by Bill Connelly
A couple of weeks ago, I began the process of working through old Varsity Numbers columns from when I began here in 2008. Every couple of weeks, I figure I'll do the same.
Today, we're talking leverage. One of the first concepts I pursued to any great detail is something I still frequently use today: the relationship between standard downs and passing downs. From 2008:
[T]he 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:
- Second-and-8 or more;
- Third-and-5 or more;
- Fourth-and-5 or more.
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.
The percentages haven't changed much through the years. In the 2007 sample I referenced in that piece, teams had a 47.4 percent success rate on standard downs and a 30.1 percent success rate on passing downs. In 2014, those percentages were 45.6 and 31.3 percent, respectively. Teams are slightly less effective at avoiding passing downs and slightly better at getting out of them. But there is still a marked difference -- you're successful on more than four of every nine plays on standard downs and fewer than one in three once behind schedule.
One other concept that I haven't done nearly as much with through the years is win correlations.
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.
In 2008, I was using them quite a bit simply because I didn't yet know what was important. Now I've got a pretty good idea. Then, it appeared the data supported big plays over all else -- a team's PPP was as predictive of wins and losses as its combined S&P (success rate and PPP).
Thanks to work with Five Factors, I like to think I have a clearer picture of what's important.
Instead of simply looking at Success Rate and PPP (Equivalent Points Per Play), what if we added together Success Rate and the PPP for only successful plays? It puts efficiency first, which isn't a surefire winner, but it frames things in an interesting way: How efficient are you, and when you're successful, how successful are you?
When you strip the efficiency aspect from the big plays, it turns out that big plays are awfully volatile and efficiency matters more. And if you use an S&P formula that weights success rates at 80 percent and my new IsoPPP at 20 percent, S&P is a stronger indicator.
Single-game win correlation, based on percentage of points:
- Non-garbage time Success Rate: 0.578 in 2007, 0.619 in 2014*
- Non-garbage time PPP: 0.682 in 2007 (PPP), 0.402 in 2014 (IsoPPP)
- Non-garbage time S&P: 0.678 in 2007 (old formula), 0.708 in 2014 (new formula)
- Standard downs S&P: 0.565 in 2007 (old formula), 0.628 in 2014 (new formula)
* My definition of garbage time has also been tweaked to correlate better. Pretty sure that's why success rate's correlations have improved.
Pretty much any quality measurement correlates reasonably well with a team's percentage of points, but the new S&P, with a four-to-one ratio of success rate to IsoPPP, matches up a little better.
Public service announcement:
For years, I've wanted to figure out a sustainable way to post Ken Pomeroy-style team profile pages for the stats I use, and after quite a bit of tinkering, I did it. At Football Study Hall, you can now find lengthy, detailed stat profiles for each of 128 FBS teams. Check it out. I'm really excited about this.