The favourite-longshot bias isn’t going anywhere for certain markets

In gambling and economics, the favourite-longshot bias is an observed phenomenon where on average, bettors tend to overvalue “long shots” and undervalue favourites. That is, in a horse race where one horse is given odds of 2-to-1, and another 100-to-1, the true odds might for example be 1.5-to-1 and 300-to-1 respectively. Betting on the “long shot” is therefore a much worse proposition than betting on the favourite. Various theories exist to explain why people willingly bet on such losing propositions, such as risk-loving behavior, risk-averse behavior (1) or simply inaccurate estimation as presented by Sobel and Raines (2).

In January 2016, a paper named “A Favorite-Longshot Bias in Fixed-Odds Betting Markets: Evidence from College Basketball and College Football” was published, authored by Jason P. Berkowitz, II Craig A. Depken and John M. Gandar. This paper provides empirical evidence that the favorite-longshot bias persists in money-line betting markets of both college basketball and college football. This is the first clear evidence of the favorite-longshot bias existing in fixed-odds money-line betting markets in the US, as the reverse favorite-longshot bias has been documented in professional baseball and hockey money- line and in US sports sides-line markets. They also document that betting on heavy favorites in both of these markets yields an average return close to zero, suggesting this strategy removes the negative expected return created by the bookmaker’s commission. This “break even” betting strategy persisted in both sports over the sample period. Thus, the evidence suggests that these two betting markets are efficient within transaction costs.

Let’s repeat the highlights:
• They provide the first clear evidence of the favorite-longshot bias existing in fixed-odds money line betting markets.
• They document that betting on heavy favorites in both markets yields the best strategy to exploit the favorite-longshot bias documented for both sports with an average return close to zero.
• While betting on heavy favorites offers a near zero return over several years, this evidence suggests that these two betting markets are efficient within transaction costs.

How to take advantage of this bias? They examined if a betting strategy is able to exploit this bias; if not, the market can be considered efficient. The strategy they found that best exploits the FLB entails betting only on an SL Heavy Favorite, defined as a 12/18 point or more favorite in basketball/football. This strategy generates an average actual return near zero in both sports, allowing a bettor to essentially eliminate the bookmaker’s commission. However, because even this strategy does not generate a positive return, the evidence suggests that the money line markets for NCAA basketball and football are efficient within transaction costs.

 

UPDATE (Match 6th, 2016)

The favorite-longshot bias… now in poker. Vaughan Williams, L., Sung, M., Fraser-Mackenzie, P.A.F., Peirson, J. and Johnson, J.E.V. (2016) has shown that:

Evidence of differential returns to bets placed with different probabilities of success has revealed a broadly systematic tendency for low/high probability events to be relatively over/under-bet, a phenomenon known as the favourite-longshot bias. While most of the literature focuses on sports, especially horse racing, we report here the existence of the same phenomenon in online poker games. We find that misperception rather than risk-love offers the best explanation for the behaviour we identify.

 

1. “We discount the chances of any party at 100/1 or bigger. The reverse of tweak 1 applies here. Almost all of these probably have effectively zero chance. Why don’t we just make them a bigger price? We don’t think we’ll take much extra money, certainly not enough to compensate us for the day we get it wrong.” Matthew Shadwick, Ladbrokes, 2010-02-25.

2. Russell S. Sobel & S. Travis Raines, 2003. “An examination of the empirical derivatives of the favourite-longshot bias in racetrack betting,” Applied Economics, Taylor and Francis Journals, vol. 35(4), pages 371-385, January

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