The 5 O’Clock Club: How predictive of NFL success is the Relative Athletic Score?

It’s 5 o’clock somewhere…

The 5 o’clock club is published from time to time during the season, and aims to provide a forum for reader-driven discussion at a time of day when there isn’t much NFL news being published. Feel free to introduce topics that interest you in the comments below.

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Kent Lee Platte is the guy who developed the Relative Athletic Score (RAS), and he regularly looks for and discusses the strengths and limitations of RAS in both blogs and social media platforms like Twitter/X.

In case you’re not familiar with RAS, let me share this pretty cogent explanation from Pro Football Network that was published just a few months ago:

[T]his evaluation formula — created by Kent Lee Platte — collects a multitude of a player’s testing metrics and physical attributes to generate a score to measure a prospect’s athleticism entering the NFL. The final number is determined by what percentile a player falls relative to their peers at a particular position.

The highest score a prospect can achieve in this tool is a 10. Prospects are graded on a scale of 1-10 for each contributing factor, which is a great reference point to help determine exactly how a player’s athleticism stacks up against incoming prospects at the same position to help aid the evaluation process.

What Measurements Contribute to RAS?

The algorithm that helps determine a prospect’s RAS can be broken into two different categories: physical measurements — which consist of a player’s height, weight, hand size, and arm length — and testing numbers — which can come from NFL Combine events/drills like the 40-yard dash, 20-yard shuttle, vertical jump, broad jump, three-cone, and bench press.

To help paint a picture of how individual testing numbers are translated into the model — if a WR has a vertical jump that lands in the 95th percentile for that position, the player would earn a score of 9.5 for that specific testing metric.

This data model certainly helps contextualize a player’s athletic profile relative to their peers in an easily digestible number.

Some perfect scores have been achieved according to this data model over the years that are worth revisiting.

Cam Newton’s elite combination of size and athleticism set a new gold standard for athleticism entering the league in 2011. His unique prospect profile translated to immense success at the next level, winning a league MVP award in 2015.

Another signal-caller who turned heads with a spectacular performance at the NFL Combine was Anthony Richardson. His 4.43 time in the 40-yard dash at 6’4″, 244 pounds, helped send his draft stock through the roof. That ultimately saw the Indianapolis Colts use the fourth pick in the 2023 NFL Draft in hopes of finding their next franchise QB.

One position that has produced some athletic specimens over the years has been wide receiver. Perhaps no player had a more impressive athletic profile entering the league than Calvin Johnson, whose time of 4.4 in the 40-yard dash proved abnormally exceptional when considering his 6’5” 239-pound frame.

This is what a RAS card looks like:

This week, Kent Lee Platte took to Twitter/X to elucidate some of the research he’s been doing of late. In this particular case, he was looking for correlation between RAS scores and the Approximate Value (AV) metric developed by Pro Football Reference. Again, if you’re unfamiliar with AV, here’s an explanation, this time, from PFR, the guys who developed it.

Created by PFR founder Doug Drinen, the Approximate Value (AV) method is an attempt to put a single number on the seasonal value of a player at any position from any year (since 1960). The way Drinen described the intent of this measurement was:

“AV is not meant to be a be-all end-all metric. Football stat lines just do not come close to capturing all the contributions of a player the way they do in baseball and basketball. If one player is a 16 and another is a 14, we can’t be very confident that the 16AV player actually had a better season than the 14AV player. But I am pretty confident that the collection of all players with 16AV played better, as an entire group, than the collection of all players with 14AV.”

“Essentially, AV is a substitute for — and a significant improvement upon, in my opinion — metrics like ‘number of seasons as a starter’ or ‘number of times making the pro bowl’ or the like. You should think of it as being essentially like those two metrics, but with interpolation in between. That is, ‘number of seasons as a starter’ is a reasonable starting point if you’re trying to measure, say, how good a particular draft class is, or what kind of player you can expect to get with the #13 pick in the draft. But obviously some starters are better than others. Starters on good teams are, as a group, better than starters on bad teams. Starting WRs who had lots of receiving yards are, as a group, better than starting WRs who did not have many receiving yards. Starters who made the pro bowl are, as a group, better than starters who didn’t, and so on. And non-starters aren’t worthless, so they get some points too.”

Now, let’s look at what Kent Lee Platte claims to have uncovered about the relationship between RAS and AV.

tl;dr

RAS is a little bit like PFF scores in that both try to aggregate a lot of data into a single number to allow comparison between players. There are, in my opinion, two key differences between PFF grades and RAS:

• The first is that PFF aims to quantify on-field play, while RAS quantifies a player’s personal measurements (height, weight, etc) and testing numbers from the NFL Combine or the player’s pro day.
• The second is that PFF has a rather opaque grading system, while RAS is fully transparent.

In his analysis, KLP has used AV as a proxy for NFL success, and then evaluated the predictive power of RAS by looking for positive correlation between RAS and AV for players with very high AV scores.

The bottom line of KLP’s analysis, if I’ve understood this collection of tweets correctly, is that when a prospect scores below 5 on RAS, it is a very strong ‘red flag’ to indicate that the player likely will struggle to succeed in the NFL. A score above 8 is not as reliable an indicator, but is a pretty good data point in projecting NFL success. Scores between 5 and 8 are not very reliable for predicting a prospect’s future success or failure in the NFL.

This would indicate that a GM who likes a specific draft prospect that has an RAS of <5 should think long and hard about the analysis of that player because the odds of NFL success will be heavily stacked against that player.

Any prospect with a score of 8+ should be seen as having the physical tools that will predispose the player to success in the NFL, which should support any positive analysis of the player’s film and character, but not override any negative analysis of those elements.

For players that achieve scores between 5 and 8 on the RAS, the metric will not really help the GM separate the wheat from the chaff as this range was not found to have significant correlation with eventual NFL success or failure.

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