Omneity® Pellets
All-In-One Vitamin & Mineral Pellet
Distribution of Losses Among Horse Race Bettors: An Analysis of Account-Based Player Tracking Data.
Abstract: Previous research on gambling loss patterns has mainly relied on self-reported data, which has some inherent biases (e.g., low accuracy). While some studies are based on large samples of horse bettors, few have examined how losses are distributed among them. The aim of this exploratory study was to examine the concentration of losses among gamblers playing horse betting games. Norsk Rikstoto, the Norwegian horse betting monopoly, provided account-based gambling data for all customers who bet with them between 2021 and 2023, covering 13 horse betting formats. To describe the shape of the loss distribution for each format, we calculated the mean-to-median loss ratio, the Gini coefficient, and the shares of total losses attributable to the top 1%, 5%, 10%, and 20% of bettors. Across all 13 betting formats combined, the mean-to-median loss ratio was 7.06 (min: 4.88, max: 11.53) and the Gini coefficient was 0.81 (min: 0.77, max: 0.87). Overall, the top 1% of bettors accounted for almost one-quarter of total losses (24.7%), while the top 20% accounted for 86.0%. This imbalance was most pronounced for single-race games (top 1%: 26.2-39.5%; top 20%: 87.3-91.5%) compared with the multi-race V-games (top 1%: 19.9-23.9%; top 20%: 81.0-87.5%). In summary, losses in horse betting are heavily concentrated among a relatively small share of bettors. The degree of inequality differs across game types, suggesting that product characteristics may play a role in shaping who loses and by how much.
© 2026. The Author(s).
Publication Date: 2026-10-10 PubMed ID: 42859324PubMed Central: 5579145DOI: 10.1007/s10899-026-10568-2Google Scholar: Lookup The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Journal Article
Summary
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
Overview
- This study analyzed detailed account data from Norwegian horse race bettors to understand how gambling losses are spread across players.
- It found that losses are highly concentrated among a small proportion of bettors, especially in specific types of betting games.
Background and Motivation
- Previous research on gambling losses often used self-reported data, which can be inaccurate or biased.
- Horse race betting has been studied with larger samples before, but the distribution of losses among bettors was less examined.
- The researchers aimed to explore how losses from horse race betting are distributed among individual players using actual betting account data.
Data and Methods
- Data Source: Norsk Rikstoto, the Norwegian horse betting monopoly, provided comprehensive account-based tracking data for all bettors from 2021 to 2023.
- Coverage: The dataset included 13 different horse betting game formats.
- Metrics to Describe Loss Distribution:
- Mean-to-Median Loss Ratio: Measures how skewed the loss distribution is (a high ratio indicates greater disparity).
- Gini Coefficient: A statistical measure of inequality ranging from 0 (perfect equality) to 1 (maximum inequality).
- Top Percentile Shares: The portion of total losses contributed by the top 1%, 5%, 10%, and 20% of bettors.
Key Findings
- Overall Inequality:
- The mean-to-median loss ratio across all 13 formats was 7.06, indicating that the average loss was more than seven times the median loss.
- The Gini coefficient was 0.81, showing a very unequal distribution of losses among bettors.
- Concentration of Losses:
- The top 1% of bettors were responsible for nearly a quarter (24.7%) of all losses.
- The top 20% accounted for 86.0% of losses, meaning most losses were concentrated within a minority of players.
- Variation by Game Type:
- Single-race games had even stronger concentration:
- Top 1% accounted for 26.2% to 39.5% of losses.
- Top 20% accounted for 87.3% to 91.5% of losses.
- Multi-race “V-games” showed less extreme but still substantial concentration:
- Top 1% accounted for 19.9% to 23.9% of losses.
- Top 20% accounted for 81.0% to 87.5% of losses.
- Single-race games had even stronger concentration:
Implications and Conclusions
- Losses in horse betting are heavily concentrated among a relatively small number of players.
- The degree of inequality varies across betting product types, suggesting how a game is structured affects who loses and how much.
- This insight could guide gambling policy, harm reduction efforts, and product design to better address problem gambling or financial risks.
- Using actual account data provides a more accurate picture of gambler loss patterns than self-report methods.
Cite This Article
APA
Buzcular K, Rossow I, Pallesen S, Leino T.
(2026).
Distribution of Losses Among Horse Race Bettors: An Analysis of Account-Based Player Tracking Data.
J Gambl Stud.
https://doi.org/10.1007/s10899-026-10568-2 Publication
Researcher Affiliations
- Department of Psychosocial Science, University of Bergen, P.O. Box 7807, Bergen, N-5020, Norway. kerembuzcu@gmail.com.
- Department of Psychology, Bursa Technical University, Bursa, 16110, Turkey. kerembuzcu@gmail.com.
- Department of Alcohol, Tobacco and Drugs, Norwegian Institute of Public Health, Oslo, Norway.
- Department of Psychosocial Science, University of Bergen, P.O. Box 7807, Bergen, N-5020, Norway.
- Norwegian Competence Center for Gambling and Gaming Research, University of Bergen, P.O. Box 7807, Bergen, N-5020, Norway.
- Department of Psychosocial Science, University of Bergen, P.O. Box 7807, Bergen, N-5020, Norway.
- Norwegian Competence Center for Gambling and Gaming Research, University of Bergen, P.O. Box 7807, Bergen, N-5020, Norway.
Conflict of Interest Statement
Declarations. Ethical Approval: The study was, according to the Norwegian Health Research Act, exempted from ethical approval as all data were provided anonymously. Informed consent was not provided as all analyses were based on player account data that the provider, Rikstoto, can make available for research purposes. Informed Consent: Informed consent was not required as the analyses utilized de-identified player account data. Competing Interests: The authors have no competing interests to declare that are relevant to the content of this article.
References
This article includes 30 references
- Auer M, Griffiths MD. Self-reported losses versus actual losses in online gambling: An empirical study. Journal of Gambling Studies 33(3), 795–806.
- Auer M, Griffiths MD. Reasons for gambling and problem gambling among Norwegian horse bettors: A real-world study utilizing combining survey data and behavioral player data. International Journal of Mental Health and Addiction 21(2), 740–755.
- Auer M, Griffiths MD. Differences in online gambling expenditure between players from Germany, Spain, Netherlands, Great Britain, US and Canada: A largescale online player tracking study. Acta Psychologica 266, 106864.
- Braverman J, Tom MA, Shaffer HJ. Accuracy of self-reported versus actual online gambling wins and losses. Psychological Assessment 26(3), 865–877.
- Catalano MT, Leise TL, Pfaff TJ. Measuring resource inequality: The Gini coefficient. Numeracy 2(2).
- Chóliz M. Experimental analysis of the game in pathological gamblers: Effect of the immediacy of the reward in slot machines. Journal of Gambling Studies 26(2), 249–256.
- Delfabbro P, Parke J. Challenges in the measurement of gambling product risk: A critical review of the ASTERIG assessment tool. Journal of Gambling Issues 47, 378–402.
- Deng X, Lesch T, Clark L. Pareto distributions in online casino gambling: Sensitivity to timeframe and associations with self-exclusion. Addictive Behaviors 120, 106968.
- Evans R, McNamee M. Sports betting, horse racing and nanobiosensors – An ethical evaluation. Sport Ethics and Philosophy 15(2), 208–226.
- Fiedler I, Kairouz S, Costes J-M, Weißmüller KS. Gambling spending and its concentration on problem gamblers. Journal of Business Research 98, 82–91.
- Forrest D, McHale IG. Patterns of play: Technical report 2 – Account data stage. .
- Grönroos T, Kouvonen A, Kontto J, Salonen AH. Sociodemographic factors, gambling behaviour, and the level of gambling expenditure: a population-based study. Journal of Gambling Studies 38(4), 1093–1109.
- Kesaite V, Wardle H, Rossow I. Gambling consumption and harm: A systematic review of the evidence. Addiction Research & Theory 32(3), 194–203.
- LaBrie RA, Kaplan SA, LaPlante DA, Nelson SE, Shaffer HJ. Inside the virtual casino: A prospective longitudinal study of actual Internet casino gambling. European Journal of Public Health 18(4), 410–416.
- Langham E, Thorne H, Browne M, Donaldson P, Rose J, Rockloff M. Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. Bmc Public Health 16(1), 80.
- Mendez MA, Miles DR, Poti JM, Sotres-Alvarez D, Popkin BM. Persistent disparities over time in the distribution of sugar-sweetened beverage intake among children in the United States. The American Journal of Clinical Nutrition 109(1), 79–89.
- Orford J, Wardle H, Griffiths M. What proportion of gambling is problem gambling? Estimates from the 2010 British Gambling Prevalence Survey. International Gambling Studies 13(1), 4–18.
- Phua YXP, Pyun DY, Leng HK. Cognitive distortions and problem gambling in sports betting. Journal of Gambling Issues 47, 1–16.
- Rose G, Day S. The population mean predicts the number of deviant individuals. Bmj 301(6759), 1031–1034.
- Rossow I, Bramness JG. The total sale of prescription drugs with an abuse potential predicts the number of excessive users: A national prescription database study. Bmc Public Health 15, 288.
- Rossow I, Hansen MB. Gambling and gambling policy in Norway – an exceptional case. Addiction 111(4), 593–598.
- Rossow I, Mäkelä P, Kerr W. The collectivity of changes in alcohol consumption revisited. Addiction 109(9), 1447–1455.
- Rossow I, Kesaite V, Pallesen S, Wardle H. Concentration of gambling spending by product type: Analysis of gambling accounts records in Norway. Addiction Research & Theory 33(2), 114–121.
- Sopala I, Suhonen N, Kainulainen T, Forrest D, Saastamoinen J. Who wagers on horse races? A large-scale investigation into the socioeconomic background of online horse bettors in the Finnish market. International Gambling Studies 26(2), 177–206.
- StataCorp. Stata statistical software: Release 18. .
- Suhonen N, Saastamoinen J, Forrest D, Kainulainen T. Does IQ predict engagement with skill-based gambling? Large-scale evidence from horserace betting. Journal of Behavioral Decision Making 36(2), e2300.
- Tom MA, LaPlante DA, Shaffer HJ. Does Pareto rule Internet gambling? Problems among the vital few & trivial many. Journal of Gambling Business and Economics 8(1), 73–100.
- Tom MA, Edson TC, Louderback ER, Nelson SE, Amichia KA, LaPlante DA. Second session at the virtual poker table: A contemporary study of actual online poker activity. Journal of Gambling Studies 39(3), 1295–1317.
- Tørdal EV, Leino T, Pallesen S. Loss limits as a predictor of future gambling behavior. Journal of Gambling Studies 42(1), 61–75.
- Wardle H, Kolesnikov A, Fiedler I, Critchlow N, Hunt K. Is the economic model of gambling dependent on problem gambling? Evidence from an online survey of regular sports bettors in Britain. International Gambling Studies 23(1), 139–159.
Citations
This article has been cited 0 times.Use Nutrition Calculator
Check if your horse's diet meets their nutrition requirements with our easy-to-use tool Check your horse's diet with our easy-to-use tool
Talk to a Nutritionist
Discuss your horse's feeding plan with our experts over a free phone consultation Discuss your horse's diet over a phone consultation
Submit Diet Evaluation
Get a customized feeding plan for your horse formulated by our equine nutritionists Get a custom feeding plan formulated by our nutritionists
