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How Predictive Are Returning Snaps for Missouri?

The Tigers are set to bring back 63.5 percent of their snaps from last year. Is that a good thing?

NCAA Football: Missouri at Florida
It’s a good thing for Missouri that Albert Okwuegbunam is back. For all the obvious reasons plus the fact that he plays in the position group from which returning snaps have been the most correlative predictor of offensive success as a whole.
Kim Klement-USA TODAY Sports

You know what I love about the fact that I’ve done comprehensive Missouri snap counts for the past six seasons, other than the severe eye strain and accompanying migraines that mysteriously seem to crop up each fall?

I can look back at past years of snap data and see what it has meant for the following season. Namely, for this post in particular, how much does it matter if the Tigers bring back, say, 51 percent of their snaps as they did from 2013-14 as opposed to 72 percent of their snaps, as they did from 2016-17.

Well, as you can see by those two examples, it doesn’t matter all that much. The team that brought back 51 percent went 11-3. The team that brought back 72 percent went 7-6.

Post over!

But, if you look at specific position groups and offensive and defensive performance, it paints a bit of a different picture.

I set the offense, defense and each position group within them as a whole to regression formulas, trying to project overall production the next season based on returning snap percentage, and saw how much the projections deviated from the actuals.

Some groups had basically no correlation. Some had stronger correlations.

Let’s see which groups have mattered the most over the past six years, shall we?

Offense

For every percentage point added in snaps returning, we expect points per game to increase by 0.2, yards per game to increase by 1.45 and yards per play to increase by 0.02.

Fitting those models to the data, points per game had a standard deviation of 8.82, yards per game was 94.5 and yards per play was 0.94.

So, not spectacular.

With 60.6 percent of its offense set to return in 2019, the model would predict Missouri to average 28.9 points per game, 423.0 yards per game and 5.84 yards per play.

Among position groups, the wide receivers and tight ends were most predictive of offensive success.

For every percentage point added in snaps returning among wideouts and tight ends, we expect points per game to increase by 0.32, yards per game to increase by 4.12 and yards per play to increase by 0.04.

Fitting those models to the data, points per game had a standard deviation of 6.73, yards per game was 42.3 and yards per play was 0.54.

Missouri returned around 30-35 percent of its receiver/tight end snaps from both 2013 and 2014, and the offense sputtered both years. Well, at least until 2014 found a run game. The Tigers returned more than 75 percent of their receiver/tight end snaps from 2015 and 2016, and the offense averaged more than 500 yards per game each year.

With a new offensive coordinator. And an emboldened Drew Lock. Yes, there are shiploads of mitigating factors. I’m choosing to ignore them for this thought exercise.

With 67.9 percent of its receiver/tight end snaps set to return in 2019, the model would predict Missouri to average 33.1 points per game, 474.5 yards per game and 6.38 yards per play.

Defense

On defense, over the course of this study, it has actually behooved Missouri to have less coming back. And the correlations are stronger than the offensive side of the ball.

For every percentage point added in snaps returning, we expect points per game to increase by 0.69, yards per game to increase by 9.89 and yards per play to increase by 0.10.

Fitting those models to the data, points per game had a standard deviation of 5.53, yards per game was 38.8 and yards per play was 0.46.

With 66.8 percent of its offense set to return in 2019 (uh ohhhhhhh), the model would predict Missouri to average giving up 31.4 points per game, 475.6 yards per game and 6.18 yards per play.

Among position groups, defensive line actually mirrors the defense as a whole pretty closely in terms of correlation.

For every percentage point added in snaps returning, we expect points per game to increase by 0.16, yards per game to increase by 2.04 and yards per play to increase by 0.02.

Fitting those models to the data, points per game had a standard deviation of 5.51, yards per game was 44.7 and yards per play was 0.49.

With 59.7 percent of its offense set to return in 2019, the model would predict Missouri to average giving up 26.0 points per game, 396.7 yards per game and 5.41 yards per play.

Whole Team

The correlation is not great when we take it out to a super macro level, as I said before.

For every percentage point increase of snaps returning, we expect a decrease of .11 percentage points in win percentage.

That’s .11 percentage points. So it barely moves at all. That results in a bunch of predictions hovering around 52-55 percent over the past six years and a standard deviation of 17.6

So 18 points of leeway on either side. So more than 4 games of difference over a 12-game season. So, taking 2019’s 63.5 percent of snaps returning and 53.4 projected win percentage, anywhere from 35.8 to 71.0.

So anywhere from 4-8 to 9-3.

You’re welcome!

Measuring it by percentage point change in win percentage from the previous season is a little better, but still not great. For every percentage point increase of snaps returning, we expect an increase of 1.63 percentage points in win percentage, with a standard deviation of 16.0.

So that 63.5-percent figure would give us a bump up to an expected 61.7 win percentage, or 7-5 over 12 games (8-5 over 13, if the bowl ban gets lifted). Again, though, with 32 total points of leeway, meaning the expected range dips as low as 45.7 and goes as high as 77.7. Or the difference between 5-7 and 9-3 in a 12-game season.

What do we take from all of this? Look for Missouri to have really good seasons on offense and defense the next time it returns a whole bunch of receivers and not that many defensive linemen.

Work is below, if you want to see: