Year: 2026
Type: preprint
Abstract: Match-fixing undermines the integrity of sport by eroding public trust and threatening the financial sustainability of clubs and leagues. The 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 in-play (live) betting data from the Italian Serie B (seasons 2018/19--2020/21) to explore statistical approaches for detecting unusual 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 betting activities. The results demonstrate how statistical modelling can contribute to the early identification of unusual betting patterns, thereby supporting integrity assurance in in-play sports betting markets.
Source: arXiv (Cornell University)
Institution: Bielefeld University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W7162768426