Bookmakers rely on sophisticated algorithms and risk management strategies to set odds, ensuring profitability while balancing exposure. They incorporate a built-in margin into their odds to maintain a consistent edge over bettors, adjusting these margins based on market conditions and bettor behavior (Cortis, 2015). Odds-setting models typically consider a range of factors, including team rankings, player statistics, and historical performance data, which are processed through statistical methods such as logistic regression and ordered probit models to estimate probabilities of different outcomes (Lisi, 2017; Graham & Stott, 2008).
To maintain balanced books, bookmakers dynamically adjust odds based on betting patterns and wager volumes, ensuring that liabilities are distributed in a way that minimizes potential losses (Cortis, 2016). However, in practice, bookmakers do not always aim for perfect balance, as they also seek to exploit bettor biases. One well-documented example is the favorite-longshot bias, where longshots tend to be systematically overpriced relative to favorites, likely due to bettor preferences for high-risk, high-reward outcomes (Makropoulou & Markellos, 2011; Hodges et al., 2013).
In setting early odds, bookmakers may incorporate premiums to account for uncertainty, particularly when key information—such as player injuries or weather conditions—has not yet fully emerged (Makropoulou & Markellos, 2011). Additionally, psychological factors play a role in how odds are framed. Support theory suggests that the way events are described influences perceived probabilities, meaning more explicitly described events can lead to higher subjective probabilities, which bookmakers may exploit in their pricing strategies (Ayton, 1997). These approaches, combining statistical modeling, behavioral insights, and market adjustments, allow bookmakers to maintain profitability despite fluctuations in betting patterns and the presence of informed bettors.
To maintain balanced books, bookmakers dynamically adjust odds based on betting patterns and wager volumes, ensuring that liabilities are distributed in a way that minimizes potential losses
Summary of: Cortis 2016
Anecdote
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Articles Cited
- “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.”
- “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.”
- “Karol Odachowski, Jacek Grekow (2012): Using Bookmaker Odds to Predict the Final Result of Football Matches, https://doi.org/10.1007/978-3-642-37343-5_20
- The paper investigates using bookmaker odds to predict the final results of football matches.”
- “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.”
- “Francesco Lisi (2017): Tennis betting: can statistics beat bookmakers?, –
- The study proposes a logistic regression model to predict the outcome of tennis matches, which is then used in an out-of-sample betting experiment against bookmakers, resulting in a 15.9% cumulative return over 501 Grand Slam matches in 2013.”
- “I. Graham, H. Stott (2008): Predicting bookmaker odds and efficiency for UK football, https://doi.org/10.1080/00036840701728799
- The paper investigates the efficiency of bookmaker odds in the UK football market by using an ordered probit model to generate predictions for English football matches and comparing these predictions to the odds of the bookmaker William Hill, as well as developing a model to predict bookmaker odds.”
- “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.”
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.
