Integrity at Stake: Statistical Detection of Anomalous In-Play Betting in Football
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.
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BibTeX
@misc{winkelmann2026integrity,
title = {{Integrity at Stake: Statistical Detection of Anomalous In-Play Betting in Football}},
author = {David Winkelmann and Maya Vienken and Christian Deutscher and Roland Langrock},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2605.30209},
eprint = {2605.30209},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.30209},
}
RIS
TY - UNPB
TI - Integrity at Stake: Statistical Detection of Anomalous In-Play Betting in Football
AU - David Winkelmann
AU - Maya Vienken
AU - Christian Deutscher
AU - Roland Langrock
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2605.30209
UR - https://arxiv.org/abs/2605.30209
AB - 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.
ER -