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.

翻译：实时足球预测模型在与博彩交易所价格（这些价格汇集了大量市场参与者的信息）的准确性比较中一直难以匹敌。我们通过结合威布尔加速失效时间模型的两项扩展来缩小这一差距：将球队实力参数校准至开赛前的Betfair交易所价格，以捕捉赛前市场信息；以及将射门后预期进球作为时变协变量，以捕捉比赛进行中的信息。这种校准方法通过平方误差最小化，将球队实力参数联合拟合至1X2市场及大小球博彩市场，适用于任何基于强度的进球到达模型，并能实现更强的实时预测。通过对140场英格兰足球超级联赛比赛进行每分钟间隔的评估，校准后的模型在分类准确率上几乎与Betfair价格持平（70.2%对70.6%），同时保留了可解释的球队层次参数和协变量效应。与另外两种经相同赛前赔率校准的连续时间得分模型的比较证实，市场校准是预测准确性的主导驱动因素。针对Betfair实时赔率进行的投注模拟显示，在17,458次投注中获得了4.5%的投资回报率（夏普比率5.94），这表明实时足球市场中存在效率不足。