Model Trees for Identifying Exceptional Players in the NHL Draft

TL;DR

This paper introduces a novel interpretable model tree approach for evaluating NHL draft prospects, combining strengths of predictive and cohort-based methods to improve accuracy and provide actionable insights.

Contribution

It develops a new model tree learning method that automatically discovers player groups and enhances draft prediction accuracy over existing approaches.

Findings

Model trees achieve competitive prediction performance.

Groups of players are discovered without predefined similarity metrics.

Player rankings highlight strengths and weaknesses effectively.

Abstract

Drafting strong players is crucial for the team success. We describe a new data-driven interpretable approach for assessing draft prospects in the National Hockey League. Successful previous approaches have built a predictive model based on player features, or derived performance predictions from the observed performance of comparable players in a cohort. This paper 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 of discrete features, or learned thresholds for continuous features. Each leaf node in the tree defines a group of players, easily described to hockey experts, with its own group 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 performance predictions of the model tree are competitive with the state-of-the-art methods, which validates our model empirically. We show in case studies that the model tree player ranking can be used to highlight strong and weak points of players.