Hisham Talukder, Data Scientist, Dow Jones
Thomas Vincent, Data Scientist Engineer, DigitalOcean
Abstract: Player injuries have long been a cause of concern to NBA team management and fans, as they can significantly affect the overall performance of a team. Over the past 2 years many high caliber players, such as Kobe Bryant, Kevin Durant and Derrick Rose, have missed significant amounts of playing time as a result of injuries. In this work we present a model that offers a quantitative and systematic approach to injury prevention by allowing teams to predict the likelihood that any given player will succumb to an injury event during the course of an upcoming game.
We apply advanced machine learning techniques to predict the probability of injury for a player. Our model is based on play-by-play game data, SportsVU data, player workload and measurements, and team schedules from the last 2 years. Our results demonstrate strong accuracy in predicting whether a player will get injured in an upcoming week. By combining these results with information on team schedules and rest days, our approach enables team management and decision-makers to identify the best time for a team to rest their star players and reduce the risk of long-term injuries, while optimizing team strategies.
Finally, we show the effect of injuries on NBA teams as well as on the fans’ experience. By accounting for the amount of money invested in each player, we can rank player injuries based on the financial cost of missed games associated with these injuries. Using our model can be used as a valuable asset for NBA team management.