Statistical Modelling 18 (5-6) (2018), 483–504
Integrating multiple data sources in match-fixing warning systems
Marius Ötting
Department of Business Administration and Economics,
Bielefeld University,
Germany.
e-mail: marius.oetting@uni-bielefeld.de
and
Department of Sports Science,
Bielefeld University,
Germany.
Roland Langrock
Department of Business Administration and Economics,
Bielefeld University,
Germany.
Christian Deutscher
Department of Sports Science,
Bielefeld University,
Germany.
Abstract:
Recent years have seen several match-fixing scandals in soccer. In order to avoid match-fixing, existing literature and fraud detection systems primarily focus on analysing betting odds provided by bookmakers. In our work, we suggest to not only analyse odds but also total volume placed on bets, thereby making use of more of the information available. As a case study for our method, we consider the second division in Italian soccer, Serie B, since for this league it has effectively been proven that some matches were fixed, such that to some extent we can ground truth our approach. For the betting volume data, we use a flexible generalized additive model for location, scale and shape (GAMLSS), with log-normal response, to account for the various complex patterns present in the data. For the betting odds, we use a GAMLSS with bivariate Poisson response to model the number of goals scored by both teams, and to subsequently derive the corresponding odds. We then conduct outlier detection in order to flag suspicious matches. Our results indicate that monitoring both betting volumes and betting odds can lead to more reliable detection of suspicious matches.
Keywords:
Bivariate Poisson; boosting, GAMLSS; semi-parametric regression; sports statistics.
Downloads:
Example data and code in
zipped archive.
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