Exploring chance in NCAA basketball

Albrecht Zimmermann (1, 2)

1 Equipe CODAG - Laboratoire GREYC - UMR6072
2 GREYC - Groupe de Recherche en Informatique, Image et Instrumentation de Caen

There seems to be an upper limit to predicting the outcome of matches in (semi-)professional sports. Recent work has proposed that this is due to chance and attempts have been made to simulate the distribution of win percentages to identify the most likely proportion of matches decided by chance. We argue that the approach that has been chosen so far makes some simplifying assumptions that cause its result to be of limited practical value. Instead, we propose to use clustering of statistical team profiles and observed scheduling information to derive limits on the predictive accuracy for particular seasons, which can be used to assess the performance of predictive models on those seasons. We show that the resulting simulated distributions are much closer to the observed distributions and give higher assessments of chance and tighter limits on predictive accuracy.

Domaines: Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]

Albrecht Zimmermann. Exploring chance in NCAA basketball. Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2015 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Sep 2015, Porto, Portugal. ⟨hal-01628005⟩