Probabilistic modeling is an effective tool for evaluating team performance and predicting outcomes in sports. However, an important question that hasn't been fully explored is whether these models can reliably reflect actual performance while assigning meaningful probabilities to rare results that differ greatly from expectations. In this study, we create an inference-based probabilistic framework built on expected goals (xG). This framework converts shot-level event data into season-level simulations of points, rankings, and outcome probabilities. Using the English Premier League 2015/16 season as a data, we demonstrate that the framework captures the overall structure of the league table. It correctly identifies the top-four contenders and relegation candidates while explaining a significant portion of the variance in final points and ranks. In a full-season evaluation, the model assigns a low probability to extreme outcomes, particularly Leicester City's historic title win, which stands out as a statistical anomaly. We then look at the ex ante inferential and early-diagnostic role of xG by only using mid-season information. With first-half data, we simulate the rest of the season and show that teams with stronger mid-season xG profiles tend to earn more points in the second half, even after considering their current league position. In this mid-season assessment, Leicester City ranks among the top teams by xG and is given a small but noteworthy chance of winning the league. This suggests that their ultimate success was unlikely but not entirely detached from their actual performance. Our analysis indicates that expected goals models work best as probabilistic baselines for analysis and early-warning diagnostics, rather than as certain predictors of rare season outcomes.

翻译：概率建模是评估球队表现和预测体育赛事结果的有效工具。然而，一个尚未被充分探讨的重要问题是：这些模型能否在给与期望值差异巨大的罕见结果赋予合理概率的同时，可靠地反映实际表现？本研究构建了一个基于预期进球的推断式概率框架，该框架将射门级别的事件数据转化为赛季级别的积分、排名及结果概率模拟。以2015/16赛季英格兰足球超级联赛为数据样本，我们证明该框架能够捕捉联赛积分表的整体结构，准确识别前四名争夺者与降级候选队伍，并解释了最终积分与排名差异的显著部分。在全赛季评估中，模型对极端结果赋予较低概率，尤其是莱斯特城历史性的夺冠事件，在统计上呈现为异常值。随后我们通过仅使用赛季中期数据，探讨预期进球的事前推断与早期诊断功能。基于上半赛季数据模拟下半赛季进程，研究发现具有更强赛季中期预期进球特征的球队往往能在下半程获得更多积分，即使已考虑其当前联赛排名。在此中期评估中，莱斯特城的预期进球指标位居联赛前列，并被赋予虽小但值得关注的夺冠概率。这表明其最终成功虽属小概率事件，但并非完全脱离实际表现。我们的分析指出，预期进球模型最适合作为分析的概率基线与早期预警诊断工具，而非罕见赛季结果的确定性预测器。