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Join Date: Apr 2004
Re: Combine Results as Relates to NFL Positions Other than WRs
The Harvard College Sports Analysis Collective:
Do NFL Teams Think the Combine Matters: Offense
REST OF THE STORY
Last February, HSAC member Kevin Meers analyzed which combine events (namely 40-yard dash, bench press, vertical leap, broad jump, 20-yard shuttle, and 3-cone drill) actually translate into future NFL success for each position. The original article used Career Approximate Value (CAV) as a measure of production so that players could be compared across positions.
After the results found in his study, I decided to see if there were any associations between combine event scores and how early the players were selected in the NFL draft using the draft record from 1999-2010. If a player was either not drafted or did not participate in the combine, he was left out of the analysis. I assume that NFL teams try to draft players within each position in rank order of their future production: teams try to draft the best tight end first before drafting the second best, etc. Kevin found out which combine events were the best predictors of CAV, but which combine events have been the best predictors of eventual draft pick? In other words, do NFL teams select their rookies based on the combine events that will ultimately lead to their future success?
The accuracy of these models is subject to scrutiny. These regressions are very similar in accuracy to those in Kevin’s article, about which, he said, “There are two ways to view the results of these regressions. On one hand, most of the combine measurements do not predict anything at all, and the statistics that are significant (at the p = 0.05 level) explain relatively little of the variance in future performance.” On the other hand, it’s surprising that one short event can actually hold predictive value. Obviously there are many more factors that come into play when evaluating a draftee besides combine score (for example, “hands” for wide receivers), so it is interesting to see how important these one-time measurements really matter in the evaluation of new player. Here’s what I found:
Quarterbacks: the events that were significant in predicting CAV were height over 74 inches (6’2”) and shuttle time. As poor of a predictor as this model was (adjusted R^2 of 0.05), it makes intuitive sense. Your quarterback will benefit from being taller (even if only a small benefit), and the lateral movement of the shuttle logically translates into skill at scrambling and evading pass rushers better than 40-yard dash time would. After all, how often does your quarterback run 40 yards at a time if his name isn’t Michael Vick? Surprisingly, none of these events made it into the most predictive model, and even more surprisingly, only broad jump did.
Pick = -3.64*broad jump (inches) + 526.41
The p-values are 0.002 and 0.000 respectively, with an adjusted R^2 of 0.0859 and a root Mean Standard Error (MSE) of 71.712, which means that broad jump explains about 8.6% of the variation in quarterback draft selection. The model suggests that for every additional inch the quarterback’s jump, he can expect to be selected three or four picks earlier in the draft. It’s possible that broad jump is just a proxy for general athletic competence, and not that NFL scouts look for quarterbacks who can jump far.
Running Backs: heavier, shorter, faster running backs were found to be more successful on average, but NFL scouts tended to ignore height and weight and instead focused on 40-yard dash time and broad jump, with p-values of 0.036 and 0.000 respectively. The model has an R^2 of 0.19 and a MSE of 67.62.
Pick = 150.95*40-yard dash(seconds) – 4.32*broad jump(inches)
The broad jump sneaks its way into this regression too. It makes intuitive sense that NFL team would look for impressive 40-yard dash times, but perhaps they should pay more attention to size and stature.
Wide Receivers: No events were significant predictors of CAV for wide receivers, which makes sense because it is a skilled position. While speed and size would seem to be important, it is even more important to have good hands and understand how to run routes. I wouldn’t expect scouts to just ignore these metrics, though, but I was surprised at the only event that made it into the final regression: bench press.
Pick = -6.84*bench press (repetitions) + 230.67
The p-values for these were 0.001 and 0.000 respectively, with an adjusted R^2 of 0.17 and a MSE of 60.96. These findings suggest that each additional bench press rep a wide receiver put up, on average, led to getting picked about seven selections earlier. Perhaps teams think that stronger wide receivers can fight through jams, but bench press was not found to affect CAV.
Tight Ends: 40-yard dash time and bench press were significant in the most accurate model for CAV for any offensive position. The scouts nailed it with the tight ends – both 40-yard dash and bench press were significant in predicting draft pick for tight ends, each with p-values of 0.006.
Pick = 133.67*40-yard dash (seconds) – 4.38*bench press(repetitions)
This model has an adjusted R^2 of 0.17 and a MSE of 62.29. It seems NFL teams have figured out the equation for tight end success – 40-yard dash to go out for passes, and also bench-press to block. Kudos.
Centers: The equation that best predicted success for centers included three transformations of their shuttle time (shuttle, shuttle^2, and shuttle^3). Assuming that nearly every center entering the draft is large and strong, quickness is what sets them apart. NFL teams tend to favor speed however, with the only 40-yard dash being significant (p-value = 0.038).
Pick = 132.66*40-yard dash(seconds) – 567.06
This is one of the worst models out of any position. The adjusted R^2 is 0.06, the MSE is 64.46, and although the constant is not significant (p-value = 0.087), without it the model would not make any sense. Without the constant, a center who ran a 5.00s dash time would expect to be chosen 663rd overall, rather than 96th with the constant. All that can really be taken away from this model is that teams tend to draft faster centers earlier.
Guards: The CAV models found that only faster 40-yard dash times led to more success as a guard, but I found that weight also played a part in their evaluation.
Pick = 91.10*40-yard dash(seconds) – 1.23*weight
This is the worst model out of any position. With coefficients with p-values equal to 0.029 and 0.008 respectively, this model has an adjusted R^2 of only 0.05 and a MSE of 64.56. Again, this just shows that front offices prefer faster, heavier players at guard.
Offensive Tackles: for tackles, it appeared that weight mattered more than it did for guards in regards to total production. Luckily, scouts recognize it, favoring tackles heavy tackles with good 40-yard dash times (p-values = 0.027, 0.000, and 0.007 respectively)
Pick = -0.76*weight + 151.39*40-yard dash(seconds) – 436.61
This has an adjusted R^2 of 0.10 and a MSE of 70.05. NFL executives hit it right on the money, drafting heavier, faster offensive tackles.
Full Backs: None of the combine events predicted production for full backs, so it was fitting that NFL teams didn’t tend to favor any particular combine event in drafting them. While they seem to know that no combine metric is enough to favor a full back, they haven’t found out what does – full back was the only position that, when regressing the CAV of players at that position on their draft pick, the result was not significant.
The Harvard College Sports Analysis Collective
Do NFL Teams Think the Combine Matters: Defense
REST OF THE STORY
As I studied the predictive value of NFL combine events on the draft picks of offensive players, this post examines which combine tests NFL front offices tend to focus on when selecting defensive recruits. This analysis, coupled with Kevin Meers’s study of the predictive value of combine measurements on eventual Career Approximate Value (CAV) will help to see whether teams tend to place emphasis on the combine events that best translate into on-field performance. Like Kevin’s article, this study includes all players who were drafted from 1999-2010. Also like the first post, this study produced rather weak models, and the most accurate model only explains 23% of the variation in draft selection. Still, the defensive models are more predictive than their offensive counterparts. Here they are:
Defensive End: The model for predicting DE production was one of the best fitting models found in Kevin’s article, containing transformations of weight, 40-yard dash, and 3-cone drill. NFL scouts seem to have figured out the keys to finding successful defensive ends (as far as the combine goes, at least) by this regression.
Pick = 181.50*40-yard dash(seconds) -2.12*weight(lbs.) +64.80*cone drill(seconds) -667.54
This model does a fair job predicting draft pick with an adjusted R^2 of 0.17, a MSE of 68.85, and respective p-values of 0.000, 0.000, 0.006, and 0.004. As obvious as it sounds, big, fast, and quick defensive ends tend to make the best ones, and NFL teams have figured that out.
Defensive Tackle: Weight, shuttle time, and bench press repetitions were each significant predictors of on-field success, but front offices tended to focus on only one of those three.
Pick = 121.84*40-yard dash -2.45*bench press(repetitions) -443.41
With p-values of 0.004, 0.010, and 0.040 respectively, an adjusted R^2 of 0.09, and a MSE of 67.88, this model is rather weak. This result makes sense because the model for finding CAV only showed a weak relationship between the combine and career outcomes. In an effort to properly evaluate defensive tackles by more than their strength, teams tend to differentiate them with what appears to be their default measurement of athleticism (the 40-yard dash) instead of the combination of weight and shuttle drill. NFL teams may want to focus more on their defensive tackles’ size and quickness rather than their speed.
Outside Linebacker: The model for outside linebacker success was relatively weak, but it found that 3-cone drill and 40-yard dash were significant. NFL teams picked up on those two predictors, but they also tended to favor weight and broad jump.
Pick = 123.84*40-yard dash -2.06*weight -2.53*broad jump(inches) +61.24*cone drill
The p-values for these are 0.016, 0.000, 0.012, and 0.014 respectively. This regression finds a relatively strong relationship, with an adjusted R^2 of 0.23 and a MSE of 58.94. While NFL teams tended to reward speed and quickness, as they should, they tended to overestimate the need for size and explosiveness through their favoritism of weight and the broad jump. It appears that rather than size and power, NFL executives should find more agile linebackers, holding all else constant.
Inside Linebacker: The CAV model for inside linebackers also did a poor job at predicting linebackers success, yet somehow it found 40-yard dash to be significant, as did the bizarre model predicting draft pick.
Pick = -35304.72*40-yard dash +9873.35*40-yard dash^2 -775.74*40-yard dash^3 +133435.6
It isn’t the friendliest looking model, but there it is with respective p-values of 0.024, 0.025, 0.025, and 0.023, and adjusted R^2 of 0.11, and a MSE of 60.48. Kevin said it best in his original post with, “These combine measurements simply do not do a good job of predicting performance for linebackers.” Effectively, the only thing that can be taken from this model is that there is much more that makes a successful linebacker than the combine is able to measure, though speed may have a small say in it.
Cornerback: The model for success at cornerback included the 40-yard dash, weight, and the 3-cone drill. In this particularly accurate model for draft pick (adjusted R^2 = 0.22, MSE = 54.78), scouts tended to see the benefits of these measurements, yet substituted explosiveness for quickness.
Pick = 256.10*40-yard dash -1.51*weight -1.87*broad jump -525.25
P-values here are 0.000, 0.001, 0.014, and 0.047. NFL teams appear to evaluate cornerbacks rather well, though there’s evidence that they should favor more agile corners over the explosive or powerful ones.
Free Safety: The model predicting the CAV of free safeties is quite terrible, only being able to explain 4% of the variation in free safety success. Essentially, this tells us that the combine has no good way of evaluating free safeties for what they are really worth. This makes it interesting that certain combine events can explain over 10% of the variation in draft pick (adjusted R^2 = 0.10, MSE = 60.28).
Pick = 166.14*40-yard dash +80.75*cone drill -1202.97
With p-values of 0.041, 0.018, and 0.006, 40-yard dash and 3-cone drill times are significant predictors of draft pick. Since no metric indicating speed showed up in the model for free safety CAV, perhaps things that the combine can’t measure, such as instincts, awareness, and tackling ability, should get the emphasis that these speed and agility metrics previously received.
Strong Safety: In another odd model, strong safety draft pick was significantly predicted by 20-yard shuttle time with surprising strength (adjusted R^2 = 0.14, MSE = 66.16), instead of by weight and 40-yard dash time, like the CAV model suggests should happen.
Pick = 180.83*shuttle(seconds) -617.67
P-values of 0.003 and 0.013 make these terms significant. Whether they should be or not is a different question entirely. While NFL teams tend to look for quickness and agility in their strong safeties, they should focus more on speed. That said, the weak relationship between CAV and any combine events here suggest that there is probably no effective way to evaluate strong safeties.