Physical predictors for retention and dismissal of professional soccer head coaches: an analysis of locomotor variables using logistic regression pipeline

Abstract

IntroductionSoccer has enormous global popularity, increasing pressure on clubs to optimize performance. In failure, the tendency is to replace the Head coach (HC). This study aimed to check the physical effects of mid-season replacements of HCs, investigating which external load variables can predict retention or dismissal.MethodsThe data was collected in training and matches of a professional adult male soccer team during three complete seasons (2020/21-2022/2023). The sample included 6 different HCs (48.8 ± 7.4 years of age; 11.2 ± 3.9 years as a HC). The 4 weeks and 4 games before and after the replacement of HCs were analysed. External load variables were collected with Global Positioning System (GPS) devices. A logistic regression (LR) model was developed to classify the HCs' retention or dismissal. A sensitivity analysis was also conducted to determine the specific locomotive variables that could predict the likelihood of HC retention or dismissal.ResultsIn competition, locomotor performance was better under the dismissed HCs, whereas the new HC had better values during training. The LR model demonstrated a good prediction accuracy of 80% with a recall and precision of 85% and 78%, respectively, amongst other model performance indicators. Meters per minute in games was the only significant variable that could serve as a potential physical marker to signal performance decline and predict the potential dismissal of an HC with an odd ratio of 32.4%.DiscussionAn in-depth analysis and further studies are needed to understand other factors' effects on HC replacement or retention.