Stampede Blue Stats: Rigoberto Sanchez is alive and kicking

Now that NFL rosters have taken shape and we are less than a week away from real football, the question on most every fan’s mind is “How good is our punter?”

Or maybe that’s just me.

Typically punters are an afterthought; someone who is needed when the offense fails. Even in modern parlance, “punting” is often an idiom for quitting. Forgotten, overlooked, under-appreciated, the NFL punter is destined for obscurity.

Or maybe not. I’ve been intrigued by the recent emphasis on punters in the draft lately. In the 2017 draft, there were 3 punters taken and in 2018 that number increased to 4, including the Texas Bowl MVP. That is the most punters selected in a two year span since 1987(1).

Maybe that is random noise or maybe the NFL is starting to change their view of punters. Either way it begs the question of just how much can a good punter help a team?

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NET YARDS

Good punting requires numerous high level skills; short operation time (snap to punt), distance, accuracy, hang time, spin etc. And all of these have to be adapted to conditions and game situation.

In terms of direct impact on games, these skills coalesce into a stat called net punt yards, which is simply a punt’s distance past the line of scrimmage less any return yardage. It should be no surprise that this metric is highly impacted by field position.

This graph shows all punts since 2000 and plots the avg net yards by line of scrimmage. Net yards are basically flat until a team is about 40 yards from their own goal, at which point the averages decrese as the opponent end zone compresses the field.

For comparison, here are Rigoberto Sanchez’s punts against these NFL averages.

If that looks really good to you, it’s because it is.

Rigo’s 42.3 net yards per punt (NY/P) ranks 2nd among all punters since 2000(2), Only Johnny Hekker’s 43.1 is better. However, since net yards are clearly dependent on field position, I really can’t compare punters directly without taking that into account.

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ADJUSTED NET YARDS

Also, there are other impacts not accounted for with net yards. If a punter forces a turnover with a muffed punt, then there is more benefit to that than just 0 return yardage. Conversely, if a punter out-kicks the coverage, a returned TD is worse than just the yards given up.

To account for these issues, I have done some mathy stuff, to create an adjusted number. First, I applied bonus/penalty yardage to account for muff-turnovers and return TDs based on commonly used yardage equivalents(3).

Then, I derived an equation(4) to calculate the league average net yards for any given field position and applied that to all punts to determine each punter’s expected total net yards.

Combining all of that, I can determine the adjusted net yards over average with the following equation:

Adj NY over average = (act total net yards + 45 * muff TOs - 20 * return TDs - exp total net yards)

Then I simply averaged that by punt and added that amount to the league average NY/P to get my final adjusted metric:

Adj NY/P = Adj NY over average / Punts + nfl NY/P

After applying all of that math, here are the top 10 punters.

Rigoberto actually overtakes Hekker in adj NY/P and becomes the #1 punter of the century (McAfee finished 27th). Of course, Rigo only has the one season under his belt so let’s not call Canton just yet.

And even if the Colts do have the best punter, so what? Can I use these ridiculously esoteric stats to provide something more meaningful like points per game or impact on wins?

Well, since I brought it up, yes I can, or at least I can make a reasonable estimate.

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POINTS

The first step is to use expected points (EP). For those not familiar, the concept of EP is that when a team has the ball, they will usually score next more often than their opponent but that likelihood varies depending upon factors like down, distance and field position.

Using historical data, for any situation, I can track all the ‘next points’, whether on a current or subsequent drive, to determine the average points that a team can expect. In other words, EP is the points a team will score, on average, before their opponent, given a specific down, distance and field position.

In regards to a punter’s impact, since the drive following a punt is always 1st and 10 (well almost always), then we already know down and distance but field position is still variable.

Or is it? The following graph may be one of the most iconic in all of NFL advanced stats.

This shows a team’s expected points in first and 10 situations, anywhere on the field. I want to clarify that the blue line is not some stat-nerd formula, it is actual historical data showing the average amount of points a team scores “next” given their starting field position(4). The dotted line is the stat-nerd formula.

This demonstrates the incredibly obvious fact that the closer a team is to their own goal line, the fewer points they are likely to score. Notice that inside the 10 yard line, EP is actually negative, meaning the opponent is more likely to score next.

Notice also, that the curve is basically linear (not exactly but pretty close). Therefore, the rate of change in EP (slope) is constant everywhere on the field.

In other words, given an X yardage differential, the difference in expected points can be determined without knowing anything about field position. Since Adj NY/P is just a yardage differential to NFL averages, I can calculate the EP differential without worrying about opponent field position.

Rigo has a 43.0 adj NY/P which is 5.9 yards above average. The EP trendline equation gives a slope of about 0.064 EP per yard, and so Rigo’s adj NY/P converts to 5.9 * 0.064 = 0.38 EP. This means that with every punt, the opponent was expected to score 0.38 fewer points than if the kick had been made by an average punter.

And at his average clip of 5.1 punts per game, that comes out to be 1.96 points per game for 2017. That is a lot.

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WINS

I can even take this one step further and convert that impact to wins.

Pythagorean Expected Wins uses a team’s season total points for and against to determine how many games they were expected to win.

Pythagorean Expected Wins = 16 * (PF ^ 2.37) / (PF ^ 2.37 + PA ^ 2.37)

In 2017, the Colts scored 263 points and gave up 404, so their Pythagorean wins was 4.2 games. (Pythagorean wins are eerily accurate. )

If Rigoberto limited opponents by 1.96 points per game, that means the season total points against would have been 404 + 1.96 * 16 = 435.3 had we had an average punter. The Pythagorean wins from that increased amount is 3.7 wins.

So, our punter’s incremental impact for the season was 4.2 - 3.7 = about 0.5 wins . . . by himself!

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CONCLUSION

It’s far too soon to tell if 2017 was a trend or a fluke for Rigo. His preseason numbers (40.8 NY/P, 39.7 Adj NY/P) are down a bit from 2017, but then again that’s preseason.

And my yardage adjustments are going to be very sensitive to decreased volume, so my points per game and incremental win impacts could easily be over-estimated. That’s not even considering the possibility that the impact of punting field position might not accumulate throughout the game . . . (but it actually does)

The bottom line is, a punter can have a big impact on a team’s season. As Colts fans, we should be very happy with Rigo’s performance so far and very optimistic about his impact going forward.

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FOOTNOTES

1) Limiting drafts to first 7 rounds only.

2) Minimum of 75 punts

3) Based on expected points added equivalents calculated by Brian Burke and Chase Stuart of 45 yards for a turnover and 20 yards for a TD.

4) Least squares curve fitting of a cubic where x = yards from own goal: -0.000137*x^3 + 0.004803*x^2 + 0.023683*x + 38.64223

5) Data was limited to 1st and 3rd quarters only to minimize the variable of time remaining, although expanding to full game data yields very similar results.