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Risk is at the heart of football strategy. Aggressive, risky gameplans should result in boom-or-bust high-variance outcomes, sometimes scoring lots of points but sometimes scoring very few. Conservative gameplans result in relatively consistent low-variance outcomes. Teams would more likely score close to their average score.

In this post, I’ll look at what high and low variance strategies would look like in terms of point totals and how they affect each team’s chances of winning. I’ll also compare the theoretical strategies to the actual distributions in the NFL. We'll see why NFL coaches should be more aggressive when they're the underdog.<!-more-->

High Variance Strategy in Basketball

Some time ago, I came across an article posted by basketball researcher Dean Oliver that analyzed high and low variance strategies for the NBA. Oliver calculated the win probability of each opponent according to the mean and standard deviation (SD) of each team’s scoring tendencies. SD represents the degree of variance. The more aggressive and riskier the strategy, the higher the SD will be. For example, a basketball team that shoots lots of 3-pointers would have a high variance.

The key to accurately modeling basketball is realizing that each team’s score is correlated with that of its opponent. The pace of a basketball game ties each team’s score together, and there is a high level of covariance. When one team scores a high number of points, the other team will tend to score more too. Game scores are interdependent.

In Football

Recently the Smart Football blog illustrated the advantage of high variance strategies for underdogs. A high variance strategy increases an underdog’s chances of winning but comes with the cost of also increasing its chances of being blown out.

In the NFL as a whole, visiting teams average about 19 points with a SD of 10 points while home teams average about 23 points with a SD of 10 points. But unlike basketball, football opponent scores are negatively correlated. This makes intuitive sense because the better one team does, the worse the other should do. If one team gets lots of first downs and doesn’t commit turnovers, its opponent will usually start drives with poor field position, and vice versa. The covariance between NFL opponent scores is -1.9 points-squared.

If NFL scores were normally distributed, this is what the typical score distribution would look like. The visitor scores are in red and the home scores are in blue.


We can calculate each team's chances of winning by summing all the probabilities with these distributions and factor in the covariance using Dean Oliver’s method. This estimates that the home team wins 56.5% of the time, which happens to be exactly the NFL actual home field advantage.

Disclaimer

There’s one problem. NFL scores are not normally distributed, primarily due to its unique scoring, which typically comes in chunks of 3 or 7. Here is what the actual distribution of scores looks like.


The good news is, if we group the scores into bins of 7 points, we get a quasi-normal distribution. (Technically, it may be more of a gamma or Poisson distribution.) I’m going to stick with normal distributions to simplify the math and to better illustrate the concepts I want to convey.



Demonstration

Here’s why underdogs should play aggressive and risky gameplans. Take an example where one team is a 7-point favorite over its underdog opponent. Say the favorite would average 24 points and the underdog would average 17 points. With a SD of 10 points for each team, the underdog upsets the favorite 31.5% of the time. The favorite’s scoring distribution is blue and the underdog’s is red.


But if the underdog plays a more aggressive high-variance strategy, increasing its SD to 15 points, it would upset the favorite 35.3% of the time.


Note that I haven’t increased the underdog’s average score in any way, just its variance. The increase in its chance of winning results due to more of its probability mass moving to the right of the favorite’s mean score of 24. In fact, the higher the variance, the wider the probability mass will be spread. Consequently, more mass will be to right side of the favorite’s average score. But more mass will also be to the left, meaning there is a higher risk of an embarrassing blowout.

Even if employing a high-variance strategy is non-optimum, it can still help an underdog. In other words, even if an aggressive gameplan results in an overall reduction in average points scored, it often still results in a better chance of winning.

The next graph plots the scoring distributions of just such a scenario. Like before, the favorite’s average score is 24 with a SD of 10. But this time the underdog’s average is reduced from 17 to 16. The increase in variance still results in a slightly better chance of winning despite its overall reduction in average points scored. In this case, it's 33.2% for the underdog.


What about the favorite? Should it increase its variance in response to an aggressive underdog? No. Ideally it should play as consistently as possible. The lower the variance the better for the favorite. The next example shows a favorite playing a low-variance game with an average of 24 points and a SD of 5 points. The underdog is playing conventionally with a 17 point average and 10 point SD. The result is an increase in the favorite’s chances of winning from 69.5% in the original example to 73.0%.


And if the underdog plays an aggressive high-variance game, the low-variance strategy is still better for the favorite. In this case the favorite still improves its chances of winning from 64.7% to 67.8%.


In Practice

So what does any of this mean in the real world? Simply put, to win more often underdogs should employ a high-variance strategy from the beginning of the game. It shouldn’t wait until the 4th quarter and become desperate. Go for it on 4th and short, run trick plays, throw deep, and blitz more often. Roll the dice from the get-go.

The real question is, what is the optimum level of risk? I’m not sure, but I do know NFL coaches are operating far from it.

Looking at games from the ’02 through ’06 seasons (a total of 1280), underdogs do not increase their variance. For example, for games in which the point spread is between 6 and 7.5 points, the underdog’s SD is 9.8 points, slightly less than the overall league average. Ideally, it should be higher. The favorite’s SD is 10.4 points when ideally
it should be lower.

The table below lists the SDs of points scored for the favorite and underdog according to the most common point spreads.


Spread Favorite SD Underdog SD
0 - 1.5 9.6 10.5
2 - 3.5 9.8 9.4
6 - 7.5 10.4 9.8
10 - 11.5 10.5 8.7


If anything, there appears to be slight trends in
the exactly wrong directions. The bigger the spread, the smaller the underdog’s variance and the bigger the favorite’s variance. It appears underdogs may get less aggressive while favorites may get more aggressive.

Conclusions

This is more evidence coaches do not coach to maximize their team’s chances of winning. My theory is coaches are delaying elimination until the latest point in the game—that is, trying to “stay in the game” for as long as possible. Underdog coaches minimize risk all game long hoping for a miracle along the way. They seem to be reducing the chances of being blown out, but this is not consistent with giving their team the best chance to win.

But if you think about it, this kind of approach might be good for the NFL as a whole. It keeps games entertaining as long as possible, and keeps viewers tuned in.

Coaches of favored teams could be accused of the same crime. They might be playing with too much variance. But there is certainly a limit to just how consistent a team can be, no matter how hard it tries. There will always be random variation in team performance. I suspect a SD of 10 points may be near that limit, and that coaches of both favorites and underdogs simply play the least risky game they can consistent with accepted conventions.