Academic journal article Economic Inquiry

Point Shaving in College Basketball: A Cautionary Tale for Forensic Economics

Academic journal article Economic Inquiry

Point Shaving in College Basketball: A Cautionary Tale for Forensic Economics

Article excerpt

The study of crime and corruption has significant interest to economists. Of central interest is the actual extent to which individuals engage in illegal behavior. The problem is that the very laws that discourage corruption (Shleifer and Vishny 1993) also make it difficult to measure illegal activity, as criminals conceal their activities to avoid punishment.

Accordingly, economists have turned to indirect approaches to measure illegal activity. For example, Persico (2002) and Anwar and Fang (2006) use patterns in arrest records to detect racial profiling by police. Jacob and Levitt (2003) use the distribution and correlation of test scores to identify cheating by teachers. Porter and Zona (1993, 1999), McAfee and McMillan (1992), and Pesendorfer (2000) exploit implications of auction theory to uncover illegal bid rigging by auction cartels from the distribution of bids. Muelbroek (1992) documents the effect of insider trading on stock returns, and Christie and Schultz (1994) use the distribution of even-and odd-eighth quotes to detect collusion by NASDAQ market makers.

Researchers have also turned to sports to investigate corruption, exploiting the fact that sports settings provide abundant clean data. (1) Duggan and Levitt (2002) document match throwing in sumo wrestling by exploiting the highly nonlinear payoff structure. Wolfers (2006) and Gibbs (2007) conclude from asymmetric distributions in winning margins around the spread that point shaving--the illegal practice by favored teams of attempting to win basketball games by less than the point spread in order to yield profits for gamblers who bet on the underdog--is pervasive throughout college and professional basketball.

The danger with indirect methods is that, by their very nature, their identification of illegal activity hinges on subtle assumptions. For example, in auctions, it is crucial to identify competitive equilibrium bidding strategies. So, too, a suspicious price or point spread pattern is not sufficient to prove corruption or to identify its source. For example, Yermack (1997) concludes from negative stock returns prior to (unobserved) executive stock option grants and positive stock returns after that firms strategically disseminate bad news before the grants and good news after. Lie (2005) argues that this pattern reflects strategic backdating of the grants, and Heron and Lie (2007, 2009) distinguish their backdating interpretation by showing the return pattern falls off sharply following a 2002 SEC disclosure requirement. Indeed, many firms are now under investigation for illegal backdating. Our article's investigation of point shaving in college basketball highlights the necessity of identifying appropriate counterfactuals and robustness checks to validate findings. Betting in basketball typically involves a point spread handicap for the favored team. A wager on the favored team wins only if the favorite wins by more than the point spread, while a wager on the underdog pays off if that team wins the game outright or loses by less than the point spread. Because players care primarily about winning, while gamblers care about the winning margin, there is scope for profitable point shaving by strong favorites who are very likely to win.

Wolfers (2006) argues that gambling-related corruption is manifest in men's NCAA college basketball, and Gibbs (2007) makes the same claim for professional NBA basketball. Both papers begin with the key premise that since "the deviation of [winning margins] from the spread is a forecast error," then the margin of victory should be symmetrically distributed around the point spread. Such symmetry would imply that if there is no point shaving, then a team favored by S points should be as likely to win by less than S points, as to win by between S and 2S points. Wolfers finds that 46.2% of strong favorites--teams favored to win by more than 12 points--win, but fail to cover the spread, while only 40. …

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