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Chang, V., Sajeev, S., Xu, Q. A., Tan, M., & Wang, H. (2024). Football Analytics: Assessing the Correlation between Workload, Injury and Performance of Football Players in the English Premier League. Applied Sciences, 14, Article 7217.
https://doi.org/10.3390/app14167217

has been cited by the following article:

• TITLE: Sport Analytics Data for Player Performance and Financial Risk Management

  AUTHORS: Gorngris Kositsakul

  KEYWORDS: Player Performance, Player Valuation, Data Analytics, Regression

  JOURNAL NAME: Journal of Financial Risk Management, Vol.14 No.3, September 1, 2025

  ABSTRACT: This study investigates the relationship between various player characteristics and the performance metrics in professional football using sport data analytics. Utilising a data set of over 10,000 observations, the analysis employs regression models to identify the significant determinants of player performance and value. Results highlight that most factors are positively correlated and that subjective opinions on metrics which normally influence player performance and values are supported by objective evidence. These insights demonstrate the positive impacts of utilizing sport data analytics in enhancing decision-making in football. The findings also provide insights into how objective performance data can support better financial decisions in football.