Missouri and its Dependence on the Pass Rush

so much depends
upon

a healthy Missouri
pass rush

terrorizing
QBs

behind the offensive
line.

What is the thing Missouri’s defense has been most known for over the past decade or so? Pass rush. You know, Marcell Frazier, Charles Harris, Shane Ray, Markus Golden, Michael Sam, Kony Ealy, Sheldon Richardson, Jacquies Smith et al.

What is one of the most unsettled things on the Tigers’ defense coming into 2018? Pass rush. You know, Tre Williams, Chris Turner, Franklin Agbasimere, Akial Byers, Nate Anderson, all those young guys who have barely played at the power-conference level.

How important has a good pass rush been to the Tigers’ defensive success? We took the past 12 years of Missouri defensive data — six in the SEC, six in the Big 12 — to see how total yards per game and yards per play (as well as pass and rush yards per game and play) fluctuate in response to changes in pass plays per sack.

For this, we counted “pass plays” as opponent pass attempts plus Missouri sacks. We also did the NFL method, wherein sacks are counted as pass plays and minus yards in the pass game, instead of in the run game.

Since 2006, the Tigers’ best season in pass plays per sack was 2014, in which Golden and Ray led a group that posted a sack once every 11.8 pass plays. That defense ended up giving 346.4 yards per game and 4.83 yards per play.

The 2012 team had the most anemic passes per sack mark (20.1), and that team gave up 390.7 yards a game and 5.50 yards a play.

Last year’s team averaged a sack every 14.4 pass plays and gave up 414.1 yards per game and 5.60 yards a play.

So how strong is the correlation? Not all that strong on first blush, but I setup a bunch of regression equations and plotted expected values based off of them to see how strong the correlation for each of the six categories (total, pass and rush yards per game; total, pass and rush yards per play) with passes per sack.

Over the past 12 years, passes per sack correlated the strongest with pass yards per game. The expected values had a standard deviation of 24.6 yards, bolstered by years such as 2011 (245.8 expected pass yards a game against; 240.1 actual) and 2014 (185.9 expected; 191.7 actual) in which the model followed reality extremely close.

The regression equation for pass yards for the past 12 years was, for every one more opponent pass attempt per Missouri sack, pass yards per game were expected to go up by 8.248 yards per game.

So the difference between the best sack year (11.8 PPS) and worst (20.1) would be expected to be 68.5 pass yards a game.

In the yards per play realm, total yards per play was actually the category in which the expected veered the least from the actual, with a standard deviation of 0.39. 2010 (4.99 expected; 5.01 actual) and 2012 (5.47 expected; 5.50 actual) hewed especially close.

For every extra pass attempt per sack, the model expected yards per play to bump up by 0.064. So the difference between the best and worst sack year would be expected to be 0.53 yards per play.

Doesn’t seem like that much, but go ahead and ask a defensive coordinator giving up 4.50 yards a play how much better he feels about his team than one giving up 5.03 yards a play.

What about the six years in the SEC? The correlations were weaker (higher standard deviations all around), but pass yards per game and total yards per play were, again, the aspects tied most closely to the pass per sack stat.

The interesting thing about the SEC numbers, though, is the slopes were more steep, on the whole. Meaning that, during Missouri’s SEC run, opponents have been more punishing against Tiger teams that didn’t sack as much than in the Big 12 days.

Another interesting thing: in the overall numbers, the run game stats are inversely correlative to the pass per sack stat. That is, the fewer pass attempts per sack, the better the run defense is expected to be.

That trend reverses in the SEC, with each added attempt per sack corresponding to a bump of 3.506 rush yards per game and 0.065 yards per rush.

So what does that mean for this year’s team?

Well, first we have to figure out how often we think the Tigers will be logging sacks.

If it hews to the average of the past 12 years, then we’d expect Missouri to notch a sack every 15.4 passes (so, say, 32 over 499 pass plays).

At that value, the expected values would be: 377.8 yards/game (215.8 pass, 162.0 rush) and 5.17 yards/game (5.69 pass, 4.58 rush) against.

Within one standard deviation in each category, we’d expect a range of 335.3-420.3 yards/game (191.2-240.4 pass, 132.4-191.6 rush) and 4.78-5.56 yards/play (5.25-6.13 pass, 4.03-5.11 yards/rush) against.

Here’s how the expected output changes if Missouri’s pass rush is better, or worse, than average.

15% better than average: 13.1 PPS; 361.6 yards/game (196.8 pass, 164.8 rush); 5.02 yards/play (5.39 pass, 4.64 rush)
10% better: 13.9 PPS; 367.2 yards/game (203.4 pass, 163.8 rush); 5.07 yards/play (5.49 pass, 4.62 rush)
5% better: 14.6 PPS; 372.1 yards/game (209.2 pass, 163.0 rush); 5.12 yards/play (5.59 pass, 4.60 rush)
5% worse: 16.2 PPS; 383.4 yards/game (222.4 pass, 161.0 rush); 5.22 yards/play (5.80 pass, 4.56 rush)
10% worse: 16.9 PPS; 388.3 yards/game (228.2 pass, 160.2 rush); 5.26 yards/play (5.89 pass, 4.54 rush)
15% worse: 17.7 PPS; 394.0 yards/game (234.8 pass, 159.2 rush); 5.32 yards/play (6.00 pass, 4.52 rush)

So there you have it: the difference between outperforming the average of the past 12 years and underperforming it by 15 percent each is 4.6 more pass attempts per sack, 32.4 yards per game and 0.30 yards per play.

What about if this year’s Tigers duplicate their best and worst sack years from 2006-17?

Best Year: 11.8 PPS; 352.4 yards/game (186.1 pass, 166.3 rush); 4.94 yards/play (5.21 pass, 4.67 rush)
Worst Year: 20.1 PPS; 410.9 yards/game (254.5 pass, 156.3 rush); 5.47 yards/play (6.32 pass, 4.42 rush)

Which is to say that’s all 1,000 words of explaining why you should hope Missouri ends up nearer to the 11.8 PPS end of the spectrum than the 20.1 end.

Here’s the work if you wanted to see it, with a bonus of expected 2018 values using the SEC regression data.