ACL Tear Rates In and Across Women's Football Leagues: Insights from a Unique 2-Year Database

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Sewell, Vanessa Crespin, Katrine Okholm Kryger, Cong Zhou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7349933/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Anterior cruciate ligament (ACL) injuries are a high injury burden in football, with a notably higher incidence rate among women compared to men. However, epidemiological studies are limited in women’s football so is presently no statistically rigorous or fully quantified context for understanding this difference. This study utilizes public news, blog, and social media data to create a statistical framework for analysing ACL injury rates in women’s football to enable better comparisons and understanding of the probability of the different numbers of injuries per team per season. Employing Poisson distributions, which are widely employed to assess the incidence of discrete events whose occurrence is relatively rare, we are able to quantify ACL injury incidence and compare it across different top women’s football leagues from various counties, and against the English Premier League for men where other longer-term data was available. The analysis includes calculating the raw mean and median injury rates, constructing confidence intervals, and evaluating the number of additional injury-free games and fewer injuries per season (for a league) required for women’s football to match the men’s injury rates. The results reveal significant differences in ACL injury rates between men’s and women’s football with an overall difference of 2.5x (p < 0.05), as expected, with unexpected variability of almost 4x in rates observed across the 12 women’s elite or premier leagues. The Swedish, Netherlands, and Mexican women’s leagues show no statistically significant difference in incidence rates to the men in this comparison though the raw data incidence is slightly higher. These insights offer an empirical foundation for targeted research into causes of these differences. Study limitations highlight the need for improved data collection and reporting practices. The overall study results underscore the importance of applying more rigorous statistical methods to this problem to better target inquiry and to assess future interventions. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors ACL Women’s Football Female Relative Risk Injury Database Figures Figure 1 1.0 INTRODUCTION Anterior cruciate ligament (ACL) tears is a rare but burdensome injury in football (soccer), and several other sports, where mobility, high energy, and speed are at a premium. ACL rehabilitation typically spans 9–12 months typically before return to play and can potentially end careers prematurely. In football, several reports have noted a significant difference between the incidence rate of men and women [ 1 , 2 ]. However, incidence rates, especially among women, have not been well-quantified in the literature [ 2 – 6 ]. More specifically, these studies often report significantly higher ACL injury rates in women compared to men, with some indicating rates 2–6 times higher [ 1 , 2 ]. Many studies are hindered by small sample sizes and/or labour-intensive processes for collecting detailed exposure data [ 6 – 8 ], such as the number of minutes played per participant. Equally, several studies mix multiple playing levels, such as elite and high school, where confounding factors such as level of training or total season exposure time vary significantly. Overall, this approach to assessing incidence can complicate some practical application and generalizability [ 5 , 6 , 9 – 11 ]. Despite extensive research on ACL injury causes and influencing factors [ 2 , 6 , 12 – 15 ], conclusive answers remain elusive. One major factor limiting understanding is the (positive) fact these injuries have relatively low incidence rates given the number of players or/and games per season. To obtain enough injury data to quantify incidence and to identify/eliminate potential risk factors with robustness and/or statistical significance would require significantly larger numbers of injuries and data for teams or leagues over time than readily available today. A robust statistical framework accurately quantifying the probability of ACL injury incidence at league or team level would enable calculation of likelihood of any identified risk factors and/or causal relationships found. It would also enable calculation of present value costs to assess the impact and cost of interventions. The relative scarcity of injury data makes this task difficult. Further, the use of normally-distributed statistics may mean statistical results do not accurately reflect the actual distribution of incidence rates [ 15 ]. This study addresses these challenges by leveraging a publicly compiled injury dataset to establish a statistical framework for ACL injury incidence across several top women’s football leagues. It then uses the Poisson distribution, widely employed to assess the incidence of discrete events whose occurrences are relatively rare and independent [ 21 – 27 ] to create distributions of incidence rate per season as games played per injury for a given league. Results are compared to injury rates from the English men’s Premier League as an international comparator for which long-term, multi-year data was available [ 28 ] to evaluate differences by sex. Evaluating by league allows assessment of any variability across leagues, which has not been considered before in smaller more focused studies, where differences across leagues might offer insight into potential causal factors. 2.0 METHODS 2.1 Data Data for this study was sourced from a specialised Twitter (X, Bastrop, TX) account dedicated to reporting injury information sourced from public news outlets, including blogs, mainstream news sources, and social media platforms where teams, players, and fans share updates [ 29 ]. The dataset was cross-checked across multiple publicly available news and social media sources to ensure the accuracy and completeness as much as possible, encompassing details such as player names, ages, teams, and specific match details (date, teams, location) which were available. Thus, the dataset and injuries presented represent a minimum number of events, where some injuries may have been missed, similar to surveillance programs. The study period spans two calendar years and includes data from three football seasons, from January 1, 2022, to January 31, 2024. This timeframe provides a comprehensive snapshot of ACL injury occurrences in top women’s football leagues during this specific period. For some leagues this is two whole seasons, but for others it covers two half seasons and one full season in between. The fixed date approach was chosen as leagues begin and end at different points in the year depending on hemisphere, continent, and league, where data before this time was sparse or not collected in this dataset. To establish a gender comparative baseline, ACL injury incidence in the English men’s Premier League were derived from historical data spanning approximately the last 13 years [ 28 ]. This long-term average serves as a reference point for understanding injury trends in men’s football and provides context for comparing injury rates across sexes in this study. In assessing relatively rare incidence events, there are essentially two options in the presence of limited data. First, to collect data for one team or league over a long period, risking bias from changing circumstances or training. Second, to examine multiple teams or leagues over a short snapshot of time, risking bias of outlier events or years. In this study, only a short period was available for the women, but over many leagues providing enough games, and thus injuries, to assess trends. In contrast, data for men’s leagues was limited to one league, but this longer 13-year study was available in the literature. As this analysis seeks to assess larger differences, if they exist, the different risks of bias are noted, but should not impact larger statistically significant differences, if they exist. Table 1 outlines the specific leagues and nations included in this analysis, encompassing women’s elite football leagues and the English Premier League for men with its median and extreme values to show the spread in this analysis. The women’s leagues included from the database had 4 or more injuries reported per season, and those with the lowest values were crosschecked extensively to find any missed injuries. This limit was imposed to minimise the potential levels of missing injuries. The games per season for each league was calculated, where additional games from multi-division cups (e.g. FA Cup in England) or international Champions Leagues were averaged over the 2 years. Importantly, a game within the league counts as two games in Table 1 , as two teams are playing one game each, where this approach allows counting games played across leagues more easily as the league would only have one team in such a game, with the other team would come from a different league. The number of injuries is as reported in the dataset and crosschecked by the authors to the best of their ability but remains a lower bound, where the men’s data is from an independent study [ 28 ]. Table 1 Leagues and demographic data in the analysis and period January 2022 to January 2024. Total games include a range of international and national cups and cups across divisions of leagues, and can vary from year to year depending on advancement in the cups and leagues. Using available data the games were counted, but up to ~ 1% may be missed due to lack of full information across leagues. Average age is for those players injured where age could be found. Games estimated or counted. ACL injuries from the database. In the sub- or second table, the Men’s data are for median injuries per season over the study period in [ 28 ] and for 1 season, where the maximum and minimum values show the range over time and variability. Premier League (Nation) # Teams Average Age of Injured Games per Season (counted) Num Injuries in 2-years National Women’s Soccer League (USA) 12 27 352 27 French D1 Arkema (France) 12 24 295 22 Women’s Soccer League (England) 12 26 324 25 Liga F (Spain) 16 25 520 29 A-League Women (Australia and New Zealand) 11 24 206 13 Liga MX (Mexico) 18 24 668 13 Damallssvenskan (Sweden) 14 24 415 10 Eredivisie Vrouwen (Netherland) 12 22 267 7 Frauen Bundesliga (Germany) 12 24 285 19 Frauen Bundesliga (Austria) 10 22 200 10 Ekstraliga (Poland) 12 22 280 9 WSL (Switzerland) 10 23 217 7 Women’s Total 4029 191 Premier League (Nation) # Teams Average Age of Injured Games per Season (counted) Num Injuries per 1 season England Men’s Premier League (median) 20 899 8 Premier League (England) Max (3x in 15yrs) 20 899 12 Premier League (England) Min (2x in 15 yrs) 20 899 5 2.2 Ethics The utilization of social media and publicly available news data [ 29 ] for research purposes introduces ethical considerations, which are influenced by diverse cultural, social, and legal norms across countries. These sources, while rich in information, can raise concerns regarding privacy and data sensitivity. To address these challenges, this study strictly employs de-identified data at the league level, ensuring specific details, such as team names or exact years, are excluded to prevent the identification of individual injured players. This approach meets the Australia-New Zealand Statements on Ethics [ 30 , 31 ]. The study adheres to guidelines set forth by the European Union, particularly under the General Data Protection Regulation (GDPR) [ 32 ],which governs the ethical use of personal data in research. Finally, the authors consulted with experts in the United States and Europe to better understand and ensure compliance with regional ethics standards and statements. Exemption from ethics approval for this study was obtained from the University of Canterbury Human Research Ethics Committee (HREC-2024-02 EX), aligning with the Australia-NZ Statement of Research Ethics [ 30 , 31 ]. 2.3 Incidence rate The data in Table 1 and limitations on identifiable data provide the basis for assessing incidence rate given the number of ACL injuries over the two calendar years of data by league as a rate of games played (by a single team) per injury. Average injury rates per team can be obtained by dividing by the number of teams, but the data is sparse enough taking the analysis to the team level would not add any robust information. Thus, for any given league, the incidence rate (IR) is defined as the games played by teams in the league, including international champions leagues and cups, per injury reported: $$\:IR=\frac{Games}{{\overline{N}}_{ACL}}$$ 1 where Games is games per season for a given league, and \(\:{\overline{N}}_{ACL}\) is mean number of ACL injuries per season for the same league using the two calendar years of data. Using the mean value filters some year to year variability for a comparison, and a similar mean over the longer period of men’s league data is used for the same reason, as well as their maximum and minimum value over the longer period of men’s data. This rate has the advantage of being readily assessed, but is an average, and not every team will see this number for a given league. By using the total time period any uncertainty or variability over the two-year period is lost, but two years is also too small for a robust estimate of this uncertainty. If distributions between men and women are very similar, this loss of uncertainty will cloud results, however, if they are far apart it will not impact any conclusions. 2.4 Poisson Distributions and Statistical Modeling of ACL Incidence Rate Poisson distributions are a fundamental statistical tool used to model rare, discrete events which occur independently within a fixed interval of time or space [ 33 ]. Poisson distributions are widely used [ 21 – 27 ] to quantify rare events because they require the estimation of only a single distribution parameter, \(\:\lambda\:\) , the mean event (incidence) rate. They can also be used to assess the statistical differences between leagues, men and women, and the likelihood of one league having greater or less numbers of injuries in a given season. Finally, they can be used to convert an injury number for any one or few years into a likelihood value, when asked if a certain number of injuries is high or low. The Poisson distribution is defined by the following probability mass function: $$\:P\left(X=k\right)=\frac{{\lambda\:}^{k}{e}^{-\lambda\:}}{k!}$$ 2 where X represents the number of ACL injuries occurring within number of games, k is the number of games played per injury from the raw data, λ is the mean rate of ACL injuries per games played, and e is the base of the natural logarithm. More explicitly, Eq. ( 2 ) defines the probability of having X = k injuries in a given season as a function of the number of games played per injury, as defined in Eq. ( 1 ), for a given league from the raw data, denoted l , and e is the natural logarithm. Thus, knowing l from the raw data, a distribution can be created to show the probability of injury rates greater, or lesser, than this value, which can be compared using statistical tests to other leagues or data for men. This statistical model of the distribution of ACL injury incidence rates is modeled for injuries within different leagues or over different seasons. It thus presents a distribution defining the likelihood of a given number of injuries per league, given a known number of games played per season. Hence, it is an incidence rate per league per year. 2.5 Analyses Comparing Poisson distributions across leagues helps standardize the analysis by accounting for variations in game frequency and league intensity. Distributions are calculated for each league listed in Table 1 , and their mode and directly calculated 95% confidence interval are compared. Incidence is assessed per Eq. ( 1 ) as games per injury, thus normalising out the differences in games played per league. Further, in comparing women’s leagues, to women over all leagues, the data for the league being compared is not removed from the set of all data for women, ensuring a more conservative comparison of 95% confidence intervals. This choice also recognises the large number of games in Table 1 , and thus the removal of any one league makes only modest to small differences to the overall distribution. Further calculations show the number of injury-free games per league per season for each women’s league to indicate how many additional games without injury would be required to match the English men’s Premier League average rate. A positive value indicates the specific women’s league had a higher rate of injury in the raw data compared to the men’s league, and a negative value would be a lower raw injury incidence as defined. This value provides further context and contextual feel for the results, where large values indicate a highly unlikely opportunity for the two leagues being compared to be the same and quantifies this difference in games or match time exposure. 3.0 Results Table 2 shows the overall results and it is clear there is variability across leagues. A first overall result shows the rate of injury, using this measure, is 2.8x larger for women than men [ 1 , 2 ]. Figure 1 shows the results graphically for selected leagues in Table 2 and clearly shows a wide range of incidence rates, some of which are not significantly different from the men’s rate, while others have far higher differences. Notably, the maximum injury rate for the men’s comparator over 15 years has an IR lower than 3 leagues and would not be statistically significantly different than a further 2 women’s leagues. The minimum IR for men’s leagues in Table 2 would be statistically significantly different from all women’s leagues in Table 2 . It is clear some women’s leagues are statistically different from all women’s leagues in general, even while including their data in the overall set for a conservative analysis. In particular, the Damsallsvenskan (Swedish) and Eriedivisie (Dutch) women’s leagues (Figs. 1 a, 1 b) are not statistically different from the men, though with higher injury rates (lower number of games per injury). More importantly, they are statistically different from women overall, including their data, showing a less than 5% chance they would have a higher rate (lower number of games per injury) than women in all other leagues, where this analysis is, again, conservative. Similar results hold for the Mexican Women’s Liga MX. In contrast, the women’s NWSL in the USA and Women’s Super League in England (Figs. 1 c, 1 e) have higher rates than women overall. The range of relative injury rates goes from 1.1x to 7.0x of the English Premier League (men’s) rate. Finally, comparing the range of men’s English Premier League results alone, the number of injury free games required to reach either the high or low limit are very large and thus show the range of possibilities in these distributions. Table 2 Incidence rate, IR , in league games played per ACL injury, IR relative to English men’s median rate and injury free games over 2 years to match the men’s median rate. Results for each league and overall. The IR is also k in Eq. ( 2 ). The first 4 columns are also in Table 1 and replicated here for context. Premier League (Nation) # Teams Games per Season Num Injuries 2-years IR IR relative to England Men’s Premier League Injury Free Games to Match England Men’s Premier League IR National Women’s Soccer League (USA) 12 352 27 26.1 4.3 1162 French D1 Arkema (France) 12 295 22 31.5 3.6 767 Women’s Soccer League (England) 12 324 25 27.6 4.1 1004 Liga F (Spain) 16 520 29 27.9 4.0 1560 A-League Women (Aus-NZ) 11 206 13 31.6 3.6 536 Liga MX (Mexico) 18 668 13 102.8 1.1 67 Damallssvenskan (Sweden) 14 415 10 83.4 1.4 166 Eredivisie Vrouwen (Netherland) 12 267 7 84.6 1.3 80 Frauen Bundesliga (Germany) 12 285 19 33.33 3.4 684 Frauen Bundesliga (Austria) 10 200 10 25.0 4.5 700 Ekstraliga (Poland) 12 280 9 16.1 7.0 1680 WSL (Switzerland) 10 217 7 16.1 7.0 1302 Women’s Total 4270 191 40.0 2.8 9708 Premier League (Nation) # Teams Games per Season Num Injuries per season IR IR relative to England Men’s Premier League Injury Free Games to Match England Men’s Premier League IR England Men’s PL 20 899 8 112.4 1.0 0 Premier League (England) Max (3x in 15yrs) 20 899 12 74.9 1.5 450 Premier League (England) Min (2x in 15 yrs) 20 899 5 179.8 0.6 -360 4.0 Discussion The overall study examines a relatively large dataset of injuries covering 8540 women’s football games over two calendar years and comparing to injury data from ~ 11650 the English Men’s Premier League games over 13 seasons. The goal was to examine relative incidence rate overall, and any differences across national women’s premier leagues. 4.1 Main Results and Key Takeaways The results reveal a pronounced disparity in ACL injury rates between men’s and women’s football. This result aligns with previous reports indicating women experience ACL injuries at rates 2–6 times greater than the men [ 1 , 2 , 5 , 6 ]. The specific value of 2.5x also matches current mainstream estimates [ 34 , 35 ], but is lower than some more popularly reported values up to 4-6x [ 36 ]. It is also higher than a more recent meta-analysis across multiple sports and levels [ 37 ]. Thus, this analysis confirms women’s football leagues generally exhibit higher ACL injury rates compared to their male counterparts, and shows it is statistically significant within an appropriate statistical modeling framework. Equally, the results compare well with prior studies, meta-analyses, and expectations within the field for elite leagues in this sport. Importantly, in comparison to prior high resolution studies examining incidence by game play time and similar, the analysis covers 2 years of play over 2–3 seasons depending on league. Thus, it includes an estimated total of 8540 women’s games where Premier League men over the same period played ~ 1790 games, noting again for clarity a game within the league counts as 2 as two teams are playing. The overall difference reported is consistent with existing literature but the greater number of games provides greater robustness to the result. The comparisons across women’s leagues also provide a more granular understanding of the variability among different leagues. Overall, to the author knowledge, this study is one of the largest of its kind in the number of teams and games considered in the women’s leagues. The use of Poisson Distributions provides a quantified level of the potential variability which might be seen year to year. In particular, injury rates can vary significantly year-to-year and over longer periods, as seen in the men’s data used [ 28 ], and variability in statistical significance when comparing their maximum and minimum injury rates to the women’s data. However, a distribution puts these values into context in terms of their likelihood of occurrence. As better data arises and/or accrues over time, these distributions can be easily updated. By comparison, in earthquake (seismic) engineering, the Poisson distribution is commonly employed in seismic hazard analysis to quantify the likelihood of ground-shaking intensity [ 38 ], and subsequently its use in determining infrastructure damage and consequences. For women’s sports this analysis and analogy would follow onto the economic impacts of an ACL injury. Given a probabilistic model to quantify likelihood, an economic net present value can be calculated for any intervention or choice to compare to an estimated cost of the injury. Such values help engineers and insurers make informed decisions on risk management and cost-benefit analysis of structural interventions or preparedness measures [ 39 ]. There is thus a good and direct analogy to the relatively rare, discrete, and highly damaging events of ACL injuries. 4.2 Variability Among Women’s Leagues A notable contribution of this study is the identification of previously undocumented variability among women’s football leagues. This variability challenges the assumption of uniform injury rates among women, suggesting factors specific to each league, such as training practices, playing conditions, or regional differences, play a significant role in injury incidence. These findings are supported by similar observations in other sports, such as ballet [ 1 , 5 , 6 , 40 ], where differences in injury rates between men and women may be due to specific training and conditioning factors in these sports. More specifically, there is no data or highlighted specific intervention which may be a cause for these results. However, these results provide starting points for specific inquiry into potential causal factors for these differences. Finally, and perhaps most importantly, the very low rates in some leagues, if further confirmed, indicate the potential for women to approach similar injury rates to the men. Overall, these particular results also show treating women as a monolithic entity or group in assessing this problem may not be valid. The differences may be due to missing data in some of the less well-known leagues, despite rigorous crosschecking and analysis. However, the results in Table 2 showing very large numbers of injury free games (for women) or increased injuries (for men, or decreased injuries for women) to reach the women’s (men’s) average rate suggest these differences may also suggest where to look for differences and potential causal factors in training or other background factors. 4.3 Impact on Injury Occurrence The metrics of injury-free games and reduction goals directly support the observed trends. For instance, the need for additional injury-free games to match men’s injury rates highlights the extent of intervention required, where potential adherence of players or teams to specific intervention programs could be low and/or highly variable. While men’s football exhibits a broader variability in injury rates over the 13 years noted, it does not significantly alter the overall conclusions, and further highlights the variability possible, which has not yet been seen in a collection (over time) of women’s league data. 4.4 Identifying New Avenues for Research The study results and the variability across leagues in particular should prompt the exploration of new research avenues beyond traditionally assessed factors like menstrual cycles or birth control use [ 2 , 14 , 41 – 44 ]. Future investigations should focus on specific mechanisms influencing individual variability in ACL injury rates. In particular, variability and factors related to biomechanics, training methods, or other contextual factors may provide greater insight [ 2 , 13 , 45 ]. 4.5 Limitations While the study provides insights, there are limitations to the data. Despite rigorous crosschecking there may be missing injuries. However, as such, the trends remain similar or greater than what is shown here. This issue is particularly true for some leagues, such as the Mexican Women’s Liga-MX where data was harder to find, than for others such as the well-known English WSL and USA NWSL. These variabilities occur to relatively different levels of coverage and media, among many factors. Again, these values are lower bounds, and the trends should be robust given the number of games involved. The use of Poisson Distributions also provides an idea of the potential variability seen year to year to avoid over-reacting to potential outliers as more data becomes available. In comparing the women’s leagues over 2 years and overall with the English Premier League men’s rates over 13 years provides large enough numbers of games (Table 1 ) to ensure robust incidence rates. However, the difference in time scales may create bias, where the original report of the men’s data noted declining rates of injury over time [ 28 ] though recent increases [ 46 ] indicate these declines were perhaps a statistical event inside the distribution in Figs. 1 and Table 1 for the English Men’s Premier League. This potential bias is mitigated by the lack of data overall for comparison, as well as by the fact the original report was reported in contrast to the occurrence of more recent increases in injury rates [ 46 ]. This latter point also indicates how injury rates may appear low, or high, for several years, but still remain within a likely range given the width of the Poisson Distributions in Fig. 1 and Table 2 . A final limitation of the data is the Mexican MX-Liga data. It was very difficult for the authors to find additional injury reports, and the values and incidence are very comparable to the men’s rates. This outcome may be valid, but likely there are injuries missed. Again, this outcome is a lower bound, but this league may be far lower than actual. The issue is mitigated in this comparison because all women’s leagues are used in the overall analysis in Fig. 1 , and any one league does not contribute excessively to this overall distribution. As seen in these limitations, data availability and quality is an issue. Datasets can be incomplete data due to voluntary reporting or confidentiality concerns among teams and athletes, though more recommendations are emerging [ 16 ]. For instance, the US NCAA Injury Surveillance Program data had not been publicly available for several years [ 10 , 17 – 19 ]. Equally, surveillance programs and recommendations [ 16 ] are often voluntary and some data may be missed, just as in this study, although in this regard, the NCAA program had high compliance of 88–93% [ 20 ]. Thus, this the results of this and other studies offer lower bounds on reported injuries, which limits the robustness of some conclusions. The study highlights the critical need for more comprehensive and high-quality data sources. Potential avenues for improvement could include leveraging insurer data from companies who provide the mandatory insurance for athletes required by the US NCAA, which could offer valuable insights into injury trends with far higher data quality and consistency over time than was available here. Additionally, advocating for standardized reporting practices by governing bodies, such as the English FA and UEFA, could enrich comparative analyses and support the development of effective injury prevention strategies. Similarly, a greater focus on leagues away from North America and Europe is a limitation, which needs to be addressed as only Mexico and the Australia-New Zealand A-League were in this study. Finally, this limitation highlights the “poverty” in the study of women’s sports, where this issue has been noted since the early 1990s [ 47 ] but still lacks larger scale contextual data despite the passing of three decades. 5.0 Conclusion This study reveals significant disparities in ACL injury rates between women’s and men’s football, using Poisson distributions to analyse a dataset of public injury reports. Overall, ACL injuries are more common in women’s football in comparison to the men’s English Premier League, with an average difference in rate of 2.5x. Furthermore, there is notable variability in injury rates among different women’s leagues, suggesting league-specific factors such as training practices and playing conditions play a crucial role. The study encompasses a large number of games to ensure robustness. The use of Poisson distributions provides a robust statistical framework to this problem for the first time, and allows statistical significance to be assessed in any comparison, as well as the ability to assess the likelihood of any given injury number per season. The low rates for some elite women’s leagues are not statistically different from the men’s league comparator and show the potential for women’s injury rates to be relatively much lower, up to 4x, in some leagues. These results would be enhanced by a larger more complete dataset over a longer period. Finally, the study highlights the need for better ethics guidelines and standards for using these data in general, and data from social media and public new sources in particular, where this study’s authors struggled to assure themselves of the best approach. Declarations Funding: The authors received no funding for this work. Author Contribution J.S. wrote the manuscript and did all analysis and computation. V.C provided support with data collection. J.G.C., T.D., J.F.K, K.O.K provided methodological and analytical input to design and analysis of the study. All authors reviewed the manuscript and provided advice/feedback. Data Availability As described in the Ethics section of the Methods. All de-identified data is in Tables 1-2, which reports injury numbers per league over the study period and number of games per season over the period. Thus, all ethically permissible data to be shared is available in the paper. Our ethics approval to use the publicly available data does not extend to reporting player names or teams which would make the players identifiable. Further, there are European jurisdictions where only the aggregate data, as reported, is permitted and since many of the injuries are reported from European leagues, this set of data in Tables 1-2 is all we can report. Hence, all data is in the paper and available for further use with citation. References Arendt, E. and R. 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Moore, I.S., et al., Female athlete health domains: a supplement to the International Olympic Committee consensus statement on methods for recording and reporting epidemiological data on injury and illness in sport. British Journal of Sports Medicine, 2023. 57 (18): p. 1164-1174. Agel, J., E.A. Arendt, and B. Bershadsky, Anterior cruciate ligament injury in national collegiate athletic association basketball and soccer: a 13-year review. Am J Sports Med, 2005. 33 (4): p. 524-30. Center, D. NCAA Injury Surveillance Program . 2018; Available from: https://www.ncaa.org/sports/2018/4/9/ncaa-injury-surveillance-program.aspx. Chandran, A., et al., Methods of the National Collegiate Athletic Association Injury Surveillance Program, 2014-2015 Through 2018-2019. J Athl Train, 2021. 56 (7): p. 616-621. Kucera, K.L., et al., Validity of soccer injury data from the National Collegiate Athletic Association's Injury Surveillance System. J Athl Train, 2011. 46 (5): p. 489-99. Allin Cornell, C. and S.R. Winterstein, Temporal and magnitude dependence in earthquake recurrence models. Bulletin of the Seismological Society of America, 1988. 78 (4): p. 1522-1537. Lord, D. and F. Mannering, The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation research part A: policy and practice, 2010. 44 (5): p. 291-305. Zhu, Y., et al., Concentration and size distribution of ultrafine particles near a major highway. Journal of the air & waste management association, 2002. 52 (9): p. 1032-1042. Camerino, D., et al., Shiftwork, work-family conflict among Italian nurses, and prevention efficacy. Chronobiology international, 2010. 27 (5): p. 1105-1123. Wang, J. and S.-C. Chang, Evidence in support of seismic hazard following Poisson distribution. Physica A: Statistical Mechanics and its Applications, 2015. 424 : p. 207-216. Wang, K., et al., A bivariate zero-inflated Poisson regression model to analyze occupational injuries. Accid Anal Prev, 2003. 35 (4): p. 625-9. Bailer, A.J., L.D. Reed, and L.T. Stayner, Modeling fatal injury rates using Poisson regression: A case study of workers in agriculture, forestry, and fishing. Journal of Safety Research, 1997. 28 (3): p. 177-186. Waldén, M., et al., ACL injuries in men's professional football: a 15-year prospective study on time trends and return-to-play rates reveals only 65% of players still play at the top level 3 years after ACL rupture. Br J Sports Med, 2016. 50 (12): p. 744-50. ACL-Womens-Football-Club, A. 2022; Available from: https://x.com/aclwfc. Zealand, H.R.C.N., HRC Research Ethics Guidelines. 2021. NHMRC, National Statement on Ethical Conduct in Human Research . 2023. commission, E., Ethics and data protection . 2021. Haight, F.A., Handbook of the Poisson Distribution . 1967. Waldén, M., et al., The epidemiology of anterior cruciate ligament injury in football (soccer): a review of the literature from a gender-related perspective. Knee surgery, sports traumatology, arthroscopy, 2011. 19 : p. 3-10. Prodromos, C.C., et al., A meta-analysis of the incidence of anterior cruciate ligament tears as a function of gender, sport, and a knee injury–reduction regimen. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 2007. 23 (12): p. 1320-1325. e6. Hewett, T.E., Neuromuscular and hormonal factors associated with knee injuries in female athletes: strategies for intervention. Sports medicine, 2000. 29 : p. 313-327. López-Valenciano, A., et al., Injury profile in women’s football: a systematic review and meta-analysis. Sports medicine, 2021. 51 : p. 423-442. Baker, J., B. Bradley, and P. Stafford, Seismic hazard and risk analysis . 2021: Cambridge University Press. Krawinkler, H. and E. Miranda, 9.1. A Perspective of Performance-Based Earthquake Engineering. Earthquake Engineering: From Engineering Seismology to Performance-Based Engineering; CRC Press: Boca Raton, FL, USA, 2004: p. 87. Emerson, R.J., Basketball knee injuries and the anterior cruciate ligament. Clin Sports Med, 1993. 12 (2): p. 317-28. Zazulak, B.T., et al., The effects of the menstrual cycle on anterior knee laxity: a systematic review. Sports Med, 2006. 36 (10): p. 847-62. Burgess, K.E., S.J. Pearson, and G.L. Onambélé, Patellar tendon properties with fluctuating menstrual cycle hormones. J Strength Cond Res, 2010. 24 (8): p. 2088-95. Dam, T.V., et al., Muscle Performance during the Menstrual Cycle Correlates with Psychological Well-Being, but Not Fluctuations in Sex Hormones. Med Sci Sports Exerc, 2022. 54 (10): p. 1678-1689. D'Souza, A.C., et al., Menstrual cycle hormones and oral contraceptives: a multimethod systems physiology-based review of their impact on key aspects of female physiology. J Appl Physiol (1985), 2023. 135 (6): p. 1284-1299. Collings, T.J., et al., Risk Factors for Lower Limb Injury in Female Team Field and Court Sports: A Systematic Review, Meta-analysis, and Best Evidence Synthesis. Sports Med, 2021. 51 (4): p. 759-776. Shephard, S., ACL injuries in men’s and women’s football: So many factors equal so much uncertainty ( https://www.nytimes.com/athletic/5120100/2023/12/08/acl-crisis-premier-league-wsl/ ), Dec 8, 2023 , in The Guardian . 2023, The Guardian: London, UK. Parsons, J.L., S.E. Coen, and S. Bekker, Anterior cruciate ligament injury: towards a gendered environmental approach. British Journal of Sports Medicine, 2021. 55 (17): p. 984-990. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7349933","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504244256,"identity":"a4e1bbc0-de87-4f66-8502-33ac1c77b15d","order_by":0,"name":"Jessica G. Sewell","email":"data:image/png;base64,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","orcid":"","institution":"Univ of Canterbury","correspondingAuthor":true,"prefix":"","firstName":"Jessica","middleName":"G.","lastName":"Sewell","suffix":""},{"id":504244257,"identity":"d0353546-fe7b-4959-8254-fa911e9dc069","order_by":1,"name":"Vanessa Crespin","email":"","orcid":"","institution":"Independent Researcher and Data Analyst","correspondingAuthor":false,"prefix":"","firstName":"Vanessa","middleName":"","lastName":"Crespin","suffix":""},{"id":504244258,"identity":"5ede563e-1d65-4add-95a0-42ff8b5e17d7","order_by":2,"name":"Katrine Okholm Kryger","email":"","orcid":"","institution":"I Union of European Football Associations (UEFA)","correspondingAuthor":false,"prefix":"","firstName":"Katrine","middleName":"Okholm","lastName":"Kryger","suffix":""},{"id":504244261,"identity":"9fcea8b7-cfed-4d5e-bfd1-0141b17c0a6a","order_by":3,"name":"Cong Zhou","email":"","orcid":"","institution":"Univ of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Zhou","suffix":""},{"id":504244263,"identity":"ddd0b235-56bb-4f05-a9ce-ed1b4cd0d4ac","order_by":4,"name":"Jean-Francois Kaux","email":"","orcid":"","institution":"University Hospital and University of Liège","correspondingAuthor":false,"prefix":"","firstName":"Jean-Francois","middleName":"","lastName":"Kaux","suffix":""},{"id":504244264,"identity":"d9058d45-ba10-41af-8cfa-6b153bf2d56a","order_by":5,"name":"Brendon A Bradley","email":"","orcid":"","institution":"Univ of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"Brendon","middleName":"A","lastName":"Bradley","suffix":""},{"id":504244265,"identity":"0c1193bf-9658-49b2-840d-fbf29dfe6747","order_by":6,"name":"Thomas Desaive","email":"","orcid":"","institution":"University of Liège, GIGA Institute","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Desaive","suffix":""},{"id":504244266,"identity":"feae5e07-9f44-4ac6-8826-cbfeb2393eeb","order_by":7,"name":"J. Geoffrey Chase","email":"","orcid":"","institution":"Univ of Canterbury","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"Geoffrey","lastName":"Chase","suffix":""}],"badges":[],"createdAt":"2025-08-11 23:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7349933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7349933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90147475,"identity":"50deb446-a9fc-4e42-90b6-c62b628901ac","added_by":"auto","created_at":"2025-08-29 06:09:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144112,"visible":true,"origin":"","legend":"\u003cp\u003eOverall Poisson Distribution modeling and confidence interval results for selected leagues in \u003cstrong\u003eTable 1\u003c/strong\u003e. Every panel from \u003cstrong\u003ea-e\u003c/strong\u003e shows the Poisson Distribution of IR or games played per injury reported for the overall Men’s median \u0026nbsp;(red-pink) in \u003cstrong\u003eTable 1\u003c/strong\u003e, and overall Women’s results for all leagues (blue) for comparison to overall values. The yellow distribution is for the specific league, where: Panels: \u003cstrong\u003ea\u003c/strong\u003e) Eredivisie Vrouwen (Netherlands); \u003cstrong\u003eb\u003c/strong\u003e) Damsallsvenskan (Sweden); \u003cstrong\u003ec\u003c/strong\u003e) NWSL (USA); \u003cstrong\u003ed\u003c/strong\u003e) Liga MX (Mexico); \u003cstrong\u003ee\u003c/strong\u003e) WSL (England); \u003cstrong\u003ef\u003c/strong\u003e) Confidence intervals all Men median, all Women, and the 5 leagues presented in panels \u003cstrong\u003ea-e\u003c/strong\u003e. All other teams in \u003cstrong\u003eTable 1\u003c/strong\u003e sit around these values and distributions based on their specific results.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7349933/v1/81ec887b6588de252c7187c6.png"},{"id":93200026,"identity":"d23c382b-86e1-4699-9267-1ad1b8a1aec5","added_by":"auto","created_at":"2025-10-10 06:46:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1302219,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7349933/v1/d5266a96-b349-4e50-bad3-8fff672910c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ACL Tear Rates In and Across Women's Football Leagues: Insights from a Unique 2-Year Database","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003eAnterior cruciate ligament (ACL) tears is a rare but burdensome injury in football (soccer), and several other sports, where mobility, high energy, and speed are at a premium. ACL rehabilitation typically spans 9\u0026ndash;12 months typically before return to play and can potentially end careers prematurely. In football, several reports have noted a significant difference between the incidence rate of men and women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, incidence rates, especially among women, have not been well-quantified in the literature [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMore specifically, these studies often report significantly higher ACL injury rates in women compared to men, with some indicating rates 2\u0026ndash;6 times higher [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Many studies are hindered by small sample sizes and/or labour-intensive processes for collecting detailed exposure data [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], such as the number of minutes played per participant. Equally, several studies mix multiple playing levels, such as elite and high school, where confounding factors such as level of training or total season exposure time vary significantly. Overall, this approach to assessing incidence can complicate some practical application and generalizability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite extensive research on ACL injury causes and influencing factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], conclusive answers remain elusive. One major factor limiting understanding is the (positive) fact these injuries have relatively low incidence rates given the number of players or/and games per season. To obtain enough injury data to quantify incidence and to identify/eliminate potential risk factors with robustness and/or statistical significance would require significantly larger numbers of injuries and data for teams or leagues over time than readily available today.\u003c/p\u003e\u003cp\u003eA robust statistical framework accurately quantifying the probability of ACL injury incidence at league or team level would enable calculation of likelihood of any identified risk factors and/or causal relationships found. It would also enable calculation of present value costs to assess the impact and cost of interventions. The relative scarcity of injury data makes this task difficult. Further, the use of normally-distributed statistics may mean statistical results do not accurately reflect the actual distribution of incidence rates [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study addresses these challenges by leveraging a publicly compiled injury dataset to establish a statistical framework for ACL injury incidence across several top women\u0026rsquo;s football leagues. It then uses the Poisson distribution, widely employed to assess the incidence of discrete events whose occurrences are relatively rare and independent [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] to create distributions of incidence rate per season as games played per injury for a given league. Results are compared to injury rates from the English men\u0026rsquo;s Premier League as an international comparator for which long-term, multi-year data was available [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] to evaluate differences by sex. Evaluating by league allows assessment of any variability across leagues, which has not been considered before in smaller more focused studies, where differences across leagues might offer insight into potential causal factors.\u003c/p\u003e"},{"header":"2.0 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data\u003c/h2\u003e\u003cp\u003eData for this study was sourced from a specialised Twitter (X, Bastrop, TX) account dedicated to reporting injury information sourced from public news outlets, including blogs, mainstream news sources, and social media platforms where teams, players, and fans share updates [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The dataset was cross-checked across multiple publicly available news and social media sources to ensure the accuracy and completeness as much as possible, encompassing details such as player names, ages, teams, and specific match details (date, teams, location) which were available. Thus, the dataset and injuries presented represent a minimum number of events, where some injuries may have been missed, similar to surveillance programs.\u003c/p\u003e\u003cp\u003eThe study period spans two calendar years and includes data from three football seasons, from January 1, 2022, to January 31, 2024. This timeframe provides a comprehensive snapshot of ACL injury occurrences in top women\u0026rsquo;s football leagues during this specific period. For some leagues this is two whole seasons, but for others it covers two half seasons and one full season in between. The fixed date approach was chosen as leagues begin and end at different points in the year depending on hemisphere, continent, and league, where data before this time was sparse or not collected in this dataset.\u003c/p\u003e\u003cp\u003eTo establish a gender comparative baseline, ACL injury incidence in the English men\u0026rsquo;s Premier League were derived from historical data spanning approximately the last 13 years [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This long-term average serves as a reference point for understanding injury trends in men\u0026rsquo;s football and provides context for comparing injury rates across sexes in this study. In assessing relatively rare incidence events, there are essentially two options in the presence of limited data. First, to collect data for one team or league over a long period, risking bias from changing circumstances or training. Second, to examine multiple teams or leagues over a short snapshot of time, risking bias of outlier events or years. In this study, only a short period was available for the women, but over many leagues providing enough games, and thus injuries, to assess trends. In contrast, data for men\u0026rsquo;s leagues was limited to one league, but this longer 13-year study was available in the literature. As this analysis seeks to assess larger differences, if they exist, the different risks of bias are noted, but should not impact larger statistically significant differences, if they exist.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the specific leagues and nations included in this analysis, encompassing women\u0026rsquo;s elite football leagues and the English Premier League for men with its median and extreme values to show the spread in this analysis. The women\u0026rsquo;s leagues included from the database had 4 or more injuries reported per season, and those with the lowest values were crosschecked extensively to find any missed injuries. This limit was imposed to minimise the potential levels of missing injuries. The games per season for each league was calculated, where additional games from multi-division cups (e.g. FA Cup in England) or international Champions Leagues were averaged over the 2 years. Importantly, a game within the league counts as two games in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, as two teams are playing one game each, where this approach allows counting games played across leagues more easily as the league would only have one team in such a game, with the other team would come from a different league. The number of injuries is as reported in the dataset and crosschecked by the authors to the best of their ability but remains a lower bound, where the men\u0026rsquo;s data is from an independent study [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLeagues and demographic data in the analysis and period January 2022 to January 2024. Total games include a range of international and national cups and cups across divisions of leagues, and can vary from year to year depending on advancement in the cups and leagues. Using available data the games were counted, but up to ~\u0026thinsp;1% may be missed due to lack of full information across leagues. Average age is for those players injured where age could be found. Games estimated or counted. ACL injuries from the database. In the sub- or second table, the Men\u0026rsquo;s data are for median injuries per season over the study period in [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and for 1 season, where the maximum and minimum values show the range over time and variability.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremier League (Nation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e# Teams\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage Age of Injured\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGames per Season (counted)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNum Injuries in 2-years\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNational Women\u0026rsquo;s Soccer League (USA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrench D1 Arkema (France)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u0026rsquo;s Soccer League (England)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiga F (Spain)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA-League Women (Australia and New Zealand)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiga MX (Mexico)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDamallssvenskan (Sweden)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEredivisie Vrouwen (Netherland)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrauen Bundesliga (Germany)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrauen Bundesliga (Austria)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkstraliga (Poland)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWSL (Switzerland)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWomen\u0026rsquo;s Total\u003c/b\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\u003cp\u003e\u003cb\u003e4029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e191\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePremier League (Nation)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e# Teams\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eAverage Age of Injured\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eGames per Season (counted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eNum Injuries per 1 season\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEngland Men\u0026rsquo;s Premier League (median)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e899\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremier League (England) \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMax\u003c/span\u003e (3x in 15yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremier League (England) \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMin\u003c/span\u003e (2x in 15 yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ethics\u003c/h2\u003e\u003cp\u003eThe utilization of social media and publicly available news data [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] for research purposes introduces ethical considerations, which are influenced by diverse cultural, social, and legal norms across countries. These sources, while rich in information, can raise concerns regarding privacy and data sensitivity. To address these challenges, this study strictly employs de-identified data at the league level, ensuring specific details, such as team names or exact years, are excluded to prevent the identification of individual injured players. This approach meets the Australia-New Zealand Statements on Ethics [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The study adheres to guidelines set forth by the European Union, particularly under the General Data Protection Regulation (GDPR) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e],which governs the ethical use of personal data in research. Finally, the authors consulted with experts in the United States and Europe to better understand and ensure compliance with regional ethics standards and statements. Exemption from ethics approval for this study was obtained from the University of Canterbury Human Research Ethics Committee (HREC-2024-02 EX), aligning with the Australia-NZ Statement of Research Ethics [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Incidence rate\u003c/h2\u003e\u003cp\u003eThe data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and limitations on identifiable data provide the basis for assessing incidence rate given the number of ACL injuries over the two calendar years of data by league as a rate of games played (by a single team) per injury. Average injury rates per team can be obtained by dividing by the number of teams, but the data is sparse enough taking the analysis to the team level would not add any robust information. Thus, for any given league, the incidence rate (IR) is defined as the games played by teams in the league, including international champions leagues and cups, per injury reported:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:IR=\\frac{Games}{{\\overline{N}}_{ACL}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eGames\u003c/em\u003e is games per season for a given league, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\overline{N}}_{ACL}\\)\u003c/span\u003e\u003c/span\u003e is mean number of ACL injuries per season for the same league using the two calendar years of data. Using the mean value filters some year to year variability for a comparison, and a similar mean over the longer period of men\u0026rsquo;s league data is used for the same reason, as well as their maximum and minimum value over the longer period of men\u0026rsquo;s data.\u003c/p\u003e\u003cp\u003eThis rate has the advantage of being readily assessed, but is an average, and not every team will see this number for a given league. By using the total time period any uncertainty or variability over the two-year period is lost, but two years is also too small for a robust estimate of this uncertainty. If distributions between men and women are very similar, this loss of uncertainty will cloud results, however, if they are far apart it will not impact any conclusions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Poisson Distributions and Statistical Modeling of ACL Incidence Rate\u003c/h2\u003e\u003cp\u003ePoisson distributions are a fundamental statistical tool used to model rare, discrete events which occur independently within a fixed interval of time or space [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Poisson distributions are widely used [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] to quantify rare events because they require the estimation of only a single distribution parameter, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e, the mean event (incidence) rate. They can also be used to assess the statistical differences between leagues, men and women, and the likelihood of one league having greater or less numbers of injuries in a given season. Finally, they can be used to convert an injury number for any one or few years into a likelihood value, when asked if a certain number of injuries is high or low.\u003c/p\u003e\u003cp\u003eThe Poisson distribution is defined by the following probability mass function:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:P\\left(X=k\\right)=\\frac{{\\lambda\\:}^{k}{e}^{-\\lambda\\:}}{k!}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eX\u003c/em\u003e represents the number of ACL injuries occurring within number of games, \u003cem\u003ek\u003c/em\u003e is the number of games played per injury from the raw data, \u003cem\u003eλ\u003c/em\u003e is the mean rate of ACL injuries per games played, and \u003cem\u003ee\u003c/em\u003e is the base of the natural logarithm.\u003c/p\u003e\u003cp\u003eMore explicitly, Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) defines the probability of having \u003cem\u003eX\u0026thinsp;=\u0026thinsp;k\u003c/em\u003e injuries in a given season as a function of the number of games played per injury, as defined in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), for a given league from the raw data, denoted \u003cem\u003el\u003c/em\u003e, and \u003cem\u003ee\u003c/em\u003e is the natural logarithm. Thus, knowing \u003cem\u003el\u003c/em\u003e from the raw data, a distribution can be created to show the probability of injury rates greater, or lesser, than this value, which can be compared using statistical tests to other leagues or data for men.\u003c/p\u003e\u003cp\u003eThis statistical model of the distribution of ACL injury incidence rates is modeled for injuries within different leagues or over different seasons. It thus presents a distribution defining the likelihood of a given number of injuries per league, given a known number of games played per season. Hence, it is an incidence rate per league per year.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Analyses\u003c/h2\u003e\u003cp\u003eComparing Poisson distributions across leagues helps standardize the analysis by accounting for variations in game frequency and league intensity. Distributions are calculated for each league listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and their mode and directly calculated 95% confidence interval are compared. Incidence is assessed per Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) as games per injury, thus normalising out the differences in games played per league. Further, in comparing women\u0026rsquo;s leagues, to women over all leagues, the data for the league being compared is not removed from the set of all data for women, ensuring a more conservative comparison of 95% confidence intervals. This choice also recognises the large number of games in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and thus the removal of any one league makes only modest to small differences to the overall distribution.\u003c/p\u003e\u003cp\u003eFurther calculations show the number of injury-free games per league per season for each women\u0026rsquo;s league to indicate how many additional games without injury would be required to match the English men\u0026rsquo;s Premier League average rate. A positive value indicates the specific women\u0026rsquo;s league had a higher rate of injury in the raw data compared to the men\u0026rsquo;s league, and a negative value would be a lower raw injury incidence as defined. This value provides further context and contextual feel for the results, where large values indicate a highly unlikely opportunity for the two leagues being compared to be the same and quantifies this difference in games or match time exposure.\u003c/p\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the overall results and it is clear there is variability across leagues. A first overall result shows the rate of injury, using this measure, is 2.8x larger for women than men [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the results graphically for selected leagues in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and clearly shows a wide range of incidence rates, some of which are not significantly different from the men\u0026rsquo;s rate, while others have far higher differences. Notably, the maximum injury rate for the men\u0026rsquo;s comparator over 15 years has an \u003cem\u003eIR\u003c/em\u003e lower than 3 leagues and would not be statistically significantly different than a further 2 women\u0026rsquo;s leagues. The minimum \u003cem\u003eIR\u003c/em\u003e for men\u0026rsquo;s leagues in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e would be statistically significantly different from all women\u0026rsquo;s leagues in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIt is clear some women\u0026rsquo;s leagues are statistically different from all women\u0026rsquo;s leagues in general, even while including their data in the overall set for a conservative analysis. In particular, the Damsallsvenskan (Swedish) and Eriedivisie (Dutch) women\u0026rsquo;s leagues (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) are not statistically different from the men, though with higher injury rates (lower number of games per injury). More importantly, they are statistically different from women overall, including their data, showing a less than 5% chance they would have a higher rate (lower number of games per injury) than women in all other leagues, where this analysis is, again, conservative. Similar results hold for the Mexican Women\u0026rsquo;s Liga MX. In contrast, the women\u0026rsquo;s NWSL in the USA and Women\u0026rsquo;s Super League in England (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee) have higher rates than women overall. The range of relative injury rates goes from 1.1x to 7.0x of the English Premier League (men\u0026rsquo;s) rate. Finally, comparing the range of men\u0026rsquo;s English Premier League results alone, the number of injury free games required to reach either the high or low limit are very large and thus show the range of possibilities in these distributions.\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIncidence rate, \u003cem\u003eIR\u003c/em\u003e, in league games played per ACL injury, \u003cem\u003eIR\u003c/em\u003e relative to English men\u0026rsquo;s median rate and injury free games over 2 years to match the men\u0026rsquo;s median rate. Results for each league and overall. The IR is also \u003cem\u003ek\u003c/em\u003e in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The first 4 columns are also in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and replicated here for context.\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\u003cp\u003ePremier League (Nation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e# Teams\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGames per Season\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNum Injuries 2-years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eIR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eIR\u003c/em\u003e relative to England Men\u0026rsquo;s Premier League\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eInjury Free Games to Match England Men\u0026rsquo;s Premier League IR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNational Women\u0026rsquo;s Soccer League (USA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrench D1 Arkema (France)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u0026rsquo;s Soccer League (England)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiga F (Spain)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA-League Women (Aus-NZ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e536\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiga MX (Mexico)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e102.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDamallssvenskan (Sweden)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEredivisie Vrouwen (Netherland)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrauen Bundesliga (Germany)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e684\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrauen Bundesliga (Austria)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEkstraliga (Poland)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWSL (Switzerland)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWomen\u0026rsquo;s Total\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e191\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e40.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e9708\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePremier League (Nation)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e# Teams\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eGames per Season\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eNum Injuries per season\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eIR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eIR\u003c/b\u003e \u003cb\u003erelative to England Men\u0026rsquo;s Premier League\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eInjury Free Games to Match England Men\u0026rsquo;s Premier League IR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEngland Men\u0026rsquo;s PL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e899\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e112.4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremier League (England) Max (3x in 15yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePremier League (England) Min (2x in 15 yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e899\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e179.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-360\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThe overall study examines a relatively large dataset of injuries covering 8540 women\u0026rsquo;s football games over two calendar years and comparing to injury data from ~\u0026thinsp;11650 the English Men\u0026rsquo;s Premier League games over 13 seasons. The goal was to examine relative incidence rate overall, and any differences across national women\u0026rsquo;s premier leagues.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Main Results and Key Takeaways\u003c/h2\u003e\u003cp\u003eThe results reveal a pronounced disparity in ACL injury rates between men\u0026rsquo;s and women\u0026rsquo;s football. This result aligns with previous reports indicating women experience ACL injuries at rates 2\u0026ndash;6 times greater than the men [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The specific value of 2.5x also matches current mainstream estimates [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], but is lower than some more popularly reported values up to 4-6x [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is also higher than a more recent meta-analysis across multiple sports and levels [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Thus, this analysis confirms women\u0026rsquo;s football leagues generally exhibit higher ACL injury rates compared to their male counterparts, and shows it is statistically significant within an appropriate statistical modeling framework. Equally, the results compare well with prior studies, meta-analyses, and expectations within the field for elite leagues in this sport.\u003c/p\u003e\u003cp\u003eImportantly, in comparison to prior high resolution studies examining incidence by game play time and similar, the analysis covers 2 years of play over 2\u0026ndash;3 seasons depending on league. Thus, it includes an estimated total of 8540 women\u0026rsquo;s games where Premier League men over the same period played\u0026thinsp;~\u0026thinsp;1790 games, noting again for clarity a game within the league counts as 2 as two teams are playing. The overall difference reported is consistent with existing literature but the greater number of games provides greater robustness to the result. The comparisons across women\u0026rsquo;s leagues also provide a more granular understanding of the variability among different leagues. Overall, to the author knowledge, this study is one of the largest of its kind in the number of teams and games considered in the women\u0026rsquo;s leagues.\u003c/p\u003e\u003cp\u003eThe use of Poisson Distributions provides a quantified level of the potential variability which might be seen year to year. In particular, injury rates can vary significantly year-to-year and over longer periods, as seen in the men\u0026rsquo;s data used [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and variability in statistical significance when comparing their maximum and minimum injury rates to the women\u0026rsquo;s data. However, a distribution puts these values into context in terms of their likelihood of occurrence. As better data arises and/or accrues over time, these distributions can be easily updated.\u003c/p\u003e\u003cp\u003eBy comparison, in earthquake (seismic) engineering, the Poisson distribution is commonly employed in seismic hazard analysis to quantify the likelihood of ground-shaking intensity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and subsequently its use in determining infrastructure damage and consequences. For women\u0026rsquo;s sports this analysis and analogy would follow onto the economic impacts of an ACL injury. Given a probabilistic model to quantify likelihood, an economic net present value can be calculated for any intervention or choice to compare to an estimated cost of the injury. Such values help engineers and insurers make informed decisions on risk management and cost-benefit analysis of structural interventions or preparedness measures [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. There is thus a good and direct analogy to the relatively rare, discrete, and highly damaging events of ACL injuries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Variability Among Women\u0026rsquo;s Leagues\u003c/h2\u003e\u003cp\u003eA notable contribution of this study is the identification of previously undocumented variability among women\u0026rsquo;s football leagues. This variability challenges the assumption of uniform injury rates among women, suggesting factors specific to each league, such as training practices, playing conditions, or regional differences, play a significant role in injury incidence. These findings are supported by similar observations in other sports, such as ballet [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], where differences in injury rates between men and women may be due to specific training and conditioning factors in these sports. More specifically, there is no data or highlighted specific intervention which may be a cause for these results. However, these results provide starting points for specific inquiry into potential causal factors for these differences. Finally, and perhaps most importantly, the very low rates in some leagues, if further confirmed, indicate the potential for women to approach similar injury rates to the men.\u003c/p\u003e\u003cp\u003eOverall, these particular results also show treating women as a monolithic entity or group in assessing this problem may not be valid. The differences may be due to missing data in some of the less well-known leagues, despite rigorous crosschecking and analysis. However, the results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showing very large numbers of injury free games (for women) or increased injuries (for men, or decreased injuries for women) to reach the women\u0026rsquo;s (men\u0026rsquo;s) average rate suggest these differences may also suggest where to look for differences and potential causal factors in training or other background factors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Impact on Injury Occurrence\u003c/h2\u003e\u003cp\u003eThe metrics of injury-free games and reduction goals directly support the observed trends. For instance, the need for additional injury-free games to match men\u0026rsquo;s injury rates highlights the extent of intervention required, where potential adherence of players or teams to specific intervention programs could be low and/or highly variable. While men\u0026rsquo;s football exhibits a broader variability in injury rates over the 13 years noted, it does not significantly alter the overall conclusions, and further highlights the variability possible, which has not yet been seen in a collection (over time) of women\u0026rsquo;s league data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Identifying New Avenues for Research\u003c/h2\u003e\u003cp\u003eThe study results and the variability across leagues in particular should prompt the exploration of new research avenues beyond traditionally assessed factors like menstrual cycles or birth control use [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Future investigations should focus on specific mechanisms influencing individual variability in ACL injury rates. In particular, variability and factors related to biomechanics, training methods, or other contextual factors may provide greater insight [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Limitations\u003c/h2\u003e\u003cp\u003eWhile the study provides insights, there are limitations to the data. Despite rigorous crosschecking there may be missing injuries. However, as such, the trends remain similar or greater than what is shown here. This issue is particularly true for some leagues, such as the Mexican Women\u0026rsquo;s Liga-MX where data was harder to find, than for others such as the well-known English WSL and USA NWSL. These variabilities occur to relatively different levels of coverage and media, among many factors. Again, these values are lower bounds, and the trends should be robust given the number of games involved. The use of Poisson Distributions also provides an idea of the potential variability seen year to year to avoid over-reacting to potential outliers as more data becomes available.\u003c/p\u003e\u003cp\u003eIn comparing the women\u0026rsquo;s leagues over 2 years and overall with the English Premier League men\u0026rsquo;s rates over 13 years provides large enough numbers of games (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to ensure robust incidence rates. However, the difference in time scales may create bias, where the original report of the men\u0026rsquo;s data noted declining rates of injury over time [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] though recent increases [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] indicate these declines were perhaps a statistical event inside the distribution in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for the English Men\u0026rsquo;s Premier League. This potential bias is mitigated by the lack of data overall for comparison, as well as by the fact the original report was reported in contrast to the occurrence of more recent increases in injury rates [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This latter point also indicates how injury rates may appear low, or high, for several years, but still remain within a likely range given the width of the Poisson Distributions in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eA final limitation of the data is the Mexican MX-Liga data. It was very difficult for the authors to find additional injury reports, and the values and incidence are very comparable to the men\u0026rsquo;s rates. This outcome may be valid, but likely there are injuries missed. Again, this outcome is a lower bound, but this league may be far lower than actual. The issue is mitigated in this comparison because all women\u0026rsquo;s leagues are used in the overall analysis in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and any one league does not contribute excessively to this overall distribution.\u003c/p\u003e\u003cp\u003eAs seen in these limitations, data availability and quality is an issue. Datasets can be incomplete data due to voluntary reporting or confidentiality concerns among teams and athletes, though more recommendations are emerging [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For instance, the US NCAA Injury Surveillance Program data had not been publicly available for several years [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Equally, surveillance programs and recommendations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] are often voluntary and some data may be missed, just as in this study, although in this regard, the NCAA program had high compliance of 88\u0026ndash;93% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Thus, this the results of this and other studies offer lower bounds on reported injuries, which limits the robustness of some conclusions.\u003c/p\u003e\u003cp\u003eThe study highlights the critical need for more comprehensive and high-quality data sources. Potential avenues for improvement could include leveraging insurer data from companies who provide the mandatory insurance for athletes required by the US NCAA, which could offer valuable insights into injury trends with far higher data quality and consistency over time than was available here. Additionally, advocating for standardized reporting practices by governing bodies, such as the English FA and UEFA, could enrich comparative analyses and support the development of effective injury prevention strategies. Similarly, a greater focus on leagues away from North America and Europe is a limitation, which needs to be addressed as only Mexico and the Australia-New Zealand A-League were in this study. Finally, this limitation highlights the \u0026ldquo;poverty\u0026rdquo; in the study of women\u0026rsquo;s sports, where this issue has been noted since the early 1990s [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] but still lacks larger scale contextual data despite the passing of three decades.\u003c/p\u003e\u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThis study reveals significant disparities in ACL injury rates between women\u0026rsquo;s and men\u0026rsquo;s football, using Poisson distributions to analyse a dataset of public injury reports. Overall, ACL injuries are more common in women\u0026rsquo;s football in comparison to the men\u0026rsquo;s English Premier League, with an average difference in rate of 2.5x. Furthermore, there is notable variability in injury rates among different women\u0026rsquo;s leagues, suggesting league-specific factors such as training practices and playing conditions play a crucial role. The study encompasses a large number of games to ensure robustness. The use of Poisson distributions provides a robust statistical framework to this problem for the first time, and allows statistical significance to be assessed in any comparison, as well as the ability to assess the likelihood of any given injury number per season. The low rates for some elite women\u0026rsquo;s leagues are not statistically different from the men\u0026rsquo;s league comparator and show the potential for women\u0026rsquo;s injury rates to be relatively much lower, up to 4x, in some leagues. These results would be enhanced by a larger more complete dataset over a longer period. Finally, the study highlights the need for better ethics guidelines and standards for using these data in general, and data from social media and public new sources in particular, where this study\u0026rsquo;s authors struggled to assure themselves of the best approach.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors received no funding for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.S. wrote the manuscript and did all analysis and computation. V.C provided support with data collection. J.G.C., T.D., J.F.K, K.O.K provided methodological and analytical input to design and analysis of the study. All authors reviewed the manuscript and provided advice/feedback.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAs described in the Ethics section of the Methods. All de-identified data is in Tables 1-2, which reports injury numbers per league over the study period and number of games per season over the period. Thus, all ethically permissible data to be shared is available in the paper. Our ethics approval to use the publicly available data does not extend to reporting player names or teams which would make the players identifiable. Further, there are European jurisdictions where only the aggregate data, as reported, is permitted and since many of the injuries are reported from European leagues, this set of data in Tables 1-2 is all we can report. Hence, all data is in the paper and available for further use with citation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArendt, E. and R. Dick, \u003cem\u003eKnee injury patterns among men and women in collegiate basketball and soccer. 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Coen, and S. Bekker, \u003cem\u003eAnterior cruciate ligament injury: towards a gendered environmental approach.\u003c/em\u003e British Journal of Sports Medicine, 2021. \u003cstrong\u003e55\u003c/strong\u003e(17): p. 984-990.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ACL, Women’s Football, Female, Relative Risk, Injury Database","lastPublishedDoi":"10.21203/rs.3.rs-7349933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7349933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnterior cruciate ligament (ACL) injuries are a high injury burden in football, with a notably higher incidence rate among women compared to men. However, epidemiological studies are limited in women\u0026rsquo;s football so is presently no statistically rigorous or fully quantified context for understanding this difference. This study utilizes public news, blog, and social media data to create a statistical framework for analysing ACL injury rates in women\u0026rsquo;s football to enable better comparisons and understanding of the probability of the different numbers of injuries per team per season. Employing Poisson distributions, which are widely employed to assess the incidence of discrete events whose occurrence is relatively rare, we are able to quantify ACL injury incidence and compare it across different top women\u0026rsquo;s football leagues from various counties, and against the English Premier League for men where other longer-term data was available. The analysis includes calculating the raw mean and median injury rates, constructing confidence intervals, and evaluating the number of additional injury-free games and fewer injuries per season (for a league) required for women\u0026rsquo;s football to match the men\u0026rsquo;s injury rates. The results reveal significant differences in ACL injury rates between men\u0026rsquo;s and women\u0026rsquo;s football with an overall difference of 2.5x (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as expected, with unexpected variability of almost 4x in rates observed across the 12 women\u0026rsquo;s elite or premier leagues. The Swedish, Netherlands, and Mexican women\u0026rsquo;s leagues show no statistically significant difference in incidence rates to the men in this comparison though the raw data incidence is slightly higher. These insights offer an empirical foundation for targeted research into causes of these differences. Study limitations highlight the need for improved data collection and reporting practices. The overall study results underscore the importance of applying more rigorous statistical methods to this problem to better target inquiry and to assess future interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"ACL Tear Rates In and Across Women's Football Leagues: Insights from a Unique 2-Year Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 06:01:49","doi":"10.21203/rs.3.rs-7349933/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86f725f2-925a-43cc-a52a-096ba683c52c","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53575591,"name":"Health sciences/Diseases"},{"id":53575592,"name":"Health sciences/Health care"},{"id":53575593,"name":"Health sciences/Medical research"},{"id":53575594,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-10T06:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-29 06:01:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7349933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7349933","identity":"rs-7349933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}