One could argue that the most underrated position in the NFL is the punter. Nobody really cares about the guys who single-handedly control the opposing team’s field position, which is obviously directly linked with scoring. Intuitively, it seems like a very important job. But it feels like fans and analysts don’t really put much focus at all on punting despite its inherent value. If I asked somebody what statistics comes to mind when you think of evaluating punters, they’d probably struggle to come up with a single statistic at all.
Well, I already trained a fairly accurate field goal model for an improved method of evaluating placekickers in the NFL. Why not stick with the special teams theme?
The goal of a punter is to start the opposing offense’s drive as far away from your end zone as possible. It’s simple — the team that scores more points wins the game, and it is easier to score points if you’re closer to the end zone. So, the punter wants to make sure the opposing offense is far away from the punting team’s end zone so that their chance of scoring points decreases.
One way people evaluate punters is with the ‘punts inside the twenty’ statistic. After all, punters aim to pin the opposing offense as close to their own end zone as possible without the punt actually going into the end zone for a touchback. Best case scenario: your punter gets the ball to rest at the one-yard line. So, it makes sense to see which punters are actually able to pin opposing offenses inside their own 20-yard line, right?
Here’s the issue: it’s far easier to pin the opposing offense inside the twenty if your offense is getting to mid-field. It’s incredibly difficult to really give the other team trouble if you’re forced to punt deep within your team’s own territory because of your offense’s inability to drive down the field. My idea is to account for this with a punting metric that takes into account the position in which a punter is in. I was pretty excited for this idea and thought it might be unique because I hadn’t ever heard of it. Of course, I’d be remiss if I didn’t mention that Harvard beat me to it over five years ago. So, this isn’t an original idea. Think of this as a five year refresher, I suppose.
Let’s go through the process of crafting this metric with some visualizations. First, I plotted the average offensive starting field position after punts from every yard-line over the past 11 years. There are 25,100 total punts in this dataset.
To understand the axes, just imagine a number line from 0-100. The punting team is punting towards point 0. In other words, the x-axis values are how far the punting team is from scoring a touchdown when they’re choosing to punt. The y-axis is essentially the x-axis minus the punt distance plus the return. The net average yardage for a punt from a team’s own one-yard line (at x=99) is about 40.65 yards. This remains rather constant until the punting team is around mid-field. Before this point, the punter is just trying to boot the ball as deep as possible — they don’t have to worry about punting it too far because … well, they’re just not physically capable of punting it that far.
I should note that the post-punt field position includes the yardage gained from the punt return. There’s an argument to be made that return yardage shouldn’t be included because punters would be punished if their team is bad at special teams coverage. However, I believe that punters should also be rewarded for punts that are not returnable. On the other side, it’s easier to return punts when the punter is backed up — which, again, is out of the punter’s control. There’s valid arguments for both sides.
Anyway, we can quantify the value of field position using Brian Burke’s Expected Points metric.
If a team starts their drive far from the opposing end zone, their EP is far lower than if they start their drive close to the opposing end zone. Of course.
The offense’s average starting distance to the end zone can be plotted with the punting team’s field position, and the offense’s EP can be plotted with their average starting distance to the end zone. So let’s put everything together and plot the opposing offense’s expected points scored by the punting team’s field position.
If an offense is 99-yards away from scoring when they’re punting, the opposing offense is going to score, on average, three points on their next drive. As the punting team gets closer to the opposing end zone, this number naturally decreases.
I’ll provide an example to understand where the idea for this metric is heading. It’s 4th-and-10 and the offense has the ball at their own 25-yard line (x=75 on above graph). The punting unit is brought onto the unit. Given historical averages, the offense is expected to receive the ball at their own 30-yard line. They’ll be 70 yards away from the end zone, which puts their Expected Points at around 1.30. That’s why the fitted line on the previous graph goes through (75, 1.30).
The hypothetical punt turns out to be better than expected. Ten yards better than expected, to be exact. The opposing offense receives the ball at their own 20-yard line. Now they’re 80 yards away from the opposing end zone. Their Expected Points on the drive drops to 0.47. The average EP for the opposing offense given the punter’s initial position was 1.30. The actual EP for the opposing offense ended up being 0.47. So, the punter is credited with saving 0.83 points on the play.
I calculated how many Expected Points all 25,100 punts in the dataset saved. With this information, I could determine every punter’s APS (Average Points Saved) and TPS (Total Points Saved), which are similar to POE and PAA from my previous article on placekicking. APS and POE are measures of efficiency, while TPS and PAA take volume into account.
You can find a full table with the cumulative data and single-season data using the site menu. For now, though, here’s a table of the cumulative punting stats I calculated for punters from 2009 to 2019.
I mentioned earlier that the idea behind this idea has been done before by the Harvard Sports Analysis Collective. While true, the results are not the same. The article from HSAC mentioned that Morstead added 11.03 points over just under five seasons, which is “equivalent to 2.32 points over average each season.” This figure was used to imply that the punter position is not particularly valuable and they might be overpaid.
My conclusions were a bit different.
I found that Johnny Hekker has added a whopping 18.44 points per season so far in his career. What does this mean? Well, HSAC’s article also mentions that Peyton Manning added 160.6 expected points above average in 2013, when he was the best QB in the NFL. Hekker’s average season point contribution is equal to 11.5% of Manning’s 2013 season point contribution. Johnny Hekker is paid $3.75M/yr. The highest paid quarterback in the NFL currently is paid $35M/yr. Hekker is paid 10.7% of that. I think that’s pretty fair.
But that doesn’t even tell the whole story. I used Hekker’s season-by-season average, while the value for Manning is from arguably his best season. In Hekker’s best season (2016), he put up a TPS of 45.93. Using the same logic I used above, his contributions were worth approximately $10M.
Therefore, I disagree with the article’s following claim: “the marginal benefit a team could expect from signing a top punter is not worth nearly the current market cost.” A more thorough analysis could be done, but I think the punter position is more valuable than HSAC’s article suggested.