Biomechanical Loads and Their Effects on Player Performance in NCAA D-I Male Basketball Games - Olthof, Sigrid B. H.; Tureen, Tahmeed; Tran, Lam; Brennan, Benjamin; Winograd, Blair; Zernicke, Ronald F. (Frontiers in Sports and Active Living, 2021)
Basketball games and practices are characterized by quick actions and many scoring attempts which pose biomechanical loads on the players’ bodies. Inertial Measurement Units capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis, and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and practices with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modelled: i) relations between team-level game stats and the team’s win/loss probabilities, ii) associations between the player-level training and game loads and their game stats, and iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses therefore included total points, 2-point field goals, and defensive rebounds that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular increased loads two days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads two days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in player’s loads and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sport scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.