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Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts

  • Yejia LiuEmail author
  • Oliver Schulte
  • Chao Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11330)

Abstract

Drafting players is crucial for a team’s success. We describe a data-driven interpretable approach for assessing prospects in the National Hockey League and National Basketball Association. Previous approaches have built a predictive model based on player features, or derived performance predictions from comparable players. Our work develops model tree learning, which incorporates strengths of both model-based and cohort-based approaches. A model tree partitions the feature space according to the values or learned thresholds of features. Each leaf node in the tree defines a group of players, with its own regression model. Compared to a single model, the model tree forms an ensemble that increases predictive power. Compared to cohort-based approaches, the groups of comparables are discovered from the data, without requiring a similarity metric. The model tree shows better predictive performance than the actual draft order from teams’ decisions. It can also be used to highlight the strongest points of players.

Keywords

Player ranking Logistic Model Trees M5 regression trees National Hockey League National Basketball Association 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computing ScienceSimon Fraser UniversityBurnabyCanada

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