Resource type
Thesis type
(Thesis) M.Sc.
Date created
2018-04-10
Authors/Contributors
Author: Liu, Yejia
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 team's decision. It can also be used to highlight strongest points of players.
Document
Identifier
etd10613
Copyright statement
Copyright is held by the author.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Schulte, Oliver
Member of collection
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etd10613_YLiu.pdf | 2.03 MB |