Pith Number

pith:ARXCWXVF

pith:2026:ARXCWXVFNLPMQOMYHHW5M2SUDP

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A market-calibrated accelerated failure time model for in-play football forecasting

John Cartlidge, Lawrence Clegg, Zixing Song

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.

arxiv:2605.16066 v1 · 2026-05-15 · stat.AP

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest 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.

C2weakest 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.

C3one 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.

References

84 extracted · 84 resolved · 0 Pith anchors

[1] 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 2025

[2] 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 2017

[3] 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 2024

[4] Capen, E. C., R. V. Clapp, and W. M. Campbell (1971). Competitive bidding in high-risk situations. Journal of Petroleum Technology\/ 23\/ (06), 641--653 1971

[5] Clegg, L. and J. Cartlidge (2025). Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks. International Journal of Forecasting\/ 41\/ (2), 798--802 2025

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Receipt and verification

First computed	2026-05-20T00:01:51.272691Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

046e2b5ea56adec8399839edd66a541beb1e6c40c4c88755443b246d9aac5df9

Aliases

arxiv: 2605.16066 · arxiv_version: 2605.16066v1 · doi: 10.48550/arxiv.2605.16066 · pith_short_12: ARXCWXVFNLPM · pith_short_16: ARXCWXVFNLPMQOMY · pith_short_8: ARXCWXVF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ARXCWXVFNLPMQOMYHHW5M2SUDP \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 046e2b5ea56adec8399839edd66a541beb1e6c40c4c88755443b246d9aac5df9

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.AP",
    "submitted_at": "2026-05-15T15:29:31Z",
    "title_canon_sha256": "32851f5e2edb523364ffe5e6c047445dbffe2af9e6e1a8a27a50d9dc1aebd760"
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