{"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/nfl-draft-modelling-loss-functional-analysis","title":"NFL Draft Modelling: Loss Functional Analysis","arxiv_id":"2504.07291","date":"2025-04-09","proceeding":null,"authors":["Tanmay Grandhisiri"],"abstract":"In the NFL draft, teams must strategically balance immediate player impact against long-term value, presenting a complex optimization challenge for draft capital management. This paper introduces a framework for evaluating the fairness and efficiency of draft pick trades using norm-based loss functions. Draft pick valuations are modelled by the Weibull distribution. Utilizing these valuation techniques, the research identifies key trade-offs between aggressive, immediate-impact strategies and conservative, risk-averse approaches. Ultimately, this framework serves as a valuable analytical tool for assessing NFL draft trade fairness and value distribution, aiding team decision-makers and enriching insights within the sports analytics community.","url_abs":"https://arxiv.org/abs/2504.07291v1","url_pdf":"https://arxiv.org/pdf/2504.07291v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"nfl-draft-modelling-loss-functional-analysis","repo_url":"https://github.com/tanmay-sketch/draftdynamics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}