It's summertime. For Americans, that means baseball season and all the simple pleasures that the game affords — from peanuts and Cracker Jack to the seventh inning stretch and renditions of "Take Me Out to the Ballgame." For many, though, the game is not the same without the opportunity to place a little (or even a big) wager on the outcome. Whether legal or not, betting is ubiquitous in baseball, and in all other sports for that matter. And of course betting is not even limited to sporting events: it has evolved into an international, multi-billion dollar industry. People now wager on the outcome of events like American Idol and the Miss American Pageant just as readily as they do the World Series or March Madness.
Given the prevalence of betting and the money at stake, it is worth considering how we pick sides. What is the best method for predicting a winner? One might expect that, for the average person, an accurate forecast depends on the careful analysis of specific, detailed information. For example, focusing on the nitty-gritty knowledge about competing teams (e.g., batting averages, recent player injuries, coaching staff) should allow one to predict the winner of a game more effectively than relying on global impressions (e.g., overall performance of the teams in recent years). But it doesn't.
In fact, recent research by Song-Oh Yoon and colleagues at the Korea University Business School suggests that when you zero in on the details of a team or event (e.g., RBIs, unforced errors, home runs), you may weigh one of those details too heavily. For example, you might consider the number of games won by a team in a recent streak, and lose sight of the total games won this season. As a result, your judgment of the likely winner of the game is skewed, and you are less accurate in predicting the outcome of the game than someone who takes a big picture approach. In other words, it is easy to lose sight of the forest for the trees.
Yoon and his research team explored the optimal process of prediction in a series of studies examining bets made on soccer matches and baseball games. In their first study, they reviewed more than one billion (yes, billion) bets placed in 2008-2010 through Korea's largest sports-betting company, "Sports ToTo." They characterized the bets in one of two ways: (a) bets that involved a general prediction (i.e., win or lose), and (b) bets that involved a specific prediction (i.e., a precise score). Critically, they wanted to know which type of bet was more likely to result in an accurate prediction of the overall winner. Despite the fact that the specific bets were arguably more difficult and involved greater effort than general bets, they led to diminished success in predicting the global outcome of the game (i.e., which team won). This disadvantage was especially pronounced for games in which the favored team won.
These findings suggest that adopting a holistic approach when predicting outcomes, even for multi-faceted events like sporting competitions, may be more effective than dwelling in the details. However, because these findings reflect performance in a natural setting, they are open to alternative interpretations. For example, different kinds of people (e.g., risk-averse versus risk-seeking) may be more prone to placing different kinds of bets (e.g., general versus specific). In addition, different opportunities for reward may influence betting behavior, thus encouraging those making specific bets to take risks on unlikely outcomes. To control for these factors, Yoon's team examined betting behavior in a controlled laboratory paradigm.
In three different experiments, participants were asked to make predictions about upcoming sporting events. In each study, half of the participants were randomly selected to make general win/lose predictions, while the other half were asked to make specific score predictions. The dependent measure was the same for both groups: Could they predict the winners?