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By The Numbers

Adjusted SPARQ: Position-Adjusted Athleticism

by Hayden Winks
Updated On: March 6, 2020, 1:45 am ET

Why “Adjusted SPARQ” is Needed

SPARQ is an athletic score originally built by Nike that’s created by using speed, strength, agility, and size data points. It’s easy to translate SPARQ scores from NFL Combine data, which has made the athletic composite score very popular in recent years. SPARQ scores are helpful for a prospect’s evaluation, but it’s a flawed metric.

SPARQ was designed to measure overall athleticism, not specific athletic traits. That’s totally fine for some sports, but the NFL is far too specialized to have one single formula determine how athletic a player is. For example, the SPARQ formula doesn’t change when measuring a defensive tackle and cornerback, but we can all agree that the “athleticism” required for each position is totally different. A DT needs more strength and size, while a CB needs more speed and agility. These position adjustments are necessary to measure true athleticism for an NFL prospect, and that’s why I created “Adjusted SPARQ”. 

 

“Adjusted SPARQ” Methodology

I built multiple linear regression models for each NFL position that predict how much production a prospect will have during his first four NFL seasons (a.k.a. rookie contract production). NFL Combine and Pro Day measurements were my regressors and Pro Football Reference’s Approximate Value (AV) metric was my output variable. I limited the dataset to players who were drafted from 2005-2016.

The coefficients of the input variables change from position to position because the value of each athletic test differs based on what the position is asked to do. I’ve included the “Adjusted SPARQ” formulas and r-squareds below, but please note that these are in-sample adjusted r-squareds and there may be overfitting issues. Also note how I split some positions (RB, WR) into different groups based on size. I did this because Hunter Renfrow and DK Metcalf shouldn’t be viewed as playing the same position. They are asked to do two totally different things, so it’s best to build separate models for the two.

 

“Adjusted SPARQ” Formulas

The correlations of Adjusted SPARQ to rookie contract success vary from position to position, but these scores have higher correlations than traditional SPARQ so it’s worth transitioning to these formulas in my opinion. Still, it’s important not to overrate adjusted athleticism because it only explains 5-25% of a player’s success in the NFL. 

 

RB (under 210 pounds) 

*Updated*

Adjusted SPARQ

Coefficient

P-Value

Constant

77.6611

0.24

Three Cone

-11.6559

0.18

Ten Split

-34.0988

0.30

Weight

0.335520

0.15

 

Adjusted R-Squared

SPARQ = 0.00 (aka completely useless)

Adjusted SPARQ = 0.03

Conclusion

Traditional SPARQ is completely useless when it comes to predicting NFL success for undersized NFL running backs, but my Adjusted SPARQ formula found that having a low center of gravity is a little important, however. Specifically, it’s looking for thicc prospects who have above-average agility (Three Cone) and short-area burst (10 Yard Split). The classic example of an undersized running back who checked these boxes was Ray Rice.

 

RB (at least 210 pounds)

*Updated*

Adjusted SPARQ

Coefficient

P-Value

Constant

-21.3685

0.71

Broad

0.401096

0.12

Three Cone

−5.75160

0.34

Speed Score

0.258178

0.06

 

Adjusted R-Squared

SPARQ = 0.03

Adjusted SPARQ = 0.10

Conclusion

Traditional SPARQ doesn’t do a good job of predicting NFL success for bigger running backs, but my Adjusted SPARQ formula found that weight-adjusted speed (Speed Score) and explosion (Broad) were the highest correlated athletic traits for big backs, while agility came in as a minimal factor. Two prospects who checked all of these boxes were Matt Forte and Adrian Peterson

 

WR (under 6’0)

Adjusted SPARQ

Coefficient

P-Value

Constant

67.4649

0.30

Speed Score

0.266259

0.08

10-Yard Split

−27.2128

0.31

Short Shuttle

−9.34978

0.28

 

Adjusted R-Squared

SPARQ = 0.00 (completely useless)

Adjusted SPARQ = 0.07 

Conclusion

Athleticism isn’t very important for undersized receivers. Of all the metrics my model looked at, the weight-adjusted forty (Speed Score) carried the most weight, while agility (Short Shuttle) and short-area burst (10 Yard Split) were minimal contributors. Ultimately, it’s not the end of the world if a sub-six foot receiver doesn’t shred the NFL Combine. Two prototype sub-six foot receivers are Brandin Cooks and John Brown.

 

WR (at least 6’0)

*Updated*

Adjusted SPARQ

Coefficient

P-Value

Constant

-30.1401

0.47

Ten Split

-6.94917

0.73

Broad

0.364881

0.04

Speed Score

0.0716564

0.51

 

Adjusted R-Squared

SPARQ = 0.04

Adjusted SPARQ = 0.04

Conclusion

Putting too much weight into NFL Combine scores for big receivers can be problematic. Both traditional SPARQ and Adjusted SPARQ do a fairly poor job of predicting NFL success, but my model did find burst (Broad Jump, Ten Split) and size-adjusted speed (Speed Score) to be a little helpful. Putting any weight into agility scores for tall receivers is a big mistake with the most recent example being DK Metcalf. Players like Dez Bryant and, of course, Julio Jones checked these boxes as big receiver prototypes. 

 

TE

Adjusted SPARQ

Coefficient

P-Value

Constant

−39.8474

0.41

Height

0.779761

0.16

Vertical

0.201368

0.48

Cone

−4.75801

0.19

Speed Score

0.137361

0.09

 

Adjusted R-Squared

SPARQ = 0.05

Adjusted SPARQ = 0.08

Conclusion

Among the athletic measurables for tight ends, there’s one key thing to pay attention to: the weight-adjusted forty (Speed Score). That metric carries the most significance, while size (Height), agility (Cone), and burst (Vertical) were secondary components. If a tight end prospect can’t run, it’s not looking good for his NFL future as a pass-catcher. Jared Cook and Travis Kelce are examples of tight end prototypes, at least if you’re looking for a producer and not a hand in the dirt blocker.