By John Ezekowitz and Andrew Cohen
The allure of March Madness is due in no small part to the complementary joys of filling out a bracket and first and second round upsets. America loves an underdog, and the first four days of the NCAA Tournament are the perfect forum for underdogs and upsets. America also loves to be right; there is nothing quite so satisfying as picking the correct 12-5 upset. While in our fallible recollections it may seem as if there are plenty of upsets (defined as a victory by a team five or more seed lines below its opposition), less than a quarter of first and second round games are upsets.
What, then, separates the underdogs that are successful in their upset bids from those that are not? Join us after the jump as we analyze a theory put forward by no less of an authority than Dean Oliver, the father of modern tempo-free basketball statistics.In his book “What Wins Basketball Games,” Oliver hypothesizes that underdogs should engage in “risky strategies,” those that increase the variance of the score. One of these strategies would be slowing down the tempo of the games, leaving teams with fewer possessions. The logic goes that the favorite would have fewer chances to press their advantage on a per possession basis, increasing the variance of the score from the natural score, which obviously favors the more talented team.
On the surface, this logic makes sense; allowing the superior team fewer opportunities to score seems like a good, if risky, strategy. Luckily, we can empirically test this theory.
Using tempo data from Ken Pomeroy’s incredible website, we have analyzed every first and second round NCAA tournament game that could produce an upset from 2004 to 2009. We made the decision to exclude 1 vs 16 and 2 vs 15 matchups, since over the last decade those games have produced no upsets. That left us with a sample of 144 games to analyze.
The results were, well, stunning. There were 35 upsets in the data set, and the average tempo of those games was 67.77, while the average tempo of the games in which the underdog lost was only 64.93. Using the statistical technique of a two-sample t test, we can find that we reject the null hypothesis that there was no difference between the tempos of successful and unsuccessful underdogs, and conclude that successful NCAA tournament underdogs played at a faster tempo, using more possessions. The P-value for this test stat was 0.0134, which is well inside the threshold for significance. This result directly conflicts with Oliver’s theory, and warrants further study.
Having seen that successful underdogs play faster, we attempted to determine how much of an advantage playing faster was. We ran a logistic regression on the dataset, with an underdog win coded as 1 and a loss as 0. The results were slightly less powerful than the t-test, as the P-value was 0.021, but again, were still statistically significant. An underdog having an extra possession increased that team’s odds of an upset by 7.7 percent.
What could be the reasons behind this rejection of the theory? One potential objection would be that teams that play faster in the tournament play faster over the course of the year, and that perhaps their faster tempo is in fact slower in the NCAA’s than during the season. However, the average adjusted year-long tempos of the successful and unsuccessful underdogs are practically the same (successful underdogs played on average .3 possessions per game faster), and successful underdogs played, on average, 1.4 possessions faster in their upsets than over the course of the season.
Perhaps playing faster helps level the playing field for smaller, mid-major teams who often do not match up with the frontcourts of the favored teams. Another reason could be that tempo of close games, which upsets often are, is artificially inflated by the fouling that occurs late in games. Similarly, the tempo of blowout games, which are games that favorites tend to win, could be artificially deflated by favorites using up the shot clock down the stretch.
We will definitely investigate this possibility in future posts. Frankly, however, we cannot think of a satisfactory explanation for why this data contradicts Oliver’s theory. Any thoughts or explanations would be appreciated in the comments.