What are Slaytics? An Intro to SPARQ, tFREAK and SLA Data
SPARQ score, tFREAK Rating and SLA score are analytic composites designed to quantify a NFL prospects measurables. This article will attempt to take a deeper dive into all three and explain the methodology behind them.
A quick note, I often use percentiles to compare and reference past players. Please remember when working with percentiles, the 0 percentile is the worst possible score in a dataset, the 50th percentile is the average, and the 100th percentile is the best score of a dataset. So if Jalen Ramsey tests in the 96.4 SPARQ percentile, that means he's a better athlete than 96.399 percent of all CBs (since 1999 at least, as that is how far my data goes).
SPARQ has been around for some time, with the first incarnation popping up in 2004. For those who don't know, SPARQ stands for "Speed, Power, Agility, Reaction and Quickness." It was orginally created by a small company (before quickly being accquired by Nike) as a recruiting tool to identify and quantify top high school athletes. Now, many NFL teams use SPARQ or something similar in talent evaluation. Pete Carroll, Chip Kelly, Howie Roseman, Urban Meyer, and many other front offices have utilized or talked about the benefits of athletic composites. While the exact original formula has never been released, I was able to re-create something very close to it through a series of regression models. Then, I modified the equation to use drills performed at the NFL Combine, instead of inputs like the Kneeling Power Ball Toss and Beep Test. My current NFL version uses the 40 Yard Dash, 10 Yard Split, Bench Press, Short Shuttle, 3-Cone, Vertical Jump, and Broad Jump as inputs. While not many people have an accurate replica of the SPARQ formula, I am certainly not the first to do this. I've always been fascinated with SPARQ and it's applications in talent evaluation, but to me it seemed like something was missing. That is how I came up with tFREAK.
While SPARQ takes many factors into account, it doesn't measure any size or length measurables other than weight. Size and length are obviously important for football players, and yet they are ignored in SPARQ. Not giving these big and long players their due can result in some big misses. Take DeForest Buckner for example. Just by looking at him, you can tell he is a freak. But he only tested in the 65th percentile for SPARQ score among defensive lineman, so clearly there is a disconnect somewhere. Players like Buckner are why I created True FREAK Rating (or tFREAK for short), aptly named to capture the "freaky" players that SPARQ misses. Think of it as a SPARQ score for size and length instead of athleticism. Anyway, back to Buckner. He ranks in the 94th percentile of tFREAK among defensive linemen since 1999, which really paints a better picture of his traits. That is just a quick example of how tFREAK effectively captures that subset of lengthy players SPARQ can miss on. The NFL covets length, and we can't ignore it in our analysis. But just like SPARQ, while tFREAK is helpful on it's own, it doesn't get every player right as a raw talent evaluation metric. So that's why I decided to make a third formula, to balance out SPARQ and tFREAK.
I named my third formula "Size, Length and Athleticism" score -- or SLA. This all-encompassing composite score seeks to solve the problems of both tFREAK and SPARQ by bringing them together. SPARQ tends to rate small explosive players very highly, and tFREAK rates long, lumbering players higher than we want. But SLA gives us the players with the most desirable measurable sets, the long and explosive players. And while it's certainly not perfect, I think SLA analytics (I call the whole data set Slaytics) are the most effective tool out there to measure an NFL prospect's measurables. In practical use, I think Slaytics are a great way to determine which players will have the highest ceiling within their respective tiers. Sure, Shawn Oakman has a great Slaytic profile (testing in the 97th percentile for SLA among defensive linemen since 1999) but I'm not saying that he should be the first defensive lineman taken. Oakman obviously shouldn't even be in that conversation, his tape is terrible at times. But, it can be used to as a tiebreaker of sorts to compare players in the same tier.
Keep in mind, Slaytics take positional averages into account, so you can use the percentiles to compare both offense and defensive players. In the example above, I compare the WRs in the 1st/2nd Round range. As you can see, TCU's Josh Doctson is clearly ahead of the field.
Slaytics can also be used to find players with major measurable red flags. Take A'Shawn Robinson for example. His Slaytic splits are in the 87th (tFREAK), 16th (SPARQ), and 41st (SLA) percentiles, which at his weight is a red flag. I'll get into specifically why later, as I plan on creating an article series in the future breaking this down at each position. But A'Shawn falls below my minimum interior defensive line Slaytic thresholds. Due to such a low success rate below this line, I wouldn't recommend drafting A'Shawn or anyone else below my thresholds. The list of successful lineman below my threshold is pretty short (Tyson Jackson, Jordan Hill, Glenn Dorsey, Cedric Thornton, and Ricky Jean-Francois are some notable "success stories") but I will get more into that in a seperate article. And yes, most of those guys were not worth the investment either.
If you like this, I have a full Slaytic dataset up for every player that I have numbers for. Check it out here.
Expect updates as more Pro Day data comes in. I also plan on writing more articles talking about the Slaytics of certain players, and minimum thresholds that can be used for talent evaluation purposes. The first one, breaking down PST for QB's, is now up. Keep an eye out for other positions coming soon!
