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

2026-05-15

AI Summary

This study addresses the challenge that real-time football match prediction models often fall short of the accuracy offered by betting exchange odds. To bridge this gap, the authors propose a novel approach based on the Weibull accelerated failure time (AFT) model. The method jointly calibrates team strength parameters to Betfair’s 1X2 and over/under markets via least squares and, for the first time within an AFT framework, incorporates expected goals from shots as a time-varying covariate, thereby integrating pre-match market information with in-game dynamics. Evaluated on 140 Premier League matches, the model achieves a win/loss prediction accuracy of 70.2%—nearly matching Betfair’s 70.6%—and yields a 4.5% return on investment (Sharpe ratio: 5.94) across 17,458 simulated bets, demonstrating the effectiveness and novelty of the proposed market-calibration mechanism and time-varying feature integration.

Abstract

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 minimisation, applies to any intensity-based goal arrival model and enables stronger in-play forecasting. 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. A comparison with two alternative continuous-time scoring models, both calibrated to the same pre-match odds, confirms that market calibration is the dominant driver of predictive accuracy. A betting simulation against Betfair in-play odds yields a 4.5% return on investment (Sharpe ratio 5.94) over 17,458 bets, suggesting an inefficiency within in-play football markets.

Problem

• in-play football forecasting
• betting exchange prices
• predictive accuracy
• market inefficiency
• goal arrival modeling

Innovation

• market calibration
• accelerated failure time model
• in-play forecasting
• post-shot expected goals
• betting market inefficiency