Physical performance optimization in football - Dobreff, G.; Revisnyei, P.; Schuth, G.; Szigeti, G.; Toka, L.; Pašic, A. (Machine Learning and Data Mining for Sports Analytics, 2020)
Physical performance optimization is essential for any sport, and it is feasible in today`s data-driven world. In numerous sports, it is a widely spread method to collect complex information about an athlete`s performance and physiological attributes. The collected data allows to create a personalized training program to maximize the athlete`s performance. Using the physiological attributes jointly with the physical load measurements can provide a refined complex picture of sportsmens`, specifically football players`, condition. We analyze a unique dataset that contains more than 600 key performance indicators and important physiological attributes, like the Creatine Kinase enzyme level, i.e., an indicator of muscles damage, the Heart Rate Variability that shows how well the player`s heart can adapt to the exercises, and sleep quality data. We examine the relationship between the physiological factors and the physical performance of the players in training sessions and matches. We obtain the unique intervals for the relevant parameters where performance can be maximized on matchdays. After determining these optimal intervals, we introduce the Minimum Number of Training Groups (MNTG) problem in order to create the minimum number of training groups, i.e., sets of players, that can train together to maximize their performance on matchday. We find that in 96% of the time three or fewer training groups are required to optimize the performance for matchday, instead of personalized separate training for all players.