Hidden Power Of Trading Activity: The FLB In Tennis Betting Exchanges

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Hidden Power Of Trading Activity: The FLB In Tennis Betting Exchanges

Isabel Abinzano
Universidad Pública de Navarra

Luis Muga
Universidad Publica de Navarra

Rafael Santamaria
Public University of Navarre

May 1, 2016


This paper examines the impact of trading activity on the Favorite-Longshot Bias (FLB) in tennis Betting Exchanges, using direct measures such as betting volume, average bet or standard deviation of the odds. According to predictions based on Disagreement Models, odds mispricing is positively associated with trading volume but negatively associated with the presence of institutional bettors. The FLB is also positively related to the degree of uncertainty in the market.

The existence of two simultaneous markets (a “main” and an “alternative” market) in this particular sports betting environment has enabled us to observe that the relative amount of attention given to the favourite versus that given to the long shot is positively associated with the FLB. It has also been possible to confirm that information is more rapidly incorporated into the odds in the market that receives more attention from bettors, an effect that is intensified by the arbitrage and hedging that occurs between the two markets.

Hidden Power Of Trading Activity: The FLB In Tennis Betting Exchanges – Introduction

An area that has begun to raise interest among researchers in the field of sports betting is that of the relationships between mispricing, trading (betting) activity and the way in which betting markets are organized. In this context, much of the literature in this field has focused on the price setting of bookmakers, and whether it can be interpreted as strategic behaviour that leads them to bias the odds in the presence of informed or sentimental bettors.

Early theoretical models (Kuypers 2000, or Levitt 2004), involving a single bookmaker, treat the total betting volume for a given event as an exogenous variable. In those models, sports bettors’ preferences have an ultimate impact on the prices set by bookmakers. This framework, however, does not seem an accurate reflection of the current reality, in which there is a large number of bookmakers competing in order to increase their market share. In view of this, Franck et al. (2011) developed a model in which, driven by competition, bookmakers tend to offer lower odds on events that attract a high proportion of sentimental bettors, thus establishing a relationship between price and trading volume, by which the presence of irrational bettors can lead to bookmaking odds bias.

The development of internet trading platforms has undoubtedly facilitated direct competition between bookmakers, but, even more importantly, it has revolutionized betting markets by enabling a form of online betting known as “Betting Exchanges”. These platforms work in a way similar to order-driven financial markets. Bettors placing bets set their own odds and state their stake, which will be exchanged if they match those of another bettor. In these markets, therefore, there is no dealer (bookmaker) to generate the bid-ask spread of the odds; it is instead determined by the best bid and ask prices from different bettors that are currently parked on the order book. Bookmakers’ markets, on the other hand, are similar to price-driven financial markets in which the bid-ask spread captures three factors: order-processing costs, inventory costs and adverse-selection costs. The first two are related, respectively, to the processing of the order, and to the operating costs of trading and maintaining a desired level of inventory subject to a risk. The third is related to the possibility of trading with an agent who is better informed than the bookmaker. Of these factors, the one with the biggest impact in bookmaker betting markets is adverse-selection costs, given that, when trading with informed agents, the bookmaker will, on average, lose the bet.

The impact on prices due to the different ways betting markets are organized is in no way trivial. Indeed, empirical evidence has shown that betting exchange odds are better than bookmakers’ odds at predicting future events (Franck et al. 2010). Put another way, price-setting errors are less common in betting exchanges, although various studies have shown that they are not entirely free of price-setting biases. Specifically, Abinzano et al. (2016) report evidence of the Favorite-Longshot Bias (FLB) in tennis betting exchanges, and Abinzano et al. (2014) present evidence of the impact on prices of overconfidence or self-attribution biases resulting from an overreaction to good news surrounding a specific event.

The organizational difference also leads to micro-structural differences between the two types of market (betting exchanges vs. bookmakers’ markets) which will presumably affect the relationship between mispricing and trading activity. In this respect, Flepp et al. (2014a), using a sample of football betting exchanges, show that the odds in bookmakers’ markets become more attractive to bettors when the liquidity for the same event is low in betting exchanges1 and Flepp et al. (2014b) show that liquidity reduces price-setting efficiency in betting exchanges for matches played at weekends, when less-informed bettors are more likely to join in the betting. In a bookmakers’ market, however, Flepp et al. (2016) find no significant relationship between bookmakers’ odds and trading volume in a context where there are sentimental bettors.

Despite the findings reported above, there remain large gaps in the analysis of the impact of trading activity on price setting in both organizational forms of market, due to the fact that, as noted by Flepp et al. (2016), it has not been easy to obtain information on trading activity in betting markets.

Favorite-Longshot Bias (FLB) in tennis betting exchanges

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