Market Efficiency and Behavioral Biases in the WNBA Betting Market

Abstract

Abstract:The betting market for the Women's National Basketball Association (WNBA) is a thin financial market, which does not attract much interest from sports bettors. Given these characteristics, it is possible that profitable wagering strategies could exist for informed bettors of the WNBA. Using betting data on the WNBA from 2007-2012, we find that simple betting strategies do not earn statistically significant returns. WNBA bettors are like NBA bettors; however, in that they strongly prefer the best teams, particularly when they are on the road. Despite this clear bias, betting against the most popular public wagers is not found to earn statistically significant profits.

How rational is gambling?

“…For example, the quality of the teams highly impacts the number of bets made in the US (NBA, NHL, WNBA) and in Australia (variety of races and sports). Moreover, more bets are made if the scores are higher, if the games are broadcast on television, and if the teams are evenly matched–indicating that the outcome is more uncertain than in games with strong favorites facing underdogs (see Gainsbury & Russell, 2015; Paul & Weinbach, 2010, 2014, 2015). Another effect is an increase in the number of bets during holidays, especially during Christmas (see Paul & Weinbach, 2010).…”

“…Paul and Weinbach (2007) found this bias as well, noting that the percentage of bets increased by 16% for road favorites. Other papers supporting the home‐underdog bias are Paul and Weinbach (2014, 2012, 2008), Dare and Dennis (2011) and Golec and Tamarkin (1991). Again, such an effect is not in line with the assumption of purely financial motives.…”

“…If one drops the assumption of purely financial motives, gambling could be interpreted as a consumption activity (see Mao et al., 2015a, 2015b; Paul & Weinbach, 2010, 2014; García et al., 2008). Consumers of gambling services could thus be seen as rational economic agents purchasing a commodity that offers entertainment and excitement, as well as some hope of acquiring a higher level of income and wealth (see Eadington, 1975, 1999).…”

Building an NCAA mens basketball predictive model and quantifying its success

“…A few landmark studies, including Harville (1980) and Stern (1991), used data from National Football League (NFL) games to argue that, in general, point spreads should act as the standards on which to judge any pre-game predictions. While recent work has looked at gambling markets within, for example, European soccer (Constantinou, Fenton, and Neil, 2013), the Women's National Basketball Association (Paul and Weinbach, 2014), the NFL (Nichols, 2014), and NCAA men's football (Linna, Moore, Paul, and Weinbach, 2014), most research into the efficiency of men's college basketball markets was produced several years ago. Colquitt, Godwin, and Caudill (2001), for example, argued that, overall, evidence of market inefficiencies in men's college basketball were limited.…”

How often does the best team win? A unified approach to understanding randomness in North American sport

“…Across time and sporting leagues, researchers have identified that it is difficult to estimate win probabilities that are more accurate than the market; i.e, the betting markets are efficient. As an incomplete list, see Harville (1980); Gandar et al (1988); Lacey (1990); Stern (1991); Carlin (1996); Colquitt, Godwin and Caudill (2001); Spann and Skiera (2009); Nichols (2012); Paul and Weinbach (2014); Lopez and Matthews (2015). Interestingly, Colquitt, Godwin and Caudill (2001) suggested that the efficiency of college basketball markets was proportional to the amount of pre-game information available-with the amount known about professional sports teams, this imsart-aoas ver.…”