Forecasting the future development in quality and value of professional football players
1 Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands；\ 2 Data Science \& Computer Vision, SciSports, 3703 HX Zeist, The Netherlands
Transfers in professional football (soccer) are risky investments because of the large transfer fees and high risks involved. Although data-driven models can be used to improve transfer decisions, existing models focus on describing players' historical progress, leaving their future performance unknown. Moreover, recent developments have called for the use of explainable models combined with uncertainty quantification of predictions. This paper assesses explainable machine learning models based on predictive accuracy and uncertainty quantification methods for the prediction of the future development in quality and transfer value of professional football players. The predictive accuracy is studied by training the models to predict the quality and value of players one year ahead. This is carried out by training them on two data sets containing data-driven indicators describing the player quality and player value in historical settings. In general, the random forest model is found to be the most suitable model because it provides accurate predictions as well as an uncertainty quantification method that naturally arises from the bagging procedure of the random forest model. Additionally, this research shows that the development of player performance contains nonlinear patterns and interactions between variables, and that time series information can provide useful information for the modeling of player performance metrics. The resulting models can help football clubs make more informed, data-driven transfer decisions by forecasting player quality and transfer value.
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Beijing Sport University（北京体育大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Universidad Politécnica de Madrid（马德里理工大学）
Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and ten types of match events with the corresponding coordinates for all the lineup players whos…
Over the last few decades, the player recruitment process in professional football has evolved into a multi-billion industry and has thus become of vital importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and advanced statistics. However, the question whether a given player would fit the team's playing style often still remains unanswered. In this paper, we aim to bridge that gap by proposing a set of 21 player roles and introducing a method for automatically identifying the most applica…
Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networks
Institute of Statistics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院统计研究所）；Karlsruher Sport-Club（卡尔斯鲁厄体育俱乐部）；Broad Institute of MIT & Harvard（麻省理工学院与哈佛大学生物医学 Broad 研究所）；German Football Association (DFB)（德国足球协会）；Chair for Sports Analytics, Saarland University（萨尔兰大学体育分析讲席）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
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