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Recruitment occurred using market research online panel sampling. To participate, respondents had to be 18 years of age or older and have gambled online during the past 4 weeks. Potential respondents received an email from the market research company providing a brief outline of the study and a URL to access the online questionnaire.
Participation was voluntary and respondents could withdraw at any time. Ethics approval for this research was received from the [deidentified] University Human Research Ethics Committee. Respondents were mostly male Fixed choice questions assessed frequency of spending real money on seven types of Internet gambling activities: lottery-type games, slot machines, race wagering, esports betting 1 , sports betting, poker, casino card or table games, and other.
Response options were at least once per day, at least once per week, or at least once in the last 4 weeks. Questions assessed age at which participants had first gambled and modes used to place bets smartphone, computer, tablet, wearable device, telephone, in venue. Age, gender, education, work status, family household income, language spoken at home, country of birth, and ethnic background.
Cronbach's alpha in this sample was 0. The data were analyzed using SPSS Assumptions testing was conducted on all measured variables, including skewness and kurtosis, univariate outliers, and multivariate outliers Mahalnobis distance. Where instances of homogeneity of variance is violated, a Satterthwaite approximation for degrees of freedom is applied. Age first gambled was highly skewed and leptokurtic, which was corrected with a log transformation.
Missing values for the in-play betting variable were excluded on a list wise basis. Chi-square tests and t -tests were used to investigate if group differences existed between sports bettors who participate in in-play betting and those who do not for single-response demographic and gambling behavior variables.
Following these comparisons, a logistic regression was conducted to determine which characteristics differentiate in-play bettors from non-in-play bettors. Twelve predictor variables were used in the logistic regression: gender, age, education level, employment status, income, ethnic background, country of birth, language other than English spoken at home, number of gambling behaviors other than sports betting , age first gambled, highest reported gambling frequency for any gambling game, and PGSI classification binary variable, classified as problem gambling for scores of 8 or higher.
These variables were selected based on established validity from other studies [see, e. For comparison testing, an alpha of 0. Where measurement of certain variables is not conducive to certain analytical procedures i. Just over one third of the participants Participants that bet in-play were statistically more also likely to have completed higher education levels e.
There were no significant differences in terms of reported household income. Comparison of the demographic profiles of participants who bet in-play vs. Table 2 displays reported gambling behaviors and history. In terms of game preference, the most popular form of gambling among participants who bet on sports was lottery-type games. Participants that bet in-play engaged in all forms of gambling at a higher frequency than those who did not bet in-play, with a notably large difference for esports betting Participants that bet in-play engaged in 4.
Comparison of gambling behaviors and history of profiles of participants who bet in-play vs. Chi-square values are not displayed where the question allowed multiple responses to be selected. Of those who indicated that they had placed in-play bets, the most popular mode of access was using online websites and apps via smartphone In-play bettors placed their bets via legal, regulated modes of access, including speaking over the telephone An initial logistic regression was applied to assess which predictor variables statistically differentiated participants who bet in-play from those who did using the 12 predictor variables described in the Methods.
Income and country of birth predictor variables were removed from analysis due to lack of significance and poor contribution to model fit statistics. Ethnic background was also excluded from the final model because sparse data effects both reduced the model fit and led to uninterpretable odds ratios. As a robustness check, the model was run with these variables included, but the model fit improved with their removal. Overall prediction success was The regression variables were assessed for multicollinearity using Variance Inflation Factor diagnostics, which were under 1.
Categorical variables used the following reference groups: gender male , education level post-graduate qualification , employment status work full-time , language other than English spoken at home yes , highest gambling frequency at least once per day , and PGSI classification score 8 or higher. Logistic regression results for characteristics differentiating participants who bet in-play vs. This study makes a significant contribution by providing insight into the characteristics of those who place in-play bets, overcoming limitations of previous studies which focus on analyzing gambling behaviors without controlling for significant personal variables and betting across different modes and activities.
The results of this study show that among the sample of participants who regularly gamble online, in-play betting is relatively common. Three in 10 participants had placed bets after an event had started, and this occurred mostly via online methods which are prohibited under Australian regulations. Demographic differences were found between those who placed bets in-play and those who did not: in-play bettors were more likely to be more highly educated, employed, younger, and from culturally and ethnically diverse backgrounds albeit not country of birth.
Individuals who received income from welfare sources including a pension, unemployment, or disability benefits, were less likely to bet in-play than respondents who work full time. As in-play betting was associated with younger age, however, this finding may reflect a likelihood of older participants to be retired.
No specific differences were found in relation to gender although the different approached significance with a greater proportion of females engaged in in-play betting. The relationship between gender and in-play betting and gambling problems warrants additional investigation particularly as several previous studies have been based on almost entirely male samples 20 , Those who placed bets in play were more involved in gambling overall in terms of frequency and number of activities.
This is consistent with previous studies 15 , 20 , Higher levels of problem gambling severity were observed among those who placed in-play bets, which is a novel finding as our results control for a greater range of relevant factors than previous research including individual characteristics, gambling behavior, and gambling history. Several of the characteristics of those who bet in-play are similar to the profile of Australians who use offshore as opposed to only domestic online gambling sites, suggesting there may be some confound or overlap given in-play betting is only available via offshore gambling sites Our hypothesis was supported as after adjusting for gambling involvement, participants who had placed bets in-play were approximately three times more likely to be classified as having a gambling problem than those who had not placed this bet type, indicating an association between in-play betting and gambling problems.
These findings are consistent with previous research 24 which is important as it demonstrates the consistency of findings across jurisdictions despite policy differences in prohibition and legalized in-play betting. As with previous studies, our results are based on cross-sectional data and we cannot draw conclusions regarding causality. The structural characteristics of in-play betting mean that these bets require a rapid decision based on quick reactions to within-game events and are more similar to continuous and rapid gaming than most other forms of wagering which is typically discontinuous with low event frequency.
These characteristics may make in-play betting more appealing and potentially problematic. For example, individuals with gambling problems are more likely to consume impulsively, using immediate forms of gambling in which the time period between bet and outcome is shorter 5 , 27 , This is likely related to findings that higher trait impulsivity is common among those with gambling problems 39 , As such, online in-play betting products may be particularly harmful for individuals who are vulnerable to experiencing gambling problems.
In addition to the lack of evidence regarding causality, our methodology included other limitations. To be eligible to participate in the study, respondents had to have gambled online in the past month, meaning that respondents were likely more frequently engaged in gambling than the broader population of online gamblers.
Further, the survey was described as a gambling study, making it more likely to catch the attention of potential respondents with a specific interest in gambling. As such, the results should be interpreted in relation to this specific sample of online gamblers rather than as an accurate level of gambling involvement or gambling problems among all those who have made in-play bets. In terms of implications, our findings support the prohibition of online in-play betting in Australia based on the principle of limiting the availability of gambling products that are strongly associated with gambling-related harm.
It is crucial to note that the association between in-play betting and gambling problems is independent of involvement in other gambling activities and is consistently found across jurisdictions regardless of policies to legalize or prohibit this gambling activity. The findings suggest that further regulatory attention needs to be paid to this gambling activity and efforts made to identify those who bet in-play to assess for gambling harms as well as to develop specific prevention interventions for in-play betting.
Since the time of data collection, efforts have been made in Australia to reduce the availability of and demand for offshore gambling sites, by which in-play betting can be accessed. The extent to which restricting in-play betting may encourage consumers to use offshore gambling sites should be continuously evaluated due to the risks associated with this activity. Further research on the mechanisms by which in-play betting may cause harm is warranted, including consideration of other gambling products that allow continuous bets to be placed within short decision periods, such as electronic gaming machines.
How to differentiate between different variants of in-play betting and whether particular variants of in-play betting should be regulated, such as those involving longer time periods for decision-making, is a matter for further research.
The studies involving human participants were reviewed and approved by University of Sydney Human Research Ethics Committee. SG and AB designed and conducted the survey. BA led the data analysis. SG led the manuscript preparation.
BA and AB contributed to the manuscript editing and refining. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The handling editor declared a past co-authorship with one of the authors AB. The funding body had no involvement in the research, including but not limited to: the conceptualisation of the manuscript; collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the article for publication.
National Center for Biotechnology Information , U. Journal List Front Psychiatry v. Front Psychiatry. Published online Oct Author information Article notes Copyright and License information Disclaimer. This article was submitted to Addictive Disorders, a section of the journal Frontiers in Psychiatry.
Received Jun 22; Accepted Sep The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Associated Data Data Availability Statement The datasets presented in this article are not readily available because this would be a violation of the conditions of ethics approval. Abstract Internationally, Internet gambling is increasingly permitted under regulated licensing conditions; however, the specific products that are legal varies between jurisdictions.
Keywords: in-play betting, live action betting, regulation, online gambling, internet gambling, problem gambling, disordered gambling, gambling addiction. The Regulatory Context Gambling policy has a significant impact on the rates of harm experienced within communities.
Structural Characteristics of Online In-Play Betting Structural characteristics of gambling—inherent features of games—can contribute to the acquisition, maintenance, and development of problem gambling behaviors The Association Between Online In-Play Betting and Problem Gambling A series of studies have been conducted using customer account data on bwin , a predominately European gambling site.
The Current Study: Aims and Hypotheses This study aimed to understand the association between online in-play betting and gambling problems in the context of online in-play betting being prohibited on licensed domestic gambling sites. Methods Recruitment occurred using market research online panel sampling. Measures Gambling Frequency and Behaviors Fixed choice questions assessed frequency of spending real money on seven types of Internet gambling activities: lottery-type games, slot machines, race wagering, esports betting 1 , sports betting, poker, casino card or table games, and other.
Demographics Age, gender, education, work status, family household income, language spoken at home, country of birth, and ethnic background. Results Just over one third of the participants Table 1 Comparison of the demographic profiles of participants who bet in-play vs. Open in a separate window.
Gambling Involvement Table 2 displays reported gambling behaviors and history. Table 2 Comparison of gambling behaviors and history of profiles of participants who bet in-play vs. Predictors of In-Play Betting Behavior An initial logistic regression was applied to assess which predictor variables statistically differentiated participants who bet in-play from those who did using the 12 predictor variables described in the Methods.
Table 3 Logistic regression results for characteristics differentiating participants who bet in-play vs. Predictor variable B S. Discussion This study makes a significant contribution by providing insight into the characteristics of those who place in-play bets, overcoming limitations of previous studies which focus on analyzing gambling behaviors without controlling for significant personal variables and betting across different modes and activities.
Data Availability Statement The datasets presented in this article are not readily available because this would be a violation of the conditions of ethics approval. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References 1. Podesta J, Thomas A. Jordan L. Beating Betting , January 1.
Gambling Commission. Gambling Participation in Behaviour, Awareness and Attitudes. Birmingham, WM: Gambling Commission; Goal Profits. Griffiths MD, Auer M. The irrelevancy of game-type in the acquisition, development, and maintenance of problem gambling.
Front Psychol. Associations between national gambling policies and disordered gambling prevalence rates within Europe. Int J Law Psychiatry. Department of Broadband, Communications, and the Digital Economy. Regulation 22 Interactive Gambling Act Cth Parke J, Griffiths MD. The role of structural characteristics in gambling.
Research and Measurement Issues in Gambling Studies. Then sharp bettors will wager at lower limits — just a few hundred dollars or low four-figures, usually. This gives those books the opportunity to gather information and create a relatively accurate price without exposing themselves to big financial risk.
The price on a particular game, once settled, has come from the wisdom of the crowds, not the almighty bookmaker. These opening bettors are really just buying and selling teams at certain prices, and lines will eventually settle once those bettors feel that the price is correct. UNC in basketball.
Bettors may bet Duke at Market Efficiency Definition, from Investopedia : Market efficiency refers to the degree to which market prices reflect all available, relevant information. Sports with the highest betting limits and most available information, like the NFL, have the most efficient betting markets.
Books have all the relevant information, including injuries and the opinions of sharp bettors. Markets like player props are far less efficient, because they have lower betting limits and many sharp bettors are not betting into them. Liquid Market Definition, from The Financial Dictionary : A market allowing the buying or selling of large quantities of an asset at any time and at low transactions costs.
Major stocks are liquid because you can theoretically trade big quantities with relative ease. Due to betting limits imposed by the sportsbooks to control risk, you cannot get as much money down as you want on even an NFL game, where the limits are high. But that sneaky angle you think you have on a game? Sports Betting. Best Books. Credit: Credit: Getty Images. Pictured: The board at the SuperBook in Vegas.
Steve Petrella. Download App. Their language may sound something like… What does the betting market think of this game?