CCU ranked #41 in Sports and Economics Research
According to the IDEAS bibliographic database, the largest database dedicated to economics, in February 2017 Coastal Carolina University ranked No. 41 in the field of sports and economics. This is in large part due to the research conducted by Andrew Weinbach, Ph.D., professor of economics at Coastal Carolina University.
Weinbach’s research includes more than 50 peer-reviewed journal articles related to sports economics, including “Market Efficiency and a Profitable Betting Rule”; “The uncertainty of outcome and scoring effects on Nielsen ratings for Monday Night Football”; “Using betting market odds to measure the uncertainty of outcome in Major League Baseball”; and “Expectations and voting in the NCAA football polls: The wisdom of point spread markets.”
Weinbach began researching sport betting markets as a graduate student at Clemson University with classmate Rodney Paul, who is now at Syracuse University, and they still collaborate on most of their research projects today. In recent years, Weinbach has also collaborated with Coastal’s Yoav Wachsman, Monica Fine and Ken Small on sports economics projects.
He explained that sport-betting markets are essentially simple versions of financial markets, and there was a widely held belief that sports betting markets should conform to the idea of “market efficiency.” In the stock market, a stock’s price is assumed to incorporate all available relevant information. Just as a stock price should reflect an unbiased forecast of the present value of a company’s earnings, by logical extension, betting odds or point spreads should reflect unbiased forecasts of game outcomes.
His research came to show that while markets appeared to be largely efficient, there were systematic deviations from the predicted efficiency across time and sports. Weinbach suggested that because most people betting on sports are sports fans who are also likely to watch the games, there is a tendency for fans to bet not strictly on a forecast of a game outcome, but also take their preferences into consideration. Therefore, betting lines may not strictly reflect a forecast of what is going to happen, but rather what people are interested in.
For example, in their first paper, Paul and Weinbach studied the NFL totals market (where bettors wager whether the combined points scored by both teams will be “over” or “under” a posted “total”). They found that for NFL games with the highest posted totals, generally featuring high-scoring teams, scores in those games were just a little more likely to be “under,” or lower than the posted total. They hypothesized that most fans preferred to watch high-scoring games and bet on the over, hoping to see high-scoring games.
Therefore, the preferences of the majority was influencing the betting lines, pushing up the posted totals higher than an accurate forecast would have been. This “apparent inefficiency” was merely a reflection of consumer preferences. Because betting markets are formed before games are actually played, Weinbach and Paul began using betting market data and knowledge of fan behavior to help generate pregame forecasts for attendance and television ratings. His research evolved from studying betting markets to studying fan behavior, or, more specifically, what drives fans to watch a game or go to live events.