{"paper":{"title":"A market-calibrated accelerated failure time model for in-play football forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Calibrating a Weibull accelerated failure time model to pre-match betting prices and adding post-shot expected goals nearly matches betting exchange accuracy for in-play football forecasts.","cross_cats":[],"primary_cat":"stat.AP","authors_text":"John Cartlidge, Lawrence Clegg, Zixing Song","submitted_at":"2026-05-15T15:29:31Z","abstract_excerpt":"In-play football forecasting models have struggled to match the accuracy of betting exchange prices, which aggregate information from many market participants. We close this gap by combining two extensions to a Weibull accelerated failure time model: calibrating team strength parameters to Betfair Exchange prices at kick-off to capture pre-match market information, and including post-shot expected goals as a time-varying covariate to capture in-play information. The calibration approach, where we jointly fit team-strength parameters to 1X2 and over/under betting markets via squared-error minim"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Evaluated across 140 English Premier League matches at minute intervals, the calibrated model almost matches Betfair's classification accuracy (70.2% versus 70.6%) while retaining interpretable team-level parameters and covariate effects.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That jointly fitting team-strength parameters to pre-match 1X2 and over/under Betfair prices via squared-error minimisation produces values that remain valid for in-play forecasting once post-shot expected goals are added as a time-varying covariate.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Market-calibrated Weibull AFT model for in-play football forecasting nearly matches Betfair accuracy (70.2% vs 70.6%) and produces 4.5% ROI in simulation against in-play odds.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Calibrating a Weibull accelerated failure time model to pre-match betting prices and adding post-shot expected goals nearly matches betting exchange accuracy for in-play football forecasts.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"66b6ddfcde1320bf2f54708036cbd3ce268d03fd196e06f22d66b3a678b958d2"},"source":{"id":"2605.16066","kind":"arxiv","version":1},"verdict":{"id":"06a61cb1-d3c6-4647-9b94-a5fdb41fb7e1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T18:53:37.443947Z","strongest_claim":"Evaluated across 140 English Premier League matches at minute intervals, the calibrated model almost matches Betfair's classification accuracy (70.2% versus 70.6%) while retaining interpretable team-level parameters and covariate effects.","one_line_summary":"Market-calibrated Weibull AFT model for in-play football forecasting nearly matches Betfair accuracy (70.2% vs 70.6%) and produces 4.5% ROI in simulation against in-play odds.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That jointly fitting team-strength parameters to pre-match 1X2 and over/under Betfair prices via squared-error minimisation produces values that remain valid for in-play forecasting once post-shot expected goals are added as a time-varying covariate.","pith_extraction_headline":"Calibrating a Weibull accelerated failure time model to pre-match betting prices and adding post-shot expected goals nearly matches betting exchange accuracy for in-play football forecasts."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16066/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T19:01:33.218091Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T19:01:18.980564Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:41.546131Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.516808Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"296918db9d6534c255da344ede822bc284033b5d45e6f7f640b20305ac6aca71"},"references":{"count":84,"sample":[{"doi":"","year":2025,"title":"Ayana, G., A. Ehlert, J. Ehlert, L. Santagata, M. Torricelli, and B. Klein (2025). Temporal dynamics of goal scoring in soccer. arXiv:2501.18606. arXiv preprint","work_id":"f4c7d990-30ca-4572-8d94-280c856376c5","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Boshnakov, G., T. Kharrat, and I. G. McHale (2017). A bivariate weibull count model for forecasting association football scores. International Journal of Forecasting\\/ 33\\/ (2), 458--466","work_id":"ca84514c-a6dc-4341-b640-78528aafd388","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Bunker, R., C. Yeung, and K. Fujii (2024). Machine learning for soccer match result prediction. In Artificial Intelligence, Optimization, and Data Sciences in Sports , pp.\\ 7--49. Springer","work_id":"4a294001-2120-4eb0-80b3-be7f61bb993f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1971,"title":"Capen, E. C., R. V. Clapp, and W. M. 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