{"paper":{"title":"Optimising football transfer strategy under budget constraints: A weighted multi-criteria approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A club's optimal football transfers can be determined by feeding performance and price predictions into a weighted multi-criteria optimization that respects budget limits.","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Rishideep Roy, Soudeep Deb, Tathagata Basu","submitted_at":"2026-05-13T15:27:51Z","abstract_excerpt":"The football transfer market is a complex, dynamic environment in which clubs compete to acquire players who strengthen their squads. While several frameworks estimate a player's worth, a comprehensive approach that captures both squad optimisation and transfer market dynamics remains limited. In this paper, we propose a quantitative framework for optimising football transfer strategy under budget constraints, integrated with a competitive bidding paradigm. Using data from professional football leagues, we construct player performance and transfer price models using linear mixed-effects framew"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The predicted ratings and estimated transfer prices are integrated into a weighted multi-criteria constrained optimisation framework that determines a club's transfer activities at the end of the season.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That linear mixed-effects models built on player characteristics, recent performance, team context, and league effects will produce reliable enough predictions of future performance and market prices to drive useful optimization decisions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A framework combining linear mixed-effects models for player ratings and prices with multi-criteria optimization and auction simulation for football transfers, illustrated on 2018-19 Premier League data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A club's optimal football transfers can be determined by feeding performance and price predictions into a weighted multi-criteria optimization that respects budget limits.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b0817d3a904777c6736fb86bf32f829ea8965a7fc3f22a9f0a0435ab002c245e"},"source":{"id":"2605.13926","kind":"arxiv","version":1},"verdict":{"id":"5d178916-fdca-4931-ae1e-22203dd98095","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:43:43.112563Z","strongest_claim":"The predicted ratings and estimated transfer prices are integrated into a weighted multi-criteria constrained optimisation framework that determines a club's transfer activities at the end of the season.","one_line_summary":"A framework combining linear mixed-effects models for player ratings and prices with multi-criteria optimization and auction simulation for football transfers, illustrated on 2018-19 Premier League data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That linear mixed-effects models built on player characteristics, recent performance, team context, and league effects will produce reliable enough predictions of future performance and market prices to drive useful optimization decisions.","pith_extraction_headline":"A club's optimal football transfers can be determined by feeding performance and price predictions into a weighted multi-criteria optimization that respects budget limits."},"references":{"count":66,"sample":[{"doi":"","year":2019,"title":"Saikia, Hemanta and Bhattacharjee, Dibyojyoti and Mukherjee, Diganta and Saikia, Hemanta and Bhattacharjee, Dibyojyoti and Mukherjee, Diganta , journal=. 2019 , publisher=","work_id":"25beed5a-b9c4-428a-8e61-32d3766f13dc","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2004,"title":"IEEE signal processing magazine , volume=","work_id":"2850dd56-fe62-4540-be71-a212272649d0","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Proceedings of the 2024 9th International Conference on Machine Learning Technologies , pages=","work_id":"e03103d5-a752-4376-bf7f-e2ee15c8e444","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Humanities and Social Sciences , volume=","work_id":"ce90f5d9-b94e-431c-808d-f040bbf2f9bd","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Depken, Craig A and Globan, Tomislav , journal=. 2021 , publisher=","work_id":"7d0a8599-39ad-4b94-bab9-81988a83b5eb","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":66,"snapshot_sha256":"bad55a42e36ca43cdb32b03960a43670a9ba4fc554b8037791410097ce6dd0d4","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}