This year's update to the playoff drive stats show that the football gods may have been on Peyton Manning's side this time. Also: Cam Newton and Alex Smith enter the mix, and why we should be comparing Andrew Luck to Dan Marino.
02 Oct 2008
by Bill Connelly
Missouri head coach Gary Pinkel likes to say that playing defense is all about leverage. In a nutshell, if two guys are pursuing a ball-carrier who is running toward the sideline, the job of the first guy isn't necessarily to tackle him, but to make him cut back toward the middle of the field to be tackled. A runner who breaks a tackle near the sidelines will often find a clear path to the end zone. If he breaks a tackle in the middle of the field, however, there will usually be about four other guys in pursuit to bring him down. It's not necessarily about making the big play yourself, it's about making it harder for the runner to make the big play. Or something like that. Pinkel describes it much better than I do.
Why am I mentioning this? Because today’s Varsity Numbers column is about win correlations, and the numbers suggest that it is not necessarily how many big defensive plays you make that determines how well you do; it is more about leveraging the offense into uncomfortable situations -- in other words, Passing Downs.
First, let us define what constitutes a Passing Down. Once I had enough data to analyze, I began to look at sack rates and success rates for different down yardages. I determined that the following situations are tipping points between successful and unsuccessful drives in college football:
There is a tremendous difference in sacks and successes for plays above and below those yardages. And I'd say the numbers back that up.
(And once again, Success Rate = FO’s Success Rate, adjusted for college data; PPP = EqPts Per Play; S&P = Success Rate + Points Per Play.)
|NCAA Offense by Situation, 2007|
|Play Type||Passing Downs||Non-Passing Downs|
|Rushing||26.7% success rate
|47.4% success rate
|Passing||31.7% success rate
|47.4% success rate
|All Plays||30.1% success rate
|47.4% success rate
You would naturally expect a pretty strong difference in levels of success between those two categories, but that is still pretty staggering.
With that background information out of the way, let’s move to today’s topic.
Win Correlations, or WinCorr for short, is the correlation between any given statistical category and wins/losses. As you’ll see, they can serve a couple of different purposes: We can use them to determine which statistical categories are truly the most important on a national level, and we can look at team-specific WinCorr’s to develop a unique footprint for each team. I will cover the former this week and the latter next week.
There are two ways to look at WinCorr on a national level: determining which statistical categories are most tied to winning a specific game, and determining which categories are most tied to winning seasons, i.e., being a good team. We'll look at both.
As I said above, we compare each statistical category with overall wins and losses, but how do we come up with a number for wins and losses when we're talking about a single game? We have a couple options:
The former is cleaner (and leads to lower correlations, obviously), but the latter is probably a bit more telling. There's a difference between winning 24-23 and winning 41-3.
A couple other things to note: First, I ran Spearman correlations for these numbers. Second, the list below shows the strongest correlations, so there is the possibility of a negative correlation on the list with positive correlations.
Also, I'm not listing the most obvious correlations. You don't need lots of stats to figure out that things like "percentage of points" and "total points" are going to be highly correlated to wins. And as you will quickly notice, for now I am sticking with my own stats and using EqPts instead of yards. Once I have pulled more of that information together, I can begin to look at both conventional stats and unconventional stats.
|Single-game WinCorr, based on percentage of points|
|Total EqPts||Non-Passing Downs||0.597|
|Total Rushing EqPts||Overall||0.587|
|Passing S&P||Close game*||0.583|
|Total Rushing EqPts||Non-Passing Downs||0.582|
|Total Rushes||Fourth quarter||0.579|
|Success Rate||Close game*||0.578|
|Passing PPP||Close game*||0.565|
|Total Rushes||First down||0.534|
|Total Rushing EqPts||First down||0.529|
|Rushing PPP||Close game*||0.529|
|Rushing S&P||Close game*||0.523|
|Total EqPts||First down||0.521|
|Total Line Yards||Non-Passing Downs||0.517|
|Total Passes||Fourth quarter||-0.516|
|Total Rushes||Overall||0.510||*Close game = Scoring margin of 16 points (two possessions) or less|
Correlations for those 25 categories were all over 0.500. This tells a few really interesting stories:
Now we’ll look at WinCorr over the course of a season, i.e. comparing season totals to a season “% of pts” total. See if you can pick out trends.
|Season WinCorr, based on percentage of points|
|EqPts Per Game||Overall||0.752|
|Rushing PPP||Non-Passing Downs||0.722|
|Rushing PPP||Close games||0.693|
|Success Rate||Close games||0.690|
|Rushing PPP||First downs||0.687|
|Rushing S&P||Non-Passing Downs||0.686|
|Rushing S&P||Close games||0.682|
|Success Rate||Non-Passing Downs||0.675|
|Success Rate||Third downs||0.674|
|Rushing S&P||First downs||0.653|
Since I spent all that time developing the "+" Number concept, you had to know I was going to look at that too. What's funny, though, is that for this one, the correlations with win percentage were significantly stronger than the correlations with percentage of points. Correlations in the 0.9 range? That's quite significant. So that's what we're going to use.
|"+"-number WinCorr, based on win percentage|
|Passing S&P+||Overall||0.801||Passing S&P+||Overall||0.787|
|Rushing S&P+||Overall||0.765||Rushing S&P+||Overall||0.775|
|Rushing EqPts+||Overall||0.763||Rushing S&P+||Close games||0.734|
|Rushing S&P+||Overall||0.745||S&P+||Non-Passing Downs||0.733|
|S&P+||Second downs||0.680||S&P+||First downs||0.730|
|Rushing S&P+||Close games||0.674||Rushing S&P+||Non-Passing Downs||0.699|
|Passing S&P+||Close games||0.669||Rushing S&P+||First downs||0.696|
So we can reach some pretty interesting conclusions from this data, and most of it comes back to the idea of leverage. I have data broken out for all quarters, all downs, the red zone, etc., and by far the most significant category is how teams perform in Non-Passing Downs.
This brings me to an interesting question: If Passing Downs are equivalent to death, on average, then would the teams with the best numbers on Passing Downs be privy to a possible turnaround in luck the next year? In other words, are Passing Downs a lot like turnovers? Is success in the category somewhat arbitrary, and does it even out over time? I only have one full year of play-by-play data, so all I can do is take a look at the best (and worst) teams in the category, speculate, and see what happens at the end of the year. When I have multi-year data, it's going to be fun to tie all these season stats to success the next season, so I can see which stats are the best predictors of future success.
A list of the top 10 offenses, based on S&P+ on Passing Downs, looks like this:
4. Texas Tech
6. Washington State
10. West Virginia
Now, Florida, Texas Tech, Tulsa, Kentucky, Hawaii, and Louisville were six of the best passing teams in the country, so their presence on the list should surprise no one. Oregon and West Virginia had great all-around offenses as well. But Nebraska? Washington State?
What if I looked at the teams with the most disproportionate success on Passing Downs? Would that give me an indication of who might be due a turnaround in 2008? Here's a list of the top 10 teams, based on the ratio of success on Passing Downs to success overall.
1. Houston (0.974)
2. Tulsa (0.965)
3. Indiana (0.921)
4. Memphis (0.906)
5. Nebraska (0.889)
6. Nevada (0.886)
7. Texas Tech (0.882)
8. Minnesota (0.876)
9. Kentucky (0.862)
10. Toledo (0.860)
11. Washington State (0.859)
12. Boise State (0.851)
13. Hawaii (0.836)
14. Bowling Green (0.836)
15. Wisconsin (0.822)
Now, A) I only have BCS games entered to date, B) it's early in the season -- some BCS teams on that list haven't played the toughest of schedules, and C) some of those teams have changed quarterbacks or even coaches (have I given enough disclaimers yet?), but let's see what a comparison of 2007 and early-2008 numbers tells us about disproportionate Passing Downs success.
|Disproportionate Passing Down success, 2007 to 2008, BCS teams only|
|Team||S&P||PD S&P||Ratio||S&P||PD S&P||Ratio||Change|
So it's quite early, and I'm pretty sure further schedule difficulty will help bump down Minnesota's numbers a bit, but half the teams on this list have seen quite a decent change of proportion. However, only two teams have seen their overall S&P drop so far. I'll be checking on these numbers at the end of the year.
One exciting thing (I hope) about getting on with Football Outsiders at this time is that, as I said in my first column, we're just on the ground floor here. Running correlations of stats to wins is something I've wanted to do for a long time -- not just for these stats, but for the standard box score stats as well -- and something like this is just the start.
I was intrigued by the fact that explosiveness (Points Per Play) is worth more than efficiency/consistency (success rates); I was also intrigued by the staggering numbers in what I've been calling "leverage" figures. It certainly seems to spell out the surest blueprint for winning: 1) Do whatever you can to stay out of Passing Downs and awkward situations that lead to turnovers and easy scores for your opponent, and 2) Have explosive players who can score at any point from anywhere. It definitely shows why there's such a premium on those top-shelf, explosive recruits, but it also shows that there's a way to win by playing smart and using leverage to your advantage.
5 comments, Last at 05 Oct 2008, 11:53am by Bowl Game Anomaly