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

TL;DR

This paper introduces a market-calibrated accelerated failure time model for in-play football forecasting that nearly matches Betfair's accuracy and reveals market inefficiencies through betting simulations.

Contribution

It develops a novel calibration method for goal arrival models using market data and post-shot expected goals, enhancing in-play forecasting accuracy.

Findings

Calibrated model achieves 70.2% accuracy, close to Betfair's 70.6%.

Market calibration is the key factor improving predictive performance.

Betting simulation shows a 4.5% ROI, indicating market inefficiency.

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.