Tom Hackett’s basic numbers suggest that he was one of the best punters in the nation in 2014. He ranked 3rd in yards per punt and 3rd in yards per game. These stats are the only traditional measures that exist to evaluate the skill of a punter, and they don’t do justice to the many facets of an expert rugby-style punter like Hackett.
Hackett’s skills include a quick release that allows him to wait patiently for his coverage to get downfield, and the ability to send a punt in a high trajectory that gives plenty of time for the coverage to get in position to make a tackle or down the ball. He can also drop a ball into a corner just ahead of the end zone, and control the deflection well enough that it usually pops straight up into the air or off to the side and out of bounds. Rugby-style punters like Tom have become hot commodities nationally, and the new degree of skill and precision punters are demonstrating demands better tools for statistical analysis.
Building Better Punting Stats
Going from something as basic as yards per punt to stats which can tell you the difference between a 35 yard punt from the opponent’s 40 and from your own 4 requires exhaustive play-by-play analysis. There were thousands of punts in college football in 2014, and the task of cataloging and databasing every single one by hand was too daunting for me to undertake. I elected instead to use every punt in PAC 12 conference games. This approach has some drawbacks, but does feature a consistent level of competition with regard to coverage and returns. A nationwide survey of punts would create a different dataset that would move the numbers slightly, but I’m confident that this approach created nationally relevant statistics.
I measured each punt based on 1) where the line of scrimmage was, 2) where the punt was caught, downed, or went out of bounds, and 3) how far the punt was returned, if at all. This more thorough approach enabled me to create several useful stats.
The New Metrics
Returnable Rate (RR)
There is a noticeable clustering in the data of punts which were returned for 0-3 yards and punts which had an explosive, meaningful return. The return rate metric measures the percentage of punts where the coverage was out of position enough that they couldn’t down the punt, force a fair catch, or tackle the returner before he could take more than a step or two. It also gives credit to a punter who can put a ball out of bounds where it cannot be caught. Any punt which is returned for 4 or more yards is deemed "returnable".
Expected Point Differential (EPD)
It’s possible to put an expected point value on field position. For example, a drive which starts at the opponent’s 11 yard line scored an average of 5.08 points per possession. A drive which starts at your team’s 37 yard line scores 2.21 points, on average. These averages can be used to put an expected point value on the field position differential created by a punt.
When Tom Hackett skied a punt from Utah’s 10 yard line that landed at the Colorado 20 and was returned for only 3 yards, he created 3.12 points of field position, on average. When he punted from 37 yards out and hit the end zone for a touchback, he only created .86 points. Every punt can be scored in this way.
Punting Success Rate (PSR)
Using these EPD numbers and a little bit of statistical math and common sense, it’s possible to create a basic rubric for punting success. It turns out that you see three spikes in the data at different field positions. These clusters of expected points occur from the punting team’s 1 yard line through the 20 yard line, from the 21 yard line through the 50 yard line, and from the 49 yard line through about the 30 (past that point it becomes a very poor expected point decision to punt; you are almost always better off either going for a fourth down conversion or attempting a field goal).
The strongest correlations to both eyeball test and EPD analysis give us the following success rubrics: a punt from the 1-20 is successful when it picks up 50% or more of available yards; a punt from the 21-50 is successful when it picks up 65% or more of available yards; and a punt from the 49-30 is successful when it picks up 75% or more of available yards. The percentage of total punts which were successful is the punter’s success rate.
Tom Hackett vs. the PAC 12
Tom Hackett’s average net punting yardage was 40.41, the entire PAC 12 averaged 37.1 net yards per punt. This 3.3 net yardage difference doesn’t give us a lot of information, but the newly created stats enable us to see the wide gulf between Tom Hackett and the rest of the PAC 12.
Tom Hackett excelled in every single area. He was astounding at long range punts, missing only one (One more yard on that one punt and he’d be a perfect 100%). He also surpassed the league average in intermediate and short range punts by a healthy margin. Only one in ten of the punts he sent downfield were able to be returned for more than 3 yards.
In 80 punts, Tom Hackett gave the Utes an average of 15.6 points of field position per game. He did more than any punter in the PAC 12 and more than earned his hardware. By any measure, Hackett's influence on the Utes' opportunities to win games was enormous.