The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues

Mehmet Ali Yalçınkaya; Murat Işık

Makale detayları

The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues

Yazarlar:

Mehmet Ali Yalçınkaya | Murat Işık

Yayıncı:

Pamukkale University

Ana Başlıklar:

Sociology of Sports

Dergi:

Pamukkale Journal of Sport Sciences Vol.15 Issue 3, pp.455-485

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337626

pISSN:

1309-0356

Anahtar kelimeler:

Özet:

 The transfer economy in football is a multi-billion-dollar industry, where accurate valuation of players is crucial for clubs financial sustainability and competitive success. This study investigates the role of performance metrics in estimating the market values of football players in Europes top five leagues Spains La Liga, Frances Ligue 1, Englands Premier League, Italys Serie A, and Germanys Bundesliga. The study collected 28 performance metrics e.g., goals, shots per game, assists, and pass success percentage for 1508 players from the Whoscored platform. Additionally, the players positions and the leagues they play in were also included as features. These data were combined with market values from the Transfermarkt platform, resulting in a comprehensive dataset. Two main analytical methods were employed: regression and classification. In the regression analysis, seven models Adaboost, Decision Tree, Gradient Boosting, K Nearest Neighbors, Random Forest, Ridge Regression, and Support Vector Machine predicted players market values. The highest accuracy was achieved with the Random Forest algorithm R-squared: 0.90. In the classification analysis, players market values were categorized into four classes low, lower-mid, upper-mid, and high, and their class memberships were predicted based on performance metrics. The CNN algorithm achieved the highest accuracy, with a success rate of 97. The results indicate that performance metrics significantly contribute to estimating football players market values, and models based on these metrics can assist clubs in making more informed, data-driven decisions during transfers.

 The transfer economy in football is a multi-billion-dollar industry, where accurate valuation of players is crucial for clubs financial sustainability and competitive success. This study investigates the role of performance metrics in estimating the market values of football players in Europes top five leagues Spains La Liga, Frances Ligue 1, Englands Premier League, Italys Serie A, and Germanys Bundesliga. The study collected 28 performance metrics e.g., goals, shots per game, assists, and pass success percentage for 1508 players from the Whoscored platform. Additionally, the players positions and the leagues they play in were also included as features. These data were combined with market values from the Transfermarkt platform, resulting in a comprehensive dataset. Two main analytical methods were employed: regression and classification. In the regression analysis, seven models Adaboost, Decision Tree, Gradient Boosting, K Nearest Neighbors, Random Forest, Ridge Regression, and Support Vector Machine predicted players market values. The highest accuracy was achieved with the Random Forest algorithm R-squared: 0.90. In the classification analysis, players market values were categorized into four classes low, lower-mid, upper-mid, and high, and their class memberships were predicted based on performance metrics. The CNN algorithm achieved the highest accuracy, with a success rate of 97. The results indicate that performance metrics significantly contribute to estimating football players market values, and models based on these metrics can assist clubs in making more informed, data-driven decisions during transfers.

 The transfer economy in football is a multi-billion-dollar industry, where accurate valuation of players is crucial for clubs financial sustainability and competitive success. This study investigates the role of performance metrics in estimating the market values of football players in Europes top five leagues Spains La Liga, Frances Ligue 1, Englands Premier League, Italys Serie A, and Germanys Bundesliga. The study collected 28 performance metrics e.g., goals, shots per game, assists, and pass success percentage for 1508 players from the Whoscored platform. Additionally, the players positions and the leagues they play in were also included as features. These data were combined with market values from the Transfermarkt platform, resulting in a comprehensive dataset. Two main analytical methods were employed: regression and classification. In the regression analysis, seven models Adaboost, Decision Tree, Gradient Boosting, K Nearest Neighbors, Random Forest, Ridge Regression, and Support Vector Machine predicted players market values. The highest accuracy was achieved with the Random Forest algorithm R-squared: 0.90. In the classification analysis, players market values were categorized into four classes low, lower-mid, upper-mid, and high, and their class memberships were predicted based on performance metrics. The CNN algorithm achieved the highest accuracy, with a success rate of 97. The results indicate that performance metrics significantly contribute to estimating football players market values, and models based on these metrics can assist clubs in making more informed, data-driven decisions during transfers.

 The transfer economy in football is a multi-billion-dollar industry, where accurate valuation of players is crucial for clubs financial sustainability and competitive success. This study investigates the role of performance metrics in estimating the market values of football players in Europes top five leagues Spains La Liga, Frances Ligue 1, Englands Premier League, Italys Serie A, and Germanys Bundesliga. The study collected 28 performance metrics e.g., goals, shots per game, assists, and pass success percentage for 1508 players from the Whoscored platform. Additionally, the players positions and the leagues they play in were also included as features. These data were combined with market values from the Transfermarkt platform, resulting in a comprehensive dataset. Two main analytical methods were employed: regression and classification. In the regression analysis, seven models Adaboost, Decision Tree, Gradient Boosting, K Nearest Neighbors, Random Forest, Ridge Regression, and Support Vector Machine predicted players market values. The highest accuracy was achieved with the Random Forest algorithm R-squared: 0.90. In the classification analysis, players market values were categorized into four classes low, lower-mid, upper-mid, and high, and their class memberships were predicted based on performance metrics. The CNN algorithm achieved the highest accuracy, with a success rate of 97. The results indicate that performance metrics significantly contribute to estimating football players market values, and models based on these metrics can assist clubs in making more informed, data-driven decisions during transfers.

Makale detayları

The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues

Yazarlar:

Mehmet Ali Yalçınkaya | Murat Işık

Yayıncı:

Pamukkale University

Ana Başlıklar:

Sociology of Sports

Dergi:

Pamukkale Journal of Sport Sciences Vol.15 Issue 3, pp.455-485

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337626

pISSN:

1309-0356

Football player valuation

Performance metrics

Transfer market analysis

Machine learning in sports

Sayı

Makale detayları

The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues

Yazarlar: Mehmet Ali Yalçınkaya | Murat Işık

Yayıncı: Pamukkale University

Ana Başlıklar: Sociology of Sports

Dergi: Pamukkale Journal of Sport Sciences Vol.15 Issue 3, pp.455-485

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001337626

pISSN: 1309-0356

Football player valuation

Performance metrics

Transfer market analysis

Machine learning in sports

Sayı

Yazarlar:

Mehmet Ali Yalçınkaya | Murat Işık

Yayıncı:

Pamukkale University

Ana Başlıklar:

Sociology of Sports

Dergi:

Pamukkale Journal of Sport Sciences Vol.15 Issue 3, pp.455-485

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337626

pISSN:

1309-0356