Predicting NBA Talent from Enormous Amounts of College Basketball Tracking Data

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Andrew Patton, Matt Scott, Nathan Walker, Alex Ottenwess, Paul Power, Aditya Cherukumudi, and Patrick Lucey (Stats Perform)


Even though the generation of tracking data from broadcast for college basketball is in itself a massive breakthrough in the field of basketball analytics – it is not enough. To showcase the value of the generated data, it is best to gauge the value through a predictive task. In this paper, we focus on the task of predicting the talent of future NBA players. We do this by predicting the probability of a player making the NBA directly from college data. We show using tracking data enables us to obtain more accurate forecasts compared to current data sources (tracking log-loss: 0.30 vs play-by-play log-loss: 0.40). The additional benefit of our approach is that we apply “interpretable machine learning“ techniques (i.e., Shapley values) to not only create accurate predictions but also identify the strengths and weaknesses of a specific player.