{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/model-trees-for-identifying-exceptional","title":"Model Trees for Identifying Exceptional Players in the NHL Draft","arxiv_id":"1802.08765","date":"2018-02-23","proceeding":null,"authors":["Oliver Schulte","Yejia Liu","Chao Li"],"abstract":"Drafting strong players is crucial for the team success. We describe a new\ndata-driven interpretable approach for assessing draft prospects in the\nNational Hockey League. Successful previous approaches have built a predictive\nmodel based on player features, or derived performance predictions from the\nobserved performance of comparable players in a cohort. This paper develops\nmodel tree learning, which incorporates strengths of both model-based and\ncohort-based approaches. A model tree partitions the feature space according to\nthe values of discrete features, or learned thresholds for continuous features.\nEach leaf node in the tree defines a group of players, easily described to\nhockey experts, with its own group regression model. Compared to a single\nmodel, the model tree forms an ensemble that increases predictive power.\nCompared to cohort-based approaches, the groups of comparables are discovered\nfrom the data, without requiring a similarity metric. The performance\npredictions of the model tree are competitive with the state-of-the-art\nmethods, which validates our model empirically. We show in case studies that\nthe model tree player ranking can be used to highlight strong and weak points\nof players.","url_abs":"http://arxiv.org/abs/1802.08765v1","url_pdf":"http://arxiv.org/pdf/1802.08765v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"model-trees-for-identifying-exceptional","repo_url":"https://github.com/liuyejia/Model_Trees_Full_Dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}