Abstract/Summary

This thesis presents three chapters that touch on the influence of information beliefs in sports betting markets. Improving on the odds implied outcome forecasts projected by bookmakers, the first chapter uses Wikipedia to reveal systematic mispricing in women’s tennis betting markets. Expanding on the first chapter, the second chapter similarly uses the Mincer and Zarnowitz (1969) economic forecast evaluation framework to identify mispricing in women’s tennis betting markets due to 1) bookmaker bias towards the more "beautiful" player relative to their opponent 2) bookmaker bias towards the player with the lighter skin tone relative to their opponent. The final chapter uses Twitter and the English Premier League to analyze the impact of belief dynamics and emotional cues on entertainment utility and consumption. Betting on a buzz, mispricing and inefficiency in online sportsbooks Bookmakers sell claims to bettors that depend on the outcomes of professional sports events. Like other financial assets, the wisdom of crowds could help sellers to price these claims more efficiently. We use the Wikipedia profile page views of professional tennis players involved in over ten thousand singles matches to construct a buzz factor. This measures the difference between players in their pre-match page views relative to the usual number of views they received over the previous year. The buzz factor significantly predicts mispricing by bookmakers. Using this fact to forecast match outcomes, we demonstrate that a strategy of betting on players who received more pre-match buzz than their opponents can generate substantial profits. These results imply that sportsbooks could price outcomes more efficiently by listening to the buzz. Beyond the Baseline: Exploring the Impact of Beauty Bias in Women’s Tennis Markets I ask whether bookmakers set prices on the outcomes they offer efficiently, given the physical characteristics of participants in sporting competitions. Using profile photos from the Women’s Tennis Association (WTA) and state-of-the-art deep learning facial recognition methods, I construct a Relative Beauty Differential between tennis match participants. Based on the predicted beauty scores, the Relative Beauty Differential measures the proportional difference in beauty between a player and their opponent. The constructed measure significantly predicts bookmaker implied-odds forecast error (mispricing, in other words). As a test of market efficiency, I use a beauty informed forecasting model to demonstrate how strategic bets on the less beautiful player would yield sustained profits. Adhering to the standards for market efficiency, this result implies inefficiency in tennis betting markets. Furthermore, I use a completely novel machine learning approach to extract skin tone measures. These measures are used to perform the same set of exercises in reference to relative racial bias, returning similar results. Exploring entertainment utility from football games Previous research exploring the role of belief dynamics for consumers in the entertainment industry has largely ignored the fact that emotional reactions are a function of the content and a consumer’s disposition towards certain participants involved in an event. By analyzing 19m tweets in combination with in-play information for 380 football matches played in the English Premier League we contribute to the literature in three ways. First, we present a setting for testing how belief dynamics drive behavior which is characterized by several desireable features for empirical research. Second, we present an approach for detecting fans and haters of a club as well as neutrals via sentiment revealed in Tweets. Third, by looking at behavioral responses to the temporal resolution of uncertainty during a game, we offer a fine-grained empirical test for the popular uncertainty-of-outcome hypothesis in sports.