# 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.

## Key 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.