Date Thesis Awarded
Honors Thesis -- Open Access
Bachelors of Science (BS)
Injuries are a significant aspect of every sport, with the ability to impact a player’s career and the success of a team in their season. As sensor data is able to pick up on a player’s physical state, recently it has been analyzed for its ability to predict player injuries. We inspect the predictive power of player stats, subjective player responses, GPS data, and training load data in forecasting game injuries from an NCAA American football team during the 2019 season. Data processing techniques are used to remove noise and decrease correlated data, and as large portions of the data is missing, multiple methods of data interpolation are tested. Survey data and player stats have the most predictive power for injuries with GPS and training load data performing at near-random levels. Overall, when modeling player stats and survey data together, injury predictions had a precision of .47, recall of .74, and an F1 score of .52 significantly outperforming random guessing.
Lyubovsky, Andrew, "A Pain Free Nociceptor: Predicting Football Injuries with Machine Learning" (2021). Undergraduate Honors Theses. William & Mary. Paper 1709.