The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues
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 Europe&...
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Pamukkale University
2024-12-01
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| Series: | Pamukkale Spor Bilimleri Dergisi |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3954238 |
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| author | Murat Işık Mehmet Ali Yalçınkaya |
| author_facet | Murat Işık Mehmet Ali Yalçınkaya |
| author_sort | Murat Işık |
| collection | DOAJ |
| description | 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 Europe's top five leagues (Spain's La Liga, France's Ligue 1, England's Premier League, Italy's Serie A, and Germany's 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. |
| format | Article |
| id | doaj-art-9e0ba36ff90549689768be804015c6b1 |
| institution | Kabale University |
| issn | 1309-0356 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Pamukkale University |
| record_format | Article |
| series | Pamukkale Spor Bilimleri Dergisi |
| spelling | doaj-art-9e0ba36ff90549689768be804015c6b12025-01-09T12:13:50ZengPamukkale UniversityPamukkale Spor Bilimleri Dergisi1309-03562024-12-0115345548510.54141/psbd.1489554218The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five LeaguesMurat Işık0https://orcid.org/0000-0003-3200-1609Mehmet Ali Yalçınkaya1https://orcid.org/0000-0002-7320-5643KIRSEHIR AHI EVRAN UNIVERSITYAhi Evran ÜniversitesiThe 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 Europe's top five leagues (Spain's La Liga, France's Ligue 1, England's Premier League, Italy's Serie A, and Germany's 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.https://dergipark.org.tr/en/download/article-file/3954238football player valuationperformance metricstransfer market analysismachine learning in sports |
| spellingShingle | Murat Işık Mehmet Ali Yalçınkaya The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues Pamukkale Spor Bilimleri Dergisi football player valuation performance metrics transfer market analysis machine learning in sports |
| title | The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues |
| title_full | The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues |
| title_fullStr | The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues |
| title_full_unstemmed | The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues |
| title_short | The Role of Performance Metrics in Estimating Market Values of Footballers in Europe's Top Five Leagues |
| title_sort | role of performance metrics in estimating market values of footballers in europe s top five leagues |
| topic | football player valuation performance metrics transfer market analysis machine learning in sports |
| url | https://dergipark.org.tr/en/download/article-file/3954238 |
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