In-game win probability is a statistical metric that provides a sports team's likelihood of winning at any given point in a game, based on the performance of historical teams in the same situation. In-game win-probability models have been extensively studied in baseball, basketball and American football. These models serve as a tool to enhance the fan experience, evaluate in game-decision making and measure the risk-reward balance for coaching decisions. In contrast, they have received less attention in association football, because its low-scoring nature makes it far more challenging to analyze. In this paper, we build an in-game win probability model for football. Specifically, we first show that porting existing approaches, both in terms of the predictive models employed and the features considered, does not yield good in-game win-probability estimates for football. Second, we introduce our own Bayesian statistical model that utilizes a set of eight variables to predict the running win, tie and loss probabilities for the home team. We train our model using event data from the last four seasons of the major European football competitions. Our results indicate that our model provides well-calibrated probabilities. Finally, we elaborate on two use cases for our win probability metric: enhancing the fan experience and evaluating performance in crucial situations.

本文提出一种基于贝叶斯统计框架的足球比赛中胜率预测模型，通过对比现有模型，构建了一个可信的、根据比赛时间变化的贝叶斯统计模型，通过对五大顶级联赛八个赛季的数据进行实证评价，表明该模型能够提供良好的结果预测，并能够提升球迷体验和评估关键比赛情况下的表现。

足球比赛中基于贝叶斯方法的胜率预测