Application of statistical methods for predicting successful adaptation of new players in a team

modern sports management requires effective tools for predicting the successful adaptation of players during transfers between teams. This article presents an integrated model for predicting athlete adaptation, based on a combination of machine learning methods, statistical analysis, and expert assessments. Objective: to develop and evaluate the effectiveness of a comprehensive model for predicting the successful adaptation of players in new teams, taking into account individual, team, and external factors. Methods: the research is based on a longitudinal analysis of transfers in professional sports, using statistical methods, Bayesian modeling, and machine learning algorithms. A composite index, considering the speed of integration and athlete performance, was used to assess the success of adaptation. Issues of predicting player adaptation were considered in the works of Smith, Ruiz, and other researchers, who proposed various approaches from expert assessments to the application of machine learning methods. Results: the developed model demonstrates a prediction accuracy of 78.2%, which exceeds existing approaches, including expert assessments (67.2%), linear regression (69.8%), and Bayesian classifier (73.6%). The highest accuracy was achieved when predicting adaptation in basketball (81.3%) and during intra-championship transfers (81.7%). Conclusion: the proposed integrated model provides high accuracy in predicting the successful adaptation of players and can serve as an effective decision support tool in sports management when planning transfer policies.

athlete adaptation, transfer prediction, machine learning in sports, multi-criteria analysis, fuzzy logic, player selection, hybrid prediction models