RACR, Fantasy Performance Comparative Analysis
Data Degenz, unite. We are Going In Raw for the first time.
I like data and tend to get lost in the sheetz of raw data a lot. I analyze raw data and what I find, I plan to provide to you, the people, to be able to have a competitive edge on your competition.
(Really, this series will be me channeling my inner nerd/economist who has a thirst for research)
For each Going In Raw Post:
I’ll explain what lead me to the post and the intent
I’ll explain the data model
I’ll provide the data with short analysis
I’ll end with a takeaway
A lot of data, clear, concise, to the point, not overly complicated.
Lead and Intent
In my breakdown of air yard leaders, I analyzed air yards over the last five years highlighting leaders in the respective air yard category.
We know that air yards is a great metric for predicting receiver opportunity as it captures coaching and QB intent. Basically, the higher the air yards, the greater the opportunity for the wide receiver.
Looking at the top 12 air yard leaders, something caught my eye. Seven of the top 12 leaders finished as #1 WRs. This is what I would have expected, but it had me think... Where is Michael Thomas?
*Note: All images can be clicked to be enlarged
Michael Thomas has finished as a first tier (top 12) fantasy producing WR each of the last four years, but has never cracked the first tier in air yards. What was more interesting is that where he lagged in air yards compared to the air yard leaders, he excelled in receiver air conversion ratio (RACR), finishing nearly first tier every year; completely opposite of the air yard leaders. Note that RACR is a metric developed by Josh Hermsmeyer. According to Hermsmeyer, RACR identifies the efficiency of a receiver at the depth of the target that a receiver is targeted the most. In a nut shell, the higher the number, the higher the efficiency, meaning that a receiver makes the most out of their targets at a most targeted depth.
My thought at this point was, if Michael Thomas who is one of the best WRs in the league is ranked at the top for RACR, how do other top performing RACR WRs perform in fantasy?
The intent of this post is to breakdown a defined player pool sample of RACR data that compares RACR to future fantasy point performance. The data identifies year-to-year consistency, but also highlights what could be potential league winners for 2020.
Data Model
All raw data is from airyards.com
One exception is fantasy points (.5 Point PPR) is from pff.com
All rankings are specific to WR only
Tiers are defined by ranges of 12 (i.e. 1-12=first tier, 13-24=second tier)
Data is for the years 2015-2019
The model compares base year performance to following year performance, so it is a four year sample
i.e. 2015 (base year) - 2016 (following year), 2016-2017, 2017-2018 , 2018-2019
The sample of data is only for WRs that had annual receptions => 45
For the WRs that had => 45 receptions, the sample of data only includes the WRs that finished with a top 18 RACR ranking
The sample only includes WRs that played a max difference of 2 games (+/-) year-to-year
i.e. Cooper Kupp finished with 5th highest RACR in 2017, but missed 7 games in 2018, which excludes him from the data. If he would have missed two games in 2018, he would be included with the data. We exclude greater than +/-2 because the intent is to identify how the RACR metric translates to future fantasy point performance the following year. If a player misses or plays say 5 more games than the year prior, it’s not exactly a good comparison.
Data and Analysis
From 2015 to 2019, there were a total of 47 players that met the above criteria.
Now diving in raw to the year-to-year comparisons.
Table 1: 2015-2016 Comparison
Variance Notes: Out of the data sample, 2015-2016 has the most red for fantasy pt. position variance. However, look at how small the variance is. Only two of the players varied greater than 10 positions.
Player Notes: Nothing too exciting except Jamison Crowder almost jumping to a WR2.
Table 2: 2016-2017 Comparison
Variance Notes: 2016-2017 there is a lot of green to the right.
Player Notes: Look at the jumps for Tyreek Hill, Adam Thielen, and Stefon Diggs. Breakout years. Michael Thomas remains flat.
Table 3: 2017-2018 Comparison
Variance Notes: 2017-2018 was mixed, but seven of the 12 wide receivers either finished in the same tier or better.
Player Notes: JuJu had the second highest RACR in 2017 and jumped to first tier in 2018. Tyreek Hill is again on the table improving to the #1 WR overall. Michael Thomas remains flat.
Table 4: 2018-2019 Comparison
Variance Notes: Again, a lot of green. 7 of 9 players ended in better positions than the year before.
Player Notes: Michael Thomas breaks his #6 hurdle and finishes as the WR1. Amari Cooper his second year in Dallas finished a top tier WR. DJ Moore breakout.
Table 5: Rolled Up Performance Trends
Below are the overall rolled up numbers calculating the year-to-year performance trends.
Variance Notes: I’m really focusing on the tier totals because it represents how tight these players' production is year-to-year. From one year to the next, 72% of the time the player either finished in the same tier or better.
Key Takeaways
It may be inferred that players within the sampled player pool perform rather consistently year-to-year as 72% of the WRs finished the following year in the same tier or better as the prior year.
As shown below, players within the sampled player pool have been breakout candidates and league winners.
As Hermsmeyer has said, RACR cannot alone predict future performance. However, it may be inferred that as RACR represents efficient production, coaches and quarterbacks may realize efficient production and award the receiver the next year with greater opportunity (targets). Below highlights the increase in targets and receptions for the league winners previously noted.
On average, targets increased by 30 and receptions increased by 21.
Alright! Now that we’ve highlighted the data and takeaways, the below represents 2019 WRs (=>45 receptions) that finished top 18 in RACR metric (the competitive advantage).
Based on the model, 75% of these players could finish in the same tier or better in 2020. That is, if they play +/- no greater than a 2 game difference in 2020 compared to 2019. This pretty much means as long as they don’t get injured because all players (minus Golden Tate) played near a full season in 2019.
A lot of these players are already getting a lot of preseason HYPE. There could be a league winner or maybe a few on this list. Who’s the next Juju? Who is the next DJ Moore?
My favorite player on this list was Deebo Samuel. With Sanders out of town, he should have seen the opportunity to get the needed bump in targets to climb the ladder. Unfortunately, Samuel broke his foot and is expected to miss time. I STILL LOVE HIM WHEN HE RETURNS.
My honorable mention risky(er) selection would be Dionate Johnson.
That’s all I have for this one.
