University of Cambridge > > Seminars arranged by Dr A C Faul > What can gambling machine data tell us about betting behaviour?

What can gambling machine data tell us about betting behaviour?

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Bookmakers’ betting machines continually attract political and media attention over their impact on problem gambling. Featurespace and NatCen partnered with the Responsible Gambling Trust to investigate harmful patterns of play on gaming machines, and draw implications for intervention. The methods and results of this ground-breaking investigation are presented, linking industry-held data from the five largest UK bookmakers with surveys of loyalty card customers (which measured customers’ Problem Gambling Severity Index score as a proxy for harmful play), integrating research methods for 10 billion individual gaming machine events. The data set included 6.7 billion bets and 333,000 customers.

This huge data set was harnessed to model actual gaming play, measure theoretical markers of harm (e.g. faster gaming), survey loyalty card customers (matching 4,001 responses with transactional data) and explore consumer interventions.

Two predictive models were built, exploring the statistical relationships between the data and the customer surveys. The results showed it is possible to distinguish between problem gamblers and non-problem gamblers in industry data:

 Player model: behavioural analyses in loyalty card holder data – 66% improvement over the current baseline model.

 Session model: proxy measurements for anonymous players rather than individual players –550% improvement in accuracy of detecting problem gamblers over the industry standard.

The research demonstrated that a combination of variables are needed to identify problem gamblers, in contrast to proposed policy suggestions of regulating individual parameters (e.g. stake size). Approach limitations are assessed, including data skewedness, and explore the challenges of incorporating big data into social scientific investigations.

This talk is part of the Seminars arranged by Dr A C Faul series.

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