Loss leaders: What is the best way to measure ‘gambling intensity’?
The issue of how to measure ‘gambling intensity’ is an important one in the gambling studies field. Gambling intensity is one of those concepts that means different things to different researchers but basically refers to how absorbed gamblers are based on the time and money they spend gambling. Over the last few years, this issue has become much more to the fore as researchers in various jurisdictions have been given access to behavioural tracking data (i.e., actual data showing what online gamblers actually do online such as the games they are playing, the time they spend online, the amount of money that they spend, etc.). This has initiated a whole new line of gambling research that is already providing insights about gambling that we never had before.
Many of these studies have used proxy measures for gambling intensity including variables such ‘bet size’ and ‘number of games played’. Another major problem with these studies is that they have tended to present data by single game type (e.g., only data from online poker players or sports bettors are presented). However, as researchers such as myself have noted, online gamblers typically gamble on a variety of games.
There are various ways to conceptualize gambling intensity. Such ways could include parameters involving the time spent gambling, the number of gambles made, and/or the amount of money won or lost while gambling. In almost all of the studies carried out to date, monetary involvement has tended to be the main proxy used measure for gambling intensity. However, I and my colleague Michael Auer have proposed a different proxy measure for the money risked while gambling. We define gambling intensity as the amount of money that players are putting at risk when playing. This might be considered easy to do (e.g., by using ‘bet size’), but the element of chance is rarely accounted for, especially when a random win occurs. For instance, two gamblers putting the same amount of money at risk might end up with very different wins or losses at the end of similar length gambling sessions because of the chance factor. For this reason, we are now using a measure that is completely independent of random events and takes into account the true amount of money that players are prepared to risk. The interesting aspect of this is that most of the time, gamblers themselves are probably not aware of the amount of money they risked at the end of a playing session.
Our first published paper in this area was a simulation study published last year in the journal Gaming Law Review and Economics. In that paper, we demonstrated that the most robust and stable measure for ‘gambling intensity’ is what we call the ‘theoretical loss’. Our fiest paper on this topic showed that all previous studies using proxy measures for ‘gambling intensity’ had failed to take into account the house advantage. Outcomes in games of chance over the long-term will always be dependent upon the house advantage of each different type of game. Dr. S. Li showed in a 2003 paper published in the Journal of Risk Research that ‘at risk’ decision-making in the short-term is totally different from decision-making over longer periods of time. Decision making over the long-term can be explained by the expected value whereas short-term decision-making does not seem to be based on any expectation rule. However, studies investigating decision-making in situations where people have to make choices assume that players have a real choice in which they can truly influence the outcome and (thus) the expected return. However, this is not the case in pure chance games. Whatever the player chooses to do in pure chance situations, the house advantage will determine the expected return in the long-term.
As we pointed out in our 2012 paper, games with a high house advantage lead to higher player losses and games with a low house advantage lead to lower player losses. Theoretical loss is the same measure that the gaming industry describes as Gross Gaming Revenue (GGR), and is the difference between ‘Total Bet’ and ‘Total Win’. The ‘theoretical loss’ of any given game is represented by the product of the bet size and the house advantage. Over very long periods of time, the theoretical loss corresponds to the GGR with increasing accuracy. The more diverse the gambling behaviour, the more that bet size deviates from the theoretical loss.
By incorporating the theoretical loss, the amount risked can be measured at a very detailed level. For instance, French roulette has a house advantage of 2.7% and keno has a house advantage of 10%. This means that a player who repeatedly bets $100 on roulette will end up with a loss of $2.7, and a player who repeatedly bets $100 on keno will end up with a loss of $10. Therefore, the product of bet size and theoretical loss represents the amount of money that player will lose in the long run. Previous studies that have used bet size (as a proxy measure for gambling intensity) would assign the same gambling of $10 intensity to the two players in the aforementioned example (and which obviously is not the case). The bet size is the one risk parameter that players are most likely to be aware of during gambling. However, it is deceptive as it does not take into account the expected return/loss that is controlled by the gaming operator via their house advantage.
Our simulation study of 300,000 online gamblers showed that bet size explained only 56% of the variance of the theoretical loss, and the number of games played explained 32% of the variance of theoretical loss. This means that when using bet size alone, 44% of the gambling behaviour remains unexplained. When using the number of games played alone, 68% of the variance is left unexplained. As this study was a simulation, we recently replicated our first study using real online gambler behavioural tracking data. There are many advantages and disadvantages with using data collected via behavioural tracking. However, the main advantages are that behavioural tracking data (a) provide a totally objective record of an individual’s gambling behaviour on a particular online gambling website, (b) provide a record of events and can be revisited after the event itself has finished, and (c) usually comprise very large sample sizes.
Our latest study on theoretical loss in the Journal of Gambling Studies comprised 100,000 online gamblers who played casino, lottery or poker games during a one-month period on the Austrian win2day gambling website. All games played by these gamblers were recorded and subsequently analysed. The game types were categorized into eight distinct groups: (i) Lottery – Draw/Instant, (ii) Casino – Card, (iii) Casino – Slot, (iv) Casino – Videopoker, (v) Casino – Table, (vi) Casino Other, (vii) Bingo and (viii) Poker. For each of the game types and each player, the ‘bet size’ and the ‘theoretical loss’ were computed for the recorded time period. In terms of house advantage these game types are very different. In general, lottery games have a relatively high house advantages (typically 50%) whereas slot machines have house advantages in the range of 1 to 5% depending on the gaming platform and the specific game. Poker on the other hand does not have a house advantage as such. In poker, the gaming involvement can be measured via the rake. The rake is a fixed percentage of the stake (bet size) that goes to the casino. The overall theoretical loss is thus comprised of the theoretical loss across all game types plus the poker rake.
Although we found a high correlation between the ‘bet size’ and the overall ‘theoretical loss’ across the eight game types for the 100,000 players, we also found the bet size alone explained only 72% of the variance of the theoretical loss (not as large as we found in our simulation study but that was most likely because we had more games in the simulation study and the games in the simulation study were approximated house advantages whereas the follow-up study used actual house advantages.
This study broadly confirmed the findings from our previous simulation study. The results of our most recent study suggest that future research and particularly those that utilize behavioural tracking approaches should measure their participants’ gambling intensity by incorporating the game-specific theoretical loss instead of using proxy measures such the bet size and/or the amount of money staked. Another implication is that previously published research could be re-analysed using the more robust measure of gambling intensity presented here (i.e., theoretical loss) rather than the proxy measures that were used in the original published studies. This study demonstrates that bet size does not reliably indicate the amount of money that players are willing to risk as it does not take into account the house advantage of each individual game that gamblers engage in. The house advantage represents the percentage held back by the gaming operator and is essential for the amount lost in the long-term and will eventually be equal to the total losses that a player accumulates. In order to further generalize our results, further empirical research utilizing data from other online gaming platforms as well as land-based casino premises needs to be carried out.
Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK
Additional input: Michael Auer
Auer, M. & Griffiths, M.D. (2013). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, in press.
Auer, M., Schneeberger, A., & Griffiths, M.D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269-273.
Broda, A., LaPlante, D. A., Nelson, S. E., LaBrie, R. A., Bosworth, L. B. & Shaffer, H. J. (2008). Virtual harm reduction efforts for Internet gambling: effects of deposit limits on actual Internet sports gambling behaviour. Harm Reduction Journal, 5, 27.
Colbert, G., Murray, D., Nieschwietz, R. (2009). The use of expected value in pricing judgements. Journal of Risk Research, 12, 199-208.
Griffiths, M.D. & Auer, M. (2011). Online versus offline gambling: Methodological considerations in empirical gambling research. Casino and Gaming International, 7(3), 45-48.
Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.
LaBrie, R.A., Kaplan, S., LaPlante, D.A., Nelson, S.E., & Shaffer, H.J. (2008). Inside the virtual casino: A prospective longitudinal study of Internet casino gambling. European Journal of Public Health, 18, 410-416
LaPlante, D. A., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Population trends in Internet sports gambling. Computers in Human Behavior, 24, 2399–2414.
Li, S. (2003). The role of Expected Value illustrated in decision-making under risk: Single-play vs multiple-play. Journal of Risk Research, 6, 113-124.
Wardle, H., Moody, A., Griffiths, M.D., Orford, J. & and Volberg, R. (2011). Defining the online gambler and patterns of behaviour integration: Evidence from the British Gambling Prevalence Survey 2010. International Gambling Studies, 11, 339-356.
Posted on July 24, 2013, in Addiction, Gambling, Gambling addiction, Games, Online gambling, Psychology and tagged Behavioural tracking, Bet size, Decision-making, Gambling, Gambling intensity, Gambling spend, Gross gaming revenue, House advantage, Online gambling, Problem gambling, Theoretical loss. Bookmark the permalink. 1 Comment.