NCAA Tourney Upsets Continued: Do Close Games Have a Higher Tempo?

By John Ezekowitz and Andrew Cohen

Last week, we analyzed the effect of tempo on NCAA Tournament upsets and found that contrary to Dean Oliver’s theory, a faster tempo (more possessions) was predictive of underdog success. We searched for potential explanations for this phenomenon and, after taking suggestions, researched into the two most popular explanations.

Many suggested that the increased tempo observed in upsets could be caused by the fact that upsets are often close games, leading to late game fouls that artificially increase the possessions. This theory certainly seemed plausible and worthy of study. Our results come after the jump.

Unfortunately, the data that would best analyze this theory, possessions after the first 35 minutes of the game rather than the full forty, was unavailable. As a proxy, we looked at margin of result (this is the absolute value).

The intuition that upsets were generally closer games than non-upsets was proved correct. After eliminating one outlier, Long Beach St’s 35-point drubbing at the hands of Tennessee, the average margin of result for upsets was 7.89, while for non-upsets it was 11.86. This difference was strongly statistically significant, with a P value<0.005.

The second part of the intuition, however, was not backed up by the data (brief stat geek interlude: because the margin of results distribution was right skewed, we used a square root transformation). There was no significant correlation between closer games (smaller margin of result) and a higher tempo (more possessions). The r2 was only 0.043. We caution that our sample size in this data set is relatively small (only 144), and that the effect may hold true for a wider swathe of games. Nonetheless, this seems to reject the theory that the fouling that is present at the end of closer games (ie upsets) led to an increased tempo.

Another potential explanation for our previous results was that despite successful underdogs playing faster than their unsuccessful compatriots, relative to their competition they were in fact playing slower. This would mean that upset victims’ s average tempos during the year. However, this theory too was not supported by the data. There was no significant difference between the average tempos of upset victims and the pace they played in their losing NCAA Tournament efforts.

Having examined the data we have and the theories so far put forward, we have little choice but to conclude that successful NCAA Tournament underdogs simply play at a faster tempo during their upsets. This, of course, does not preclude another explanation for the phenomenon, and if you have further thoughts, please chime in in the comments.

Please stay tuned to HSAC as we attempt to create a model that predicts successful underdogs in the NCAA Tournament using tempo-free stats. Perhaps we can help you successfully pick that 12 vs 5 upset this March.

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  • I think that there are a couple reasons that might make a higher tempo lead to more upsets: first, I think that games that aren’t upsets are more likely to be lopsided, and when teams are up by a lot in the second half, they usually try to take up as much of the shot clock as possible (longer possessions in leads to slower tempo); and an easier theory to check would be to see if favorites who generally run at a lower tempo are more prone to be upset.

  • Its likely that upsets are generated when high -risk high-reward strategies end up being successful. Things like running and gunning, full court presses, etc speed up the game without providing an increase in quality possessions, which is where good teams make their money.

    Lets say Clemson is a 9 Seed and wins, and feeds into Kansas. How is Clemson going to win this game? By matching their half court offense against Kansas’? No. They are going to press, soft press, go for quick outlet passes, and take a lot of threes. They don’t want to have to face Kansas’ half court defense once its set up and Aldrich is back defending the paint. They certainly don’t want Collins to come up the court, run a pick-and-roll with Aldrich with weapons and outlets all over for easy buckets.

    So you do some gimmicks that you normally wouldn’t pull against a team your level, but if you play your average game your gonna lose, so you go for it.

    • Thanks for the thoughts, Alexander.

      This theory is certainly appealing, and has been pushed by Malcolm Gladwell, among others. But less a couple high-profile examples (UAB over Kentucky comes to mind), there simply hasn’t been much hard evidence to support this. Our upsets were significantly faster, but only by 3 possessions a game on average. This doesn’t imply teams were pressing the whole game, or even part of it.

      Of course, this doesn’t mean the theory is wrong; it probably means there is some inefficiency and that it could be exploited. For whatever reason, teams are afraid to. It would be interesting to look at teams that press and see if they preform above expectations in the NCAA Tournament.

  • Thanks for an interesting series of articles.

    Maybe the theory is valuing possessions too much.

    In last year’s tournament, the most widely separated 3-14 seeds in terms of scoring efficiency were Villanova and American, who also happened to play each other. According to the difference in scoring efficiency, we’d expect Villanova to pick up one point on American every 5 possessions. As it happens, Villanova won by 13 in 70 possessions – basically what we would expect. Say American keeps the pace at 60; to get the upset, they still need to make up 12 points – not the kind of thing that would happen all that often. Basically, the greater the difference in per possession efficiency, and therefore the greater the benefit for the underdog to keep the number of possessions down, the less likely they are to win in the first place.

    The other teams in the tournament were grouped tighter than Villanova and American, so a possession is worth less for them. It might be 8, 10, 12, 15 possessions before that one point difference appeared. And the nearer two teams are in efficiency, the more evenly they are matched. Why should an underdog turn into Princeton if it already had a decent chance to beat its opponent straight up?

    • Eric:

      That is a very interesting analysis. My intention in these two articles was never to predict NCAA Tournament upsets, it was simply to analyze a theory that was already out there. I think your explanation makes more sense than most that I have heard about why Oliver’s theory doesn’t hold.

      Please check back in early next week as I actually do try to find factors that predict NCAA Tournament upsets.

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