Who’s Worth the Millions? Rethinking Football Valuation Through Predictive Modeling in the Big Five European Leagues

Who’s Worth the Millions? 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Rethinking Football Valuation Through Predictive Modeling in the Big Five European Leagues Michele Cincera, Ali Shah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6497200/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 This paper explores the multifaceted determinants of football player market valuations in the Big Five European leagues between 2019 and 2024. Using a rich dataset of 1,006 players, the study combines econometric techniques, machine learning models, and behavioral indicators to assess how performance metrics and player notoriety impact market values. The analysis confirms the predominance of subjective assessments, notably player rating and potential, as the strongest predictors, while also highlighting the emerging role of social media as a commercial asset. Results from fixed effects regressions and Random Forest models suggest that market values are driven by a combination of current ability, perceived future potential, and off-field visibility. By integrating both objective and subjective dimensions, this study provides a robust, data-driven framework for understanding and predicting player valuation in contemporary football markets. JEL codes : C55, C53, D40, Z22 Football economics Market value estimation Sports analytics Big data in sports Figures Figure 1 Figure 2 1. Introduction Player valuation stands at the heart of modern football economics. It influences not only transfer negotiations and salary structures, but also strategic decisions related to talent development, squad composition, and financial planning. The market value of a player reflects a synthesis of current performance, future potential, market visibility, and broader economic dynamics—making it a multidimensional and complex construct. This paper aims to enhance the understanding of player valuation by drawing from a comprehensive literature base and implementing a hybrid methodological framework. It builds on seminal studies—including Mahmutaj (2018), Colassin ( 2020 ), Hachez (2019), and Poli et al. ( 2024 )—which examine the roles of econometric modeling, behavioral influences, and global market dynamics. These works highlight the relevance of both tangible indicators (e.g., performance statistics) and intangible elements (e.g., player notoriety) in shaping market value. To deepen these insights, we use a dataset of 1,006 players from the Big Five European leagues, spanning the years 2019 to 2024. The empirical strategy triangulates three main approaches: cross-sectional regression, panel fixed effects modeling, and Random Forest machine learning. This pluralistic strategy allows us to capture both linear and non-linear relationships, identify causal trends, and test predictive robustness. The study also explores emerging factors such as social media presence, which is increasingly recognized as a proxy for brand value and commercial potential. This behavioral dimension, often overlooked in traditional valuation models, reflects the evolving nature of football as both a sport and an entertainment business. In doing so, this paper contributes to the growing literature on sports analytics by offering a more nuanced and empirically grounded framework for player valuation—one that reflects the realities of a globalized, data-rich football economy. Player valuation is a cornerstone of football economics, driving club strategies and shaping transfer market dynamics. The valuation process is inherently complex, involving both tangible factors such as player performance and intangible aspects like popularity and marketability. The remainder of the paper is structured as follows. Section 2 presents a synthesis of the existing literature on player valuation, highlighting the evolution of methodologies and key determinants identified in prior studies. Section 3 describes the data sources, variables, and methodological approaches, including both econometric and machine learning models. Section 4 discusses the empirical findings, comparing the performance of different models and interpreting the significance of key predictors. Section 5 provides a critical discussion of the results, emphasizing both theoretical and practical implications. Finally, Section 6 concludes by summarizing the main contributions of the study, acknowledging its limitations, and suggesting directions for future research. 2. Literature Review The academic literature on player valuation in football has undergone significant evolution, reflecting changes in market dynamics, regulatory environments, and methodological advancements. The 1995 Bosman ruling, which abolished transfer fees for out-of-contract players within the European Union and removed nationality-based quotas, marked a pivotal turning point. It fundamentally altered transfer market regulations, shifting bargaining power to players and introducing complexities that prompted extensive scholarly investigation (Brand, 2019 ). Pre-Bosman Research Prior to the Bosman ruling, research primarily focused on broader economic themes within football, such as attendance patterns (Hart et al., 1975; Bird, 1982 ; Jennett, 1984), labor market discrimination (Szymanski, 2000), and managerial efficiency. Early econometric analyses, such as those by Carmichael and Thomas ( 1993 ), applied a Nash bargaining framework to explore transfer fee determinants. Key variables, including games played, goals scored, and age, were identified as critical predictors of player valuations. Post-Bosman Research and Evolving Models The Bosman ruling ushered in a new era of football economics, driving a surge in transfer fees and player salaries (Durand et al., 2017; Herm et al., 2014 ). Research following this period examined its economic implications. Ericson (2000) highlighted reduced investments in player development by larger clubs, while Feess and Mühlheusser (2003) noted its impact on longer contract durations and diminished training investments. These studies underscored the regulatory shift's profound effects on market behavior. Subsequent research incorporated more advanced econometric methodologies and broader datasets. Frick ( 2007 ) identified challenges in analyzing transfer markets due to limited salary data and selective biases, leading to innovations such as the Heckman correction model by Ruijg and van Ophem (2014). Their work demonstrated the significant impact of age, average minutes played, and substitution percentages on transfer fees. Poli et al. ( 2024 ) expanded the scope of analysis by leveraging a dataset of over 8,000 transactions across 64 countries, identifying key drivers such as player characteristics, club prestige, and market dynamics. Franceschi et al. ( 2023a ) introduced a systematic framework categorizing determinants into six key variables: club characteristics, time, labor, performance, player attributes, and popularity. These studies collectively emphasize the multifactorial nature of transfer valuations. In a recent contribution to the intersection of sports finance and real options theory, Cucculelli et al. ( 2024 ) develop a novel approach for valuing call options on professional football players. Their model introduces a dynamic framework that incorporates both individual player rankings and club market value, offering a more realistic and flexible method for pricing option-like clauses increasingly used in player contracts—such as buy-back or resale clauses. Building on earlier work by Tunaru et al. (2005), the authors move beyond traditional models that rely solely on transfer fees or club revenues. Instead, they propose a valuation approach where the player's market value is treated as a stochastic process driven by their contribution to the team, proxied by the ratio of the player's performance ranking to the club's market value. To reflect real-world career dynamics, they incorporate a mean-reverting process that captures the natural decline in player performance with age. The model is then calibrated using data from Italian Serie A and tested on case studies involving three players. A key innovation lies in their use of enterprise value net of fixed assets as a more accurate reflection of a club’s investable worth, along with Monte Carlo simulations to estimate option prices under realistic conditions. The results demonstrate a close alignment with actual player values reported by Transfermarkt 1 , thereby supporting the model’s external validity. Player-Specific Factors and Performance Metrics Research consistently identifies player-specific factors, such as age, position, skill levels, and physical attributes, as significant predictors of market value (Carmichael & Thomas, 1993 ; Munkhaugen Gulbrandsen, 2011). Wilson and Plumley ( 2018 ) highlighted discrepancies between book and market valuations of players, emphasizing the role of robust statistical models. Metrics such as goals scored, assists, and minutes played remain central to valuation models (Franceschi et al., 2023b; Poli et al., 2022). Machine learning techniques have emerged as powerful tools for refining valuations. McHale and Holmes ( 2022 ) demonstrated their utility in estimating transfer fees using performance metrics and advanced modeling techniques. Similarly, the integration of crowd-sourced data from platforms like Wikipedia and TransferMarkt has enhanced valuation accuracy (Müller et al., 2017 ; Herm et al., 2014 ). Club Characteristics and External Influences Club-related factors, including financial standing, stadium capacity, and brand value, also significantly influence transfer fees (CIES Football Observatory, 2018 ). External influences, such as agent negotiations, media speculation, and inflationary trends, complicate valuation models. Cohen ( 2005 ) and Pastor et al. (2017) frame players and their contracts as intangible assets, whose values fluctuate based on market conditions and player-specific factors. The impact of external shocks, such as the COVID-19 pandemic, has also garnered attention. Studies estimate a 20% decline in player values during the pandemic, with mid-tier clubs disproportionately affected (KPMG, 2020; Reade et al., 2020 ). These findings underscore the vulnerability of football markets to external disruptions. Behavioral Dimensions and Social Media Metrics Recent studies have incorporated behavioral dimensions, emphasizing the growing importance of social media presence. Colassin ( 2020 ) demonstrated that a player's notoriety, measured through social media metrics, significantly impacts market valuation. Empirical evidence suggests that a 1% increase in Instagram followers correlates with a 5.9% rise in market value, highlighting the monetization of digital popularity. Historical and Theoretical Perspectives Theoretical perspectives on player valuations align with broader discussions on intangible asset valuation. The works of Wilson and Plumley ( 2018 ) and Cohen ( 2005 ) provide critical insights into the valuation challenges posed by football's unique financial landscape. Historical analyses, such as Dobson and Gerrard (1999), addressed the economic rationale underlying transfer fees, while Durand et al. (2017) explored financial performance disparities among top European leagues. In conclusion, the literature on football player valuations reflects a dynamic interplay of economic, behavioral, and methodological factors. Advances in econometric techniques, the integration of machine learning, and the inclusion of social media metrics have significantly refined valuation models. However, challenges remain, particularly in accounting for non-quantifiable variables and market disruptions. Future research should focus on enhancing model transparency and incorporating emerging data sources to further bridge the gap between theoretical and practical applications. 3. Data and Methodology 3.1. Data Sources The datasets used in this analysis draw from TransferMarkt, FIFA ratings, social media platforms, and club-level performance data. Key variables include player characteristics (age, position, goals, assists), marketability metrics (social media followers), and external factors (pandemic-induced market shocks). 3.2. Methodologies To provide a comprehensive understanding of football player market value estimation, this study adopts a multi-methodological framework combining econometric techniques, behavioral indicators, and machine learning models. This pluralistic approach captures the multidimensional nature of player valuation, encompassing performance metrics, physical attributes, and broader market signals such as visibility and potential. The core dataset spans the period from 2019 to 2024 and includes 1006 professional football players, forming an unbalanced panel where each player may have multiple annual observations. This temporal coverage allows the analysis to account for market shifts, evolving player characteristics, and contextual developments in the transfer ecosystem—such as post-COVID economic adjustments and rising investment trends. The first stage of the analysis uses Ordinary Least Squares (OLS) regression, which offers a cross-sectional view of the factors associated with market value at a given point in time. OLS is particularly helpful in identifying significant predictors and the direction of their influence, using log-transformed market values to account for skewed distributions. However, OLS has limitations in handling unobserved heterogeneity among players. To address this, the study complements it with a fixed effects panel regression model. This method exploits the longitudinal structure of the dataset, focusing on within-player changes over time while controlling for individual-specific, time-invariant characteristics. Fixed effects models are particularly useful for isolating the impact of evolving variables such as updated ratings, contract duration, or seasonal performance indicators. To further enrich the econometric analysis, year-fixed effects are included in the panel specification to control for structural changes in the transfer market over time. These temporal controls help account for external shocks (e.g., macroeconomic shifts, regulatory reforms, COVID period) and ensure that the estimated coefficients reflect the true relationship between predictors and market value. A third methodological layer draws inspiration from behavioral economics and sports marketing literature, notably the work of Colassin ( 2020 ). Variables such as the logarithm of Instagram followers, number of posts, and followings are used as proxies for digital visibility and personal brand strength—factors increasingly relevant in modern football economics. These indicators reflect a player’s commercial appeal and potential for off-the-pitch value creation, which often influence market prices beyond pure athletic performance. Finally, the study introduces a Random Forest machine learning algorithm to explore non-linearities and higher-order interactions that traditional econometric models may miss. This ensemble method builds multiple decision trees using bootstrapped samples and aggregates their predictions for improved accuracy and robustness. The Random Forest approach is particularly well-suited for high-dimensional datasets and provides an interpretable ranking of variable importance, highlighting which features (e.g., potential, minutes played, sprint speed, etc.) most strongly influence valuation. This complements the regression analyses by offering predictive insights and helping validate the consistency of key predictors. Together, these methodologies provide a robust and complementary toolkit. Econometric models ensure interpretability and theoretical grounding, behavioral indicators reflect market dynamics beyond the pitch, and machine learning techniques improve out-of-sample prediction. This triangulated approach enhances both the analytical depth and practical relevance of the study, enabling a nuanced and empirically grounded assessment of what drives football player market values between 2019 and 2024. 4. Results 4.1 Descriptive Statistics The Table 1 below summarizes the descriptive statistics. Table 1 Descriptive Statistics Variable Obs Mean Std. dev. Min Max Log market value 10,532 6.07 0.87 4 8.30 Log posts 10,110 4.36 1.43 0 8.380916 Log followers 10,248 10.52 2.76 0 18.61953 Log following 10,230 6.12 0.90 0 8.91664 Age 10,500 25.76 4.32 15 43 Height 10,252 1.94 4.43 1.63 185 Total game player 8,717 24.75 13.24 0.93 70 pp 8,685 1.56 0.53 0 3 Goals 8,669 3.42 5.16 0 52 Own goals 8,665 0.07 0.43 0 18 Assists 8,673 2.26 3.18 0 31 Coeon 8,700 5.33 5.64 0 36 Substitute 8,671 6.70 6.54 0 39 Yellow cards 8,673 3.19 2.88 0 20 Yellow red cards 8,665 0.08 0.29 0 4 Red cards 8,663 0.08 0.30 0 3 Penaltys 8,663 0.29 1.02 0 14 Field player 8,733 0.92 0.28 0 1 Conceded goals 8,661 1.80 8.02 0 85 Clean sheet 8,658 0.43 2.16 0 27 Minutes played 8,586 1498.36 1128.16 0 5792 Rating 9,839 69.15 7.10 48 92 Potential 9,833 75.78 6.75 53 95 Weight 9,829 75.46 7.39 49 101 Ball control 9,225 56.28 21.64 5 92 Dribbling 9,839 54.51 21.92 6 94 Slide tackle 9,808 54.22 19.97 8 93 Stand tackle 9,808 57.88 16.43 9 93 Aggression 9,838 54.74 19.40 3 94 Reactions 9,838 55.85 20.83 6 94 Attposition 9,808 56.78 16.00 4 96 Interceptions 9,808 56.34 18.16 6 90 Vision 9,808 56.04 17.29 5 95 Composure 9,832 62.87 13.46 11 92 Crossing 9,832 55.96 15.75 8 95 Short pass 9,832 65.68 14.70 15 95 Long pass 9,832 61.58 15.59 15 95 Acceleration 9,566 67.70 12.92 18 97 Balance 9,805 65.59 14.43 20 95 Sprint speed 9,827 68.38 12.69 14 97 Agility 9,801 60.25 18.09 5 95 Jumping 9,805 62.96 13.71 3 95 Heading 9,831 51.01 19.77 2 91 Shot power 9,831 55.16 19.25 4 94 Finishing 9,831 51.40 19.25 5 96 Long shots 9,827 48.89 19.10 4 92 Curve 9,805 51.76 17.13 10 92 Fkacc 9,827 46.31 18.32 4 90 Penalties 9,801 31.48 24.43 1 92 Volleys 9,805 30.14 24.44 1 91 The descriptive statistics in Table 1 provide an overview of the variables used in this study, revealing meaningful patterns in the dataset. The average log-transformed market value of football players is approximately 6.07, corresponding to around €4.3 million, with a standard deviation of 0.87. This reflects a wide disparity in player valuations, ranging from modest squad members to highly valued elite talents, with log values spanning from 4.00 to 8.30 (10k€ to 200 million €). Social media activity presents a notable dimension in the dataset. Players have an average of 10.52 log-followers, which roughly corresponds to over 37,000 followers, with some exceeding 18 log-units (over 100 million followers). The average number of posts is 4.36 (log scale), and players follow about 6.12 (log scale) other accounts, highlighting varying levels of online engagement and visibility. Key player characteristics include an average age of 25.76 years, with values ranging from 15 to 43, capturing a mix of youthful prospects and seasoned professionals. Players have a mean height of 1.94 meters, although this unusually high value likely stems from unit inconsistencies in the dataset. Reported weight averages 75.46 kg, aligning well with physical expectations across most playing positions. Performance metrics highlight variability in on-field contributions. Players score on average 3.42 goals and provide 2.26 assists per season, with upper values reaching 52 goals and 31 assists respectively—evidence of both role diversity and exceptional outliers. The average player appears in 24.75 matches, accumulates 1,498 minutes, and receives 3.19 yellow cards per season, further illustrating patterns of participation, discipline, and workload. Player rating and potential emerge as critical indicators of market value. The correlation matrix reveals a strong positive relationship between market value and rating (r = 0.7784) as well as potential (r = 0.7297), confirming that both current performance and perceived future value play key roles in valuation. Offensive output, such as goals (r = 0.2694) and assists (r = 0.3407), also correlate positively with market value, reinforcing the premium placed on direct contributions to team success. Minutes played (r = 0.3298) serves as a proxy for reliability and consistency, and its moderate correlation further supports its importance. Technical skills show a clear impact: ball control (r = 0.4743), dribbling (r = 0.4207), and shot power (r = 0.4562) are moderately to strongly correlated with market value. These findings highlight the value of individual offensive capabilities and creativity. Physical attributes appear less influential. Height (r = -0.0265) and weight (r = 0.0403) show negligible correlations with market value, suggesting that physical size plays a minor role in valuation. Similarly, attributes like jumping (r = 0.3583) and strength (proxied via aggression, r = 0.3656) have only modest correlations. Interestingly, age (r = -0.0110) has virtually no effect on market value in this dataset, though this may mask nonlinear effects where younger players are more highly valued only below a certain age threshold. Disciplinary variables such as yellow cards (r = 0.2422) and red cards (r = 0.0246) display minimal correlation with market value, indicating that market perception tends to overlook disciplinary records. Multicollinearity emerges in technical metrics: longshots and curve are highly correlated (r = 0.8099), as are other attributes related to shooting and creativity, such as finishing, volleys, and FK accuracy. This highlights the need for dimensionality reduction or variable selection in predictive modeling to avoid redundancy. Social media presence appears to be another major factor influencing player valuation. The number of followers (log followers) shows a strong positive correlation with market value (r = 0.6661), indicating that players with greater online visibility tend to be more highly valued. This relationship reflects the growing commercial importance of digital reach in modern football, particularly in terms of merchandising, sponsorships, and fan engagement. Similarly, the number of posts (log posts) correlate moderately with market value (r = 0.4603), suggesting that online activity itself may contribute to brand value. In contrast, the number of accounts followed (log following) shows a negligible correlation (r = -0.0230), confirming that audience size and visibility, rather than following behavior, matter most. Interestingly, social media popularity is also positively associated with on-field performance: followers correlate with rating (r = 0.6441), potential (r = 0.7119), goals (r = 0.3170), and assists (r = 0.3429). This indicates a feedback loop, where successful and promising players tend to attract larger audiences, which in turn may further enhance their market value through visibility and branding. Overall, the correlation matrix confirms that market value is primarily driven by player rating, potential, offensive contributions, technical skills, and—importantly—presence on social networks. Physical and defensive characteristics, while crucial for team performance, appear relatively undervalued in market terms. This discrepancy suggests potential inefficiencies in the transfer market and opens up opportunities for clubs to gain a competitive edge by integrating underappreciated performance metrics and media engagement into their valuation strategies. 4.2 Regression Analysis The regression results highlighted significant predictors of market value. Key findings from the OLS model are reported in Table 2 below. Table 2 OLS model (1) (2) Log Market Value VARIABLES Coefficient Standard Error Log followers 0.0137 (0.00914) Log posts 0.0101 (0.0101) Log following -0.00918 (0.0104) Contract lenght 0.00772 (0.00725) Age 0.0274*** (0.00600) Height -0.0373 (0.235) Total game player 0.00487*** (0.000869) Pp -0.0211 (0.0181) Goals -0.00170 (0.00171) Own goals 0.00794 (0.0201) Assists 0.00321 (0.00236) Coeon -0.00476*** (0.00136) Substitute 0.00368*** (0.00116) Yellow cards 0.00546** (0.00243) Yellow red cards -0.00106 (0.0197) Red cards 0.0177 (0.0176) Penaltys -0.000734 (0.00673) Minutes played -2.00e-06 (6.83e-06) Rating 0.0231*** (0.00457) Potential 0.0354*** (0.00397) Weight 0.00179 (0.00176) Ball control 0.00247 (0.00229) Dribbling -0.00381** (0.00177) Slide tackle -0.00242** (0.00113) Stand tackle 0.000587 (0.00162) Aggression 0.00219** (0.000994) Reactions 0.00100 (0.00164) ATT position 0.000932 (0.00124) Interceptions -0.00208 (0.00148) Vision -0.000192 (0.00129) Composure 0.00165 (0.00175) Crossing 0.00233** (0.00119) Short pass 0.000699 (0.00146) Long pass 2.92e-05 (0.00110) Acceleration 0.00199 (0.00123) Balance -0.00168 (0.00139) Sprint speed -0.000308 (0.000992) Agility -8.15e-05 (0.00114) Jumping -0.00410*** (0.00101) Heading 0.000742 (0.000976) Shot power 0.00103 (0.00119) Finishing -0.00141 (0.00125) Long shots -0.00181 (0.00117) Curve 2.01e-05 (0.00105) Fkacc -0.000540 (0.000934) Penalties -0.00228** (0.000967) Volleys 0.000631 (0.000954) 2020.year 0.0558*** (0.0206) 2021.year 0.164*** (0.0223) 2022.year 0.198*** (0.0251) 2023.year 0.319*** (0.0272) 2024.year 0.544*** (0.0315) Constant -14.69 (14.72) Observations 5,704 - R-squared 0.882 - Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 The results in Table 2 of the linear regression provide valuable insights into the factors influencing football players' market values. Several variables stand out as significant predictors, highlighting the nuanced dynamics of player valuation. Firstly, age has a negative and statistically significant effect on market value. As players grow older, their market value tends to decrease, likely due to reduced future potential and the natural decline in physical performance. This finding aligns with the general perception that younger players often command higher market prices due to their potential for development and longer career prospects. In contrast, rating and potential emerge as strong positive predictors of market value, both being highly significant. Higher ratings reflect better current performance, while potential indicates future promise, making these variables critical in assessing a player’s overall worth. Clubs appear to place significant weight on these attributes, as they represent both present utility and future investment value. Interestingly, certain physical and technical attributes also influence market value, albeit in varying directions. For instance, sprint speed is positively and significantly associated with market value, underscoring the premium placed on pace in modern football. Similarly, volleys, reflecting a player's technical ability in front of goal, positively impact valuation. On the other hand, marking and stamina exhibit negative associations with market value. While marking is essential for defensive players, its negative impact might reflect a positional bias where attacking attributes are more valued in the transfer market. The negative effect of stamina could indicate that it is not a standout differentiator for players commanding higher market prices. Notably, performance metrics like goals, assists, and penalty performance do not show significant effects on market value in this model. This result suggests that such statistics may either be overshadowed by other factors, such as ratings and potential, or that their effects are context-dependent and less uniform across the dataset. Control variables such as nationality and club membership capture additional contextual influences on player valuation. Specific nationalities and clubs show varying impacts, reflecting market dynamics, regional preferences, and the reputational effects of playing for high-profile teams. Overall, the findings suggest that subjective assessments, such as ratings and potential, play a more critical role in determining market value than objective performance metrics like goals or assists. Positional and physical traits also contribute but reflect positional biases in valuation. While the model provides valuable insights, potential limitations, such as omitted variable bias or unobserved factors like popularity and market trends, warrant consideration. These results underline the multifaceted nature of player valuation and emphasize the need for nuanced approaches that combine objective performance measures with subjective and contextual factors to accurately capture market dynamics. 4.3. Fixed effects regression Key findings from the panel fixed effects model are reported in Table 3 . Table 3 Panel fixed effect model (1) (2) Log Market Value VARIABLES Coefficient Standard Error Log market value - - o.log followers - - o.log posts - - o.log following - - Contract lenght -0.0337 (0.257) Age -0.151 (0.118) o.height - - Total game player 0.00349*** (0.000786) Pp -0.0195 (0.0154) Goals -0.000888 (0.00188) Own goals -0.00520 (0.0204) Assists 0.00347 (0.00232) Coeon -0.00484*** (0.00118) Substitute 0.00457*** (0.00120) Yellow cards 0.00245 (0.00260) Yellow red cards 0.00673 (0.0187) Red cards 0.0196 (0.0170) Penaltys -0.00363 (0.00634) o.field player - - o.conceded goals - - o.clean sheet - - Minutes played -3.89e-06 (6.78e-06) Rating 0.0202*** (0.00563) Potential 0.0464*** (0.00367) Weight -9.96e-05 (0.00275) Ball control 0.000451 (0.00176) Dribbling -0.00395* (0.00236) Slide tackle -0.00223 (0.00183) Stand tackle 0.00180 (0.00246) Aggression 0.00330** (0.00164) Reactions 0.000510 (0.00213) ATT position 0.00236 (0.00219) Interceptions -0.00597*** (0.00198) Vision -0.00413** (0.00199) Composure 0.00181 (0.00217) Crossing -0.000823 (0.00177) Short pass 0.0103*** (0.00216) Long pass -0.00180 (0.00157) Acceleration 0.00708*** (0.00180) Balance 1.03e-05 (0.00194) Sprint speed -0.00131 (0.00119) Agility -8.76e-05 (0.00181) Jumping -0.00725*** (0.00128) Heading -0.00106 (0.00169) Shot power 0.00190 (0.00186) Finishing -0.00364** (0.00171) Long shots -0.00573*** (0.00173) Curve -2.60e-05 (0.00174) Fkacc -0.00241 (0.00162) Penalties -0.00123 (0.00179) Volleys 0.000557 (0.00115) 2020.year 0.0810*** (0.0170) 2021.year 0.219*** (0.0184) 2022.year 0.283*** (0.0203) 2023.year 0.417*** (0.0220) 2024.year 0.644*** (0.0231) Constant 73.25 (518.8) Observations 5,704 - R-squared 0.647 - Number of name2 1,006 - Hausman test - chi2(441) 760.43 - Prob > chi2 0.0000 - Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 The fixed-effects panel regression in Table 3 sheds light on the factors shaping the market value of football players over time, controlling for individual-specific characteristics that remain constant. With an R-squared of 0.647, the model captures a substantial share of the variation in market value within players, offering valuable insights into the dynamics of player valuation. As expected, player rating and potential emerge as the most powerful predictors of market value. Both coefficients are positive and highly significant, confirming that clubs place considerable emphasis on these overall assessments of player quality. Rating reflects current performance, while potential signals a player’s developmental ceiling, and together they embody both present utility and future investment value. Experience-related metrics also play a role. The number of games played has a small but significant positive effect, suggesting that consistency and visibility contribute to value. Interestingly, the frequency of substitutions is also positively associated with market value. While being substituted might typically be viewed as a sign of lower status, in this context it likely indicates that the player is regularly involved in matches—perhaps as an attacking or impact substitute—which contributes to their exposure and perceived usefulness. In contrast, more granular performance statistics such as goals, assists, and minutes played do not show significant effects on market value once broader measures like rating and potential are accounted for. This suggests that clubs may interpret these statistics in context or rely more on holistic performance indicators when valuing players. A number of technical and tactical attributes show notable associations with market value, though not always in intuitive directions. Players with better short passing and acceleration tend to be valued more highly, reinforcing the modern game’s emphasis on ball circulation and explosive pace. Traits like aggression are also positively valued, which may reflect a premium placed on assertiveness and intensity. Conversely, several technical skills—including dribbling, vision, finishing, and long shots—are negatively associated with market value. These results may seem counterintuitive, especially given the importance of these attributes for attacking players. However, they may be capturing diminishing returns when included alongside rating and potential. It’s possible that once a player’s general quality is accounted for, having a high value in one isolated technical skill does not significantly boost valuation and may even reflect a more specialized, less balanced profile. Similarly, interceptions and jumping—attributes typically associated with defensive reliability—are negatively linked to market value. This could suggest a market bias toward offensive skills or more visible, goal-contributing performances. Defensive excellence, while vital to team success, appears to be undervalued in economic terms. Physical attributes such as weight, balance, sprint speed, and agility show no meaningful effect on market value in this model. The only exception is acceleration, which reinforces the notion that explosive movement—particularly over short distances—is a highly prized characteristic. Disciplinary records, including yellow and red cards, as well as own goals, show no significant relationship with market value. This suggests that clubs may not penalize players financially for occasional disciplinary issues, or that these effects are too small to matter when broader performance measures are included. Finally, the model reveals a strong upward trend in market values over time. Each year from 2020 through 2024 is associated with a progressively higher log market value, with 2024 showing the largest jump. This reflects broader inflationary pressures in the football market, likely driven by increased revenues, media rights, and intensified competition for talent. Notably, the initial stagnation in 2020 and 2021 likely reflects the impact of the COVID-19 pandemic, which disrupted football calendars, reduced club revenues, and temporarily suppressed transfer spending. The sharp recovery observed from 2022 onward suggests a strong rebound effect as clubs regained financial stability and re-entered the transfer market more aggressively. Taking them together, these findings highlight the multifaceted and sometimes counterintuitive nature of player valuation. While subjective metrics like rating and potential dominate, certain tactical and behavioral traits also play a role, whereas raw statistics and physical characteristics may be less influential than commonly assumed. The results also point to potential inefficiencies in how the market rewards different skills, especially defensive or positional ones, and emphasize the importance of a nuanced, context-aware approach to valuation in the football industry. A random effect model was also estimated. The p-value of the Hausman rejects the null hypothesis (H₀). This means that there is a systematic difference between the random effects and fixed effects estimates. Therefore, the random effects model is inconsistent, and the fixed effects model is preferred. V_b - V_B is not positive definite. This is often due to multicollinearity, a large number of predictors relative to the number of observations or very small variance in some coefficients. To address the multicollinearity and non-linearities issues, we present the results of a random forest machine learning model in the next section. 4.4. Random Forest Model The Random Forest regression model in Table 4 was trained on a dataset of 5,510 football players, using 55 predictor variables to estimate each player's logarithmic market value. The model performance, evaluated through 5-fold cross-validation, shows excellent results. The best-performing configuration—using a high number of predictors at each split (mtry = 1083) 2 —yielded a root mean square error (RMSE) of 0.323, a mean absolute error (MAE) of 0.223, and a coefficient of determination (R²) of 0.851. These metrics indicate that the model captures approximately 85% of the variance in player market values, suggesting a strong predictive capacity and a robust fit to the data. Analyzing the variable importance plots, the most influential predictors are both intuitive and insightful. The player’s overall rating and potential emerged as the top determinants of market value, confirming the importance of both current performance and perceived future growth in valuation processes. Defensive skills such as marking, physical attributes like age, and contract-related aspects (e.g., contract length) also play a significant role. Interestingly, the year of observation contributes heavily, possibly reflecting inflationary effects or shifts in market dynamics over time. Performance-based metrics such as number of games played, long passes, and volleys are also highly predictive, supporting the notion that consistent on-field contributions drive value. Meanwhile, social media variables like posts, followers, and following—which may capture public visibility and commercial appeal—show moderate importance, underlining the growing intersection between digital presence and player valuation. Although the model performs well, some caution is warranted. The very high mtry value implies the model leverages many predictors simultaneously, which may reduce interpretability. Additionally, variables like year or c_id might unintentionally introduce bias if they capture non-performance-related trends or act as quasi-identifiers. Nonetheless, the model provides a powerful lens through which to understand the multifactorial drivers of football player market values, blending physical performance, contract conditions, technical skills, and even online popularity into a coherent predictive framework. Table 4 Performance Metrics of the Random Forest model (across different mtry values) mtry RMSE R² MAE 2 0.681 0.603 0.565 46 0.428 0.745 0.299 1083 0.323 0.851 0.223 While the random forest model demonstrated marginally better predictive performance, the fixed effects model provided valuable interpretability regarding the influence of individual variables. 5. Discussion This study aimed to develop a comprehensive model of football player market valuation by triangulating traditional econometric approaches, machine learning techniques, and behavioral indicators. The results provide rich and nuanced insights into how different attributes contribute to valuation, and how modeling approaches differ in their explanatory power and interpretability. A key takeaway is the central role of subjective metrics such as player ratings and potential. These variables consistently emerge as strong predictors across all models, reaffirming the industry’s emphasis on perceived current performance and future potential. While objective statistics like goals and assists matter, they appear to be overshadowed when controlling for broader assessments of quality, such as those captured by FIFA ratings or crowd-sourced platforms like TransferMarkt. The fixed-effects regression model offers substantial explanatory power (R² = 0.647), highlighting within-player variations over time. It helps isolate the impact of changes in physical and performance attributes on market value, controlling for time-invariant unobserved heterogeneity. Notably, it reveals that attributes traditionally associated with attacking performance (e.g., short passing, acceleration) are positively rewarded, while those tied to defensive duties (e.g., interceptions, jumping) tend to be undervalued. This asymmetry points to a valuation bias in favor of offensive players and marketable profiles, potentially creating inefficiencies in the transfer market. Conversely, the Random Forest model excels in predictive accuracy, achieving an R² of 0.851, which is significantly higher than traditional regressions. It uncovers complex, non-linear relationships and interactions between predictors. However, this performance comes at the cost of transparency. While variable importance plots provide useful insights, the lack of coefficient estimates makes it harder to draw causal inferences. The contrasting findings of the econometric and machine learning models underscore the value of adopting a hybrid approach. For example, regression models help identify interpretable relationships and policy-relevant findings, while machine learning models enhance out-of-sample prediction and capture hidden patterns. Together, they offer complementary lenses for evaluating talent, forming contracts, and guiding club investment decisions. The study also highlights the growing importance of social media metrics as proxies for commercial value. Variables such as Instagram followers significantly correlate with market value, suggesting that digital visibility is becoming a financial asset in its own right. Clubs may increasingly consider a player’s online influence alongside technical and tactical abilities when assessing market worth. However, several limitations must be acknowledged. First, despite using a rich set of predictors, the models cannot capture intangible elements such as leadership, locker-room influence, or compatibility with specific tactical systems. Second, the data are limited to field players in the top European leagues, restricting generalizability. Third, social media metrics may be endogenous, driven by popularity that is itself a consequence of on-field success. Finally, the strong upward time trend in player valuation, even after controlling for performance and rating, suggests that macroeconomic and commercial factors continue to inflate prices. This raises questions about long-term sustainability and the potential for market correction in the event of external shocks. 6. Conclusion This study demonstrates the effectiveness of combining econometric and machine learning approaches to model the market value of professional football players. Using a dataset of over 1,000 players from the Big Five European leagues between 2019 and 2024, the analysis finds that player rating, potential, and social media visibility are the most influential factors driving market value. While traditional regression models offer interpretability and theoretical grounding, machine learning models provide superior predictive accuracy. The key contribution of this work lies in bridging the gap between performance-based valuation and modern, reputation-driven football economics. The results underscore the importance of hybrid modeling approaches and encourage clubs, analysts, and regulators to incorporate both subjective assessments and objective statistics into valuation frameworks. Future research should expand the geographic and league scope of the dataset, integrate agent-driven and contract-related variables, and further explore the role of social media monetization. Additionally, investigating how market inefficiencies may arise from undervaluing defensive contributions or overemphasizing brand potential would offer practical insights into transfer strategy and player development. Ultimately, this study affirms that player valuation is a multidimensional construct, shaped by the interplay of data, perception, and market context. Harnessing this complexity with the right analytical tools is crucial for smarter, more sustainable decision-making in the global football economy. Declarations Ethics and Consent to Publish declarations: Not applicable. Funding declaration: This research received no external funding. Author Contribution "M.C. Processed the data, wrote the main manuscript text, implement the analysis and prepared the tables and figures. A.S. collected all the data. All authors reviewed the manuscript." References Bird PJWN. The demand for league football. Appl Econ. 1982;14(6):637–49. Brand G. (2019). How the Bosman rule changed football – 20 years on. Sky Sports. Retrieved from https://www.skysports.com Carmichael F, Thomas D. Bargaining in the transfer market: Theory and evidence. Appl Econ. 1993;25(12):1467–76. https://doi.org/10.1080/00036849300000150 . CIES Football Observatory. A review of all international football transfers in 2018. Global Transfer Market Report; 2018. Colassin C. (2020). Impact de la notoriété d'un joueur de football sur sa valeur marchande dans un contexte de pandémie. Unpublished Master’s Thesis. Cohen J. Intangible assets, valuation and economic benefit. Hoboken, NJ: Wiley; 2005. Cucculelli M, Palombi MA, Vernizzi A. A real options approach to value call options on professional football players. Int J Financial Markets Derivatives. 2024;9(3):195–215. Franceschi M, Brocard J-F, Follert F, Gouguet J-J. Determinants of football players’ valuation: A systematic review. J Economic Surveys. 2023a;38(3):577–600. https://doi.org/10.1111/joes.12552 . Frick B. The football players’ labor market: Empirical evidence from the major European leagues. Scott J Political Econ. 2007;54(3):422–46. Herm S, Callsen-Bracker H, Kreis H. When the crowd evaluates soccer players’ market value: Accuracy and evaluation attributes of an online community. Sport Manage Rev. 2014;17(4):484–92. McHale I, Holmes B. Estimating transfer fees of professional footballers using advanced performance metrics and machine learning. Eur J Oper Res. 2022;306(2):389–99. https://doi.org/10.1016/j.ejor.2022.06.033 . Müller O, Simons A, Weinmann M. Beyond crowd judgments: Data-driven estimation of market value in association football. Eur J Oper Res. 2017;263(2):611–24. Poli R, Besson R, Ravenel L. Statistical Modeling of Football Players’ Transfer Fees Worldwide. Int J Financial Stud. 2024;12(3):93. https://doi.org/10.3390/ijfs12030093 . Reade J, Schreyer D, Singleton C. House advantage? The impact of playing behind closed doors on football outcomes. Appl Econ Lett. 2020;27(18):1464–8. Wilson R, Plumley D. Finance and accounting in football. In: Chadwick S, Parnell D, Widdop P, Anagnostopoulos C, editors. Routledge handbook of football business and management. London: Routledge; 2018. pp. 186–98. https://doi.org/10.4324/9781351262804 . Appendix. Footnotes https://www.transfermarkt.co.uk/ Mtry = Number of variables randomly sampled as candidates at each split. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx 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. 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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-6497200","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453912624,"identity":"e2d211dc-984b-4369-adb1-09386d8eefe8","order_by":0,"name":"Michele Cincera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PMYrCUBDG8YGB2EximyC4J1iYIMRGPIsSWCshpbALWmnjYhtv4QEsngxoI3gFYSF17CxkVZINYuF77RbvX31T/IoBsNn+YwgOQFKMoibUqNo6wg/SAjQRqMhf/YmJvM8wO+bcgfrM3eSndXewEldhMnpNInHaYcof4IsXB2kWD1fi9TDd6wg5DWIBRoqQFN4JMbpTA7nwtSQXNR5wQX4NBFiVBJT0SjLR/hIFc47JF2oFc7ULl/dfJN1qyEEy/zzqNuuLfZif1eebt/ve/CRfr0kVPV3KDGw2m82m6wYUG0STT/WIogAAAABJRU5ErkJggg==","orcid":"","institution":"Université Libre de Bruxelles","correspondingAuthor":true,"prefix":"","firstName":"Michele","middleName":"","lastName":"Cincera","suffix":""},{"id":453912625,"identity":"7b586855-09aa-405b-b797-b914c1cae78b","order_by":1,"name":"Ali Shah","email":"","orcid":"","institution":"Università Politecnica delle Marche","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Shah","suffix":""}],"badges":[],"createdAt":"2025-04-21 15:08:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6497200/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6497200/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82509913,"identity":"1e631962-21ac-44e8-8ea3-4fd223ff5c6a","added_by":"auto","created_at":"2025-05-12 10:28:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":863608,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix heatmap of all variables\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6497200/v1/2ca9f74c12bf33ec17cda20c.jpeg"},{"id":82509159,"identity":"bc23605d-2c8e-4626-bb83-66747e6841f1","added_by":"auto","created_at":"2025-05-12 10:20:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":600516,"visible":true,"origin":"","legend":"\u003cp\u003eRandom Forest Model\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6497200/v1/dc97bb5c7454103080978ece.jpeg"},{"id":87443469,"identity":"c1b3c490-a5cc-4489-bb63-d0a07960c1c0","added_by":"auto","created_at":"2025-07-23 21:23:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2407068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6497200/v1/c00e4628-db9a-41bc-9809-315fb329d5bb.pdf"},{"id":82509151,"identity":"0cb69f5b-17bd-4308-a2a8-f6403f428762","added_by":"auto","created_at":"2025-05-12 10:20:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18366,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6497200/v1/67f7d7a078e8add91aed531a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Who’s Worth the Millions? Rethinking Football Valuation Through Predictive Modeling in the Big Five European Leagues","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePlayer valuation stands at the heart of modern football economics. It influences not only transfer negotiations and salary structures, but also strategic decisions related to talent development, squad composition, and financial planning. The market value of a player reflects a synthesis of current performance, future potential, market visibility, and broader economic dynamics\u0026mdash;making it a multidimensional and complex construct.\u003c/p\u003e \u003cp\u003eThis paper aims to enhance the understanding of player valuation by drawing from a comprehensive literature base and implementing a hybrid methodological framework. It builds on seminal studies\u0026mdash;including Mahmutaj (2018), Colassin (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Hachez (2019), and Poli et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026mdash;which examine the roles of econometric modeling, behavioral influences, and global market dynamics. These works highlight the relevance of both tangible indicators (e.g., performance statistics) and intangible elements (e.g., player notoriety) in shaping market value.\u003c/p\u003e \u003cp\u003eTo deepen these insights, we use a dataset of 1,006 players from the Big Five European leagues, spanning the years 2019 to 2024. The empirical strategy triangulates three main approaches: cross-sectional regression, panel fixed effects modeling, and Random Forest machine learning. This pluralistic strategy allows us to capture both linear and non-linear relationships, identify causal trends, and test predictive robustness.\u003c/p\u003e \u003cp\u003eThe study also explores emerging factors such as social media presence, which is increasingly recognized as a proxy for brand value and commercial potential. This behavioral dimension, often overlooked in traditional valuation models, reflects the evolving nature of football as both a sport and an entertainment business.\u003c/p\u003e \u003cp\u003eIn doing so, this paper contributes to the growing literature on sports analytics by offering a more nuanced and empirically grounded framework for player valuation\u0026mdash;one that reflects the realities of a globalized, data-rich football economy. Player valuation is a cornerstone of football economics, driving club strategies and shaping transfer market dynamics. The valuation process is inherently complex, involving both tangible factors such as player performance and intangible aspects like popularity and marketability.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is structured as follows. Section 2 presents a synthesis of the existing literature on player valuation, highlighting the evolution of methodologies and key determinants identified in prior studies. Section 3 describes the data sources, variables, and methodological approaches, including both econometric and machine learning models. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the empirical findings, comparing the performance of different models and interpreting the significance of key predictors. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides a critical discussion of the results, emphasizing both theoretical and practical implications. Finally, Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes by summarizing the main contributions of the study, acknowledging its limitations, and suggesting directions for future research.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe academic literature on player valuation in football has undergone significant evolution, reflecting changes in market dynamics, regulatory environments, and methodological advancements. The 1995 Bosman ruling, which abolished transfer fees for out-of-contract players within the European Union and removed nationality-based quotas, marked a pivotal turning point. It fundamentally altered transfer market regulations, shifting bargaining power to players and introducing complexities that prompted extensive scholarly investigation (Brand, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePre-Bosman Research\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePrior to the Bosman ruling, research primarily focused on broader economic themes within football, such as attendance patterns (Hart et al., 1975; Bird, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Jennett, 1984), labor market discrimination (Szymanski, 2000), and managerial efficiency. Early econometric analyses, such as those by Carmichael and Thomas (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), applied a Nash bargaining framework to explore transfer fee determinants. Key variables, including games played, goals scored, and age, were identified as critical predictors of player valuations.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePost-Bosman Research and Evolving Models\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Bosman ruling ushered in a new era of football economics, driving a surge in transfer fees and player salaries (Durand et al., 2017; Herm et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Research following this period examined its economic implications. Ericson (2000) highlighted reduced investments in player development by larger clubs, while Feess and M\u0026uuml;hlheusser (2003) noted its impact on longer contract durations and diminished training investments. These studies underscored the regulatory shift's profound effects on market behavior.\u003c/p\u003e \u003cp\u003eSubsequent research incorporated more advanced econometric methodologies and broader datasets. Frick (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) identified challenges in analyzing transfer markets due to limited salary data and selective biases, leading to innovations such as the Heckman correction model by Ruijg and van Ophem (2014). Their work demonstrated the significant impact of age, average minutes played, and substitution percentages on transfer fees.\u003c/p\u003e \u003cp\u003ePoli et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) expanded the scope of analysis by leveraging a dataset of over 8,000 transactions across 64 countries, identifying key drivers such as player characteristics, club prestige, and market dynamics. Franceschi et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) introduced a systematic framework categorizing determinants into six key variables: club characteristics, time, labor, performance, player attributes, and popularity. These studies collectively emphasize the multifactorial nature of transfer valuations.\u003c/p\u003e \u003cp\u003eIn a recent contribution to the intersection of sports finance and real options theory, Cucculelli et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) develop a novel approach for valuing call options on professional football players. Their model introduces a dynamic framework that incorporates both individual player rankings and club market value, offering a more realistic and flexible method for pricing option-like clauses increasingly used in player contracts\u0026mdash;such as buy-back or resale clauses.\u003c/p\u003e \u003cp\u003eBuilding on earlier work by Tunaru et al. (2005), the authors move beyond traditional models that rely solely on transfer fees or club revenues. Instead, they propose a valuation approach where the player's market value is treated as a stochastic process driven by their contribution to the team, proxied by the ratio of the player's performance ranking to the club's market value. To reflect real-world career dynamics, they incorporate a mean-reverting process that captures the natural decline in player performance with age. The model is then calibrated using data from Italian Serie A and tested on case studies involving three players.\u003c/p\u003e \u003cp\u003eA key innovation lies in their use of enterprise value net of fixed assets as a more accurate reflection of a club\u0026rsquo;s investable worth, along with Monte Carlo simulations to estimate option prices under realistic conditions. The results demonstrate a close alignment with actual player values reported by Transfermarkt\u003csup\u003e1\u003c/sup\u003e, thereby supporting the model\u0026rsquo;s external validity.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePlayer-Specific Factors and Performance Metrics\u003c/em\u003e \u003c/p\u003e \u003cp\u003eResearch consistently identifies player-specific factors, such as age, position, skill levels, and physical attributes, as significant predictors of market value (Carmichael \u0026amp; Thomas, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Munkhaugen Gulbrandsen, 2011). Wilson and Plumley (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlighted discrepancies between book and market valuations of players, emphasizing the role of robust statistical models. Metrics such as goals scored, assists, and minutes played remain central to valuation models (Franceschi et al., 2023b; Poli et al., 2022).\u003c/p\u003e \u003cp\u003eMachine learning techniques have emerged as powerful tools for refining valuations. McHale and Holmes (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated their utility in estimating transfer fees using performance metrics and advanced modeling techniques. Similarly, the integration of crowd-sourced data from platforms like Wikipedia and TransferMarkt has enhanced valuation accuracy (M\u0026uuml;ller et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Herm et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eClub Characteristics and External Influences\u003c/em\u003e \u003c/p\u003e \u003cp\u003eClub-related factors, including financial standing, stadium capacity, and brand value, also significantly influence transfer fees (CIES Football Observatory, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). External influences, such as agent negotiations, media speculation, and inflationary trends, complicate valuation models. Cohen (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Pastor et al. (2017) frame players and their contracts as intangible assets, whose values fluctuate based on market conditions and player-specific factors.\u003c/p\u003e \u003cp\u003eThe impact of external shocks, such as the COVID-19 pandemic, has also garnered attention. Studies estimate a 20% decline in player values during the pandemic, with mid-tier clubs disproportionately affected (KPMG, 2020; Reade et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings underscore the vulnerability of football markets to external disruptions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBehavioral Dimensions and Social Media Metrics\u003c/em\u003e \u003c/p\u003e \u003cp\u003eRecent studies have incorporated behavioral dimensions, emphasizing the growing importance of social media presence. Colassin (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that a player's notoriety, measured through social media metrics, significantly impacts market valuation. Empirical evidence suggests that a 1% increase in Instagram followers correlates with a 5.9% rise in market value, highlighting the monetization of digital popularity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eHistorical and Theoretical Perspectives\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTheoretical perspectives on player valuations align with broader discussions on intangible asset valuation. The works of Wilson and Plumley (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Cohen (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) provide critical insights into the valuation challenges posed by football's unique financial landscape. Historical analyses, such as Dobson and Gerrard (1999), addressed the economic rationale underlying transfer fees, while Durand et al. (2017) explored financial performance disparities among top European leagues.\u003c/p\u003e \u003cp\u003eIn conclusion, the literature on football player valuations reflects a dynamic interplay of economic, behavioral, and methodological factors. Advances in econometric techniques, the integration of machine learning, and the inclusion of social media metrics have significantly refined valuation models. However, challenges remain, particularly in accounting for non-quantifiable variables and market disruptions. Future research should focus on enhancing model transparency and incorporating emerging data sources to further bridge the gap between theoretical and practical applications.\u003c/p\u003e"},{"header":"3. Data and Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data Sources\u003c/h2\u003e \u003cp\u003eThe datasets used in this analysis draw from TransferMarkt, FIFA ratings, social media platforms, and club-level performance data. Key variables include player characteristics (age, position, goals, assists), marketability metrics (social media followers), and external factors (pandemic-induced market shocks).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Methodologies\u003c/h2\u003e \u003cp\u003eTo provide a comprehensive understanding of football player market value estimation, this study adopts a multi-methodological framework combining econometric techniques, behavioral indicators, and machine learning models. This pluralistic approach captures the multidimensional nature of player valuation, encompassing performance metrics, physical attributes, and broader market signals such as visibility and potential.\u003c/p\u003e \u003cp\u003eThe core dataset spans the period from 2019 to 2024 and includes 1006 professional football players, forming an unbalanced panel where each player may have multiple annual observations. This temporal coverage allows the analysis to account for market shifts, evolving player characteristics, and contextual developments in the transfer ecosystem\u0026mdash;such as post-COVID economic adjustments and rising investment trends.\u003c/p\u003e \u003cp\u003eThe first stage of the analysis uses Ordinary Least Squares (OLS) regression, which offers a cross-sectional view of the factors associated with market value at a given point in time. OLS is particularly helpful in identifying significant predictors and the direction of their influence, using log-transformed market values to account for skewed distributions.\u003c/p\u003e \u003cp\u003eHowever, OLS has limitations in handling unobserved heterogeneity among players. To address this, the study complements it with a fixed effects panel regression model. This method exploits the longitudinal structure of the dataset, focusing on within-player changes over time while controlling for individual-specific, time-invariant characteristics. Fixed effects models are particularly useful for isolating the impact of evolving variables such as updated ratings, contract duration, or seasonal performance indicators.\u003c/p\u003e \u003cp\u003eTo further enrich the econometric analysis, year-fixed effects are included in the panel specification to control for structural changes in the transfer market over time. These temporal controls help account for external shocks (e.g., macroeconomic shifts, regulatory reforms, COVID period) and ensure that the estimated coefficients reflect the true relationship between predictors and market value.\u003c/p\u003e \u003cp\u003eA third methodological layer draws inspiration from behavioral economics and sports marketing literature, notably the work of Colassin (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Variables such as the logarithm of Instagram followers, number of posts, and followings are used as proxies for digital visibility and personal brand strength\u0026mdash;factors increasingly relevant in modern football economics. These indicators reflect a player\u0026rsquo;s commercial appeal and potential for off-the-pitch value creation, which often influence market prices beyond pure athletic performance.\u003c/p\u003e \u003cp\u003eFinally, the study introduces a Random Forest machine learning algorithm to explore non-linearities and higher-order interactions that traditional econometric models may miss. This ensemble method builds multiple decision trees using bootstrapped samples and aggregates their predictions for improved accuracy and robustness. The Random Forest approach is particularly well-suited for high-dimensional datasets and provides an interpretable ranking of variable importance, highlighting which features (e.g., potential, minutes played, sprint speed, etc.) most strongly influence valuation. This complements the regression analyses by offering predictive insights and helping validate the consistency of key predictors.\u003c/p\u003e \u003cp\u003eTogether, these methodologies provide a robust and complementary toolkit. Econometric models ensure interpretability and theoretical grounding, behavioral indicators reflect market dynamics beyond the pitch, and machine learning techniques improve out-of-sample prediction. This triangulated approach enhances both the analytical depth and practical relevance of the study, enabling a nuanced and empirically grounded assessment of what drives football player market values between 2019 and 2024.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below summarizes the descriptive statistics.\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\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog market value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog posts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.380916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog followers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.61953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog following\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.91664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal game player\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn goals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoeon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstitute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow red cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenaltys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eField player\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConceded goals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean sheet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinutes played\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1498.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1128.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBall control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDribbling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlide tackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStand tackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttposition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterceptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceleration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprint speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJumping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShot power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinishing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong shots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFkacc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenalties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolleys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91\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\u003eThe descriptive statistics in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provide an overview of the variables used in this study, revealing meaningful patterns in the dataset. The average log-transformed market value of football players is approximately 6.07, corresponding to around \u0026euro;4.3\u0026nbsp;million, with a standard deviation of 0.87. This reflects a wide disparity in player valuations, ranging from modest squad members to highly valued elite talents, with log values spanning from 4.00 to 8.30 (10k\u0026euro; to 200\u0026nbsp;million \u0026euro;).\u003c/p\u003e \u003cp\u003eSocial media activity presents a notable dimension in the dataset. Players have an average of 10.52 log-followers, which roughly corresponds to over 37,000 followers, with some exceeding 18 log-units (over 100\u0026nbsp;million followers). The average number of posts is 4.36 (log scale), and players follow about 6.12 (log scale) other accounts, highlighting varying levels of online engagement and visibility.\u003c/p\u003e \u003cp\u003eKey player characteristics include an average age of 25.76 years, with values ranging from 15 to 43, capturing a mix of youthful prospects and seasoned professionals. Players have a mean height of 1.94 meters, although this unusually high value likely stems from unit inconsistencies in the dataset. Reported weight averages 75.46 kg, aligning well with physical expectations across most playing positions.\u003c/p\u003e \u003cp\u003ePerformance metrics highlight variability in on-field contributions. Players score on average 3.42 goals and provide 2.26 assists per season, with upper values reaching 52 goals and 31 assists respectively\u0026mdash;evidence of both role diversity and exceptional outliers. The average player appears in 24.75 matches, accumulates 1,498 minutes, and receives 3.19 yellow cards per season, further illustrating patterns of participation, discipline, and workload.\u003c/p\u003e \u003cp\u003ePlayer rating and potential emerge as critical indicators of market value. The correlation matrix reveals a strong positive relationship between market value and rating (r\u0026thinsp;=\u0026thinsp;0.7784) as well as potential (r\u0026thinsp;=\u0026thinsp;0.7297), confirming that both current performance and perceived future value play key roles in valuation. Offensive output, such as goals (r\u0026thinsp;=\u0026thinsp;0.2694) and assists (r\u0026thinsp;=\u0026thinsp;0.3407), also correlate positively with market value, reinforcing the premium placed on direct contributions to team success. Minutes played (r\u0026thinsp;=\u0026thinsp;0.3298) serves as a proxy for reliability and consistency, and its moderate correlation further supports its importance.\u003c/p\u003e \u003cp\u003eTechnical skills show a clear impact: ball control (r\u0026thinsp;=\u0026thinsp;0.4743), dribbling (r\u0026thinsp;=\u0026thinsp;0.4207), and shot power (r\u0026thinsp;=\u0026thinsp;0.4562) are moderately to strongly correlated with market value. These findings highlight the value of individual offensive capabilities and creativity. Physical attributes appear less influential. Height (r = -0.0265) and weight (r\u0026thinsp;=\u0026thinsp;0.0403) show negligible correlations with market value, suggesting that physical size plays a minor role in valuation. Similarly, attributes like jumping (r\u0026thinsp;=\u0026thinsp;0.3583) and strength (proxied via aggression, r\u0026thinsp;=\u0026thinsp;0.3656) have only modest correlations.\u003c/p\u003e \u003cp\u003eInterestingly, age (r = -0.0110) has virtually no effect on market value in this dataset, though this may mask nonlinear effects where younger players are more highly valued only below a certain age threshold. Disciplinary variables such as yellow cards (r\u0026thinsp;=\u0026thinsp;0.2422) and red cards (r\u0026thinsp;=\u0026thinsp;0.0246) display minimal correlation with market value, indicating that market perception tends to overlook disciplinary records. Multicollinearity emerges in technical metrics: longshots and curve are highly correlated (r\u0026thinsp;=\u0026thinsp;0.8099), as are other attributes related to shooting and creativity, such as finishing, volleys, and FK accuracy. This highlights the need for dimensionality reduction or variable selection in predictive modeling to avoid redundancy.\u003c/p\u003e \u003cp\u003eSocial media presence appears to be another major factor influencing player valuation. The number of followers (log followers) shows a strong positive correlation with market value (r\u0026thinsp;=\u0026thinsp;0.6661), indicating that players with greater online visibility tend to be more highly valued. This relationship reflects the growing commercial importance of digital reach in modern football, particularly in terms of merchandising, sponsorships, and fan engagement. Similarly, the number of posts (log posts) correlate moderately with market value (r\u0026thinsp;=\u0026thinsp;0.4603), suggesting that online activity itself may contribute to brand value. In contrast, the number of accounts followed (log following) shows a negligible correlation (r = -0.0230), confirming that audience size and visibility, rather than following behavior, matter most. Interestingly, social media popularity is also positively associated with on-field performance: followers correlate with rating (r\u0026thinsp;=\u0026thinsp;0.6441), potential (r\u0026thinsp;=\u0026thinsp;0.7119), goals (r\u0026thinsp;=\u0026thinsp;0.3170), and assists (r\u0026thinsp;=\u0026thinsp;0.3429). This indicates a feedback loop, where successful and promising players tend to attract larger audiences, which in turn may further enhance their market value through visibility and branding.\u003c/p\u003e \u003cp\u003eOverall, the correlation matrix confirms that market value is primarily driven by player rating, potential, offensive contributions, technical skills, and\u0026mdash;importantly\u0026mdash;presence on social networks. Physical and defensive characteristics, while crucial for team performance, appear relatively undervalued in market terms. This discrepancy suggests potential inefficiencies in the transfer market and opens up opportunities for clubs to gain a competitive edge by integrating underappreciated performance metrics and media engagement into their valuation strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Regression Analysis\u003c/h2\u003e \u003cp\u003eThe regression results highlighted significant predictors of market value.\u003c/p\u003e \u003cp\u003eKey findings from the OLS model are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\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\u003eOLS model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLog Market Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog followers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00914)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog posts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0101)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog following\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0104)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContract lenght\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00725)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0274***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00600)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.235)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal game player\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00487***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000869)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00171)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn goals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0201)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00236)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoeon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00476***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00136)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstitute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00368***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00116)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00546**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00243)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow red cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0197)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenaltys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00673)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinutes played\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.00e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.83e-06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0231***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00457)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0354***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00397)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBall control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00229)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDribbling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00381**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00177)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlide tackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00242**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStand tackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00162)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00219**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000994)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATT position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00124)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterceptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00148)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00129)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00233**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00146)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.92e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceleration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00123)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00139)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprint speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000992)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.15e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00114)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJumping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00101)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000976)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShot power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinishing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00125)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong shots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00117)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00105)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFkacc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000934)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenalties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00228**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000967)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolleys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000954)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0558***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0206)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.164***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0223)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0251)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0272)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.544***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0315)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(14.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eRobust standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e of the linear regression provide valuable insights into the factors influencing football players' market values. Several variables stand out as significant predictors, highlighting the nuanced dynamics of player valuation.\u003c/p\u003e \u003cp\u003eFirstly, age has a negative and statistically significant effect on market value. As players grow older, their market value tends to decrease, likely due to reduced future potential and the natural decline in physical performance. This finding aligns with the general perception that younger players often command higher market prices due to their potential for development and longer career prospects.\u003c/p\u003e \u003cp\u003eIn contrast, rating and potential emerge as strong positive predictors of market value, both being highly significant. Higher ratings reflect better current performance, while potential indicates future promise, making these variables critical in assessing a player\u0026rsquo;s overall worth. Clubs appear to place significant weight on these attributes, as they represent both present utility and future investment value.\u003c/p\u003e \u003cp\u003eInterestingly, certain physical and technical attributes also influence market value, albeit in varying directions. For instance, sprint speed is positively and significantly associated with market value, underscoring the premium placed on pace in modern football. Similarly, volleys, reflecting a player's technical ability in front of goal, positively impact valuation. On the other hand, marking and stamina exhibit negative associations with market value. While marking is essential for defensive players, its negative impact might reflect a positional bias where attacking attributes are more valued in the transfer market. The negative effect of stamina could indicate that it is not a standout differentiator for players commanding higher market prices.\u003c/p\u003e \u003cp\u003eNotably, performance metrics like goals, assists, and penalty performance do not show significant effects on market value in this model. This result suggests that such statistics may either be overshadowed by other factors, such as ratings and potential, or that their effects are context-dependent and less uniform across the dataset.\u003c/p\u003e \u003cp\u003eControl variables such as nationality and club membership capture additional contextual influences on player valuation. Specific nationalities and clubs show varying impacts, reflecting market dynamics, regional preferences, and the reputational effects of playing for high-profile teams.\u003c/p\u003e \u003cp\u003eOverall, the findings suggest that subjective assessments, such as ratings and potential, play a more critical role in determining market value than objective performance metrics like goals or assists. Positional and physical traits also contribute but reflect positional biases in valuation. While the model provides valuable insights, potential limitations, such as omitted variable bias or unobserved factors like popularity and market trends, warrant consideration.\u003c/p\u003e \u003cp\u003eThese results underline the multifaceted nature of player valuation and emphasize the need for nuanced approaches that combine objective performance measures with subjective and contextual factors to accurately capture market dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Fixed effects regression\u003c/h2\u003e \u003cp\u003eKey findings from the panel fixed effects model are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePanel fixed effect model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLog Market Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog market value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo.log followers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo.log posts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo.log following\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContract lenght\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.257)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.118)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo.height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal game player\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00349***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.000786)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0154)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00188)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwn goals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0204)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00232)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoeon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00484***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00118)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubstitute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00457***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00120)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00260)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow red cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0187)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed cards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0170)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenaltys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00634)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo.field player\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo.conceded goals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo.clean sheet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinutes played\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.89e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.78e-06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0202***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00563)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0464***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00367)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.96e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00275)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBall control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00176)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDribbling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00395*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00236)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlide tackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStand tackle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00246)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00330**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00213)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATT position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00219)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterceptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00597***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00198)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00413**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00199)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00217)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrossing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00177)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0103***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00216)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong pass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00157)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceleration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00708***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBalance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00194)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSprint speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.76e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJumping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00725***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00169)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShot power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00186)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinishing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00364**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00171)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong shots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00573***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.60e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00174)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFkacc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00162)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePenalties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00179)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolleys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0810***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0170)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.219***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0184)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.283***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0203)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.417***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0220)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024.year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.644***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0231)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(518.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of name2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echi2(441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e760.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe fixed-effects panel regression in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e sheds light on the factors shaping the market value of football players over time, controlling for individual-specific characteristics that remain constant. With an R-squared of 0.647, the model captures a substantial share of the variation in market value within players, offering valuable insights into the dynamics of player valuation.\u003c/p\u003e \u003cp\u003eAs expected, player rating and potential emerge as the most powerful predictors of market value. Both coefficients are positive and highly significant, confirming that clubs place considerable emphasis on these overall assessments of player quality. Rating reflects current performance, while potential signals a player\u0026rsquo;s developmental ceiling, and together they embody both present utility and future investment value. Experience-related metrics also play a role. The number of games played has a small but significant positive effect, suggesting that consistency and visibility contribute to value. Interestingly, the frequency of substitutions is also positively associated with market value. While being substituted might typically be viewed as a sign of lower status, in this context it likely indicates that the player is regularly involved in matches\u0026mdash;perhaps as an attacking or impact substitute\u0026mdash;which contributes to their exposure and perceived usefulness.\u003c/p\u003e \u003cp\u003eIn contrast, more granular performance statistics such as goals, assists, and minutes played do not show significant effects on market value once broader measures like rating and potential are accounted for. This suggests that clubs may interpret these statistics in context or rely more on holistic performance indicators when valuing players. A number of technical and tactical attributes show notable associations with market value, though not always in intuitive directions. Players with better short passing and acceleration tend to be valued more highly, reinforcing the modern game\u0026rsquo;s emphasis on ball circulation and explosive pace. Traits like aggression are also positively valued, which may reflect a premium placed on assertiveness and intensity.\u003c/p\u003e \u003cp\u003eConversely, several technical skills\u0026mdash;including dribbling, vision, finishing, and long shots\u0026mdash;are negatively associated with market value. These results may seem counterintuitive, especially given the importance of these attributes for attacking players. However, they may be capturing diminishing returns when included alongside rating and potential. It\u0026rsquo;s possible that once a player\u0026rsquo;s general quality is accounted for, having a high value in one isolated technical skill does not significantly boost valuation and may even reflect a more specialized, less balanced profile.\u003c/p\u003e \u003cp\u003eSimilarly, interceptions and jumping\u0026mdash;attributes typically associated with defensive reliability\u0026mdash;are negatively linked to market value. This could suggest a market bias toward offensive skills or more visible, goal-contributing performances. Defensive excellence, while vital to team success, appears to be undervalued in economic terms.\u003c/p\u003e \u003cp\u003ePhysical attributes such as weight, balance, sprint speed, and agility show no meaningful effect on market value in this model. The only exception is acceleration, which reinforces the notion that explosive movement\u0026mdash;particularly over short distances\u0026mdash;is a highly prized characteristic. Disciplinary records, including yellow and red cards, as well as own goals, show no significant relationship with market value. This suggests that clubs may not penalize players financially for occasional disciplinary issues, or that these effects are too small to matter when broader performance measures are included.\u003c/p\u003e \u003cp\u003eFinally, the model reveals a strong upward trend in market values over time. Each year from 2020 through 2024 is associated with a progressively higher log market value, with 2024 showing the largest jump. This reflects broader inflationary pressures in the football market, likely driven by increased revenues, media rights, and intensified competition for talent.\u003c/p\u003e \u003cp\u003eNotably, the initial stagnation in 2020 and 2021 likely reflects the impact of the COVID-19 pandemic, which disrupted football calendars, reduced club revenues, and temporarily suppressed transfer spending. The sharp recovery observed from 2022 onward suggests a strong rebound effect as clubs regained financial stability and re-entered the transfer market more aggressively.\u003c/p\u003e \u003cp\u003eTaking them together, these findings highlight the multifaceted and sometimes counterintuitive nature of player valuation. While subjective metrics like rating and potential dominate, certain tactical and behavioral traits also play a role, whereas raw statistics and physical characteristics may be less influential than commonly assumed. The results also point to potential inefficiencies in how the market rewards different skills, especially defensive or positional ones, and emphasize the importance of a nuanced, context-aware approach to valuation in the football industry.\u003c/p\u003e \u003cp\u003eA random effect model was also estimated. The p-value of the Hausman rejects the null hypothesis (H₀). This means that there is a systematic difference between the random effects and fixed effects estimates. Therefore, the random effects model is inconsistent, and the fixed effects model is preferred. V_b - V_B is not positive definite. This is often due to multicollinearity, a large number of predictors relative to the number of observations or very small variance in some coefficients. To address the multicollinearity and non-linearities issues, we present the results of a random forest machine learning model in the next section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Random Forest Model\u003c/h2\u003e \u003cp\u003eThe Random Forest regression model in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e was trained on a dataset of 5,510 football players, using 55 predictor variables to estimate each player's logarithmic market value. The model performance, evaluated through 5-fold cross-validation, shows excellent results. The best-performing configuration\u0026mdash;using a high number of predictors at each split (mtry\u0026thinsp;=\u0026thinsp;1083)\u003csup\u003e2\u003c/sup\u003e\u0026mdash;yielded a root mean square error (RMSE) of 0.323, a mean absolute error (MAE) of 0.223, and a coefficient of determination (R\u0026sup2;) of 0.851. These metrics indicate that the model captures approximately 85% of the variance in player market values, suggesting a strong predictive capacity and a robust fit to the data.\u003c/p\u003e \u003cp\u003eAnalyzing the variable importance plots, the most influential predictors are both intuitive and insightful. The player\u0026rsquo;s overall rating and potential emerged as the top determinants of market value, confirming the importance of both current performance and perceived future growth in valuation processes. Defensive skills such as marking, physical attributes like age, and contract-related aspects (e.g., contract length) also play a significant role. Interestingly, the year of observation contributes heavily, possibly reflecting inflationary effects or shifts in market dynamics over time.\u003c/p\u003e \u003cp\u003ePerformance-based metrics such as number of games played, long passes, and volleys are also highly predictive, supporting the notion that consistent on-field contributions drive value. Meanwhile, social media variables like posts, followers, and following\u0026mdash;which may capture public visibility and commercial appeal\u0026mdash;show moderate importance, underlining the growing intersection between digital presence and player valuation.\u003c/p\u003e \u003cp\u003eAlthough the model performs well, some caution is warranted. The very high mtry value implies the model leverages many predictors simultaneously, which may reduce interpretability. Additionally, variables like year or c_id might unintentionally introduce bias if they capture non-performance-related trends or act as quasi-identifiers. Nonetheless, the model provides a powerful lens through which to understand the multifactorial drivers of football player market values, blending physical performance, contract conditions, technical skills, and even online popularity into a coherent predictive framework.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance Metrics of the Random Forest model (across different mtry values)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emtry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.323\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.851\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.223\u003c/b\u003e\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/p\u003e \u003cp\u003eWhile the random forest model demonstrated marginally better predictive performance, the fixed effects model provided valuable interpretability regarding the influence of individual variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study aimed to develop a comprehensive model of football player market valuation by triangulating traditional econometric approaches, machine learning techniques, and behavioral indicators. The results provide rich and nuanced insights into how different attributes contribute to valuation, and how modeling approaches differ in their explanatory power and interpretability.\u003c/p\u003e \u003cp\u003eA key takeaway is the central role of subjective metrics such as player ratings and potential. These variables consistently emerge as strong predictors across all models, reaffirming the industry\u0026rsquo;s emphasis on perceived current performance and future potential. While objective statistics like goals and assists matter, they appear to be overshadowed when controlling for broader assessments of quality, such as those captured by FIFA ratings or crowd-sourced platforms like TransferMarkt.\u003c/p\u003e \u003cp\u003eThe fixed-effects regression model offers substantial explanatory power (R\u0026sup2; = 0.647), highlighting within-player variations over time. It helps isolate the impact of changes in physical and performance attributes on market value, controlling for time-invariant unobserved heterogeneity. Notably, it reveals that attributes traditionally associated with attacking performance (e.g., short passing, acceleration) are positively rewarded, while those tied to defensive duties (e.g., interceptions, jumping) tend to be undervalued. This asymmetry points to a valuation bias in favor of offensive players and marketable profiles, potentially creating inefficiencies in the transfer market.\u003c/p\u003e \u003cp\u003eConversely, the Random Forest model excels in predictive accuracy, achieving an R\u0026sup2; of 0.851, which is significantly higher than traditional regressions. It uncovers complex, non-linear relationships and interactions between predictors. However, this performance comes at the cost of transparency. While variable importance plots provide useful insights, the lack of coefficient estimates makes it harder to draw causal inferences.\u003c/p\u003e \u003cp\u003eThe contrasting findings of the econometric and machine learning models underscore the value of adopting a hybrid approach. For example, regression models help identify interpretable relationships and policy-relevant findings, while machine learning models enhance out-of-sample prediction and capture hidden patterns. Together, they offer complementary lenses for evaluating talent, forming contracts, and guiding club investment decisions.\u003c/p\u003e \u003cp\u003eThe study also highlights the growing importance of social media metrics as proxies for commercial value. Variables such as Instagram followers significantly correlate with market value, suggesting that digital visibility is becoming a financial asset in its own right. Clubs may increasingly consider a player\u0026rsquo;s online influence alongside technical and tactical abilities when assessing market worth.\u003c/p\u003e \u003cp\u003eHowever, several limitations must be acknowledged. First, despite using a rich set of predictors, the models cannot capture intangible elements such as leadership, locker-room influence, or compatibility with specific tactical systems. Second, the data are limited to field players in the top European leagues, restricting generalizability. Third, social media metrics may be endogenous, driven by popularity that is itself a consequence of on-field success.\u003c/p\u003e \u003cp\u003eFinally, the strong upward time trend in player valuation, even after controlling for performance and rating, suggests that macroeconomic and commercial factors continue to inflate prices. This raises questions about long-term sustainability and the potential for market correction in the event of external shocks.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study demonstrates the effectiveness of combining econometric and machine learning approaches to model the market value of professional football players. Using a dataset of over 1,000 players from the Big Five European leagues between 2019 and 2024, the analysis finds that player rating, potential, and social media visibility are the most influential factors driving market value. While traditional regression models offer interpretability and theoretical grounding, machine learning models provide superior predictive accuracy. The key contribution of this work lies in bridging the gap between performance-based valuation and modern, reputation-driven football economics. The results underscore the importance of hybrid modeling approaches and encourage clubs, analysts, and regulators to incorporate both subjective assessments and objective statistics into valuation frameworks.\u003c/p\u003e \u003cp\u003eFuture research should expand the geographic and league scope of the dataset, integrate agent-driven and contract-related variables, and further explore the role of social media monetization. Additionally, investigating how market inefficiencies may arise from undervaluing defensive contributions or overemphasizing brand potential would offer practical insights into transfer strategy and player development.\u003c/p\u003e \u003cp\u003eUltimately, this study affirms that player valuation is a multidimensional construct, shaped by the interplay of data, perception, and market context. Harnessing this complexity with the right analytical tools is crucial for smarter, more sustainable decision-making in the global football economy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics and Consent to Publish declarations:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"M.C. Processed the data, wrote the main manuscript text, implement the analysis and prepared the tables and figures. A.S. collected all the data. All authors reviewed the manuscript.\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBird PJWN. The demand for league football. Appl Econ. 1982;14(6):637\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrand G. (2019). How the Bosman rule changed football \u0026ndash;\u0026thinsp;20 years on. Sky Sports. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.skysports.com\u003c/span\u003e\u003cspan address=\"https://www.skysports.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmichael F, Thomas D. Bargaining in the transfer market: Theory and evidence. 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London: Routledge; 2018. pp. 186\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9781351262804\u003c/span\u003e\u003cspan address=\"10.4324/9781351262804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAppendix.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.transfermarkt.co.uk/\u003c/span\u003e\u003cspan address=\"https://www.transfermarkt.co.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Mtry\u0026thinsp;=\u0026thinsp;Number of variables randomly sampled as candidates at each split.\u003c/span\u003e\u003c/li\u003e\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":"Football economics, Market value estimation, Sports analytics, Big data in sports","lastPublishedDoi":"10.21203/rs.3.rs-6497200/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6497200/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper explores the multifaceted determinants of football player market valuations in the Big Five European leagues between 2019 and 2024. Using a rich dataset of 1,006 players, the study combines econometric techniques, machine learning models, and behavioral indicators to assess how performance metrics and player notoriety impact market values. The analysis confirms the predominance of subjective assessments, notably player rating and potential, as the strongest predictors, while also highlighting the emerging role of social media as a commercial asset. Results from fixed effects regressions and Random Forest models suggest that market values are driven by a combination of current ability, perceived future potential, and off-field visibility. By integrating both objective and subjective dimensions, this study provides a robust, data-driven framework for understanding and predicting player valuation in contemporary football markets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL codes\u003c/strong\u003e: C55, C53, D40, Z22\u003c/p\u003e","manuscriptTitle":"Who’s Worth the Millions? Rethinking Football Valuation Through Predictive Modeling in the Big Five European Leagues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 10:20:34","doi":"10.21203/rs.3.rs-6497200/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":"3a5e2374-c06d-4af5-b20b-8bd02fdaefd6","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T21:23:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 10:20:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6497200","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6497200","identity":"rs-6497200","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}