{"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/nuclear-penalized-multinomial-regression-with","title":"Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball","arxiv_id":"1706.10272","date":"2017-06-30","proceeding":null,"authors":["Scott Powers","Trevor Hastie","Robert Tibshirani"],"abstract":"We propose the nuclear norm penalty as an alternative to the ridge penalty\nfor regularized multinomial regression. This convex relaxation of reduced-rank\nmultinomial regression has the advantage of leveraging underlying structure\namong the response categories to make better predictions. We apply our method,\nnuclear penalized multinomial regression (NPMR), to Major League Baseball\nplay-by-play data to predict outcome probabilities based on batter-pitcher\nmatchups. The interpretation of the results meshes well with subject-area\nexpertise and also suggests a novel understanding of what differentiates\nplayers.","url_abs":"http://arxiv.org/abs/1706.10272v1","url_pdf":"http://arxiv.org/pdf/1706.10272v1.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":"nuclear-penalized-multinomial-regression-with","repo_url":"https://github.com/saberpowers/npmr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}