Optimal Price Setting in Fixed‐odds Betting Markets Under Information Uncertainty

Abstract

This paper develops a model of optimal pricing under information uncertainty for fixed‐odds betting markets. The model suggests that bookmakers require a premium for quoting the odds several days before an event. This premium reflects the uncertainty of public information that can be exploited by expert bettors. The model predicts that when bookmakers set optimal prices, expected returns to bettors increase as a monotonic function of winning probabilities. In this manner, an information‐based explanation is given for the celebrated favourite‐longshot bias in fixed‐odds. Using an extensive data‐set of football odds from two major European bookmakers, we estimate the probability of informed betting.

Over the moon or sick as a parrot? The effects of football results on a club's share price

“…Therefore, the odds may contain biases designed to exploit the preferences of bettors -for example, the favouritelongshot bias and the sentimental attachment of bettors to particular clubs. Bernile and Lyandres (2008), Cain, Law and Peel (2000), Deschamps and Gergaud (2007), Graham andStott (2008), Makropoulou andMarkellos (2007) and Vlastakis, Dotsis and Markellos (2009) have found evidence supporting a favourite-longshot bias in football odds, with the odds on longshots (high scores) being unfavourable to the bettor, relative to the odds on the favourite (low scores), while Dixon and Pope (2004) and Dobson and Goddard (2001, p. 408) found a reverse favourite-longshot bias, and Franck, Verbeek and Nuesch (2008) found no evidence of a favourite-longshot bias.…”

Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions

“…The odds will change over the betting period in response to a number of factors. These include protection against insider trading activity [26] and public news about fundamentals such as player injuries [27]. Bookmakers will change their subjective probability of match outcomes as they attempt to maximize their objective function.…”

Sentiment Bias in National Basketball Association Betting

“…Makropoulou and Markellos (2011) identified three types of participants in sports betting markets: informed bettors who make profit-maximizing decisions based upon information available in the public domain; insider bettors who hold information about game or match outcomes unknown to bookmakers; and noise bettors who bet randomly (Makropoulou and Markellos, 2011). As an example of randomness, Makropoulou and Markellos (2011) observe that noise bettors may bet for their favorite team at any price. The presence of noise bettors with sentiment bias in the market can affect the odds or point spreads offered by bookmakers.…”