# Calculating SPARQ

By Max Mulitz

The Seahawks have been using SPARQ for some time now to supplement their draft preparation. Zach Whitman has an entire website dedicated to estimating and calculating SPARQ scores by position. He doesn’t publish the formula but here Josh Hermsmeyer recreates WR SPARQ using multiple linear regression.

I thought I would use the 2016 Edge SPARQ Scores on Zach’s website to recalculate the formula for Z-Scores (normalized SPARQ Scores) for edge players. It seems SPARQ is just a multiple linear regression, as my adjusted R Squared was .986, almost a perfect fit. Results below.

 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.777706 1.000752 6.773 8.81E-10 *** Weight 0.02147 0.001098 19.545 <2.00E-16 *** Forty -1.566179 0.164255 -9.535 1.04E-15 *** Ten -2.35099 0.300435 -7.825 5.39E-12 *** Three Cone -0.771297 0.080434 -9.589 7.88E-16 *** Short Shuttle -1.385896 0.14201 -9.759 3.34E-16 *** Bench 0.017426 0.002895 6.019 2.91E-08 *** Broad 0.056072 0.003036 18.472 <2.00E-16 *** Vertical 0.105705 0.006333 16.692 <2.00E-16 ***

The way to read the coefficients is that for every pound increase in Weight, a players Z Score increases by .02. For every .1 second increase in the Forty, a EDGE player’s Z score decreases by .156. For every one inch increase in Broad Jump, a players Z score increases by .056, etc.

SPARQ is by no means the end all be all of athletic measurement, but Zach has shown it’s strongly correlated with performance and there is certainly value in being able to view a players combine performance as a single number as a starting point in integrating that information into an evaluation.