Identifying high-risk online gamblers: a comparison of data mining procedures

Abstract

Using play data from a sample of virtual live action sports betting gamblers, this study evaluates a set of classification and regression algorithms to determine which techniques are more effective in identifying probable disordered gamblers. This study identifies a clear need for validating results using players not appearing in the original sample, as even methods that use in-sample cross-validation can show substantial differences in performance from one data set to another. Many methods are found to be quite accurate in correctly identifying player types in training data, but perform poorly when used on new samples. Artificial neural networks appear to be the most reliable classification method overall, but still fail to identify a large group of likely problem gamblers. Bet intensity, variability, frequency and trajectory, as well as age and gender are noted to be insufficient variables to classify probable disordered gamblers with arbitrarily reasonable accuracy.

Keywords::

• supervised learning algorithm
• responsible gambling
• classification
• internet gambling

Acknowledgements

This paper utilized data from the Transparency Project (http://www.thetransparencyproject.org), Division on Addiction, the Cambridge Health Alliance, a teaching affiliate of Harvard Medical School.

Table 3 SLA classification rate on testing data.

Notes

^1. As a matter of vocabulary, Random Forests do not overfit (Breiman, 2001) but since each tree grows to maximum size they perform poorly when generalized to the testing data, creating a similar issue as the typical overfitting problem.

^2.

^3. Despite the use of the 10-fold cross-validation technique, the training/testing data partition remains in order for an equal comparison with the other algorithms.

Additional information

Funding

Funding

Funding for this study was provided by the Conrad N. Hilton Foundation.

Notes on contributors

Kahlil S. Philander

Kahlil Philander is a Visiting Assistant Professor at the William F. Harrah College of Hotel Administration, University of Nevada, Las Vegas. He is also the Director of Research at the International Gambling Institute, University of Nevada, Las Vegas. His research interests include the economics of gambling, gambling policy, and responsible gambling.