Question: How do bookmakers set odds for greyhound races?

Bookmakers set odds for greyhound races using a combination of risk management techniques and market analysis to maximize profits while maintaining balanced liability exposures. They incorporate both inside information from punters and publicly available data, adjusting prices dynamically to reflect betting patterns and uncertainty (Fingleton & Waldron, 1996; Makropoulou & Markellos, 2011). The stochastic nature of betting demand means that bookmakers must anticipate fluctuations and hedge against unexpected liabilities, often leading to risk-averse behavior (Hodges et al., 2013). One widely studied phenomenon in fixed-odds betting markets is the favorite-longshot bias, where longshots are systematically overbet while favorites are underbet, allowing bookmakers to price longshots less favorably to enhance margins (Ayton, 1997; Makropoulou & Markellos, 2011).

To increase profitability, bookmakers encourage multiple or accumulator bets, which amplify expected margins by compounding the effects of probability distortions (Cortis, 2015, 2016). Machine learning techniques, such as Support Vector Regression, have been explored to refine race outcome predictions and identify mispriced longshot opportunities, although bookmakers continually adjust their models to mitigate potential exploitation (Schumaker & Johnson, 2008). Some bookmakers employ a minimax strategy to limit their exposure to extreme losses, balancing expected profitability with worst-case risk scenarios (Barry & Hartigan, 1996). Overall, the process of setting odds involves a complex interplay of statistical modeling, behavioral economics, and strategic risk management to ensure both market efficiency and sustained bookmaker profitability.

They incorporate both inside information from punters and publicly available data, adjusting prices dynamically to reflect betting patterns and uncertainty

Summary of: Fingleton & Waldron, 1996; Makropoulou & Markellos, 2011

Anecdote

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Articles Cited

• “J. Fingleton, P. Waldron (1996): Optimal Determination of Bookmakers’ Betting Odds: Theory and Tests, –
  • This paper develops a theoretical model of how bookmakers’ odds are determined, given varying levels of insider information among bettors, and then tests this model using data on 1,696 horse races in Ireland in 1993.”
• “Dominic Cortis (2015): Expected values and variances in bookmaker payouts: A theoretical approach towards setting limits on odds, https://doi.org/10.5750/JPM.V9I1.987
  • The paper provides a theoretical approach towards setting limits on odds for bookmakers, summarizing key methods of displaying probabilities as odds, estimating expected bookmaker profit, and providing guidelines for bookmakers to increase profitability and lower variation in payouts.”
• “P. Ayton (1997): How to Be IncoherentandSeductive: Bookmakers’ Odds and Support Theory☆☆☆, https://doi.org/10.1006/OBHD.1997.2732
  • The paper examines bookmakers’ odds in relation to support theory, finding that odds for general hypotheses are subadditive compared to the sum of odds for more specific, unpacked hypotheses, but that the sum of odds for race horses increases with the number of horses, contradicting support theory’s additivity prediction.”
• “S. Hodges, Hao Lin, Lan Liu (2013): Fixed Odds Bookmaking with Stochastic Betting Demands, https://doi.org/10.1111/j.1468-036X.2012.00601.x
  • This paper studies fixed odds bookmaking in the market for bets in a British horse race, where the bookmaker faces the risk of unbalanced liability exposures and sets appropriate odds to influence the betting flow and mitigate this risk.”
• “Vasiliki Makropoulou, Raphael N. Markellos (2011): Optimal Price Setting in Fixed‐Odds Betting Markets Under Information Uncertainty, https://doi.org/10.1111/j.1467-9485.2011.00557.x
  • The paper develops a model of optimal pricing for fixed-odds betting markets that accounts for information uncertainty, and uses this model to explain the favorite-longshot bias observed in these markets.”
• “Dominic Cortis (2016): Betting Markets: Defining odds restrictions, exploring market inefficiencies and measuring bookmaker solvency, –
  • The paper by Dominic Cortis (2016) provides mathematical models and analyses related to betting markets, including defining odds restrictions, exploring market inefficiencies, and measuring bookmaker solvency.”
• “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. Barry, J. Hartigan (1996): The minimax bookie, https://doi.org/10.2307/3214988
  • The paper considers strategies a bookmaker might use to set and adjust odds for a horse race in order to minimize their expected maximum loss, as the bookmaker tries to behave conservatively.”

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.