Contrasting NFL rush defense by yards per carry, success rate

In the past, I’ve looked how well NFL teams run the ball by yards per carry. I take the raw numbers and adjust for schedule with a least squares method.

Here are the NFL rankings for rush defense through the 2019 Divisional Playoff games, which highlights the 4 remaining playoff teams. The number gives an expected yards per carry against an average offense, with an NFL average of 4.4 in 2018.

1. New Orleans, 3.50
2. Baltimore, 3.56
3. Houston, 3.61
4. Chicago, 3.70
5. Dallas, 3.81
6. San Francisco, 4.03
7. Pittsburgh, 4.08
8. Indianapolis, 4.15
9. Los Angeles Chargers, 4.21
10. Buffalo, 4.22
11. Minnesota, 4.23
12. Jacksonville, 4.29
13. New York Giants, 4.36
14. Detroit, 4.38
15. Tennessee, 4.41
16. Green Bay, 4.42
17. Denver, 4.47
18. Washington, 4.54
19. Oakland, 4.56
20. New York Jets, 4.59
21. Tampa Bay, 4.60
22. Philadelphia, 4.60
23. Cincinnati, 4.62
24. Cleveland, 4.74
25. Arizona, 4.77
26. Carolina, 4.78
27. New England, 4.88
28. Atlanta, 4.89
29. Miami, 4.92
30. Seattle, 4.93
31. Los Angeles Rams, 5.03
32. Kansas City, 5.04

New Orleans has the best rush defense in the NFL, while the other 3 playoff teams bring up the bottom of the NFL.

My research suggests that yards per carry isn’t a great way to evaluate how well teams run the ball. Big plays can skew this metric.

For example, suppose a team breaks off a 86 yard run, then gets stuffed for a 2 yard loss on the next 3 plays. 20 yards per carry seems awesome, but in this sequence the team had to settle for a field goal attempt.

This season, I’ve started looking at rush defense by success rate. A rush is a success if it gains half the yards on 1st down, 70% of the yards on 2nd down, and all the necessary yards on 3rd and 4th down.

In the four play sequence from above, the team had a 25% success rate. This rate better captures what happened on these 4 plays than the 20 yards per carry.

After making schedule adjustments with a least squares method, here are rankings for rush defense by success rate. The number gives an expected success rate against an average NFL offense (41.7% average in 2018).

1. Houston, 0.34
2. Chicago, 0.35
3. Jacksonville, 0.36
4. Baltimore, 0.37
5. Pittsburgh, 0.38
6. Denver, 0.39
7. New Orleans, 0.39
8. Philadelphia, 0.39
9. Detroit, 0.39
10. San Francisco, 0.40
11. Carolina, 0.41
12. Dallas, 0.41
13. New York Giants, 0.41
14. Los Angeles Chargers, 0.41
15. New York Jets, 0.41
16. Green Bay, 0.42
17. Tennessee, 0.42
18. Indianapolis, 0.42
19. Los Angeles Rams, 0.42
20. Buffalo, 0.42
21. Oakland, 0.42
22. Arizona, 0.43
23. Miami, 0.43
24. New England, 0.43
25. Seattle, 0.43
26. Cleveland, 0.44
27. Minnesota, 0.44
28. Washington, 0.45
29. Cincinnati, 0.45
30. Atlanta, 0.45
31. Tampa Bay, 0.47
32. Kansas City, 0.51

These numbers tell a different story. The New Orleans rush defense is closer to average, and the Rams look respectable. The Chiefs are still dead last, which I believe will play a big role in their game against New England.

Why should we believe success rate more than yards per carry?

Bill Connelly did some excellent work with college football statistics to show success rate is more sticky from early to late season that explosiveness. His study inspired my interest in success rate.

Dr. Ben Baldwin mentioned on my podcast that rushing success rate correlates to winning more than yards per carry.

I hope to look into this more myself in the off season.

Podcast: Dr. Eric Eager on football analytics, NFL conference championship games

On this episode of The Football Analytics Show, Dr. Eric Eager, data scientist at Pro Football Focus, joins me to talk analytics and make predictions. Eric got his Ph.D. at the University of Nebraska Lincoln and was a professor at the University of Wisconsin La Crosse.

Among other topics, we discuss:

  • whether you need a Ph.D. to do data science in 2019 (7:30)
  • how PFF makes predictions based on charting grades (9:17)
  • the predictive stat for quarterbacks relevant to Tom Brady vs Pat Mahomes (23:28)
  • how to evaluate pass rush better than looking at sacks (30:38)
  • the frontier in football analytics that most excites Eric (35:50)

To listen here, click on the right pointing triangle:

To listen on Apple Podcasts, click here.

Podcast: Whale Capper on predicting NFL Divisional Playoffs based on seismic engineering

On this episode of The Football Analytics, I talk with Drew, who goes by Whale Capper on Twitter. He uses his background in earthquake engineering to handicap NFL games.

Among other topics, we discuss:

  • How preparing buildings for earthquakes translates to predicting NFL games (3:09)
  • How the total can help you find value on a side (7:54)
  • How Drew uses numbers to find value in the NFL markets, an approach I wholeheartedly endore (12:20)
  • The volatile NFL team that hits the road for their next playoff game (17:23)
  • The one player who has befuddled Whale Capper over the past 2 seasons (25:21)

The breakdown of the NFL Divisional Playoff games start at 9:20.

To listen here, click on the right pointing triangle.

To listen on Apple Podcasts, click here.

In the last section, Drew and I talk about having dinner with Isaac Newton (31:08). This reminds me the Cosmos episode that tells the amazing story of his collaboration with Edmund Halley. To watch this episode, click here.

Podcast: College football title game and Super Bowl odds

On this episode of The Football Analytics Show, I start with the college football championship game between Alabama and Clemson, and how it’s a much different game from last year’s match up. (1:12)

Then I dig into my new calculations for Super Bowl win probabilities and identify the team with the best chance. But can this team’s defense hold up? (5:29)

Last, I look at a sleeper team to win the Super Bowl. But really, the NFL playoff system is working against this team. (9:00)

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To listen on Apple Podcasts, click here.

Member Super Bowl probabilities for 2019

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