[PAST EVENT] Departmental Honors Thesis Defense: Andrew Lyubovsky 

May 13, 2021
2pm - 3:30pm
Location not specified
Access & Features
  • Open to the public

Andrew Lyubovsky

William & Mary

A Pain Free Nociceptor: Predicting Football Injuries with Machine Learning


Meeting ID: 986 1397 4839

Password: 605045


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 are 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.