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Drafting strong players is crucial for the team success. We describe a newdata-driven interpretable approach for assessing draft prospects in theNational Hockey League. Successful previous approaches have built a predictivemodel based on player features, or derived performance predictions from theobserved performance of comparable players in a cohort. This paper developsmodel tree learning, which incorporates strengths of both model-based andcohort-based approaches. A model tree partitions the feature space according tothe values of discrete features, or learned thresholds for continuous features.Each leaf node in the tree defines a group of players, easily described tohockey experts, with its own group regression model. Compared to a singlemodel, the model tree forms an ensemble that increases predictive power.Compared to cohort-based approaches, the groups of comparables are discoveredfrom the data, without requiring a similarity metric. The performancepredictions of the model tree are competitive with the state-of-the-artmethods, which validates our model empirically. We show in case studies thatthe model tree player ranking can be used to highlight strong and weak pointsof players.

Model Trees for Identifying Exceptional Players in the NHL Draft