Predicting Physiological Variables of Players that Make a Winning Football Team: A Machine Learning Approach

Football Match Line-Up Prediction Based on Physiological Variables: A Machine Learning Approach

“…These variables are dependent on the player's position, displaying differences depending on the position as they are related to specific demands of the player function and team strategy. The results are presented in Table 5 and were obtained in a previous study conducted by the authors [8]. Source: [8].…”

“…The results are presented in Table 5 and were obtained in a previous study conducted by the authors [8]. Source: [8].…”

“…To build the index several steps were followed: first, the physiological variables of the players, by field position (Central Defender (CD), Full Back (FB), Centre Midfielder (CM), Offensive Midfielder (OM), Winger (W) and Forward (F)), that most contribute to victory were identified using Recursive Feature Elimination (RFE) [19,20] (already carried out in a previous study [8]. Then ML algorithms were trained to determine if the variables selected by the RFE algorithm produced better results than using all variables.…”

“…The work presented in this paper is an extension of the paper "Predicting physiological variables of players that make a winning football team: a machine learning approach" presented by the authors at ICCSA 2021 conference. The objective of the work presented at the conference was to identify if there was a strong correlation between the football match outcome (wins) and the physiological variables of the players, having achieved an accuracy of 79% with the XgB algorithm [8].…”