The Undroppables co-founder and host of Unscripted Randal Kennedy gives his own spin on the upcoming NFL Draft. Read on to see where the top prospects might land come April.
For more NFL draft takes, check out Unscripted wherever podcasts are found. You can find Randal on Twitter, @FF_Terminator.
You can also check out our latest rookie mock draft for fantasy purposes by clicking here.
This week on “The Undrafted”, a fantasy football podcast focusing on dynasty game theory, Scott (@DynoGameTheory) brings on fellow Undroppable Paul Lundgaard (@PauliesSleepers). Paulie talks about what he’s found as he has begun diving into this incoming rookie class, where he currently stands on guys like Terrance Marshall Jr., and the guys explore some current NFL players and their situations. Tune in or be tuned out!
There has been a lot of discussion on social media and in the fantasy football community the past several years about Body Mass Index (BMI) in fantasy football prospects – specifically wide receivers. The analytical data suggests that bigger WRs are more dominant and better fantasy assets. These WRs have a higher likelihood of succeeding in the NFL and therefore yielding more fantasy football points.
This season there is one prospect in particular that has stirred up the ole BMI argument again. The aforementioned player is the 2020 Heisman Winner from Alabama, DeVonta Smith. While it might seem crazy to you that people would be doubting Smith after the season he just had, it would appear that a chink in his armor is his small/slim stature. In other words, his low BMI. While Justin Jefferson, Ceedee Lamb and AJ Brown might not be in Smith’s range of outcomes, I show why he compares well to players like Adam Thielen and Calvin Ridley.
First, let’s establish what BMI stands for, what it measures, and what it does not. Body Mass Index (BMI) is designed to be a simple way to determine someone’s mass/body fat based on their weight.
Formula:
weight (kg) / [height (m)]2 or 703 * [weight(lbs)]/[height(in)²]
The number that this formula yields is used to determine if someone is underweight, appropriate weight, or obese based on others in their demographic.
That’s essentially it. Weight and height. BMI does not tell you what % of their weight is muscle or fat. It does not tell you how strong the person is. It doesn’t tell you if they are athletic or physically in good shape. You could see where there might be some limitations with this measurement.
As more focus was put into the metrics in fantasy football, analysts started to find what attributes/production was useful for predicting top prospects. For NFL WRs a high BMI seemed to correlate to fantasy production. A prime example is the sheer number of top-24 WRs the past three seasons who have a large BMI (>26). 
On average ~86% of the WRs that finish in the top-24 have a BMI over 26. It’s easy to see why this higher BMI craze has caught on.
However, when you look at the sheer volume of players in the NFL that actually have a BMI >26 over that same time period, one could also infer that the sample is skewed.
*Data includes WRs who produced any stats during their respective season.
When I limited those numbers to just fantasy-relevant players (WRs who finished in the top-100) the data set was slightly more skewed.
This data suggests that the league is saturated with WRs who have a BMI greater than 26. Roughly 30% of fantasy-relevant WRs have a BMI under 26. Forecasting that we should see that the sample to top-24 WRs would represent the whole population. Although not exact, nearly 20% of WRs who finish in the top-24 have a BMI under 26. The fantasy football community should be finding what these “smaller” WRs have in common and what allows them to succeed in the NFL.
Additionally, anyone in the analytic fantasy football community would tell you that one single measurement or data point is not great at predicting future prospects’ success in the NFL, but rather multiple factors should be taken into consideration. With that said, some data points are better than others. Peter Howard’s (@pahowdy on Twitter) database shows that BMI is terrible at pending future WR success, with r² values of 0.002 for both average expected point in year one and average PPR points in years 1-3 (meaning there is virtually no correlation between BMI and fantasy production).
For anyone interested in more of Howard’s work, I highly recommend you check out his Patreon. It’s only $1 a month and I can guarantee no one is giving you the amount of information for less money than @pahowdy.
If BMI is not really good at indicating which WRs will succeed at the next level and the majority of WRs with a BMI over 26 surely it’s good for something? Maybe durability. Are these bigger players more durable? Short answer: sort of.
To look at the effect BMI has on injuries I pulled in Undroppables math/analytic’s expert @BpoFSU – Brian O’Connell. We looked at wide receivers since 2018 that have had at least a 20% target share, to weed out irrelevant WRs that would have skewed the data.
(Nerd alert. These next few paragraphs are about the specifics of our findings and for full transparency I included them. If you just care about the results, scroll to “Results”).
Players were either injured or not, giving me binomial values of 0 or 1. To figure if BMI was related to injuries, I used logistic regression which allowed me to discover trends in the data. The two main outputs of logistic regression included the odds ratio (listed as “Odds” in the data set) and the Wald statistic with a corresponding p-value. The odds ratio tells us the increase or decrease in the probability of injury due to a one unit increase in BMI. For example, in our data set the odds ratio for any injury is 1.073. This means for every 1 unit increase in BMI, the likelihood of a receiver being injured increases by 1.073. If the odds ratio is below 1 then the probability will decrease as BMI increases. As for the Wald statistic, think of that as logistic regression’s version of a z-score, which allows us to find the p-value for our regression. A p-value is the probability that this data is due to random chance so the lower the p-value, the better. We decided for this study that the significance level would be 0.10 and any values that are lower than that would be considered statistically significant.
The likelihood of all types of injuries increases with a larger BMI by 1.073 per one unit increase of BMI. While injuries overall were not statistically significant, skeletal injuries (such as bone bruises and fractures were (p = 0.073). They increased by 1.351 suggesting that larger BMIs lead to more skeletal injuries.
This sample is significantly smaller (n = 58) but still gave us some good results.This trend is not even close to being statistically significant (p = 0.55). This indicates there are other factors that contribute to injuries besides BMI. The trend of lower BMI leading to a higher probability of injuries is also the case for soft tissue injuries and concussions but again they are also not significant. Skeletal issues and their relation to BMI in rookies have a very similar odds ratio to the whole sample size mentioned earlier. While the p-value is not statistically significant, this is something that might warrant further investigation as there are hints to a trend with skeletal injuries and BMI.
Focusing on DeVonta Smith, this data suggests that Smith has a 4.64% chance of suffering a skeletal injury over the first three years of his career and a 2.13% chance of that type of injury during his rookie season. On average the time missed for a skeletal injury in the NFL was 5.7 games, slightly larger than the 5.2 games for soft tissue injuries.
I mentioned before about focusing on attributes of successful WRs with sub-26 BMIs. I went back as far as 2016 to find the WRs who had a BMI under 26 and were able to finish the season as a WR1 or WR2. Next to their name is the rate that they finished as a WR1 or WR2 over their career in PPR leagues:
This is a list of nine players. A small list yes, but as I pointed out before, there are not many WRs in the league with BMIs under 26.
In an effort to try and forecast DeVonta Smith’s chances of success I analyzed what these WRs had in common in college along with measurements taken at the NFL combine. I used seven metrics: 40 Yard Dash Time, SPARQ-x score (a measure of athleticism via Playerprofiler.com), Breakout Age (BOA), College Yards Per Reception (YPR), College Dominator Rating, College Target Share, and Best College Yardage Share. Below are the averages for this group:
Every WR listed above met at least 4/7 criteria with Calvin Ridley and AJ Green being the only two that were a perfect 7/7. One could argue that the closer you get to hitting those seven marks the better fantasy asset Ridley is currently a converted dynasty fantasy football WR and AJ was a top-5 play at his position for most the last decade.
Using those seven metrics above, we can try and forecast DeVonta’s Smith success rate. Without a combine/pro day score, we won’t have a 40 time or SPARQ-x score but Smith has 4/5 college metrics with the exception of BOA.
Another thing people are worried about with DeVonta Smith is his ability to handle press coverage. Because he is small, analysts are concerned that DBs will be able to jam Smith at the line of scrimmage and disrupt his routes. For this issue I turned to The Undroppables very own film guru, @2on1FFB – Tommy Mo to look at how DeVonta handled press coverage at Alabama.
Tommy’s video breakdown of DeVonta Smith vs. Press Coverage showed that in college teams were not pressing Smith very much. The reasoning behind this was because DBs were not very good at it when it came to facing off with Smith. Most college DBs anticipated getting beat by Smith or were afraid of his speed. Even when they lined up close to the line of scrimmage against Smith, they quickly retreated. The few times defenders did try to get hands on Smith, he used his feet, route-running skills and speed to separate. This is likely the rationale behind coordinators electing not to press him. NFL DBs are definitely more skilled and will likely try to jam Smith, but in college at least these attempts proved futile.
There are holes in DeVonta’s Smith’s profile. He does not check all the boxes. He had a late breakout age, and we will have to wait and see what his final numbers look like when he weighs in and runs at his pro-day. I understand why the skeptics are worried. Smith is an outlier, and we typically try to avoid those guys in fantasy football. But as I pointed out, the NFL is saturated with larger WRs which bias the data. My research shows that there is not a correlation between smaller BMIs, and fantasy production. If anything bigger WRs have a propensity to skeletal type injuries. There are success stories for WRs with a sub-26 BMI and Smith’s college profile fits that mold. If you have a top-5 rookie pick in your dynasty leagues this might not be the player I would select. However, getting the next Thielen, Ridley, or Sanders is a valuable cornerstone for dynasty rosters.
So does BMI really matter? Kind of. But that title isn’t as catchy.
I would be remiss if I did not thank the people who helped me with this content. A special thanks to The Undroppables Team – specifically @2on1FFB for the film breakdown and @BpoFSU for the countless hours sifting through data and helping me incorporate it into this piece. I also want to thank Fantasy Football Astronauts for the film study resource that came in handy along with PlayerProfiler and @pahowdy for a lot of the data used in this article.
Stay tuned these next couple of weeks for The Undroppables Rookie Profiles which are coming soon!
Link to data used: WR BMI Data
Andrew Mackens is the Editor-in-Chief of The Undroppables website and a 15-year veteran of playing fantasy football. He also has passions outside of Fantasy (who knew!).
Mack of All Trades will be a variety-style show with a variety of awesome guests talking primarily about fantasy football, while also venturing into the interests of Andrew and his guests. We hope you join us on this new entertainment venture!
This year, rookies outperformed against all odds. Without preseasons to establish chemistry, develop their skill sets, and learn offensive schemes, I predicted that rookies (especially WR/TE) should be faded as a whole, with only a few exceptions. Yet as players like Justin Jefferson and James Robinson proved, this rookie class was ready for action, bursting onto the scene at an unprecedented level. So what caused this? Is this a one-off year due to the historically strong 2020 rookie class, or is this predictive of future years? Do we need to re-evaluate how we think about positional breakout timings? Let’s examine these questions below.
Here are the notable rookie finishes of 2020 (PPR, through 16 games):
(Click the image to view the entire database)
It’s unprecedented to have such rookie success from multiple positions, not restricted to the RB breakouts that occur relatively often. Here, the data speaks for itself regarding this year’s success, but how does this stack up against past years? Was this merely an anomaly?
Click here for rookie finish data from the past five years.
What does this data in the link above tell us? Let’s examine this data in windows. Let’s start in a three-year time period. Keep in mind that the 2019 and 2018 classes have had 2 or 3 years respectively to make an impact, so their recent production could skew the perception of the classes’ success in its rookie year. Regardless, we can see that last year’s class was superb, producing two (or three including Jalen Hurts) fantasy relevant QBs in their rookie seasons, with Justin Herbert leading the way as an every-week option for the majority of the season. Running back featured THREE RB1s, as well as two more RB2s. Wide Receivers were arguably the most shocking position, finishing with one WR1 (Justin Jefferson)! Additionally, three more WRs finished as top-25 options at the position. This year’s tight end class was weak, however intriguing prospects like Cole Kmet flashed this season, boding well for a breakout later on. Here is a summary of that data.
*Top-25 is included instead of top-24 since there were quite a few #25 finishes between multiple years, and I felt it best to include them in this summary. When examining these tables, keep in mind that the top 12 and top-25 columns are unequal, since becoming a top 12 player at a position is more challenging than a top-25 player. Thus, it is not appropriate to simply add the numbers in each table to assess the strength of a class. Additionally, positional finishes are unequal due to the competition and available players at a position (e.g. with WRs vs TEs, it’s arguably easier to become a top-25 TE than a top-25 WR due to competition at the position).
| 2020 | Top-12 | Top-25 |
| QB | 1 | 1 |
| RB | 3 | 2 |
| WR | 1 | 3 |
| TE | 0 | 0 |
Similar breakdowns for 2018 and 2019 are listed in the tables below.
2018 produced zero QB1s, two RB1s, one RB2, zero WR1s, one WR2, and three TE2s.
| 2018 | Top-12 | Top-25 |
| QB | 0 | 2 |
| RB | 2 | 1 |
| WR | 0 | 1 |
| TE | 0 | 3 |
In comparison, 2019 produced one QB 1, two top-25 QBs, zero RB1s, three top-25 RBs, no WR1s, two top-25 wideouts, and one TE2.
| 2019 | Top-12 | Top-25 |
| QB | 1 | 2 |
| RB | 0 | 3 |
| WR | 0 | 2 |
| TE | 0 | 1 |
This means that the 2020 class produced more top-12 RBs, more top-12 WRs, and just as many top-12 QBs/top-25 WRs than the previous two years combined! This is despite not having a preseason, and having modified online workouts take place of some practices.
Zooming out to a five-year view, we can see that the 2017 class was one of the best running back classes in NFL history, producing four rookie RB1s. There were no RB2s or RB3s in the class, however four RB1s is the highest total of any of the past five classes. When looking at the names though, it’s no surprise that this class had such high success, with Leonard Fournette, Alvin Kamara, Christian McCaffrey, and Kareem Hunt (the four RB1s), as well as Dalvin Cook, Joe Mixon, Marlon Mack, Aaron Jones, James Conner, Tarik Cohen, and Chris Carson. The remainder of the 2017 and 2016 classes are summarized below.
2017 produced one top-25 QB, four RB1s, one top-25 WR, one TE1, and three TE2s.
| 2017 | Top-12 | Top-25 |
| QB | 0 | 1 |
| RB | 4 | 0 |
| WR | 0 | 1 |
| TE | 1 | 3 |
2016 produced one QB1, one QB2, two RB1s, one WR1, another top-25 receiver, and one TE2.
| 2016 | Top-12 | Top-25 |
| QB | 1 | 1 |
| RB | 2 | 0 |
| WR | 1 | 1 |
| TE | 0 | 1 |
Looking at the entire data set, it appears that the 2020 season was significantly better than the average season during this interval. Even the strong 2017 season with its four rookie RB1s falls behind the 2020 season due to the differences in rookie WR production. There is no concrete, easily discernible rookie production trend over this time table (2017 and even 2016 were better in some areas than 2019 or 2018). Since position-by-position consistency varies year over year pretty significantly, it appears that the strength of a class (measured through scouting and evaluations pre- and post- draft) supersedes positional trends over time (for instance if the number of productive RBs steadily increased over time, or the number of QB1s/2s was seeing consistent growth year over year). Still, it seems that players are breaking out sooner than they have in the past. I think it’s time to reassess our old way of thinking about rookies, and acknowledge that rookies can breakout earlier on than previously thought, even in year one! This especially goes for rookie RBs, talented WRs with competent QB play, and even the occasional QB (especially with rushing capabilities, a la Kyler Murray). Let’s dive deeper into positional breakout timings.
*If you are interested in rookie data from before 2016, here is a graphic showing rookie finishes from 1990-2019 from Luke Neuendorf.
(Common timings I’ve seen before → New proposed timings (purely my opinion))
QB: 3 years → 2 years (The notorious year-2 breakout for QBs has yielded some of the top QBs for fantasy recently, eg. Patrick Mahomes, Lamar Jackson, Kyler Murray).
RB: 2 years → 1-early/mid-2 (Slight upgrade, however running back breakout timings have been relatively early for a while now, so the movement isn’t as large as it is for other positions).
WR: 3 years → 2-3 years (3rd year might be better, however I anticipate mini-breakouts in year 2 that yield most of the points of a 3rd year breakout).
TE: 3-4 years → 3 years (I think this breakout timing moves up slightly, however still within the 3-4 year range for the most part. I do believe this will catch up with the rest of the increases in due time, as TEs continue to be integral parts of successful NFL offenses, providing benefits as both large, powerful slot receivers and as an additional blocker. This dual-purpose is a contributor to the later breakouts, as tight ends are frequently called upon to perform both of these roles, which can slow breakout times. Due to the increasing importance of TEs however, I believe that there will be more emphasis placed on developing TEs in college, accelerating future breakouts).
Rookies are coming into the NFL more prepared, ready to make their impact on the game sooner than in past years. With franchises searching for coaches from the collegiate level to fill coordinator or head coach positions, schemes and play-calling continue to become more similar, closing the gap between college and the pros. Additionally, coaches and front offices are attached and attributed to their draft selections, and with the high turnover rate for most coaching staffs, teams are incentivized to use their rookies early and bring them along faster than ever before. This is especially true for high impact positions like QB, and this has only been exacerbated by the successful trend of surrounding a rookie-contract QB with elite talent, capitalizing on the few years prior to a major QB deal. Here is a graphic below detailing the Super Bowl winners’ QB salary cap percentage from 1994-2017 (Credit: Steven Ruiz of ForTheWin).
Additionally, preparation has improved, with better camps and training methods available for players even before college, as high schoolers or even younger. With the expansion of recruiting and social media, players are gaining exposure early on; they are provided unique opportunities that were not available before. Furthermore, with recent rule changes favoring offensive players, offenses are set to continue their recent successes, boding well for fantasy football.
While this inflates stats from players at different positions, with the focus on QB safety and preventing injuries, passing benefits a lot, supporting higher numbers for QBs and WRs. With the growing shift towards RBBCs in the NFL, this could help close the scoring gap between RBs and WRs in fantasy, which could eventually lead to new scoring formats as the balance shifts in the NFL.
We need to reevaluate how we perceive rookies, and recognize that rookie breakouts are possible, especially at running back. Additionally, as breakouts happen earlier and earlier, our mindset should change to reflect that. In order to find which rookies to target in your leagues, here’s some advice from Jax Falcone: “In the long term, talent wins out. In the short term, opportunity is key.” For redraft, opportunity reigns supreme, at least early on. In contrast, targeting talent (even in muddled situations) can yield massive returns in dynasty leagues.
Follow Vivek Iyer on Twitter for more fantasy football content! Special thank you to Jax, Chalk, and Marc for their help on this article! Thank you for reading!
