Balls and Strikes: The Effects on Domestic Violence

Balls and Strikes: The Effects on Domestic Violence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Balls and Strikes: The Effects on Domestic Violence Kalvin Mudrow This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8099255/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This paper explores the effect of umpire inaccuracies on domestic violence reports. Using pitch-by-pitch data from Major League Baseball and crime reports from the National Incident-Based Reporting System, I show an increase in the number of domestic violence reports when MLB teams lose with an inaccurate umpire. This effect is focused to losses in games with extremely inaccurate umpires. Additionally, the effect is driven by umpire’s calls favoring the opposing team. I find this effect to be robust to different specifications of domestic violence timing and driven by domestic violence that occurs after a game. JEL Codes : J12; D91; K42 intimate partner violence domestic abuse emotional cues crime Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Emotional responses to sporting events have been shown to have real world consequences on familial, judicial, and crime outcomes (Card and Dahl, 2011 ; Eren and Mocan, 2018 ; Lindo et al., 2018 ; Rees and Schnepel, 2009 ). Notably, Card and Dahl ( 2011 ) explored how emotional cues from NFL football game outcomes led to an increase in family violence, finding that upsets during home games led to a 10% increase in domestic violence immediately following the game. This framework has been applied to other areas to measure the effect of emotional cues from sports events on individual behaviors including changes in individual identity, tipping behavior, and charitable donations (Depetris-Chauvin et al., 2020 ; Ge, 2018 ; Minnich, 2022 ). Sporting events contain numerous potential emotional cues, yet previous research predominantly focuses on the outcome of the game. Additionally, much of the previous work attributes this increase, at least partially, to an increase in alcohol consumption (Trendl et al., 2021 ; Rees and Schnepel, 2009 ; Lindo et al., 2018 ; Ivandi´c et al., 2024 ; Klick and MacDonald, 2021 ). I expand this framework to consider another significant emotional cue mechanism: officiating accuracy. In this work, I extend the existing literature and framework to examine how Major League Baseball (MLB) games, specifically umpire accuracy during these games, can affect domestic violence rates. Specifically, I answer the research question: Does MLB home plate umpire accuracy affect domestic violence rates in the home county of a team? I hypothesize that inaccurate umpiring increases frustration among fans, particularly in the case of calls that disadvantage the home team. This heightened emotional response then spills over into increased domestic violence incidents, particularly in high-stakes games. To address this question, I acquire pitch-by-pitch data from 2009 to 2019 from baseballsavant.mlb.com, and crime data from the National Incident-Based Reporting System (NIBRS). I focus on reports of maleto-female assault and intimidation committed by spouses, partners, or boyfriends between the hours of 6:00 p.m. and 6:00 a.m., which is when most domestic violence occurs. This provides new insights into the broader implications of emotional cues from sports on societal behavior. MLB provides an ideal setting for studying the effects of officiating accuracy on emotional responses due to the availability and quality of data on umpire decision-making. Unlike many other sports, where officiating decisions are highly subjective and difficult to quantify, MLB benefits from precise pitch-tracking technology that records the location of every pitch relative to the strike zone. This allows for a somewhat objective measure of umpire accuracy, making it possible to analyze the direct impact of officiating on game outcomes and subsequent emotional responses. Additionally, many MLB broadcasts display a version of the strike zone on-screen, providing fans with a visual reference for judging the fairness of calls, even if this representation may not perfectly align with the official strike zone. Because home plate umpires have significant discretion in calling balls and strikes, their decisions can substantially influence the flow and outcome of a game, particularly in high-stakes moments. Additionally, MLB offers a uniquely rich dataset due to its long season, with each team playing 162 games per year. This volume of games provides a much larger sample size compared to other professional sports leagues, allowing for more robust statistical analysis and greater variation in umpire accuracy, game outcomes, and emotional responses from fans. To capture the effect of umpire accuracy on domestic violence, I use a Poisson model with fixed effects for time and space. This allows me to measure how umpire accuracy and the event of a loss affect domestic violence rates within the same time frame and location. I find that umpire inaccuracy increases domestic violence, particularly following a loss. Specifically, less accurate umpires increase the incidence of domestic violence reporting. This effect is strongest for the most inaccurate umpires and is concentrated in regular season games, particularly on Thursdays and Saturdays, with the largest impact occurring in September. I also provide evidence of loss aversion and find this effect is driven by inaccuracies that favor the other team. My findings are robust to multiple specifications, and I present evidence that this effect is not found for female-to-male domestic violence or other crimes. While there is a substantial literature on unexpected football losses stemming from Card and Dahl ( 2011 ), other research has tried to extend this framework to estimate the effect of sports outcomes outside of football on domestic violence. Kirby et al. ( 2014 ) shows a link between soccer outcomes and domestic violence reports in the UK. Similarly, Depetris-Chauvin et al. ( 2020 ) find that individuals are more likely to trust others and report reduced violence rates following soccer victories. Munyo and Rossi ( 2013 ) also examines soccer games and finds that the outcome of these games affects violent crime in the hour immediately following. Dickson et al. ( 2016 ) show increases in domestic violence following rivalry games. Ge ( 2018 ) finds that consumers give more generous tips to taxi drivers after unexpected basketball wins, attributing the lack of worse tips after losses to social norms mitigating negative emotional cues while allowing positive ones to influence behavior. Most recently, Cardazzi et al. ( 2022 ) finds an increase in domestic violence around unexpected losses in the NBA. They also examine the effect of referee fatigue (and therefore accuracy) as a mechanism for this finding. My work builds on this previous research by examining this potential mechanism driving these effects: officiating rather than just the outcome of the game. Other research on baseball officiating provides relevant context. Most similar to my current work, Archsmith et al. ( 2018 ) estimates the causal relationship between air pollution and umpire accuracy using rich fixed effects and high-quality data, finding that decreases in air quality led to decreases in umpire accuracy. Similarly, Fesselmeyer ( 2021 ) finds that umpire accuracy worsens as the temperature increases. Chen et al. ( 2016 ) finds evidence that umpires fall victim to the gambler’s fallacy, specifically that umpires are more likely to call a ball if the last pitch was a ball and a strike if the last pitch was a strike. In addition to this, there is a body of literature on how baseball games can have effects on crime in the surrounding area. Mares and Blackburn ( 2019 ) shows an increase in various crimes on MLB game days, with these effects increasing as attendance rises, suggesting that viewership may be an important component of these effects. Similarly, Pyun ( 2019 ) finds an increase in assaults in Washington, DC when the Nationals, an MLB team, moved from Montreal to Washington, DC. Klick and MacDonald ( 2021 ) finds that longer games lead to less crime outcomes, due to stadiums stoppage of alcohol serving following the seventh inning. My study extends this literature by examining the role of umpire accuracy in these crime outcomes. There are many potential emotional cues happening during sporting events. By incorporating the accuracy of umpiring into the analysis of emotional cues and their impact on domestic violence, my research contributes to a deeper understanding of the mechanisms driving how sports events influence social outcomes. This extension of the literature provides a more comprehensive view of how emotional and environmental factors associated with sporting events can lead to real-world consequences. Understanding the consequences of officiating accuracy has important policy implications, as it suggests that improvements in umpire accuracy may have unintended social benefits by reducing emotionally driven domestic violence. DATA According to the MLB official rulebook, the strike zone is defined as, “the area over home plate from the midpoint between a batter’s shoulders and the top of the uniform pants – when the batter is in his stance and prepared to swing at a pitched ball – and a point just below the kneecap. In order to get a strike call, part of the ball must cross over part of home plate while in the aforementioned area. Strikes and balls are called by the home-plate umpire after every pitch has passed the batter, unless the batter makes contact with the baseball (in which case the pitch is automatically a strike).” The home plate umpire makes calls on balls and strikes after every pitch has passed the hitter. If the umpire deems a pitch a strike, it counts as a strike regardless of the spot where it crosses the plate. However, the umpire’s judgment can sometimes lead to incorrect calls. This creates a situation where an umpire’s accuracy can change a baseball game. To capture umpire accuracy, I use data from baseballsavant.mlb.com. MLB tracks an extensive amount of data for each pitch thrown during every game. One key variable provided is the strike zone area, set by an operator using a camera when the ball is halfway from the pitcher’s mound to the plate. Although broadcasters provide a similar strike zone for viewers, it is important to note that these are not identical to the official strike zone that I use. For the purposes of this study, I assume that the strike zone reported by Baseball Savant is the correct one and that umpires should use this strike zone when making calls. I classify calls made within the strike zone as strikes and calls made outside the strike zone as balls as correct calls. Conversely, calls made inside the strike zone called as balls and those made outside the strike zone called as strikes are classified as incorrect calls. I then aggregate these calls to the game level and create a variable defined as inaccuracy, which measures the percentage of calls made by the home plate umpire that are not correct in a game. An inaccuracy measure of 10.47 (the mean) means that an umpire called 10.47% of all pitches called in that game incorrectly. Additionally, there are 288 possible states in any inning of a baseball game, depending on the base runners, outs, and the count of balls and strikes. Each of these states has a calculated expected number of runs for a team to score before the end of the inning, based on historical data from the previous five years. Each pitch affects this run expectancy: correct calls change the run expectancy accurately, while incorrect calls change it inaccurately. I measure the effect of umpire accuracy on run expectancy using a variable called Home Favor, which captures the change in run expectancy for the home team due to incorrect calls. For example, a Home Favor of + 1 indicates that incorrect calls over the course of the game led to the home team having a one run higher expectancy than they should have had. Conversely, a Home Favor of -0.5 implies that the away team’s run expectancy increased by half a run due to umpire inaccuracies. A perfect umpire with 0% inaccuracy would have a Home Favor of zero, but an umpire who misses calls equally for both teams would also have a Home Favor of zero. This is an important possibility, as I use this later to investigate loss aversion by fans. I also collect data on temperature, attendance, game start-time, game end-time, location, game outcome, and other game-level information from MLB.com. I remove observations from game days where there is a double header. It would be very difficult to discern between the two umpire accuracies on these days. Additionally, I remove any game days that don’t have a reported attendance. I supplement this game data with air quality data from the Environmental Protection Agency’s Air Quality System. Air quality estimates are captured from the station nearest to the stadium. Specifically, I use the Air Quality Index as a measure of pollution. For data on crime, I turn to the National Incident-Based Reporting System (NIBRS) to capture family violence. NIBRS includes individual-level data on crime reports made to police agencies, including demographics of the victim and offender, and details of the crime reported. The demographics include the relationship of the victim to the offender, and the crime details include the type of crime, time and date, injuries sustained by the victim, and the reporting agency. I aggregate these crime reports to the county level and include only the home counties for the MLB teams in my sample. Each crime in NIBRS is reported with at least one offense code, and up to ten offense codes. I only look at the first offense code and assume this primary offense captures the entire report . I primarily focus on incidence of simple assault, aggravated assault, and intimidation. However, I supplement this later with reports of rape, larceny, robbery, pocket picking, drug violations, and vandalism or destruction of property. Similar to Cardazzi et al. ( 2022 ) and Card and Dahl ( 2011 ), I measure domestic violence as incidents of male-to-female assault or intimidation committed by a spouse, partner, or boyfriend. To capture the effect of domestic violence after the game, I define the day of a game as starting at 6 p.m. and ending at 6 a.m. of the following day, capturing domestic violence that occurs throughout the night. This specification is used because most domestic violence occurs during this time period. Later adjustments to this specification do not significantly impact the results. Additionally, I supplement my results by investigating female-to-male incidence of assault or intimidation. I create this variable in the same way but flip the genders of the offender and victim. NIBRS captures reports, not arrests, which is advantageous as it includes cases where no arrest is made. However, one limitation of using this as my measure of domestic violence is that it does not capture victims who do not report the crime. Another limitation is that not all agencies report incidents to NIBRS. However, as long as victim reporting and agency reporting are not correlated with umpire accuracy, this is not a threat to validity. The low reporting rates to NIBRS restrict the sample of MLB teams available for study, reducing the list of thirty MLB teams to nine . Table 2 shows summary statistics for domestic violence by team in my dataset. The counties for the Minnesota Twins (MIN) and the Texas Rangers (TEX) began reporting in 2019 and 2016, respectively, so they are not included in years prior to that. My results are robust when excluding them. Additionally, the Houston Astros do report to NIBRS starting in 2018, but their reporting behavior is much different than all other counties included, so I do not include them in my analysis. Table 2 The Effect of Umpire Accuracy on Domestic Violence (1) (2) (3) (4) (5) (6) Panel A: Wins and Losses Combined Inaccuracy 0.003 0.002 0.002 0.001 0.002 0.001 (0.012) (0.011) (0.001) (0.003) (0.001) (0.001) N Panel B: Losses 12621 12621 12461 12621 12461 12317 Inaccuracy 0.004 0.003 0.004*** 0.003 0.004*** 0.003** (0.012) (0.012) (0.001) (0.003) (0.001) (0.001) N Panel C: Wins 6449 6449 6354 6449 6354 6290 Inaccuracy 0.002 0.001 -0.000 -0.001 -0.000 -0.001 (0.012) (0.011) (0.002) (0.004) (0.002) (0.002) N 6172 6172 6107 6172 6107 6024 Controls No Yes No Yes Yes Yes Day-of-Week Fixed Effects No No Yes Yes Yes Yes County Fixed Effects No No Yes Yes — — Year Fixed Effects No No Yes Yes — — Month Fixed Effects No No Yes Yes Yes — County-by-Year Fixed Effects No No No No Yes — County-by-Year-by-Month Fixed Effects No No No No No Yes Notes: This table presents the relationship between umpire accuracy and male-to-female domestic violence. Controls include variables for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the day of the week, month, county, and year in different combinations. Standard errors are clustered to the county level. *p < .1, **p < .05, ***p < .01 [Table 1 here] Table 1 Summary Statistics Mean SD Median Min Max Inaccuracy 10.50 3.21 10.29 0.74 28.95 Loss 0.51 0.50 1.00 0.00 1.00 Home Favor 0.04 0.74 0.04 −3.54 4.49 Home Score 4.61 3.12 4.00 0.00 22.00 Away Score 4.44 3.16 4.00 0.00 22.00 Temperature 73.21 11.31 74.00 27.00 107.00 AQI 52.89 22.13 48.00 8.00 208.00 Year – – – 2009 2019 Month – – – 3 10 DV Count (6pm-6am) 3.75 2.90 3.00 0.00 21.00 Team Mean SD Median Min Max CIN 2.30 1.98 2.00 0.00 13.00 CLE 5.65 2.69 5.00 0.00 17.00 COL 2.30 1.81 2.00 0.00 12.00 DET 6.49 2.96 6.00 0.00 21.00 KC 3.70 2.36 4.00 0.00 13.00 MIL 4.38 2.97 4.00 0.00 19.00 MIN 2.28 1.65 2.00 0.00 8.00 SEA 1.87 1.45 2.00 0.00 9.00 TEX 1.49 1.57 1.00 0.00 10.00 Notes: This table presents summary statistics. The top panel shows different variables used as measures of treatment or controls. The bottom panel shows domestic violence counts by team. Inaccuracy measures overall home plate umpire inaccuracy in calling balls and strikes. Home Favor measures the change in run expectancy from inaccurate calls aggregated to the game level. AQI is the air quality index. Table 1 shows summary statistics for many of the variables used in my main analysis. The inaccuracy of umpires ranges from 0.74% to 28.95%. This allows me to identify some kind of effect based on this variation of accuracy. The average umpire in this sample calls 10.5% of balls and strikes incorrectly. To better understand this variable, I present a histogram of inaccuracy by team in Fig. 1 . This shows that most teams see a fairly normal distribution of inaccuracy centered around a mean of about 10. [Figure 1 here] The Home Favor variable shows that the average umpire does not change run expectancy in a major way, but there are some cases that umpires can change run expectancy by nearly four and a half runs. These are major changes in the outcome of the game when taken in consideration with the average scores being separated by less than 0.2 runs. Additionally, there is variation in the count of domestic violence cases in a night, ranging from 0 to 21, with the average night in my data having 3.75 reports of domestic violence. The bottom panel of Table 1 shows domestic violence rates by home team county. The Texas Rangers, housed in Tarrant County, Texas, have the lowest average domestic violence reports, at just below 1.5 reports of domestic violence per game day. While, the Detroit Tigers, housed in Wayne County, Michigan, have the highest average domestic violence reports, at 6.49 reports of domestic violence per game day. Additionally, I present histograms for each of these counties in Fig. 2 . Most locations show a positively skewed distribution, with many places having many days with fewer than 4 reports of domestic violence, but some days that extend past 10 reports. [Figure 2 here] METHODS To assess the impact of umpire accuracy on domestic violence, I employ a Poisson model with fixed effects for space and time. This approach allows me to estimate how an umpire’s decisions, particularly incorrect calls, influence domestic violence rates in the home county of a baseball team. Specifically, I estimate the following Poisson model, where I model the expected number of domestic violence reports as a function of umpire accuracy: log( µ st ) = λ Inaccuracy st + βX st + γ st + ϵ st (1) where µ st represents the count of domestic violence incidents in county s on day t . The key independent variable, Inaccuracy st , measures the percentage of all called pitches that are incorrectly judged by the umpire. The vector X st includes a range of game-specific controls such as temperature, air quality index (AQI), attendance, home and away team records, game end time, and an indicator for whether the game was played on a holiday. These covariates help mitigate potential biases due to omitted variable concerns by accounting for temporal and environmental factors that could independently influence domestic violence rates. Fixed effects γ st control for both spatial and temporal heterogeneity. My preferred specification includes county-by-year fixed effects ( γ sy ), month fixed effects ( γ m ), and day-of-week fixed effects ( γ d ). The inclusion of county-by-year fixed effects is particularly crucial, as it accounts for time-invariant county-specific characteristics while allowing for differential trends across counties. This absorbs unobserved heterogeneity related to regional law enforcement practices, economic conditions, and other local factors. By incorporating month and day-of-week fixed effects, I control for cyclical patterns in domestic violence, such as seasonal variations and weekend versus weekday reporting dynamics. I prefer county-by-year fixed effects to separate county and year fixed effects because they allow for differential trends across counties rather than imposing a uniform yearly effect across all locations. Year fixed effects account for common time shocks, and county fixed effects control for time-invariant differences across locations, but together they do not fully absorb local, time-varying shocks. By including county-by-year fixed effects, I control for county-specific trends and allow yearly effects to vary across counties. This approach ensures that local shocks, such as policy changes, economic downturns, or shifts in law enforcement practices, do not bias the estimates. Controlling for these county-level annual shocks is crucial, as they may differentially affect counties in ways correlated with domestic violence. A central challenge in this analysis is ensuring that umpire accuracy is exogenous to domestic violence outcomes after controlling for observed factors. One concern is that unobservable characteristics associated with game days such as fan aggression, alcohol consumption, or general social unrest may confound the relationship between umpire accuracy and domestic violence. To address this concern, my identification strategy hinges on the assumption that game days with more accurate umpires serve as a valid counterfactual for game days with less accurate umpires. This assumption implies that, conditional on fixed effects and included controls, the variation in umpire inaccuracy is plausibly exogenous. Given that umpires are assigned to games well in advance and that their performance is largely unpredictable ex-ante, this assumption is reasonable. Additionally, because I focus on within-game-day variation in inaccuracy rather than simply comparing game days to non-game days, I avoid conflating the effects of baseball games themselves with the effects of umpire performance. A related concern is whether domestic violence reporting is systematically different on days with inaccurate umpiring. If, for instance, frustration over incorrect calls leads to an increase in reporting rather than an increase in actual incidents, this could bias my estimates. However, this would require that victims respond to umpire accuracy in a systematic way, which seems unlikely. Additionally, the inclusion of county-by-year fixed effects helps control for broader trends in reporting behavior. Another potential concern is that unobserved factors related to game competitiveness or team-specific characteristics may confound the relationship between umpire accuracy and domestic violence. More competitive games, for instance, may heighten emotional investment and fan aggression, potentially influencing both domestic violence rates and umpire performance. To account for this, I control for both teams’ records, which serve as proxies for team strength and fan expectations. Additionally, I control for game attendance, which captures the popularity and perceived importance of a game, as higher attendance could reflect greater fan engagement and emotional stakes. Game end time is another key factor that I control for, as domestic violence incidents tend to be more frequent at night, and longer games may lead to greater fan frustration while also coinciding with natural peaks in domestic violence. These controls help ensure that my estimates capture the impact of umpire inaccuracy itself rather than broader game-related dynamics. Much of the existing literature on game day effects, such as Lindo et al. ( 2018 ), focuses on outcomes like rape and attributes observed increases to broader, indirect mechanisms, such as alcohol consumption. These studies often rely on telling stories about how game-day dynamics lead to these outcomes, without directly testing the causal pathway. In contrast, my analysis provides a more direct test of the effect of umpire accuracy on domestic violence by focusing on variation within the game itself. While it is certainly possible that factors like alcohol consumption or heightened emotions could mediate the effect of poor umpiring, my approach isolates the causal effect of umpire inaccuracy on domestic violence, rather than relying on narrative explanations for how game-day dynamics influence these outcomes. By focusing on within-gameday variation, I avoid the need to speculate about the mechanisms and instead offer a clearer, more direct measure of the impact of umpire accuracy on domestic violence. Finally, I focus much of my analysis on losses, following prior research by Card and Dahl ( 2011 ) and Cardazzi et al. ( 2022 ), which finds that the emotional effects of losses drive increases in domestic violence. This refinement strengthens my identification strategy by ensuring that comparisons are made within a subset of games where emotions are more likely to be heightened. By comparing losses with high inaccuracy to losses with low inaccuracy, I further isolate the impact of umpire decisions on domestic violence outcomes. Taken together, my empirical strategy capitalizes on within-game-day variation in umpire accuracy while incorporating a comprehensive set of fixed effects and time-varying controls. By isolating the impact of umpire decisions from broader game dynamics, temporal patterns, and team-specific factors, this design strengthens causal identification. The inclusion of county-by-year fixed effects, game-specific covariates, and a refined focus on losses ensures that my estimates capture the true effect of umpire inaccuracy on domestic violence, rather than reflecting confounding influences tied to the game environment or broader social trends. RESULTS Using a Poisson regression approach, I find evidence that umpire inaccuracies and losses lead to increases in domestic violence in the home counties of MLB teams. Specifically, when a home plate umpire makes inaccurate calls on balls and strikes, domestic violence rates increase. I find that this effect is driven primarily by the most inaccurate umpires. This effect is evident during the regular season, but I find no such effect in the playoffs. Moreover, I observe that the impact is more pronounced later in the season, on Thursdays and Saturdays, and in games where the teams have similar records. Additionally, I find that the effect is driven by instances where umpire inaccuracies favor the opposing team in losses. All regressions control for factors such as temperature, air quality, attendance, team records, game end times, and holidays. Furthermore, all regressions include fixed effects for country, day of the week, month, and year in some combination. Table 2 presents the main results for Eq. (1). Column (1) displays the simple relationship between umpire inaccuracy and domestic violence. Column (2) adds controls to the model, while column (3) introduces fixed effects and removes controls. Column (4) includes additive fixed effects along with controls. Column (5) is the preferred specification, including day-of-week, month, and county-by-year fixed effects along with controls. Column (6) includes county-by-year-by-month fixed effects. Table 2 is divided into three panels. Panel A presents results for all game days in the sample, Panel B focuses on losses, and Panel C includes only wins. [Table 2 here] In Panel A, there is no statistically significant relationship between umpire inaccuracy and domestic violence. Turning to Panel B, we observe that umpire inaccuracy has a significant effect on domestic violence in the context of losses. Specifically, in columns (3), (5), and (6), I find a positive relationship between umpire inaccuracy and domestic violence. In the preferred specification (column 5), the coefficient of 0.004 suggests that for each percentage point increase in umpire inaccuracy, domestic violence incidence increases by 0.4%. Using the average inaccuracy rating of umpires in my data, this coefficient implies that a typical umpire in a loss could lead to a little over a 4% increase in domestic violence. The most inaccurate umpire, on the other hand, would lead to a 12% increase in domestic violence. For comparison, Lindo et al. ( 2018 ) find that the incidence of rape increases by 28% on game days, Card and Dahl ( 2011 ) report a 10.5% increase in domestic violence following upset losses in the NFL, and Cardazzi et al. ( 2022 ) find a nearly 25% increase in domestic violence in response to upset losses in the NBA on weekends. It is understandable that umpire inaccuracy has a smaller effect than these other sports, as baseball has a much higher frequency of games compared to football and basketball. The effect I observe is robust across multiple specifications. Panel C, however, shows there is no significant effect of umpire inaccuracy on domestic violence following wins. Taken together, these results indicate that umpire inaccuracy in losses can lead to increased domestic violence, with this effect holding across different specifications. This finding provides evidence of loss aversion, which I explore further later in the paper. Given that this effect is observed exclusively for losses, I will focus on reporting estimates for the subset of games that end in a loss moving forward, unless otherwise noted. To further investigate these effects, I analyze umpire inaccuracy by categorizing it into distinct bins. These bins represent different percentile levels of inaccuracy: the bottom 5%, 5%-25%, 25%-50%, 50%-75%, 75%-95%, and above 95%. The most accurate umpires (bottom 5% of inaccuracy) serve as the reference category to prevent multicollinearity. I estimate the relationship between these accuracy bins and domestic violence, restricting the analysis to game days that end in a loss. This regression includes controls and fixed effects for the day of the week, month, and county-by-year. Figure 3 presents the estimated coefficients for inaccuracy across these bins, along with 95% confidence intervals. [Figure 3 here] These results indicate that there is no statistically significant effect for any accuracy bin except the most inaccurate umpires. Umpires in the highest inaccuracy bin (above 95%) have a coefficient estimate of 0.061, which is statistically significant at the 95% level. This finding provides a more complete picture of what is driving the increase in domestic violence. It is not just general umpire inaccuracy but specifically the most inaccurate umpires that lead to these effects. Given the high frequency of baseball games, it makes sense that average umpire inaccuracy would not have a meaningful impact, even in losses. However, when an umpire is especially inaccurate, it may serve as a strong emotional trigger for individuals predisposed to committing domestic violence. Next, I examine how these effects vary across different days of the week and months of the year. To do this, I rerun Eq. (1) on two subsets of games. First for games on each day of the week and again for each month of the year. The results are presented in Table 3 . Panel A of Table 3 shows the effects across different days of the week, excluding day-of-the-week fixed effects. Panel B displays the effects across months, excluding month fixed effects. Both regressions still include county-by-year fixed effects and controls, and I again focus only on games that end in losses. Table 3 Effects Of Umpire Accuracy On Domestic Violence At Different Times Panel A: Days Mon Tue Wed Thu Fri Sat Sun Inaccuracy −0.006 0.002 −0.008 0.014** 0.005 0.009** −0.006 (0.008) (0.007) (0.009) (0.007) (0.005) (0.004) (0.005) N Panel B: Months 704 967 951 642 991 1006 1044 Apr May June July Aug Sept Oct Inaccuracy −0.003 0.002 0.000 0.007 0.001 0.010** 0.003 (0.003) (0.006) (0.006) (0.005) (0.005) (0.004) (0.017) N 929 1069 1041 1004 1072 1020 127 Notes: This table presents the relationship between umpire accuracy and domestic violence. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, the regressions in Panel A include fixed effects for the county-by-year and month, while the regressions in Panel B include fixed effects for the countyby-year and day-of-the-week. Standard errors are clustered to the county level. *p < .1, **p < .05, ***p < .01 [Table 3 here] Looking at Panel A, I find that umpire inaccuracy leads to increases in domestic violence specifically on Thursdays and Saturdays. Notably, Thursday has the highest domestic violence rates outside of the weekend (if Friday night is considered part of the weekend), while Saturday has the highest overall domestic violence rates. This suggests that Thursdays and Saturdays may already be high-risk days for domestic violence, and inaccurate umpiring could act as an additional emotional trigger for individuals on the intensive margin who are predisposed to committing domestic violence. Panel B examines how the effects change across months. Here, I find that September is the only month where umpire inaccuracies lead to increases in domestic violence. There are several potential explanations for this. September marks the final stretch of the baseball season, with teams fighting for postseason spots, making these games more meaningful and potentially more emotionally charged for fans. It is reassuring to find that the effect is concentrated in a specific month rather than occurring uniformly throughout the season. Given the sheer number of games played and the inevitable presence of umpire inaccuracies, it would be surprising to see this effect persist across all months. Instead, the concentration of the effect in September suggests that the emotional weight of these games may amplify the response. To test this idea more directly, I next examine games with higher stakes. Table 4 presents regression results across different subsets of losses, focusing on games that may be more watched. A key issue with studying this topic during baseball games is that baseball plays so many games that a single game outcome may not be much of an emotional trigger. By focusing on games that may have higher stakes, I aim to see if this effect is focused to games that more fans watch or view as more important. As in previous analyses, all specifications include day-of-the-week, month, and county-by-year fixed effects. Table 4 Effects In Higher Stakes Games (1) (2) (3) (4) (5) (6) Panel A: Regular Season Losses Inaccuracy 0.005 0.004 0.004*** 0.003 0.004*** 0.003** (0.012) (0.011) (0.001) (0.003) (0.001) (0.001) N Panel B: Post Season Losses 6378 6378 6283 6378 6283 6220 Inaccuracy -0.010 -0.010 -0.017 -0.037* -0.033** -0.033** (0.043) (0.038) (0.019) (0.021) (0.016) (0.016) N Panel C: Close Games Losses 71 71 68 68 68 68 Inaccuracy 0.002 0.000 -0.001 -0.001 -0.002 -0.003 (0.011) (0.010) (0.002) (0.002) (0.002) (0.004) N Panel D: Close Records Losses 1800 1800 1775 1800 1775 1758 Inaccuracy -0.000 0.001 0.007** 0.004 0.008*** 0.005 (0.014) (0.013) (0.003) (0.004) (0.003) (0.003) N 2033 2033 2002 2033 2002 1990 Controls No Yes No Yes Yes Yes Day-of-Week Fixed Effects No No Yes Yes Yes Yes County Fixed Effects No No Yes Yes — — Year Fixed Effects No No Yes Yes — — Month Fixed Effects No No Yes Yes Yes — County-by-Year Fixed Effects No No No No Yes — County-by-Year-by-Month Fixed Effects No No No No No Yes Notes: This table presents the relationship between umpire inaccuracies and domestic violence rates across a subset of losses in specific games. Regular Season captures all regular season game days in my sample. Post Season captures all playoff game days in my sample. Close games are defined as games decided by one run. Close records are defined as games where teams have records within 5 percentage points of each other. Controls include variables for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the day of the week, month, county, and year in different combinations. Standard errors are clustered to the county level. *p < .1, **p < .05, ***p < .01 For brevity, I continue to focus on games that end in losses. [Table 4 here] Panels A and B of Table 4 examine the effects of umpire inaccuracy on domestic violence during regular season and postseason losses, respectively. The results indicate that the effects are driven by regular season games, which is not surprising given that the dataset includes only 71 playoff losses, making it difficult to detect statistically significant effects in that subset. While Panel B shows a large and significant negative coefficient, implying that umpire inaccuracies are associated with less domestic violence reporting. This result is driven by the inclusion of high-dimensional fixed effects in an already limited sample. In such cases, coefficient estimates can become unstable and subject to finite-sample bias. In particular, these postseason estimates appear to reflect two game days with unusually high domestic violence counts in two counties, rather than a meaningful underlying relationship. Panels C and D of Table 4 examine the effects in games that may draw more attention, specifically close games and matchups between teams with similar records. I define close games as those decided by a single run and teams with close records as those whose winning percentages differ by five percentage points or less. The results show no evidence that inaccuracy increases domestic violence in close games. However, in matchups between teams with close records, I do find evidence of an effect. In fact, this estimated effect is larger than in my main results. This suggests that the increase in domestic violence associated with umpire inaccuracy may be driven by games where teams are closely matched, providing additional support for the earlier finding that the effects are concentrated in September because this is when games get important. As the season winds down, games may take on greater importance because of playoff seeding or pennant races, making inaccurate calls more of an emotional trigger for fans. Next, I examine how the umpire’s favor within their inaccuracy affects domestic violence rates. This analysis provides additional evidence of loss aversion. Table 5 presents results separately for cases where the umpire’s calls favored the team (Favored) and where they favored the opposing team (Not Favored). Recall that the Home Favor variable captures the composite change in run expectancy based on each individual call made by the umpire. On average, this variable is 0.04 runs, but it ranges from as high as 4.49 runs to as low as -3.54 runs. A positive value indicates that the umpire’s calls benefited the team (Favored), while a negative value indicates that the umpire’s calls benefited the opponent (Not Favored). I split Table 5 into two panels. Panel A presents the results for losses, while Panel B presents the results for wins. Given that this analysis primarily relates to loss aversion, making this distinction is particularly valuable. Table 5: Effects Across Favor Of Calls (1) (2) (3) (4) (5) (6) Panel A: Losses Inaccuracy -0.001 0.001 0.001 0.008 0.005 0.007*** (0.011) (0.003) (0.003) (0.013) (0.004) (0.002) N Panel B: Wins 3130 3130 3096 3319 3319 3258 Inaccuracy -0.007 -0.002 -0.001 0.009 0.001 -0.001 (0.009) (0.004) (0.002) (0.013) (0.004) (0.003) N 3152 3152 3111 3020 3020 2946 Controls Yes Yes Yes Yes Yes Yes Day-of-Week Fixed Effects No Yes Yes No Yes Yes Month Fixed Effects No Yes Yes No Yes Yes County Fixed Effects No Yes — No Yes — Year Fixed Effects No Yes — No Yes — County-by-Year Fixed Effects No No Yes No No Yes Notes: Favored captures all games where the home county team is favored by the inaccuracies of the umpire. A full description of how this is calculated can be found in the text. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the county-by-year, day of the week, and month. Standard errors are clustered to the county level. *p < .1, **p < .05, ***p < .01 [Table 5 here] Looking at Panel B, which examines wins, I find no evidence that umpire favor, whether for or against a team, affects domestic violence rates. However, in Panel A, which focuses on losses, the results reveal a significant interaction between umpire favor and inaccuracy. When umpires favor a losing team, their inaccuracies have no effect on domestic violence rates. However, when umpires favor the opposing team in a loss, their inaccuracies lead to a significant increase in domestic violence incidents. Once again, this effect is larger than the findings in my main results. These findings provide further insight into the loss aversion of fans. However, it’s not just that a team loses a game, it’s the combination of losing and perceiving that the umpire favored the opposing team that appears to drive the increase in domestic violence. Next, I conduct three robustness checks to validate my findings. First, I vary the time frame used to measure domestic violence incidents. Second, I examine female-to-male domestic violence to assess whether the observed effects are specific to male-perpetrated incidents. Finally, I extend my analysis to all reported crimes, rather than restricting it to domestic violence committed by males against their female spouses, girlfriends, or partners. Table 6 presents the results for varying the time frame of domestic violence, once again focusing solely on games that end in a loss for brevity. The first column replicates my baseline results using the standard 6:00 Table 6 Varying Timeframe for Domestic Violence 6pm − 6am 4pm − 4am 8pm − 6am 10pm − 6am Post-Game Pre-Game Inaccuracy 0.004*** 0.002** 0.004*** 0.002 0.003*** 0.000 (0.001) (0.001) (0.001) (0.002) (0.001) (0.001) N 6354 6354 6354 6354 6354 6354 Notes: This table shows the effects of umpire accuracy on domestic violence across various timeframes of domestic violence. 6pm-6am is the main specification I use across all other regressions. Post-Game captures all domestic violence that occurs after the hour the game ends through 6am of the following morning. Pre-Game captures all domestic violence that occurs from 7am through the final hour of the game. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the county-by-year, day of the week, and month. Standard errors are clustered to the county level. *p < .1, **p < .05, ***p < .01 p.m. to 6:00 a.m. window, which aligns with prior literature and captures the period when most domestic violence incidents occur. The second column expands this window to 4:00 p.m. to 4:00 a.m., while the third and fourth columns narrow the window to 8:00 p.m. to 6:00 a.m. and 10:00 p.m. to 6:00 a.m., respectively. In the fifth column, I use a post-game measure, which captures all domestic violence incidents occurring from the hour after the game ends through 6:00 a.m. the following morning. I also use a Pre-Game variable, which captures domestic violence incidents from 7:00 a.m. until the final hour of the game. [Table 6 here] Examining these specifications, I find positive and significant effects for the 4:00 p.m. to 4:00 a.m. and 8:00 p.m. to 6:00 a.m. windows, indicating that my results are not overly sensitive to the precise time frame chosen. Additionally, I find that inaccuracies in losses primarily lead to increases in domestic violence during the post-game period, with no evidence of an effect in the pre-game period. This is an important validation check because inaccuracies in losses should not be associated with increases in domestic violence before the game ended. If this were true, it would undermine the causal interpretation of my findings, as the game’s outcome would not yet be known. However, finding no evidence of a relationship here supports my causal interpretation. Taken together, the results in Table 6 demonstrate that my findings are robust to different time specifications and that the observed effects are not driven by incidents occurring before the game concludes. Table 7 presents the results of the female-to-male domestic violence model, structured identically to Table Table 7 Female-to-Male Domestic Violence (1) (2) (3) (4) (5) (6) Panel A: Wins and Losses Combined Inaccuracy -0.020* -0.020* -0.000 -0.002 -0.000 0.001 (0.011) (0.010) (0.002) (0.004) (0.002) (0.002) N Panel B: Losses 12621 12621 12461 12621 12461 12284 Inaccuracy -0.021* -0.021* -0.002 -0.004 -0.001 0.001 (0.012) (0.012) (0.004) (0.005) (0.004) (0.004) N Panel C: Wins 6449 6449 6354 6449 6354 6252 Inaccuracy -0.019 -0.019* 0.001 -0.000 0.001 0.001 (0.011) (0.011) (0.004) (0.006) (0.004) (0.004) N 6172 6172 6012 6172 6012 5944 Controls No Yes No Yes Yes Yes Day-of-Week Fixed Effects No No Yes Yes Yes Yes County Fixed Effects No No Yes Yes — — Year Fixed Effects No No Yes Yes — — Month Fixed Effects No No Yes Yes Yes — County-by-Year Fixed Effects No No No No Yes — County-by-Year-by-Month Fixed Effects No No No No No Yes Notes: This table presents the relationship between umpire accuracy and female-to-male domestic violence. Controls include variables for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the day of the week, month, county, and year in different combinations. Standard errors are clustered to the county level. *p < .1, **p < .05, ***p < .01 2, which examined male-to-female domestic violence. As before, I divide the results into three panels: Panel A combines wins and losses, Panel B isolates losses, and Panel C isolates wins. The model specification follows the same progression as in Table 2 , beginning with a naive specification that excludes controls and fixed effects, then sequentially adding controls, fixed effects, and alternative fixed effect structures. [Table 7 here] Previously, in Table 2 , I found that umpire inaccuracies in losses led to an increase in male-to-female domestic violence. However, when examining female-to-male domestic violence in Table 7 , I find no such relationship. In the simplest specifications (Columns 1 and 2), there is a negative correlation between inaccuracy and domestic violence, statistically significant at the 10% level. However, once I introduce fixed effects, these effects disappear, and I find no evidence of any relationship between umpire inaccuracies and female-to-male domestic violence. This result provides further evidence that the observed effects are driven primarily by male viewers, who may be more susceptible to the emotional triggers associated with the game and its outcomes. While it is possible that women could also be influenced by umpire inaccuracies, the absence of a significant relationship in the female-to-male model suggests that the effect is largely concentrated among male viewers, whose emotional responses appear more closely tied to game outcomes. This distinction reinforces the interpretation that the relationship between umpire inaccuracies and domestic violence is predominantly a male-to-female phenomenon. There is also prior evidence suggesting that outcomes of major sporting events can influence a range of behaviors beyond domestic violence, affecting both male and female offenders and victims. To explore whether similar patterns emerge in my data, I extend the analysis to include all reported crimes, regardless of the gender of the offender or victim. The results are summarized in Table 8 . The first column presents estimates for simple assault, followed by intimidation, aggravated assault, rape, larceny, robbery, pocketpicking, drug violations, and vandalism or destruction of property. As in the previous regressions, all models include fixed effects for the day of the week, month, and county-by-year. The results are presented in three panels: Panel A combines wins and losses, Panel B isolates losses, and Panel C isolates wins. Table 8 All Crime Simple AssaultIntimidation Aggravated Assault Rape Larceny Robbery Pocket Picking Drugs Vandalism Panel A: Wins and Losses Combined Inaccuracy 0.001 -0.002 -0.002 -0.005 0.001 -0.001 -0.010 0.000 -0.002 (0.001) (0.003) (0.003) (0.004) (0.002) (0.002) (0.010) (0.002) (0.002) N Panel B: Losses 12461 12461 12296 12461 12461 12296 11807 10544 12461 Inaccuracy 0.002 0.001 0.002 -0.012* 0.001 -0.002 -0.013 0.000 -0.003 (0.002) (0.002) (0.002) (0.007) (0.003) (0.005) (0.020) (0.004) (0.002) N Panel C: Wins 6354 6269 6284 6284 6284 6284 5929 5371 6284 Inaccuracy -0.000 -0.005 -0.006 0.000 0.002 -0.001 -0.008 -0.000 -0.000 (0.002) (0.005) (0.003) (0.003) (0.003) (0.002) (0.011) (0.002) (0.002) N 6107 6012 6012 6107 6107 6012 5669 5173 6107 Notes: This table shows the effects of umpire accuracy on several crime outcomes. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the county-by-year, day of the week, and month. Standard errors are clustered to the county level. *p < .1, **p < .05, ***p < .01 [Table 8 here] Across all these crimes, I find no evidence of any significant relationship between these outcomes and umpire accuracy. This lack of a relationship is unsurprising given that much of the literature linking such effects to crime partially attributes them to an increase in the number of people in an area. Since I am only examining game days, I do not capture the influx of people that might otherwise be associated with sporting events. Additionally, if crimes are committed due to people attending games, it may be difficult for attendees to discern whether an umpire is calling a game accurately or inaccurately. This suggests that the observed effect is likely driven by male-to-female domestic violence within the home, as these individuals are the ones watching the games and can more clearly observe the umpire’s performance. CONCLUSION In this study, I use NIBRS data from the home counties of nine Major League Baseball teams, focusing specifically on cases of male-to-female domestic violence, supplemented by pitch-by-pitch data from MLB. I present evidence that domestic violence increases following losses in Major League Baseball games when the umpire is inaccurate. These findings extend the literature on emotional cues and domestic violence by demonstrating that factors beyond game outcomes can influence emotional responses. Using various regression models, I show that these effects are particularly pronounced for regular season games, games played on Thursdays and Saturdays, and games played in September. Utilizing the change in run expectancy caused by incorrect umpire calls, I show these effects are concentrated in games where the umpire favors the opposing team. These results indicate that the emotional responses found in other settings may be shaped by specific emotional cues that extend beyond a simple win-loss framework. Here, umpire inaccuracies serve as a particularly salient cue, with higher accuracy mitigating frustration and lower accuracy exacerbating it. This provides broader evidence that perceptions of fairness play a crucial role in shaping emotional responses and subsequent behaviors. This implies that fair officiating has broader social implications than just competitive integrity. While my work highlights the impact of umpire accuracy on domestic violence, it does not capture all aspects of officiating decisions. My analysis focuses exclusively on the accuracy of home plate umpires in calling balls and strikes. However, umpires make numerous other crucial decisions throughout a game, including safe/out calls at each base, determining whether a batter has swung at a pitch, and issuing ejections to players and coaches. These other forms of officiating discretion may also generate emotional cues similar to those presented in this study, representing a key area for future research. Additionally, my work is restricted to a subset of nine MLB teams, limiting the generalizability of the findings across all franchises and fanbases. Future research could expand to include more teams and a broader range of officiating decisions to provide a more comprehensive understanding of the effects of referee accuracy on emotional responses. Another promising avenue for future research involves developing methods to quantify particularly salient blown calls. Not all officiating errors carry the same weight. An incorrect strike call in the first inning likely generates a weaker emotional response than a missed call in a high-leverage situation. While my study captures broad trends in umpire accuracy, future work could seek to identify and measure the impact of game-changing blown calls. This is particularly relevant in other sports such as football, where each game carries more significance due to a shorter season. For example, a blown call in the final moments of an NFL game may have a more pronounced emotional impact than a single missed strike call in a 162-game MLB season. Developing a framework to quantify the emotional salience of officiating errors across different sports and contexts could significantly enhance our understanding of the role that perceptions of fairness play in shaping fan behavior and societal outcomes. An important policy implication of this research concerns Major League Baseball’s ongoing efforts to enhance officiating accuracy. Since 2008, MLB has gradually expanded its use of replay review, initially allowing umpires to use video review at their discretion and later implementing manager challenges to correct missed calls. However, these systems currently exclude balls and strikes. MLB has recently begun experimenting with automated strike zones, or “robot umpires,” which allow for balls and strikes to be challenged. After testing automated ball-strike (ABS) systems in Minor League Baseball, MLB introduced them in spring training and during the 2025 All-Star Game, and has now approved their use in the 2026 season as part of the challenge system. This development could fundamentally alter the role of home plate umpires. Given the evidence that umpire inaccuracy can exacerbate negative emotional responses among viewers, the adoption of robot umpires may not only improve the integrity of the game but also help reduce the unintended social consequences associated with officiating errors. My findings align with the work of Bradbury ( 2019 ), which shows how umpire performance is particularly sensitive to league directives and monitoring. My effects are found primarily in September, suggesting that as the season progresses and games increase in importance, umpire accuracy may become even more crucial in shaping emotional responses. MLB may consider increasing monitoring or providing additional guidance to umpires as the season winds down to minimize poor umpiring that could increase domestic violence. Finally, my findings underscore the broader importance of fair play in professional sports, extending beyond competitive fairness to real-world social consequences. As MLB continues to refine its officiating practices, ensuring greater accuracy and reducing perceived unfairness may not only improve the game itself but also mitigate the unintended negative effects that extend beyond the ballpark. Declarations TABLES & FIGURES Table 5: Effects Across Favor Of Calls FavoredNot FavoredFavoredNot Favored Author Contribution K.M. is the sole author for this paper. Acknowledgement I would like to thank Dan Grossman, my PhD advisor, for his guidance, support, and valuable feedback throughout the development of this project. Data Availability All data and code used in this study are publicly available. MLB pitch-level data can be accessed at www.mlb.com (this was scraped using the baseballr package in R), and county-level domestic violence statistics can be accessed at https://www.openicpsr.org/openicpsr/project/118281/version/V11/view. The replication code used for the analyses is available at www.kalvinmudrow.com. References Archsmith, J., A. Heyes, and S. Saberian. 2018. Air quality and error quantity: Pollution and performance in a high-skilled, quality-focused occupation. Journal of the Association of Environmental and Resource Economists 5(4):827–863. Bradbury, J. C. 2019. Monitoring and employee shirking: Evidence from MLB umpires. Journal of Sports Economics 20(6):850–872. Card, D., and G. B. Dahl. 2011. Family violence and football: The effect of unexpected emotional cues on violent behavior. The quarterly journal of economics 126(1):103–143. Cardazzi, A., B. C. McCannon, B. R. Humphreys, and Z. Rodriguez. 2022. Emotional cues and violent behavior: Unexpected basketball losses increase incidents of family violence . The Journal of Law, Economics, and Organization. Chen, D. L., T. J. Moskowitz, and K. Shue. 2016. Decision making under the gambler’s fallacy: Evidence from asylum judges, loan officers, and baseball umpires. The Quarterly Journal of Economics 131(3):1181–1242. Depetris-Chauvin, E., R. Durante, and F. Campante. 2020. Building nations through shared experiences: Evidence from African football. American Economic Review 110(5):1572–1602. Dickson, A., C. Jennings, and G. Koop. 2016. Domestic violence and football in Glasgow: are reference points relevant? Oxford Bulletin of Economics and Statistics 78(1):1–21. Eren, O., and N. Mocan. 2018. Emotional judges and unlucky juveniles. American Economic Journal: Applied Economics 10(3):171–205. Fesselmeyer, E. 2021. The impact of temperature on labor quality: Umpire accuracy in major league baseball. Southern Economic Journal 88(2):545–567. Ge, Q. 2018. Sports sentiment and tipping behavior. Journal of Economic Behavior & Organization 145:95–113. Ivandi´c, R., T. Kirchmaier, Y. Saeidi, and N. T. Blas. 2024. Football, alcohol, and domestic abuse. Journal of public economics 230:105031. Kaplan, J. 2025. Jacob Kaplan’s concatenated files: National incident-based reporting system (NIBRS) data, 1991–2024 . Inter-university Consortium for Political and Social Research [distributor]. ICPSR Data CollectionAnn Arbor, MI:. Kirby, S., B. Francis, and R. O’Flaherty. 2014. Can the FIFA world cup football (soccer) tournament be associated with an increase in domestic abuse? Journal of research in crime and delinquency 51(3):259–276. Klick, J., and J. MacDonald. 2021. Sobering up after the seventh inning: Alcohol and crime around the ballpark. Journal of quantitative criminology 37:813–834. Lindo, J. M., P. Siminski, and I. D. Swensen. 2018. College party culture and sexual assault. American Economic Journal: Applied Economics 10(1):236–265. Mares, D., and E. Blackburn. 2019. Major league baseball and crime: Opportunity, spatial patterns, and team rivalry at st. louis cardinal games. Journal of Sports Economics 20(7):875–902. Minnich, A. 2022. Do fans’ emotions influence charitable donations? evidence from monetary and returnable cup donations in German soccer stadiums. Journal of Behavioral and Experimental Economics 96:101807. Munyo, I., and M. A. Rossi. 2013. Frustration, euphoria, and violent crime. Journal of Economic Behavior & Organization 89:136–142. Pyun, H. 2019. Exploring causal relationship between major league baseball games and crime: A synthetic control analysis. Empirical economics 57:365–383. Rees, D. I., and K. T. Schnepel. 2009. College football games and crime. Journal of sports Economics 10(1):68–87. Trendl, A., N. Stewart, and T. L. Mullett. 2021. The role of alcohol in the link between national football (soccer) tournaments and domestic abuse-evidence from England. Social science & medicine 268:113457. Footnotes Some pitches are not captured in the data because their strike zones are not recorded by the operator. I exclude these pitches from the analysis, meaning they do not count for or against umpire accuracy ratings. These make up about 0.8% of all pitches and appear to be randomly missing. There are 3 possible out states (0 outs, 1 out, 2 outs), 8 possible runner states, and 12 possible balls and strikes states that a hitter may face prior to a pitch being thrown. Specifically, I use concatenated NIBRS files from Kaplan ( 2025 ), which are compiled and cleaned versions of the NIBRS raw data. For the 2009 data, over 92% of all reports in the NIBRS data had only one offense code listed. In 2019, over 91% of all reports only had one offense code. Cincinnati Reds, Cleveland Guardians, Colorado Rockies, Detroit Tigers, Kansas City Royals, Milwaukee Brewers, Minnesota Twins, Seattle Mariners, and the Texas Rangers Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Jan, 2026 Reviews received at journal 14 Jan, 2026 Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 14 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 12 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8099255","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546516158,"identity":"8f96e6ed-22b3-4949-a045-9a8fb7a24b45","order_by":0,"name":"Kalvin Mudrow","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACCQbmBoYEMBNEVkCFefBqYUTWcoZYLQwwLYxtRGiRbD/YJvFwB0O0wfHch49559XKy89IYHzwtg23FmmexDaJxDMMuRvOPDc25t123HDDjQRmw7l4tMgxJDYbJLYBtdxIY5PO3XYswUAigU2aF58W/ofIWuYcSwA6jP03Pi3SEomNDxBaGmoSGG4ksDHj0yI54yFIi0TuzDPPmI3/HDtguOHMw2bJOedwa5E4n3zg4M82m9y+42mMD2fU1MnLtycf/PCmDLcWmE4Y4zAQw2OKOFBHkupRMApGwSgYGQAAnBlUSJJNFPgAAAAASUVORK5CYII=","orcid":"","institution":"Georgia College \u0026 State University","correspondingAuthor":true,"prefix":"","firstName":"Kalvin","middleName":"","lastName":"Mudrow","suffix":""}],"badges":[],"createdAt":"2025-11-12 19:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8099255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8099255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96918077,"identity":"f9ea314d-3947-45d4-a4ac-42acc0326e67","added_by":"auto","created_at":"2025-11-27 14:11:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163991,"visible":true,"origin":"","legend":"","description":"","filename":"BallsandStrikesfinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/0263b5026dd80c578eb89c7a.docx"},{"id":96918158,"identity":"cb7d21c9-1c75-48bc-8655-00903fb8ab53","added_by":"auto","created_at":"2025-11-27 14:11:13","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2875,"visible":true,"origin":"","legend":"","description":"","filename":"932f5ed2decb470ab51e47b64cdea2cf.json","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/ee6eb08ba4485077d3630662.json"},{"id":96822062,"identity":"dbc0a4f4-0cc5-4da6-9f02-34097028d2b0","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156825,"visible":true,"origin":"","legend":"","description":"","filename":"932f5ed2decb470ab51e47b64cdea2cf1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/858ca5c0b32bd2e98106c9ea.xml"},{"id":96822059,"identity":"2effb2b5-72ca-4a3a-94d6-e04175142679","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3849,"visible":true,"origin":"","legend":"","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/18a592fd27e862173f6c840a.jpeg"},{"id":96822055,"identity":"295ff2ba-d59b-4028-b874-660166a39e41","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14395,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/eafb02f1daaa6a4dcb18d45d.png"},{"id":96822057,"identity":"4861b4f5-cdff-49ba-9754-0a9db0286b57","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13077,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/764fc8ef0392a8826e27f0b6.png"},{"id":96822054,"identity":"199eaabb-6f2c-4ba6-947e-98a616323ec8","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2032,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/140f2b68102c9abe46b2e6da.png"},{"id":96916319,"identity":"6a6ee846-5b3d-4f6f-80e5-b440dffb1cea","added_by":"auto","created_at":"2025-11-27 14:08:28","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4921,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinegroupimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/da7dc92839c5922d6e1d55a0.png"},{"id":96822061,"identity":"85bcb6a0-3859-4ca9-8a9d-22e2c6556f87","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155245,"visible":true,"origin":"","legend":"","description":"","filename":"932f5ed2decb470ab51e47b64cdea2cf1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/c16eac1619a2d45a685f5a35.xml"},{"id":96918004,"identity":"62c9233f-484d-4579-93df-a3a1b7e26fa8","added_by":"auto","created_at":"2025-11-27 14:10:58","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159710,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/3bce27e4c6d57215efa41ae3.html"},{"id":96822053,"identity":"5993b3e9-ecc9-476d-b085-0eab80d67422","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":251319,"visible":true,"origin":"","legend":"\u003cp\u003eInaccuracy Distributions\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/6072f590235e3ccd664d3541.png"},{"id":96822052,"identity":"d95be04d-60ba-4c46-81a0-bfe49c6e3518","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":237424,"visible":true,"origin":"","legend":"\u003cp\u003eDomestic Violence Distributions\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/88bcdecc95922c0c57e9774e.png"},{"id":96822051,"identity":"9aeb664c-8493-4c16-818b-0bd8fff7c117","added_by":"auto","created_at":"2025-11-26 12:11:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38163,"visible":true,"origin":"","legend":"\u003cp\u003eInaccuracy Bins Regression\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/ffbfb51633910375d99f6b75.jpg"},{"id":96922994,"identity":"8ef43482-92c1-4a81-95ef-7409b5a44ab7","added_by":"auto","created_at":"2025-11-27 14:20:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1519480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8099255/v1/f8287bc6-7eb0-46b8-841f-9dde51418894.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Balls and Strikes: The Effects on Domestic Violence","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEmotional responses to sporting events have been shown to have real world consequences on familial, judicial, and crime outcomes (Card and Dahl, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Eren and Mocan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lindo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rees and Schnepel, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Notably, Card and Dahl (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) explored how emotional cues from NFL football game outcomes led to an increase in family violence, finding that upsets during home games led to a 10% increase in domestic violence immediately following the game. This framework has been applied to other areas to measure the effect of emotional cues from sports events on individual behaviors including changes in individual identity, tipping behavior, and charitable donations (Depetris-Chauvin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ge, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Minnich, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSporting events contain numerous potential emotional cues, yet previous research predominantly focuses on the outcome of the game. Additionally, much of the previous work attributes this increase, at least partially, to an increase in alcohol consumption (Trendl et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rees and Schnepel, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lindo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ivandi\u0026acute;c et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Klick and MacDonald, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). I expand this framework to consider another significant emotional cue mechanism: officiating accuracy. In this work, I extend the existing literature and framework to examine how Major League Baseball (MLB) games, specifically umpire accuracy during these games, can affect domestic violence rates. Specifically, I answer the research question: Does MLB home plate umpire accuracy affect domestic violence rates in the home county of a team? I hypothesize that inaccurate umpiring increases frustration among fans, particularly in the case of calls that disadvantage the home team. This heightened emotional response then spills over into increased domestic violence incidents, particularly in high-stakes games.\u003c/p\u003e\u003cp\u003eTo address this question, I acquire pitch-by-pitch data from 2009 to 2019 from baseballsavant.mlb.com, and crime data from the National Incident-Based Reporting System (NIBRS). I focus on reports of maleto-female assault and intimidation committed by spouses, partners, or boyfriends between the hours of 6:00 p.m. and 6:00 a.m., which is when most domestic violence occurs. This provides new insights into the broader implications of emotional cues from sports on societal behavior.\u003c/p\u003e\u003cp\u003eMLB provides an ideal setting for studying the effects of officiating accuracy on emotional responses due to the availability and quality of data on umpire decision-making. Unlike many other sports, where officiating decisions are highly subjective and difficult to quantify, MLB benefits from precise pitch-tracking technology that records the location of every pitch relative to the strike zone. This allows for a somewhat objective measure of umpire accuracy, making it possible to analyze the direct impact of officiating on game outcomes and subsequent emotional responses. Additionally, many MLB broadcasts display a version of the strike zone on-screen, providing fans with a visual reference for judging the fairness of calls, even if this representation may not perfectly align with the official strike zone. Because home plate umpires have significant discretion in calling balls and strikes, their decisions can substantially influence the flow and outcome of a game, particularly in high-stakes moments. Additionally, MLB offers a uniquely rich dataset due to its long season, with each team playing 162 games per year. This volume of games provides a much larger sample size compared to other professional sports leagues, allowing for more robust statistical analysis and greater variation in umpire accuracy, game outcomes, and emotional responses from fans.\u003c/p\u003e\u003cp\u003eTo capture the effect of umpire accuracy on domestic violence, I use a Poisson model with fixed effects for time and space. This allows me to measure how umpire accuracy and the event of a loss affect domestic violence rates within the same time frame and location. I find that umpire inaccuracy increases domestic violence, particularly following a loss. Specifically, less accurate umpires increase the incidence of domestic violence reporting. This effect is strongest for the most inaccurate umpires and is concentrated in regular season games, particularly on Thursdays and Saturdays, with the largest impact occurring in September. I also provide evidence of loss aversion and find this effect is driven by inaccuracies that favor the other team. My findings are robust to multiple specifications, and I present evidence that this effect is not found for female-to-male domestic violence or other crimes.\u003c/p\u003e\u003cp\u003eWhile there is a substantial literature on unexpected football losses stemming from Card and Dahl (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), other research has tried to extend this framework to estimate the effect of sports outcomes outside of football on domestic violence. Kirby et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) shows a link between soccer outcomes and domestic violence reports in the UK. Similarly, Depetris-Chauvin et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) find that individuals are more likely to trust others and report reduced violence rates following soccer victories. Munyo and Rossi (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also examines soccer games and finds that the outcome of these games affects violent crime in the hour immediately following. Dickson et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) show increases in domestic violence following rivalry games. Ge (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) finds that consumers give more generous tips to taxi drivers after unexpected basketball wins, attributing the lack of worse tips after losses to social norms mitigating negative emotional cues while allowing positive ones to influence behavior. Most recently, Cardazzi et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) finds an increase in domestic violence around unexpected losses in the NBA. They also examine the effect of referee fatigue (and therefore accuracy) as a mechanism for this finding. My work builds on this previous research by examining this potential mechanism driving these effects: officiating rather than just the outcome of the game.\u003c/p\u003e\u003cp\u003eOther research on baseball officiating provides relevant context. Most similar to my current work, Archsmith et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) estimates the causal relationship between air pollution and umpire accuracy using rich fixed effects and high-quality data, finding that decreases in air quality led to decreases in umpire accuracy.\u003c/p\u003e\u003cp\u003eSimilarly, Fesselmeyer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) finds that umpire accuracy worsens as the temperature increases. Chen et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) finds evidence that umpires fall victim to the gambler\u0026rsquo;s fallacy, specifically that umpires are more likely to call a ball if the last pitch was a ball and a strike if the last pitch was a strike.\u003c/p\u003e\u003cp\u003eIn addition to this, there is a body of literature on how baseball games can have effects on crime in the surrounding area. Mares and Blackburn (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) shows an increase in various crimes on MLB game days, with these effects increasing as attendance rises, suggesting that viewership may be an important component of these effects. Similarly, Pyun (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) finds an increase in assaults in Washington, DC when the Nationals, an MLB team, moved from Montreal to Washington, DC. Klick and MacDonald (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) finds that longer games lead to less crime outcomes, due to stadiums stoppage of alcohol serving following the seventh inning. My study extends this literature by examining the role of umpire accuracy in these crime outcomes.\u003c/p\u003e\u003cp\u003eThere are many potential emotional cues happening during sporting events. By incorporating the accuracy of umpiring into the analysis of emotional cues and their impact on domestic violence, my research contributes to a deeper understanding of the mechanisms driving how sports events influence social outcomes. This extension of the literature provides a more comprehensive view of how emotional and environmental factors associated with sporting events can lead to real-world consequences. Understanding the consequences of officiating accuracy has important policy implications, as it suggests that improvements in umpire accuracy may have unintended social benefits by reducing emotionally driven domestic violence.\u003c/p\u003e"},{"header":"DATA","content":"\u003cp\u003eAccording to the MLB official rulebook, the strike zone is defined as, \u0026ldquo;the area over home plate from the midpoint between a batter\u0026rsquo;s shoulders and the top of the uniform pants \u0026ndash; when the batter is in his stance and prepared to swing at a pitched ball \u0026ndash; and a point just below the kneecap. In order to get a strike call, part of the ball must cross over part of home plate while in the aforementioned area. Strikes and balls are called by the home-plate umpire after every pitch has passed the batter, unless the batter makes contact with the baseball (in which case the pitch is automatically a strike).\u0026rdquo; The home plate umpire makes calls on balls and strikes after every pitch has passed the hitter. If the umpire deems a pitch a strike, it counts as a strike regardless of the spot where it crosses the plate. However, the umpire\u0026rsquo;s judgment can sometimes lead to incorrect calls. This creates a situation where an umpire\u0026rsquo;s accuracy can change a baseball game.\u003c/p\u003e\u003cp\u003eTo capture umpire accuracy, I use data from baseballsavant.mlb.com. MLB tracks an extensive amount of data for each\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e pitch thrown during every game. One key variable provided is the strike zone area, set by an operator using a camera when the ball is halfway from the pitcher\u0026rsquo;s mound to the plate. Although broadcasters provide a similar strike zone for viewers, it is important to note that these are not identical to the official strike zone that I use. For the purposes of this study, I assume that the strike zone reported by Baseball Savant is the correct one and that umpires should use this strike zone when making calls. I classify calls made within the strike zone as strikes and calls made outside the strike zone as balls as correct calls. Conversely, calls made inside the strike zone called as balls and those made outside the strike zone called as strikes are classified as incorrect calls. I then aggregate these calls to the game level and create a variable defined as inaccuracy, which measures the percentage of calls made by the home plate umpire that are not correct in a game. An inaccuracy measure of 10.47 (the mean) means that an umpire called 10.47% of all pitches called in that game incorrectly.\u003c/p\u003e\u003cp\u003eAdditionally, there are 288\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e possible states in any inning of a baseball game, depending on the base runners, outs, and the count of balls and strikes. Each of these states has a calculated expected number of runs for a team to score before the end of the inning, based on historical data from the previous five years. Each pitch affects this run expectancy: correct calls change the run expectancy accurately, while incorrect calls change it inaccurately. I measure the effect of umpire accuracy on run expectancy using a variable called Home Favor, which captures the change in run expectancy for the home team due to incorrect calls. For example, a Home Favor of +\u0026thinsp;1 indicates that incorrect calls over the course of the game led to the home team having a one run higher expectancy than they should have had. Conversely, a Home Favor of -0.5 implies that the away team\u0026rsquo;s run expectancy increased by half a run due to umpire inaccuracies. A perfect umpire with 0% inaccuracy would have a Home Favor of zero, but an umpire who misses calls equally for both teams would also have a Home Favor of zero. This is an important possibility, as I use this later to investigate loss aversion by fans.\u003c/p\u003e\u003cp\u003eI also collect data on temperature, attendance, game start-time, game end-time, location, game outcome, and other game-level information from MLB.com. I remove observations from game days where there is a double header. It would be very difficult to discern between the two umpire accuracies on these days. Additionally, I remove any game days that don\u0026rsquo;t have a reported attendance. I supplement this game data with air quality data from the Environmental Protection Agency\u0026rsquo;s Air Quality System. Air quality estimates are captured from the station nearest to the stadium. Specifically, I use the Air Quality Index as a measure of pollution.\u003c/p\u003e\u003cp\u003eFor data on crime, I turn to the National Incident-Based Reporting System (NIBRS) to capture family violence.\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e NIBRS includes individual-level data on crime reports made to police agencies, including demographics of the victim and offender, and details of the crime reported. The demographics include the relationship of the victim to the offender, and the crime details include the type of crime, time and date, injuries sustained by the victim, and the reporting agency. I aggregate these crime reports to the county level and include only the home counties for the MLB teams in my sample.\u003c/p\u003e\u003cp\u003eEach crime in NIBRS is reported with at least one offense code, and up to ten offense codes. I only look at the first offense code and assume this primary offense captures the entire report\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e. I primarily focus on incidence of simple assault, aggravated assault, and intimidation. However, I supplement this later with reports of rape, larceny, robbery, pocket picking, drug violations, and vandalism or destruction of property.\u003c/p\u003e\u003cp\u003eSimilar to Cardazzi et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Card and Dahl (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), I measure domestic violence as incidents of male-to-female assault or intimidation committed by a spouse, partner, or boyfriend. To capture the effect of domestic violence after the game, I define the day of a game as starting at 6 p.m. and ending at 6 a.m. of the following day, capturing domestic violence that occurs throughout the night. This specification is used because most domestic violence occurs during this time period. Later adjustments to this specification do not significantly impact the results. Additionally, I supplement my results by investigating female-to-male incidence of assault or intimidation. I create this variable in the same way but flip the genders of the offender and victim.\u003c/p\u003e\u003cp\u003eNIBRS captures reports, not arrests, which is advantageous as it includes cases where no arrest is made. However, one limitation of using this as my measure of domestic violence is that it does not capture victims who do not report the crime. Another limitation is that not all agencies report incidents to NIBRS. However, as long as victim reporting and agency reporting are not correlated with umpire accuracy, this is not a threat to validity.\u003c/p\u003e\u003cp\u003eThe low reporting rates to NIBRS restrict the sample of MLB teams available for study, reducing the list of thirty MLB teams to nine\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows summary statistics for domestic violence by team in my dataset. The counties for the Minnesota Twins (MIN) and the Texas Rangers (TEX) began reporting in 2019 and 2016, respectively, so they are not included in years prior to that. My results are robust when excluding them. Additionally, the Houston Astros do report to NIBRS starting in 2018, but their reporting behavior is much different than all other counties included, so I do not include them in my analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Effect of Umpire Accuracy on Domestic Violence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePanel A: Wins and Losses Combined\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel B: Losses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel C: Wins\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay-of-Week Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty-by-Year Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty-by-Year-by-Month Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: This table presents the relationship between umpire accuracy and male-to-female domestic violence. Controls include variables for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the day of the week, month, county, and year in different combinations. Standard errors are clustered to the county level. *p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.1, **p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.05, ***p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome Favor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026minus;3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAway Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e107.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAQI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e208.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDV Count (6pm-6am)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDET\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: This table presents summary statistics. The top panel shows different variables used as measures of treatment or controls. The bottom panel shows domestic violence counts by team. Inaccuracy measures overall home plate umpire inaccuracy in calling balls and strikes. Home Favor measures the change in run expectancy from inaccurate calls aggregated to the game level. AQI is the air quality index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows summary statistics for many of the variables used in my main analysis. The inaccuracy of umpires ranges from 0.74% to 28.95%. This allows me to identify some kind of effect based on this variation of accuracy. The average umpire in this sample calls 10.5% of balls and strikes incorrectly. To better understand this variable, I present a histogram of inaccuracy by team in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This shows that most teams see a fairly normal distribution of inaccuracy centered around a mean of about 10.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eThe Home Favor variable shows that the average umpire does not change run expectancy in a major way, but there are some cases that umpires can change run expectancy by nearly four and a half runs. These are major changes in the outcome of the game when taken in consideration with the average scores being separated by less than 0.2 runs. Additionally, there is variation in the count of domestic violence cases in a night, ranging from 0 to 21, with the average night in my data having 3.75 reports of domestic violence.\u003c/p\u003e\u003cp\u003eThe bottom panel of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows domestic violence rates by home team county. The Texas Rangers, housed in Tarrant County, Texas, have the lowest average domestic violence reports, at just below 1.5 reports of domestic violence per game day. While, the Detroit Tigers, housed in Wayne County, Michigan, have the highest average domestic violence reports, at 6.49 reports of domestic violence per game day. Additionally, I present histograms for each of these counties in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Most locations show a positively skewed distribution, with many places having many days with fewer than 4 reports of domestic violence, but some days that extend past 10 reports.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cp\u003eTo assess the impact of umpire accuracy on domestic violence, I employ a Poisson model with fixed effects for space and time. This approach allows me to estimate how an umpire\u0026rsquo;s decisions, particularly incorrect calls, influence domestic violence rates in the home county of a baseball team. Specifically, I estimate the following Poisson model, where I model the expected number of domestic violence reports as a function of umpire accuracy:\u003c/p\u003e\u003cp\u003elog(\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e) = \u003cem\u003eλ\u003c/em\u003eInaccuracy\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβX\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e + \u003cem\u003eϵ\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e (1)\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e represents the count of domestic violence incidents in county \u003cem\u003es\u003c/em\u003e on day \u003cem\u003et\u003c/em\u003e. The key independent variable, Inaccuracy\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e, measures the percentage of all called pitches that are incorrectly judged by the umpire. The vector \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e includes a range of game-specific controls such as temperature, air quality index (AQI), attendance, home and away team records, game end time, and an indicator for whether the game was played on a holiday. These covariates help mitigate potential biases due to omitted variable concerns by accounting for temporal and environmental factors that could independently influence domestic violence\u003c/p\u003e\u003cp\u003erates.\u003c/p\u003e\u003cp\u003eFixed effects \u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003est\u003c/em\u003e\u003c/sub\u003e control for both spatial and temporal heterogeneity. My preferred specification includes county-by-year fixed effects (\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003esy\u003c/em\u003e\u003c/sub\u003e), month fixed effects (\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e), and day-of-week fixed effects (\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sub\u003e). The inclusion of county-by-year fixed effects is particularly crucial, as it accounts for time-invariant county-specific characteristics while allowing for differential trends across counties. This absorbs unobserved heterogeneity related to regional law enforcement practices, economic conditions, and other local factors. By incorporating month and day-of-week fixed effects, I control for cyclical patterns in domestic violence, such as seasonal variations and weekend versus weekday reporting dynamics.\u003c/p\u003e\u003cp\u003eI prefer county-by-year fixed effects to separate county and year fixed effects because they allow for differential trends across counties rather than imposing a uniform yearly effect across all locations. Year fixed effects account for common time shocks, and county fixed effects control for time-invariant differences across locations, but together they do not fully absorb local, time-varying shocks. By including county-by-year fixed effects, I control for county-specific trends and allow yearly effects to vary across counties. This approach ensures that local shocks, such as policy changes, economic downturns, or shifts in law enforcement practices, do not bias the estimates. Controlling for these county-level annual shocks is crucial, as they may differentially affect counties in ways correlated with domestic violence.\u003c/p\u003e\u003cp\u003eA central challenge in this analysis is ensuring that umpire accuracy is exogenous to domestic violence outcomes after controlling for observed factors. One concern is that unobservable characteristics associated with game days such as fan aggression, alcohol consumption, or general social unrest may confound the relationship between umpire accuracy and domestic violence. To address this concern, my identification strategy hinges on the assumption that game days with more accurate umpires serve as a valid counterfactual for game days with less accurate umpires. This assumption implies that, conditional on fixed effects and included controls, the variation in umpire inaccuracy is plausibly exogenous. Given that umpires are assigned to games well in advance and that their performance is largely unpredictable ex-ante, this assumption is reasonable. Additionally, because I focus on within-game-day variation in inaccuracy rather than simply comparing game days to non-game days, I avoid conflating the effects of baseball games themselves with the effects of umpire performance.\u003c/p\u003e\u003cp\u003eA related concern is whether domestic violence reporting is systematically different on days with inaccurate umpiring. If, for instance, frustration over incorrect calls leads to an increase in reporting rather than an increase in actual incidents, this could bias my estimates. However, this would require that victims respond to umpire accuracy in a systematic way, which seems unlikely. Additionally, the inclusion of county-by-year fixed effects helps control for broader trends in reporting behavior.\u003c/p\u003e\u003cp\u003eAnother potential concern is that unobserved factors related to game competitiveness or team-specific characteristics may confound the relationship between umpire accuracy and domestic violence. More competitive games, for instance, may heighten emotional investment and fan aggression, potentially influencing both domestic violence rates and umpire performance. To account for this, I control for both teams\u0026rsquo; records, which serve as proxies for team strength and fan expectations. Additionally, I control for game attendance, which captures the popularity and perceived importance of a game, as higher attendance could reflect greater fan engagement and emotional stakes. Game end time is another key factor that I control for, as domestic violence incidents tend to be more frequent at night, and longer games may lead to greater fan frustration while also coinciding with natural peaks in domestic violence. These controls help ensure that my estimates capture the impact of umpire inaccuracy itself rather than broader game-related dynamics.\u003c/p\u003e\u003cp\u003eMuch of the existing literature on game day effects, such as Lindo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), focuses on outcomes like rape and attributes observed increases to broader, indirect mechanisms, such as alcohol consumption. These studies often rely on telling stories about how game-day dynamics lead to these outcomes, without directly testing the causal pathway. In contrast, my analysis provides a more direct test of the effect of umpire accuracy on domestic violence by focusing on variation within the game itself. While it is certainly possible that factors like alcohol consumption or heightened emotions could mediate the effect of poor umpiring, my approach isolates the causal effect of umpire inaccuracy on domestic violence, rather than relying on narrative explanations for how game-day dynamics influence these outcomes. By focusing on within-gameday variation, I avoid the need to speculate about the mechanisms and instead offer a clearer, more direct measure of the impact of umpire accuracy on domestic violence.\u003c/p\u003e\u003cp\u003eFinally, I focus much of my analysis on losses, following prior research by Card and Dahl (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Cardazzi et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which finds that the emotional effects of losses drive increases in domestic violence. This refinement strengthens my identification strategy by ensuring that comparisons are made within a subset of games where emotions are more likely to be heightened. By comparing losses with high inaccuracy to losses with low inaccuracy, I further isolate the impact of umpire decisions on domestic violence outcomes.\u003c/p\u003e\u003cp\u003eTaken together, my empirical strategy capitalizes on within-game-day variation in umpire accuracy while incorporating a comprehensive set of fixed effects and time-varying controls. By isolating the impact of umpire decisions from broader game dynamics, temporal patterns, and team-specific factors, this design strengthens causal identification. The inclusion of county-by-year fixed effects, game-specific covariates, and a refined focus on losses ensures that my estimates capture the true effect of umpire inaccuracy on domestic violence, rather than reflecting confounding influences tied to the game environment or broader social trends.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eUsing a Poisson regression approach, I find evidence that umpire inaccuracies and losses lead to increases in domestic violence in the home counties of MLB teams. Specifically, when a home plate umpire makes inaccurate calls on balls and strikes, domestic violence rates increase. I find that this effect is driven primarily by the most inaccurate umpires. This effect is evident during the regular season, but I find no such effect in the playoffs. Moreover, I observe that the impact is more pronounced later in the season, on Thursdays and Saturdays, and in games where the teams have similar records. Additionally, I find that the effect is driven by instances where umpire inaccuracies favor the opposing team in losses. All regressions control for factors such as temperature, air quality, attendance, team records, game end times, and holidays. Furthermore, all regressions include fixed effects for country, day of the week, month, and year in some combination.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the main results for Eq.\u0026nbsp;(1). Column (1) displays the simple relationship between umpire inaccuracy and domestic violence. Column (2) adds controls to the model, while column (3) introduces fixed effects and removes controls. Column (4) includes additive fixed effects along with controls. Column (5) is the preferred specification, including day-of-week, month, and county-by-year fixed effects along with controls. Column (6) includes county-by-year-by-month fixed effects. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e is divided into three panels. Panel A presents results for all game days in the sample, Panel B focuses on losses, and Panel C includes only wins.\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eIn Panel A, there is no statistically significant relationship between umpire inaccuracy and domestic violence. Turning to Panel B, we observe that umpire inaccuracy has a significant effect on domestic violence in the context of losses. Specifically, in columns (3), (5), and (6), I find a positive relationship between umpire inaccuracy and domestic violence. In the preferred specification (column 5), the coefficient of 0.004 suggests that for each percentage point increase in umpire inaccuracy, domestic violence incidence increases by 0.4%. Using the average inaccuracy rating of umpires in my data, this coefficient implies that a typical umpire in a loss could lead to a little over a 4% increase in domestic violence. The most inaccurate umpire, on the other hand, would lead to a 12% increase in domestic violence. For comparison, Lindo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) find that the incidence of rape increases by 28% on game days, Card and Dahl (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) report a 10.5% increase in domestic violence following upset losses in the NFL, and Cardazzi et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) find a nearly 25% increase in domestic violence in response to upset losses in the NBA on weekends. It is understandable that umpire inaccuracy has a smaller effect than these other sports, as baseball has a much higher frequency of games compared to football and basketball.\u003c/p\u003e\u003cp\u003eThe effect I observe is robust across multiple specifications. Panel C, however, shows there is no significant effect of umpire inaccuracy on domestic violence following wins. Taken together, these results indicate that umpire inaccuracy in losses can lead to increased domestic violence, with this effect holding across different specifications. This finding provides evidence of loss aversion, which I explore further later in the paper. Given that this effect is observed exclusively for losses, I will focus on reporting estimates for the subset of games that end in a loss moving forward, unless otherwise noted.\u003c/p\u003e\u003cp\u003eTo further investigate these effects, I analyze umpire inaccuracy by categorizing it into distinct bins.\u003c/p\u003e\u003cp\u003eThese bins represent different percentile levels of inaccuracy: the bottom 5%, 5%-25%, 25%-50%, 50%-75%, 75%-95%, and above 95%. The most accurate umpires (bottom 5% of inaccuracy) serve as the reference category to prevent multicollinearity. I estimate the relationship between these accuracy bins and domestic violence, restricting the analysis to game days that end in a loss. This regression includes controls and fixed effects for the day of the week, month, and county-by-year. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the estimated coefficients for inaccuracy across these bins, along with 95% confidence intervals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eThese results indicate that there is no statistically significant effect for any accuracy bin except the most inaccurate umpires. Umpires in the highest inaccuracy bin (above 95%) have a coefficient estimate of 0.061, which is statistically significant at the 95% level. This finding provides a more complete picture of what is driving the increase in domestic violence. It is not just general umpire inaccuracy but specifically the most inaccurate umpires that lead to these effects. Given the high frequency of baseball games, it makes sense that average umpire inaccuracy would not have a meaningful impact, even in losses. However, when an umpire is especially inaccurate, it may serve as a strong emotional trigger for individuals predisposed to committing domestic violence.\u003c/p\u003e\u003cp\u003eNext, I examine how these effects vary across different days of the week and months of the year. To do this, I rerun Eq.\u0026nbsp;(1) on two subsets of games. First for games on each day of the week and again for each month of the year. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Panel A of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the effects across different days of the week, excluding day-of-the-week fixed effects. Panel B displays the effects across months, excluding month fixed effects. Both regressions still include county-by-year fixed effects and controls, and I again focus only on games that end in losses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects Of Umpire Accuracy On Domestic Violence At Different Times\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePanel A: Days\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eThu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSun\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.009**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel B: Months\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eApr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJuly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAug\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOct\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: This table presents the relationship between umpire accuracy and domestic violence. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, the regressions in Panel A include fixed effects for the county-by-year and month, while the regressions in Panel B include fixed effects for the countyby-year and day-of-the-week. Standard errors are clustered to the county level. *p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.1, **p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.05, ***p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eLooking at Panel A, I find that umpire inaccuracy leads to increases in domestic violence specifically on\u003c/p\u003e\u003cp\u003eThursdays and Saturdays. Notably, Thursday has the highest domestic violence rates outside of the weekend (if Friday night is considered part of the weekend), while Saturday has the highest overall domestic violence rates. This suggests that Thursdays and Saturdays may already be high-risk days for domestic violence, and inaccurate umpiring could act as an additional emotional trigger for individuals on the intensive margin who are predisposed to committing domestic violence.\u003c/p\u003e\u003cp\u003ePanel B examines how the effects change across months. Here, I find that September is the only month where umpire inaccuracies lead to increases in domestic violence. There are several potential explanations for this. September marks the final stretch of the baseball season, with teams fighting for postseason spots, making these games more meaningful and potentially more emotionally charged for fans. It is reassuring to find that the effect is concentrated in a specific month rather than occurring uniformly throughout the season. Given the sheer number of games played and the inevitable presence of umpire inaccuracies, it would be surprising to see this effect persist across all months. Instead, the concentration of the effect in September suggests that the emotional weight of these games may amplify the response. To test this idea more directly, I next examine games with higher stakes.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents regression results across different subsets of losses, focusing on games that may be more watched. A key issue with studying this topic during baseball games is that baseball plays so many games that a single game outcome may not be much of an emotional trigger. By focusing on games that may have higher stakes, I aim to see if this effect is focused to games that more fans watch or view as more important. As in previous analyses, all specifications include day-of-the-week, month, and county-by-year fixed effects.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects In Higher Stakes Games\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePanel A: Regular Season Losses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel B: Post Season Losses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6220\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.037*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.033**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.033**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.016)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel C: Close Games Losses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel D: Close Records Losses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1990\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay-of-Week Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty-by-Year Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty-by-Year-by-Month Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: This table presents the relationship between umpire inaccuracies and domestic violence rates across a subset of losses in specific games. Regular Season captures all regular season game days in my sample. Post Season captures all playoff game days in my sample. Close games are defined as games decided by one run. Close records are defined as games where teams have records within 5 percentage points of each other. Controls include variables for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the day of the week, month, county, and year in different combinations. Standard errors are clustered to the county level. *p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.1, **p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.05, ***p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor brevity, I continue to focus on games that end in losses.\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003ePanels A and B of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e examine the effects of umpire inaccuracy on domestic violence during regular season and postseason losses, respectively. The results indicate that the effects are driven by regular season games, which is not surprising given that the dataset includes only 71 playoff losses, making it difficult to detect statistically significant effects in that subset. While Panel B shows a large and significant negative coefficient, implying that umpire inaccuracies are associated with less domestic violence reporting. This result is driven by the inclusion of high-dimensional fixed effects in an already limited sample. In such cases, coefficient estimates can become unstable and subject to finite-sample bias. In particular, these postseason estimates appear to reflect two game days with unusually high domestic violence counts in two counties, rather than a meaningful underlying relationship.\u003c/p\u003e\u003cp\u003ePanels C and D of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e examine the effects in games that may draw more attention, specifically close games and matchups between teams with similar records. I define close games as those decided by a single run and teams with close records as those whose winning percentages differ by five percentage points or less. The results show no evidence that inaccuracy increases domestic violence in close games. However, in matchups between teams with close records, I do find evidence of an effect. In fact, this estimated effect is larger than in my main results. This suggests that the increase in domestic violence associated with umpire inaccuracy may be driven by games where teams are closely matched, providing additional support for the earlier finding that the effects are concentrated in September because this is when games get important. As the season winds down, games may take on greater importance because of playoff seeding or pennant races, making inaccurate calls more of an emotional trigger for fans.\u003c/p\u003e\u003cp\u003eNext, I examine how the umpire\u0026rsquo;s favor within their inaccuracy affects domestic violence rates. This analysis provides additional evidence of loss aversion. Table\u0026nbsp;5 presents results separately for cases where the umpire\u0026rsquo;s calls favored the team (Favored) and where they favored the opposing team (Not Favored). Recall that the Home Favor variable captures the composite change in run expectancy based on each individual call made by the umpire. On average, this variable is 0.04 runs, but it ranges from as high as 4.49 runs to as low as -3.54 runs. A positive value indicates that the umpire\u0026rsquo;s calls benefited the team (Favored), while a negative value indicates that the umpire\u0026rsquo;s calls benefited the opponent (Not Favored). I split Table\u0026nbsp;5 into two panels. Panel A presents the results for losses, while Panel B presents the results for wins. Given that this analysis primarily relates to loss aversion, making this distinction is particularly valuable.\u003c/p\u003e\n\u003cp\u003eTable 5: Effects Across Favor Of Calls\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"537\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cem\u003ePanel A: Losses\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eInaccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.007***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePanel B: Wins\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eInaccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eDay-of-Week Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eMonth Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eCounty Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eYear Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003eCounty-by-Year Fixed Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Favored captures all games where the home county team is favored by the inaccuracies of the umpire. A full description of how this is calculated can be found in the text. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the county-by-year, day of the week, and month. Standard errors are clustered to the county level. *p \u003cem\u003e\u0026lt;\u0026nbsp;\u003c/em\u003e.1, **p \u003cem\u003e\u0026lt;\u0026nbsp;\u003c/em\u003e.05, ***p \u003cem\u003e\u0026lt;\u0026nbsp;\u003c/em\u003e.01\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;5 here]\u003c/p\u003e\u003cp\u003eLooking at Panel B, which examines wins, I find no evidence that umpire favor, whether for or against a team, affects domestic violence rates. However, in Panel A, which focuses on losses, the results reveal a significant interaction between umpire favor and inaccuracy. When umpires favor a losing team, their inaccuracies have no effect on domestic violence rates. However, when umpires favor the opposing team in a loss, their inaccuracies lead to a significant increase in domestic violence incidents. Once again, this effect is larger than the findings in my main results. These findings provide further insight into the loss aversion of fans. However, it\u0026rsquo;s not just that a team loses a game, it\u0026rsquo;s the combination of losing and perceiving that the umpire favored the opposing team that appears to drive the increase in domestic violence.\u003c/p\u003e\u003cp\u003eNext, I conduct three robustness checks to validate my findings. First, I vary the time frame used to measure domestic violence incidents. Second, I examine female-to-male domestic violence to assess whether the observed effects are specific to male-perpetrated incidents. Finally, I extend my analysis to all reported crimes, rather than restricting it to domestic violence committed by males against their female spouses, girlfriends, or partners.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the results for varying the time frame of domestic violence, once again focusing solely on games that end in a loss for brevity. The first column replicates my baseline results using the standard 6:00\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVarying Timeframe for Domestic Violence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6pm \u0026minus;\u0026thinsp;6am\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4pm \u0026minus;\u0026thinsp;4am\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8pm \u0026minus;\u0026thinsp;6am\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10pm \u0026minus;\u0026thinsp;6am\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePost-Game\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePre-Game\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: This table shows the effects of umpire accuracy on domestic violence across various timeframes of domestic violence. 6pm-6am is the main specification I use across all other regressions. Post-Game captures all domestic violence that occurs after the hour the game ends through 6am of the following morning. Pre-Game captures all domestic violence that occurs from 7am through the final hour of the game. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the county-by-year, day of the week, and month. Standard errors are clustered to the county level. *p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.1, **p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.05, ***p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ep.m. to 6:00 a.m. window, which aligns with prior literature and captures the period when most domestic violence incidents occur. The second column expands this window to 4:00 p.m. to 4:00 a.m., while the third and fourth columns narrow the window to 8:00 p.m. to 6:00 a.m. and 10:00 p.m. to 6:00 a.m., respectively. In the fifth column, I use a post-game measure, which captures all domestic violence incidents occurring from the hour after the game ends through 6:00 a.m. the following morning. I also use a Pre-Game variable, which captures domestic violence incidents from 7:00 a.m. until the final hour of the game.\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eExamining these specifications, I find positive and significant effects for the 4:00 p.m. to 4:00 a.m. and 8:00 p.m. to 6:00 a.m. windows, indicating that my results are not overly sensitive to the precise time frame chosen. Additionally, I find that inaccuracies in losses primarily lead to increases in domestic violence during the post-game period, with no evidence of an effect in the pre-game period. This is an important validation check because inaccuracies in losses should not be associated with increases in domestic violence before the game ended. If this were true, it would undermine the causal interpretation of my findings, as the game\u0026rsquo;s outcome would not yet be known. However, finding no evidence of a relationship here supports my causal interpretation. Taken together, the results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrate that my findings are robust to different time specifications and that the observed effects are not driven by incidents occurring before the game concludes.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the results of the female-to-male domestic violence model, structured identically to Table\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFemale-to-Male Domestic Violence\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePanel A: Wins and Losses Combined\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.020*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.020*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel B: Losses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.021*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel C: Wins\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.019*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5944\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDay-of-Week Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonth Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty-by-Year Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty-by-Year-by-Month Fixed Effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: This table presents the relationship between umpire accuracy and female-to-male domestic violence. Controls include variables for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the day of the week, month, county, and year in different combinations. Standard errors are clustered to the county level. *p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.1, **p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.05, ***p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e2, which examined male-to-female domestic violence. As before, I divide the results into three panels: Panel A combines wins and losses, Panel B isolates losses, and Panel C isolates wins. The model specification follows the same progression as in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, beginning with a naive specification that excludes controls and fixed effects, then sequentially adding controls, fixed effects, and alternative fixed effect structures.\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003ePreviously, in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, I found that umpire inaccuracies in losses led to an increase in male-to-female domestic violence. However, when examining female-to-male domestic violence in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, I find no such relationship. In the simplest specifications (Columns 1 and 2), there is a negative correlation between inaccuracy and domestic violence, statistically significant at the 10% level. However, once I introduce fixed effects, these effects disappear, and I find no evidence of any relationship between umpire inaccuracies and female-to-male domestic violence.\u003c/p\u003e\u003cp\u003eThis result provides further evidence that the observed effects are driven primarily by male viewers, who may be more susceptible to the emotional triggers associated with the game and its outcomes. While it is possible that women could also be influenced by umpire inaccuracies, the absence of a significant relationship in the female-to-male model suggests that the effect is largely concentrated among male viewers, whose emotional responses appear more closely tied to game outcomes. This distinction reinforces the interpretation that the relationship between umpire inaccuracies and domestic violence is predominantly a male-to-female phenomenon.\u003c/p\u003e\u003cp\u003eThere is also prior evidence suggesting that outcomes of major sporting events can influence a range of behaviors beyond domestic violence, affecting both male and female offenders and victims. To explore whether similar patterns emerge in my data, I extend the analysis to include all reported crimes, regardless of the gender of the offender or victim. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The first column presents estimates for simple assault, followed by intimidation, aggravated assault, rape, larceny, robbery, pocketpicking, drug violations, and vandalism or destruction of property. As in the previous regressions, all models include fixed effects for the day of the week, month, and county-by-year. The results are presented in three panels: Panel A combines wins and losses, Panel B isolates losses, and Panel C isolates wins.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAll Crime\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSimple AssaultIntimidation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAggravated Assault\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRape\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarceny\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRobbery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePocket Picking\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDrugs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eVandalism\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePanel A: Wins and Losses Combined\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel B: Losses\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.012*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003cp\u003e\u003cem\u003ePanel C: Wins\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInaccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNotes: This table shows the effects of umpire accuracy on several crime outcomes. All regressions include controls for temperature, air quality, attendance, team records, game end times, and holidays. Additionally, these regressions include fixed effects for the county-by-year, day of the week, and month. Standard errors are clustered to the county level. *p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.1, **p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.05, ***p\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e here]\u003c/p\u003e\u003cp\u003eAcross all these crimes, I find no evidence of any significant relationship between these outcomes and umpire accuracy. This lack of a relationship is unsurprising given that much of the literature linking such effects to crime partially attributes them to an increase in the number of people in an area. Since I am only examining game days, I do not capture the influx of people that might otherwise be associated with sporting events. Additionally, if crimes are committed due to people attending games, it may be difficult for attendees to discern whether an umpire is calling a game accurately or inaccurately. This suggests that the observed effect is likely driven by male-to-female domestic violence within the home, as these individuals are the ones watching the games and can more clearly observe the umpire\u0026rsquo;s performance.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this study, I use NIBRS data from the home counties of nine Major League Baseball teams, focusing specifically on cases of male-to-female domestic violence, supplemented by pitch-by-pitch data from MLB. I present evidence that domestic violence increases following losses in Major League Baseball games when the umpire is inaccurate. These findings extend the literature on emotional cues and domestic violence by demonstrating that factors beyond game outcomes can influence emotional responses. Using various regression models, I show that these effects are particularly pronounced for regular season games, games played on Thursdays and Saturdays, and games played in September. Utilizing the change in run expectancy caused by incorrect umpire calls, I show these effects are concentrated in games where the umpire favors the opposing team.\u003c/p\u003e\u003cp\u003eThese results indicate that the emotional responses found in other settings may be shaped by specific emotional cues that extend beyond a simple win-loss framework. Here, umpire inaccuracies serve as a particularly salient cue, with higher accuracy mitigating frustration and lower accuracy exacerbating it. This provides broader evidence that perceptions of fairness play a crucial role in shaping emotional responses and subsequent behaviors. This implies that fair officiating has broader social implications than just competitive\u003c/p\u003e\u003cp\u003eintegrity.\u003c/p\u003e\u003cp\u003eWhile my work highlights the impact of umpire accuracy on domestic violence, it does not capture all aspects of officiating decisions. My analysis focuses exclusively on the accuracy of home plate umpires in calling balls and strikes. However, umpires make numerous other crucial decisions throughout a game, including safe/out calls at each base, determining whether a batter has swung at a pitch, and issuing ejections to players and coaches. These other forms of officiating discretion may also generate emotional cues similar to those presented in this study, representing a key area for future research. Additionally, my work is restricted to a subset of nine MLB teams, limiting the generalizability of the findings across all franchises and fanbases. Future research could expand to include more teams and a broader range of officiating decisions to provide a more comprehensive understanding of the effects of referee accuracy on emotional responses.\u003c/p\u003e\u003cp\u003eAnother promising avenue for future research involves developing methods to quantify particularly salient blown calls. Not all officiating errors carry the same weight. An incorrect strike call in the first inning likely generates a weaker emotional response than a missed call in a high-leverage situation. While my study captures broad trends in umpire accuracy, future work could seek to identify and measure the impact of game-changing blown calls. This is particularly relevant in other sports such as football, where each game carries more significance due to a shorter season. For example, a blown call in the final moments of an NFL game may have a more pronounced emotional impact than a single missed strike call in a 162-game MLB season. Developing a framework to quantify the emotional salience of officiating errors across different sports and contexts could significantly enhance our understanding of the role that perceptions of fairness play in shaping fan behavior and societal outcomes.\u003c/p\u003e\u003cp\u003eAn important policy implication of this research concerns Major League Baseball\u0026rsquo;s ongoing efforts to enhance officiating accuracy. Since 2008, MLB has gradually expanded its use of replay review, initially allowing umpires to use video review at their discretion and later implementing manager challenges to correct missed calls. However, these systems currently exclude balls and strikes. MLB has recently begun experimenting with automated strike zones, or \u0026ldquo;robot umpires,\u0026rdquo; which allow for balls and strikes to be challenged. After testing automated ball-strike (ABS) systems in Minor League Baseball, MLB introduced them in spring training and during the 2025 All-Star Game, and has now approved their use in the 2026 season as part of the challenge system. This development could fundamentally alter the role of home plate umpires. Given the evidence that umpire inaccuracy can exacerbate negative emotional responses among viewers, the adoption of robot umpires may not only improve the integrity of the game but also help reduce the unintended social consequences associated with officiating errors.\u003c/p\u003e\u003cp\u003eMy findings align with the work of Bradbury (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which shows how umpire performance is particularly sensitive to league directives and monitoring. My effects are found primarily in September, suggesting that as the season progresses and games increase in importance, umpire accuracy may become even more crucial in shaping emotional responses. MLB may consider increasing monitoring or providing additional guidance to umpires as the season winds down to minimize poor umpiring that could increase domestic violence.\u003c/p\u003e\u003cp\u003eFinally, my findings underscore the broader importance of fair play in professional sports, extending beyond competitive fairness to real-world social consequences. As MLB continues to refine its officiating practices, ensuring greater accuracy and reducing perceived unfairness may not only improve the game itself but also mitigate the unintended negative effects that extend beyond the ballpark.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eTABLES \u0026amp; FIGURES\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;5: Effects Across Favor Of Calls FavoredNot FavoredFavoredNot Favored\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.M. is the sole author for this paper.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI would like to thank Dan Grossman, my PhD advisor, for his guidance, support, and valuable feedback throughout the development of this project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data and code used in this study are publicly available. MLB pitch-level data can be accessed at www.mlb.com (this was scraped using the baseballr package in R), and county-level domestic violence statistics can be accessed at https://www.openicpsr.org/openicpsr/project/118281/version/V11/view. The replication code used for the analyses is available at www.kalvinmudrow.com.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArchsmith, J., A. Heyes, and S. Saberian. 2018. Air quality and error quantity: Pollution and performance in a high-skilled, quality-focused occupation. \u003cem\u003eJournal of the Association of Environmental and Resource Economists\u003c/em\u003e 5(4):827\u0026ndash;863.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradbury, J. C. 2019. Monitoring and employee shirking: Evidence from MLB umpires. \u003cem\u003eJournal of Sports Economics\u003c/em\u003e 20(6):850\u0026ndash;872.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCard, D., and G. B. Dahl. 2011. Family violence and football: The effect of unexpected emotional cues on violent behavior. \u003cem\u003eThe quarterly journal of economics\u003c/em\u003e 126(1):103\u0026ndash;143.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCardazzi, A., B. C. McCannon, B. R. Humphreys, and Z. Rodriguez. 2022. \u003cem\u003eEmotional cues and violent behavior: Unexpected basketball losses increase incidents of family violence\u003c/em\u003e. The Journal of Law, Economics, and Organization.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, D. L., T. J. Moskowitz, and K. Shue. 2016. Decision making under the gambler\u0026rsquo;s fallacy: Evidence from asylum judges, loan officers, and baseball umpires. \u003cem\u003eThe Quarterly Journal of Economics\u003c/em\u003e 131(3):1181\u0026ndash;1242.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDepetris-Chauvin, E., R. Durante, and F. Campante. 2020. Building nations through shared experiences: Evidence from African football. \u003cem\u003eAmerican Economic Review\u003c/em\u003e 110(5):1572\u0026ndash;1602.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDickson, A., C. Jennings, and G. Koop. 2016. Domestic violence and football in Glasgow: are reference points relevant? \u003cem\u003eOxford Bulletin of Economics and Statistics\u003c/em\u003e 78(1):1\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEren, O., and N. Mocan. 2018. Emotional judges and unlucky juveniles. \u003cem\u003eAmerican Economic Journal: Applied Economics\u003c/em\u003e 10(3):171\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFesselmeyer, E. 2021. The impact of temperature on labor quality: Umpire accuracy in major league baseball. \u003cem\u003eSouthern Economic Journal\u003c/em\u003e 88(2):545\u0026ndash;567.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGe, Q. 2018. Sports sentiment and tipping behavior. \u003cem\u003eJournal of Economic Behavior \u0026amp; Organization\u003c/em\u003e 145:95\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIvandi\u0026acute;c, R., T. Kirchmaier, Y. Saeidi, and N. T. Blas. 2024. Football, alcohol, and domestic abuse. \u003cem\u003eJournal of public economics\u003c/em\u003e 230:105031.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaplan, J. 2025. \u003cem\u003eJacob Kaplan\u0026rsquo;s concatenated files: National incident-based reporting system (NIBRS) data, 1991\u0026ndash;2024\u003c/em\u003e. Inter-university Consortium for Political and Social Research [distributor]. ICPSR Data CollectionAnn Arbor, MI:.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirby, S., B. Francis, and R. O\u0026rsquo;Flaherty. 2014. Can the FIFA world cup football (soccer) tournament be associated with an increase in domestic abuse? \u003cem\u003eJournal of research in crime and delinquency\u003c/em\u003e 51(3):259\u0026ndash;276.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlick, J., and J. MacDonald. 2021. Sobering up after the seventh inning: Alcohol and crime around the ballpark. \u003cem\u003eJournal of quantitative criminology\u003c/em\u003e 37:813\u0026ndash;834.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindo, J. M., P. Siminski, and I. D. Swensen. 2018. College party culture and sexual assault. \u003cem\u003eAmerican Economic Journal: Applied Economics\u003c/em\u003e 10(1):236\u0026ndash;265.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMares, D., and E. Blackburn. 2019. Major league baseball and crime: Opportunity, spatial patterns, and team rivalry at st. louis cardinal games. \u003cem\u003eJournal of Sports Economics\u003c/em\u003e 20(7):875\u0026ndash;902.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinnich, A. 2022. Do fans\u0026rsquo; emotions influence charitable donations? evidence from monetary and returnable cup donations in German soccer stadiums. \u003cem\u003eJournal of Behavioral and Experimental Economics\u003c/em\u003e 96:101807.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMunyo, I., and M. A. Rossi. 2013. Frustration, euphoria, and violent crime. \u003cem\u003eJournal of Economic Behavior \u0026amp; Organization\u003c/em\u003e 89:136\u0026ndash;142.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePyun, H. 2019. Exploring causal relationship between major league baseball games and crime: A synthetic control analysis. \u003cem\u003eEmpirical economics\u003c/em\u003e 57:365\u0026ndash;383.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRees, D. I., and K. T. Schnepel. 2009. College football games and crime. \u003cem\u003eJournal of sports Economics\u003c/em\u003e 10(1):68\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrendl, A., N. Stewart, and T. L. Mullett. 2021. The role of alcohol in the link between national football (soccer) tournaments and domestic abuse-evidence from England. \u003cem\u003eSocial science \u0026amp; medicine\u003c/em\u003e 268:113457.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Some pitches are not captured in the data because their strike zones are not recorded by the operator. I exclude these pitches from the analysis, meaning they do not count for or against umpire accuracy ratings. These make up about 0.8% of all pitches and appear to be randomly missing.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e There are 3 possible out states (0 outs, 1 out, 2 outs), 8 possible runner states, and 12 possible balls and strikes states that a hitter may face prior to a pitch being thrown.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Specifically, I use concatenated NIBRS files from Kaplan (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which are compiled and cleaned versions of the NIBRS raw data.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e For the 2009 data, over 92% of all reports in the NIBRS data had only one offense code listed. In 2019, over 91% of all reports only had one offense code.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Cincinnati Reds, Cleveland Guardians, Colorado Rockies, Detroit Tigers, Kansas City Royals, Milwaukee Brewers, Minnesota Twins, Seattle Mariners, and the Texas Rangers\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"eastern-economic-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Eastern Economic Journal](https://www.palgrave.com/gp/journal/41302)","snPcode":"41302","submissionUrl":"https://submission.springernature.com/new-submission/41302/3","title":"Eastern Economic Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Other/Unknown","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"intimate partner violence, domestic abuse, emotional cues, crime","lastPublishedDoi":"10.21203/rs.3.rs-8099255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8099255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the effect of umpire inaccuracies on domestic violence reports. Using pitch-by-pitch data from Major League Baseball and crime reports from the National Incident-Based Reporting System, I show an increase in the number of domestic violence reports when MLB teams lose with an inaccurate umpire. This effect is focused to losses in games with extremely inaccurate umpires. Additionally, the effect is driven by umpire’s calls favoring the opposing team. I find this effect to be robust to different specifications of domestic violence timing and driven by domestic violence that occurs after a game.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Codes\u003c/strong\u003e: J12; D91; K42\u003c/p\u003e","manuscriptTitle":"Balls and Strikes: The Effects on Domestic Violence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 12:11:27","doi":"10.21203/rs.3.rs-8099255/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T20:01:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T17:03:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T00:27:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139267299750879763340659305103844576537","date":"2025-11-17T19:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241655331697742310017817386674920698066","date":"2025-11-14T17:35:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T17:32:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-14T17:28:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T21:39:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Eastern Economic Journal","date":"2025-11-12T19:04:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"eastern-economic-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Eastern Economic Journal](https://www.palgrave.com/gp/journal/41302)","snPcode":"41302","submissionUrl":"https://submission.springernature.com/new-submission/41302/3","title":"Eastern Economic Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Other/Unknown","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"35e8fb18-5da7-4bfd-a31e-90d64ee154af","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T18:09:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 12:11:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8099255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8099255","identity":"rs-8099255","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}