English league transfer prices: is there a racial dimension? A re-examination with new data

Medcalfe, Simon

doi:10.1080/13504850600949178

Cited by 14 publications

(12 citation statements)

References 4 publications

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“…Also, since Frick (2007) and Torgler and Schmidt (2007) provide very thorough reviews of the early performancepay studies in soccer, we focus on post-2007 studies. There is also a related literature on the determinants of transfer prices in soccer (see, e.g., Medcalfe, 2008). 3.…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Performance and Actual Pay in Norwegian Soccer

Thrane

2019

Journal of Sports Economics

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Data on actual earnings for soccer players are virtually impossible to obtain. For this reason, most performance–pay studies on soccer use market values as a proxy for earnings. No study to date has questioned whether this fact has had any bearing on the conclusions reached in the literature. This study examines this question by comparing how certain performance variables and player characteristics affect the actual earnings and market values of soccer players in the Norwegian version of the “Premier League,” the so-called Eliteserien. In several important respects, the analyses show that the regression coefficient estimates differ for actual earnings and market values. It is concluded that caution is warranted when interpreting performance variables’ and player characteristics’ effects on market values as they were effects on actual earnings.

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Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Performance and Actual Pay in Norwegian Soccer

Thrane

2019

Journal of Sports Economics

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“…A similar OLS regression approach has been used by Carmichael and Thomas (1993), Reilly and Witt (1995), Speight & Thomas (1997), Medcalfe (2008) and Ruijg and Van Ophem (2015) to determine the impact of a number of factors on transfer market values in the UK and by Eschweiler and Vieth (2003) in Germany.…”

Section: Methodsmentioning

confidence: 99%

Does the quality of a youth academy impact a football player's market value?

Balliauw

Bosmans

Pauwels³

2021

SBM

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PurposeFootball clubs invest in the implementation of scientific insights that improve the quality of youth academies. In the long run, clubs expect their youth academy investments to result in better trained players. The purpose of this paper is to quantify the impact of the attended youth academies' quality on the future market value of a player.Design/methodology/approachA dataset containing 94 players trained in 13 different academies has been constructed. The dataset contains characteristics of the players and information on the quality of their attended academies. The impact of the quality of the attended academies on players' future market values was estimated empirically through multiple regression analysis.FindingsThe quality of a youth academy has a significant positive impact on a player's market value, which in turn is correlated with higher future wages for players and transfer fees for clubs.Research limitations/implicationsClubs are advised to pay sufficient attention to investments in their youth academy. This will eventually lead to better trained players and higher revenues. Players in turn should strive to be part of the best academies that provide good training and the opportunity to become a top-earning player. For policymakers, such as football federations, the results imply that stimulating club investments in academies can lead to better national team performances.Originality/valueThe impact of the quality of a youth academy on an individual professional football player's career has never been quantified in the literature before. To this end, a new variable has been constructed using scientific assessments of youth academies.

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“…Apart from the abovementioned metric, many researchers used other performance metrics that helped explain the value and the fees. Passing are used frequently [7]; duelling (or tackles) in the form of clearances; dribbles [21]; committed fouls [20]; and yellow and red cards [22].…”

Section: Player Performancementioning

confidence: 99%

Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques

Al-Asadi

Taşdemir

2022

IEEE Access

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Football is a popular sport; however, it is a big business as well. From a managerial perspective, the important decisions that team managers make -Concerning player transfers, issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees-prices that could be paid for a player on the football market. Therefore, market values play an important role in transfer negotiations. The market has traditionally been estimated by football experts. However, expert judgments are inaccurate and not transparent. Data analytics may thus provide a sound alternative or a complementary approach to experts-based estimations of market value. In this study, we propose an objective quantitative method to determine football players' market values. The method is based on the application of machine learning algorithms to the performance data of football players. The data used in the experiment are FIFA 20 video game data, collected from sofifa.com. We estimate players' market values using four regression models that were tested on the full set of featureslinear regression, multiple linear regression, decision trees, and random forests. Moreover, we seek to analyze the data and identify the most important factors affecting the determination of the market value. In the experimental results, random forest performed better than other algorithms for predicting the players' market values. It has achieved the highest accuracy score and lowest error ratio compared to baseline. The results show that our methods are capable to address this task efficiently, surpassing the performance reported in previous works. Finally, we believe our results can play an important role in the negotiations that take place between football clubs and a player's agents. This model can be used as a baseline to simplify the negotiation process and estimate a player's market value in an objective quantitative way.INDEX TERMS Player value prediction, regression, Machine learning, Football analytics, FIFA Video game data.

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English league transfer prices: is there a racial dimension? A re-examination with new data

Cited by 14 publications

References 4 publications

Performance and Actual Pay in Norwegian Soccer

Performance and Actual Pay in Norwegian Soccer

Does the quality of a youth academy impact a football player's market value?

Predict the Value of Football Players Using FIFA Video Game Data and Machine Learning Techniques

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