Betting Frequency: Impact on Gambling

One of the key factors for sports gamblers' analysis is bet frequency. Players with higher frequency appear to be more engaged in the activity. Often, therefore, these players would obtain higher knowledge of gambling markets and spend a long time optimizing their bets to minimize risks. Ultimately, more frequent players appear to have lower losses than their less frequent counterparts. Yet for the gambling providers, more frequent players are somewhat more appealing customers. Considering that more than one-third (1/3) of money wagered is being lost by all players, highly involved players continue bringing higher income with their high activity. Thus, this players’ segment might appear at a higher risk of problem gambling.

Authors of this review:

Nikita Goncharenko

Date of Publication:

26/06/2022

Academic Reference:

Gainsbury, S., Sadeque, S., Mizerski, D., Blaszczynski, A. (2012). Wagering in Australia: A retrospective behavioural analysis of betting patterns based on player account data. Journal of Gambling Business and Economics. 6. 50-68.

Tags:sports gambling data analytics

Key Ideas

One important parameter of analysis to be added to the type, frequency, and size of the bet is the proportion of lost bets. From the research of LaBrie et al. (2007), there has been identified a group of players with a higher frequency of bets and a lower percentage of losses. This subgroup could be considered, as having lower control over their play.

Gambling account holders' data provides the opportunity for researchers to identify different subgroups of gamblers, as well as to predict certain gambling patterns for future behavior.

One limitation that should not be forgotten is the contextual variables (e.g. emotions, surroundings), which can't be measured using an account-based approach. [However, this was also impossible to evaluate them before using the self-report method.]

The analysis of players' accounts could also be limited to acquisition, retention, or engagement practices. Different behavioral patterns would be provoked depending on the time when players join and the impact that gambling providers apply on a certain segment.

Interestingly, only 50% of players with registered accounts made at least one bet. This means, there is a significant number of players who register but never make a bet. Thus, that raises a question: Why do these players complete the registration in the first place?

No surprise is that frequent bettors would spread their bets around different markets, while non-frequent bettors spend less time researching betting markets.

Most frequent bettors (1% of the whole database of the study) could be seen, as punters (semi-professional/professional wagerers). This subgroup tends to spend longer time researching gambling markets and stake higher bet amounts. This group could be in danger of problem gambling, as gambling operators might be of interest to exercise retention marketing strategies in this segment.

It is possible to pre-conclude that "more frequent bettors are more skilled, or put more time and effort into placing smaller, but more frequent bets to minimize losses as compared to less frequent bettors who place larger single bets and tend to be less successful."

Citations

"Frequency of play has been found to be associated with and predictive of problem gambling" (Currie et al., 2006; Griffiths, Wardle, Orford, Sproston, & Erens, 2009; Hopley & Nicki, 2010; Lam & Mizerski, 2009).

"players characterised by both high frequency of gambling and variability of bet sizes during their first month of gambling were at higher risk of closing accounts due to gambling problems." (Braverman & Shaffer, 2010)

"analyses indicated that 91.78% of players lost money, on average players lost 34.07% of the total amount wagered"

"The more and less frequent bettor groups significantly differ in their patterns of gambling."

"The results indicate that a greater number of betting days and total bet value was predictive of being a more frequent bettor. A greater minimum bet value was predictive of being a less frequent bettor."

"future research may benefit by taking a multimodal approach and considering both behavioural and self-report data to further the understanding of consumer gambling behaviour."

"ongoing research should consider other variables relevant to betting involvement in examining subgroups of bettors, including expenditure, bet size and the number of betting days."

External References

Currie, S., Hodgins, D., Wang, J., el-Guebaly, N., Wynne, H., and Chen, S. (2006). Risk of harm from gambling in the general population as a function of level of participation in gambling activities. Addiction (Abingdon, England). 101. 570-80.

Griffiths, M., Wardle, H., Orford, J., Sproston, K., and Erens, B. (2011). Internet Gambling, Health, Smoking and Alcohol Use: Findings from the 2007 British Gambling Prevalence Survey. International Journal of Mental Health and Addiction. 9. 1-11.

Hopley, A. and Nicki, R. (2010). Predictive Factors of Excessive Online Poker Playing. Cyberpsychology, behavior and social networking. 13. 379-85.

Lam, D., and Mizerski, R. (2009). An Investigation Into Gambling Purchases Using The NBD And NBD-Dirichlet Models. Marketing Letters. 20. 263-276.

Braverman, J. and Shaffer, H. (2010). How do gamblers start gambling: Identifying behavioral markers for high-risk internet gambling. European journal of public health. 22. 273-8.