  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.
