Match-fixing undermines the integrity of sport by eroding public trust and threatening the financial sustainability of clubs and leagues. The global expansion of sports betting markets has created new incentives and opportunities for manipulation, calling for rigorous, data-driven monitoring tools. Football, which accounts for the largest share of global betting turnover, remains particularly exposed: integrity reports continue to flag several suspicious matches, with past scandals in Italy and Turkey underlining the problem's persistence. This study uses high-frequency live-betting data from the Italian Serie B (2018/19-2020/21) to explore statistical approaches for detecting abnormal betting behaviour. A state-space modelling framework is employed to describe standard betting market dynamics and to predict expected betting volumes conditional on match characteristics. Deviations from these expectations can then be analysed using outlier detection techniques to identify potentially suspicious periods. The results demonstrate how statistical modelling can contribute to the early identification of irregular betting patterns, thereby supporting integrity assurance in live sports betting markets.

翻译：假球行为通过侵蚀公众信任并威胁俱乐部与联赛的财务可持续性，严重损害体育诚信。全球体育博彩市场的扩张为操纵行为创造了新的动机与机会，亟需基于数据的严谨监控工具。在全球博彩交易额中占比最高的足球领域尤为脆弱：诚信报告持续标记多场可疑比赛，而意大利与土耳其的过往丑闻更突显该问题的长期性。本研究利用意大利足球乙级联赛（2018/19-2020/21赛季）的高频实时博彩数据，探索检测异常投注行为的统计方法。采用状态空间建模框架描述标准博彩市场动态，并基于比赛特征预测预期投注量。随后，通过异常值检测技术分析实际投注与预期值的偏差，以识别潜在可疑时段。研究结果证明，统计建模方法有助于早期发现非常规投注模式，从而为实时体育博彩市场的诚信保障提供支持。