The NFL draft has come and gone, and throughout the time before, during, and after the draft, commentators and pundits reference a draft eligible player’s times, repetitions, and measurements that were recorded at their collegiate pro day and/or NFL combine.

Talking Heads assume that these numbers are relevant or immaterial to a player’s future performance (really, whatever serves their argument at the time), but, to my knowledge, there hasn’t been any empirical, statistically robust research that looks at whether or not and how much these drills matter.

This post will look to see, with the data that is currently available, which combine drills and measurements matter the most for pass rushers—more specifically, four-three defensive ends—and, more importantly, how much they matter.

To answer the questions of which NFL combine drills matter the most, we will take two different approaches.

Both approaches will look at combine statistics and how they relate to the amount of pressures—hurries, plus hits, plus sacks—a player was able to produce in a given season.

The population includes four-three defensive ends that played between 2007 and 2014 that there are combine statistics for on NFL Savant, which has aggregated combine data into one easy download. Our population is limited to 2007 to 2014, because those are the only years that are on record for pressure data; we have Pro Football Focus to thank for the data that exists. There are playing time limits that are used and specified within each of the approaches.

Methodology

Value Metric

Because of differences in playing time, we can’t compare players on the pure amount of pressures that they were able to produce. For example, Player A may have gotten three more pressures than Player B in one season, but if Player A played 800 snaps while Player B played 200 snaps, we can’t say that Player A was a better pass rusher when he received four times the amount of plays. To account for differences in playing time, we will use pressures-per-600 snaps (PRS/600)—a prorated number that puts all players and their production on the same playing time scale—as our value metric.

A Poor Man’s Regression

The first approach we will use to look at the relationship between combine statistics and pass rush ability is through what Mitchel Lichtman has dubbed a poor man’s regression. For this study specifically, a poor man’s regression is the process by which we will put players into buckets—thresholds/ranges of a measurement—with regards to a particular measurement—40-time, broad jump, bench press repetitions, etc.—and take all of the players that fall within that bucket/threshold/range and average—mean and weighted arithmetic mean—their PRS/600 scores together.

When we look at the averages for each drill/measurement, we will use two different types of averages, and when look at the two different averages, we will look at the averages for two different groups of players, which adds up to a total of four categories; we will look at the average for all players, the weighted average for all players, the average for players with a minimum of 200 snaps in a season, and the weighted average for players with a minimum of 200 snaps in a season. We use the weighted arithmetic mean as an additional frame of reference and use snaps played as our weight. The weighted average allows us to emphasize players who played more snaps than others and who are more representative of the population than a defensive end who played ten snaps in a season.

There are benefits and disadvantages to each of these classifications (i.e. lines on the graph), most notably with regards to the playing time threshold of 200 snaps. While we want to factor in the players that did not play very many snaps, because a deficiency in athleticism could be the reason that they weren’t able to amass any production or get on the field to begin with, when we put the playing time minimum at one snap, we don’t give enough time for PRS/600 to stabilize; that’s why the weighted arithmetic mean is a good compromise. The differences that you will see in each of these techniques are very minimal in most cases, but there are advantages and insights to be gleaned from each process; however, your attention should be primarily directed at the classifications that have the playing time minimums of 200 snaps.

The poor man’s regression works on three different levels. To start, it shows us if there is a linear relationship between the amount of pressures a player gets and a certain measurement. The second advantage is that while scatter charts can accomplish this same goal, a poor man’s regression gives a stronger visual narrative. The final benefit that we get from a poor man’s regression is its ability to reveal certain thresholds that are important for players (e.g. players that have a 40-yard dash below “X” are significantly more likely to succeed than players with a 40-yard dash above “X”).

Traditional Approach

Our research will conclude with a list of correlations, which will show the strength of the relationship between each specific combine drill and PRS/600 for player seasons—for four-three defensive ends—with a minimum of 200 snaps between 2007 and 2014 that we have combine results and pressure data for. While the poor mans regression will tell us if there is a linear relationship between a drill and the amount of pressures a player is able to produce, this will tell us how much that relationship matters.

Results

Because there are nine combine and pro day drills and measurements to go through, some explanations and commentaries will be longer than others, while some drills/measurements that have no correlation with performance will be left to the visual.

BMI

This shows us that there is a linear relationship between BMI and PRS/600. More specifically, defensive ends with a BMI around 35 or over have significantly worse outcomes than players with a BMI lower than 35.

These results are ultimately a selection bias in the sense that players with lower BMIs, on average, are faster than players with higher BMIs. One could assume that players with lower BMIs, as a generalization, are faster than players with higher BMIs, so what looks like a relationship between BMI and PRS/600 is most likely a relationship between speed and PRS/600. In other words, it doesn’t mean that players with high BMIs can’t be good; you just have to be fast with a high BMI to be successful, which is difficult.

Arm Length

While it appears there is a relationship between arm length and PRS/600 for four-three defensive ends, we will find out when we look at our correlations that the data we have suggests that there isn’t one at all. This could ultimately stem from a small sample of players, but what is interesting to note is that when I looked at the correlation between arm length and PRS/600 for three-four outside linebackers, the relationship was quite strong.

Hand Size

Bench Press

40-Yard Dash

As we will find out later, and we can see echoed here, a player’s 40-yard dash has a strong linear relationship with a defensive end’s ability to rush the passer.

20-Yard Short Shuttle

Beyond the visible association between a player’s short shuttle and their PRS/600, we can see that players who have a short shuttle of around 4.8 seconds or slower perform much worse than other players. The second noticeable threshold that we can see is the one at 4.1 seconds at which point players with that time or faster perform better than others. The only defensive end to run a short shuttle faster than 4.1 seconds in 2015 is Frank Clark. Classified as a Force player and one of the top SPARQ athletes for edge rushers in 2014, Clark, when we put aside his off field controversies, should provide surplus value based off where he was selected at the end of the second round.

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Three-Cone Drill

Because of players with a small amount of snaps and pressures, the blue line, which represents players with no minimums on their snap counts and doesn’t weight player contributions by playing time, takes a shape in opposition to the other three categories. When we put aside the category that places no minimums or weights on playing time, we can see that there is a direct relationship between a player’s three-cone drill and their pass rush ability. While there are other drills that we have limited data on, the three-cone drill in particular has very little available data.

Vertical Jump

A player’s vertical jump is a significant factor when it comes to a defensive end’s ability to disrupt the quarterback. Players with verticals around 41 inches and over appear to be more successful than other groups. Shane Ray and Dante Fowler Jr. were measured at 33 and 32.5 inches respectively, which may incline one to be skeptical of the future of either pass rusher.

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Broad Jump

The blue line, which is the average for the classification with no snap minimum or weight, disrupts a graph that otherwise tells a nice story; there are probably a small number of players with few snaps who disrupt the group of players with a broad jump of 130 to 134 inches; these player seasons are teased out and/or accounted for in the other classifications. With a 138 inch broad jump, Bud Dupree set the record for longest broad jump by a defensive end and has the fifth longest jump by a player of any position; to put his 138 inch jump into perspective, Calvin Johnson is one of four players at any position with a longer broad jump, and Dupree’s jump bests Justin Hunter’s jump, who, although the two jumps are slightly different, was a “long shot” to make the 2012 Olympics in the long jump.

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Correlations

Now that we have an idea of which combine drills and measurements have a general relationship with PRS/600, lets look at the strength of the relationship between the two variables.

The chart above ranks each drill/measurement by the strength of its relationship—positive or negative—with PRS/600 for all four-three defensive ends with available combine data from 2007 to 2014 who also have a minimum of 200 snaps in a season.

If you are unfamiliar with correlations and/or want a frame of reference for what these numbers mean, this link gives a good overview of how to interpret the data above.

While the 40-yard dash has a strong inverse correlation with PRS/600, the broad jump and vertical jump also have meaningful relationships with PRS/600.

Conclusions

Now that we know that there is a direct, measured relationship between combine drills and on field performance, there are many implications for this research.

In specific instances, some teams have ignored combine data when it comes to the final decision of who to select on draft day. Jarvis Jones (below) is an example of a player who was successful at the collegiate level, but was unable to translate into the NFL, and one may assume that his limited athleticism may have played a role in his lack of production as a professional. From what we’ve observed, athleticism matters when it comes to the transition of collegiate pass rusher into the NFL.

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Beyond that, teams that look at just 40-yard dash for pass rushers should understand that there is more to a pass rushers ability to get after the quarterback than speed. Justis Mosqueda has used combine metrics to come up with a formula that looks to predict success for edge rushers with great results, while Zach Whitman has uncovered an approximate estimate for the equation used to calculate SPARQ scores, which is a formula developed by Nike and “measures player athleticism by outputting a single composite score.”

The research in this article has been conducted with the most readily available data, but the data that exists only goes back so far, and one should take a restrained approach and understand that the results of this research, and other research like it, has reached it’s conclusion(s) with the information available (i.e. the limited sample size should dissuade any superlatives of affirmation).

More work needs to be done to find out which combine metrics are most relevant to each position (e.g. is the 40-yard dash the optimal distance to judge the speed of players at every position?), but the ultimate goal is to come up with better drills to more accurately measure the most important aspects of athleticism for each position; this conversation has already started to happen, and it’s enactment would allow teams to make better decisions with more relevant information.

Photo Credit to Mike Morbeck