Machine learning can level the playing field against match fixing – helping regulators spot cheating
Auckland, Sep 10 (The Conversation) On the eve of the Rugby World Cup kicking off, there have already been whispers of teams spying on each other. Inevitable gamesmanship, perhaps, but there's no doubt cheating in sport is a problem authorities struggle to combat.
Our new machine learning model could be a game changer when it comes to detecting questionable behaviour and unusual outcomes – especially the practice of match fixing.
Currently, the act of altering match outcomes for personal or team gain is largely picked up through abnormalities in sports betting markets. When bookmakers notice unusual odds or changes in the betting line, they alert regulators.
But this approach is limited and often fails to identify all match fixing, particularly in less popular sports or leagues. Here is where machine learning can help.
Essentially a subset of artificial intelligence (AI), machine learning acts as a digital probe: mining sports data, revealing hidden patterns, and flagging unusual events.
Machines can delve into team performance and unexpected fluctuations, exploring all facets of sports events.
As part of our research, we introduced the concept of “anomalous match identification”, which involved identifying irregular outcomes in games, no matter what the underlying causes might be.
There could be various factors at play, from strategic losses for future advantage – such as the practice of “tanking” in the US National Basketball league (NBA) – to marketing tactics to boost ticket sales, or just a day of poor performance.
Our research model allows us to flag unusual game results and turn them over to regulators for deeper investigation.
By leveraging machine learning, we can spot abnormal matches by comparing our predictions with
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