Research on greyhound and horse racing betting markets has examined their efficiency and the potential for profitable strategies. Systematic biases, such as the tendency for bettors to overbet longshots and underbet favorites, have been widely documented, influencing market prices and potential inefficiencies (Asch et al., 1984; Hausch et al., 1981). While win betting markets generally reflect efficient pricing and do not consistently yield profits, place and show betting markets have shown signs of inefficiencies that may allow for profitable opportunities under specific conditions (Asch et al., 1984; Hausch et al., 1981).
The application of machine learning techniques, particularly Support Vector Regression, has provided new insights into race outcome prediction and the identification of profitable betting strategies, demonstrating varying degrees of success depending on data quality and model calibration (Schumaker & Johnson, 2008; Schumaker et al., 2010). Studies on market efficiency indicate that track odds largely incorporate publicly available information, discounting published forecasts and limiting the profitability of conventional handicapping strategies (Figlewski, 1979).
However, inefficiencies persist, particularly in off-track betting markets where information dissemination and market dynamics differ from on-track settings (Figlewski, 1979). Recent developments include the use of stochastic programming optimization models by professional betting syndicates, treating racing as a financial market and leveraging advanced risk management techniques to maximize expected returns (Ziemba, 2021). These approaches suggest that while individual bettors may struggle to consistently outperform the market, sophisticated data-driven strategies and capital-intensive syndicate betting can exploit remaining inefficiencies. Understanding the intersection of racing form, market behavior, and statistical modeling remains critical for identifying and capitalizing on profitable betting opportunities.
While win betting markets generally reflect efficient pricing and do not consistently yield profits, place and show betting markets have shown signs of inefficiencies that may allow for profitable opportunities under specific conditions
Summary of: Asch et al, 1984; Hausch et al, 1981
Anecdote
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Articles Cited
- “Robert P. Schumaker, James Johnson (2008): An Investigation of SVM Regression to Predict Longshot Greyhound Races, https://doi.org/10.58729/1941-6687.1082
- The paper investigates the use of Support Vector Regression (SVR) to predict the outcomes of Greyhound races, examining the accuracy, profitability, and impact of exotic wagers on the system’s performance.”
- “D. Hausch, W. Ziemba, M. Rubinstein. (1981): Efficiency of the Market for Racetrack Betting, https://doi.org/10.1287/MNSC.27.12.1435
- The authors describe a technical betting system for place and show bets at racetracks that can produce substantial positive profits, demonstrating market inefficiency in a weak form sense.”
- “W. W. Snyder (1978): Horse Racing: Testing the efficient markets model, https://doi.org/10.1111/J.1540-6261.1978.TB02051.X
- The paper examines the efficient markets hypothesis in the context of horse racing parimutuel betting.”
- “D. Terrell, A. Farmer (1996): Optimal Betting and Efficiency in Parimutuel Betting Markets with Information Costs, https://doi.org/10.2307/2235361
- The paper presents a model of parimutuel betting markets that explains several empirical observations, such as market odds failing to accurately predict outcomes and longshots earning lower expected values, as consequences of the track’s takeout and the presence of informed bettors who purchase true probabilities of events.”
- “P. Asch, B. Malkiel, R. Quandt (1984): Market Efficiency in Racetrack Betting, https://doi.org/10.1086/296257
- The paper examines the efficiency of the racetrack betting market and whether profitable betting strategies can be devised based on observed betting patterns, finding that while such strategies may exist, they may not be exploitable on a substantial scale, and that the betting market does not necessarily exhibit irrational behavior.”
- “Stephen Figlewski (1979): Subjective Information and Market Efficiency in a Betting Market, https://doi.org/10.1086/260740
- The paper examines whether the published forecasts of professional handicappers in the horse racing betting market are fully discounted by the track odds, and finds that the handicapper information is not fully discounted, especially for those betting through off-track betting systems.”
- “Robert P. Schumaker, Osama K. Solieman, Hsinchun Chen (2010): Greyhound Racing Using Support Vector Machines: A Case Study, https://doi.org/10.1007/978-1-4419-6730-5_11
- This paper explores the use of Support Vector Regression to predict the outcomes of Greyhound races across multiple dog tracks.”
- “W. Ziemba (2021): Parimutuel Betting Markets: Racetracks and Lotteries Revisited, https://doi.org/10.2139/ssrn.3865785
- The paper provides a comprehensive overview of research on racetrack and lottery investment markets, covering topics such as market efficiency, pricing of wagers, arbitrage, syndicates, behavioral biases, and similarities with other financial markets.”
Insufficient Detail?
At times it is difficult to answer the question as there are not enough relevant published journal articles to relate. It could be that the topic is niche, there’s a significant edge (and researchers prefer not to publish), there is no edge or simply no one has thought to investigate.
