Multiple regression analysis for competitive performance assessment of professional soccer players

BACKGROUND: Being in peak physical condition and having specific motor abilities are necessity for every top-level soccer player in order to achieve success in competition. In order to correctly assess soccer players' performance, this research uses laboratory and field measurements, as well as results of competitive performance obtained by direct software measurements of players' movement during the actual soccer game. OBJECTIVE: The main goal of this research is to give insight into the key abilities that soccer players need to have in order to perform in competitive tournaments. Besides training adjustments, this research also gives insight into what variables need to be tracked in order to accurately assess the efficiency and functionality of the players. METHODS: The collected data need to be analyzed using descriptive statistics. Collected data is also used as input for multiple regression models that can predict certain key measurements: total distance covered, percent of effective movements and high index of effective performance movements. RESULTS: Most of the calculated regression models have high predictability level with statistically significant variables. CONCLUSION: Based on the results of regression analysis it can be deduced that motor abilities are important factor in measuring soccer player's competitive performance and team's success in the match.