Our research analysing internet casino data has now been published in the latest edition of International Gambling Studies (Vol. 11, No.3), a special edition on Internet Gambling. The paper outlines the phase 1 findings from research undertaken by Bet Buddy in partnership with GTECH and City University London which utilised anonymised player data from a research cohort of 128,788 players from three internet gambling sites licensed in Malta offering internet casino and poker to regulated markets. Whilst the research builds on the methodology adopted by the Harvard Medical School and bwin Interactive Entertainment AG (specifically Braverman and Shaffer (2010)), which analysed live action sports betting internet data relative to gambling risk factors using k-means clustering analysis, our research explores the casino results in new contexts not covered in previous research. To the best of our knowledge this research, along with the Harvard/bwin collaboration (an overview of the collaboration can be found here), is the only peer-reviewed research to be published that analyses actual internet gambling data, and is the only research to analyse casino internet gambling data in the context of risk.
Whilst we discussed some early insights from the research in a previous blog this paper contains the full results and analysis. Our results are analysed in the context of risk factors, game structure, player education and clinical models for problem gambling. For example, we suggest that the analysis of some risk factors, such as loss chasing, could prove problematic when using current gambling screens (such as DSM-IV) in the context of internet gambling. Our results showed that real active money gamblers with the highest intensity and frequency levels gambled predominantly on slots type casino games, in comparison to the most moderate gamblers who preferred table games. We also examined how behavioural analysis and feedback mechanisms can help players to regulate gambling behaviour (for example how medical data is being used to help people make more informed choices - see this TED video). We explore how the opportunities that data analysis offers can help move beyond the traditional applications of data analytics in the gambling industry such as in marketing and risk management, in that applying advanced data analysis in new contexts (e.g. healthcare) can identify individuals who would benefit from proactive intervention or lifestyle changes (McKinsey's Big Data report discusses the application of data analytics in industry in general in greater detail). When the results were analysed in the context of clinical models for pathological and problem gambling (such as the Pathways Model) we found certain limitations. For example, we would have benefited from augmenting our existing internet gambling data sets with new data sets, such as call centre data, which can also be used to help predict gambling behaviours (see Haefeli, J., Lischer, S., & Schwarz, J.(2011)).
We believe that this research is an important step in furthering our understanding of internet gambling behaviours in the context of risk and player protection and welcome feedback from industry and academic practitioners. If you interested in finding out more about our research and product offerings then please contact us.
Whilst we discussed some early insights from the research in a previous blog this paper contains the full results and analysis. Our results are analysed in the context of risk factors, game structure, player education and clinical models for problem gambling. For example, we suggest that the analysis of some risk factors, such as loss chasing, could prove problematic when using current gambling screens (such as DSM-IV) in the context of internet gambling. Our results showed that real active money gamblers with the highest intensity and frequency levels gambled predominantly on slots type casino games, in comparison to the most moderate gamblers who preferred table games. We also examined how behavioural analysis and feedback mechanisms can help players to regulate gambling behaviour (for example how medical data is being used to help people make more informed choices - see this TED video). We explore how the opportunities that data analysis offers can help move beyond the traditional applications of data analytics in the gambling industry such as in marketing and risk management, in that applying advanced data analysis in new contexts (e.g. healthcare) can identify individuals who would benefit from proactive intervention or lifestyle changes (McKinsey's Big Data report discusses the application of data analytics in industry in general in greater detail). When the results were analysed in the context of clinical models for pathological and problem gambling (such as the Pathways Model) we found certain limitations. For example, we would have benefited from augmenting our existing internet gambling data sets with new data sets, such as call centre data, which can also be used to help predict gambling behaviours (see Haefeli, J., Lischer, S., & Schwarz, J.(2011)).
We believe that this research is an important step in furthering our understanding of internet gambling behaviours in the context of risk and player protection and welcome feedback from industry and academic practitioners. If you interested in finding out more about our research and product offerings then please contact us.
