Because Speed Kills & Size Matters

The hardest part about fantasy football is predicting how much success NFL rookies will have. Who is the next unheralded rookie, like Alvin Kamara or Terry McLauren, that weren’t being talked about by the fantasy experts, but will strike gold? Even NFL teams, despite all of their resources and knowledge, struggle at projecting rookies (e.g., Ryan Leaf and Cedric Benson). The so called “experts” are often wrong. Remember when Trent Richardson (1st round pick), Eddie Lacy (2nd round pick), and T.J. Yeldon (2nd round pick) were supposed to be the next great Alabama backs to succeed in the NFL? In our rookie running back model their analytical score resulted in a draft grade of 2nd, 3rd, and 5th round, respectively.

Our approach to NFL rookie evaluation is based on the simple premise that the players most likely to achieve NFL success are the best athletes that succeeded in college against the toughest competition. In other words, we want the 227-pound running back that ran a 4.52-second forty and scored 1.15 fantasy points per touch against SEC competition over the 203-pound back who ran a 4.66-second forty and scored 1.01 fantasy points per touch against Conference USA competition. The former is Nick Chubb and the latter is Devin Singletary; and they are not the same. Chubb was selected in the second round of the 2018 draft and received our first-round grade, while Singletary was selected in the third round of the 2019 draft and received our undrafted free agent grade. Singletary hasn’t been a complete bust, but he hasn’t been Nick Chubb either.

We were tired of drafting rookies based on the so-called experts’ opinions only to have them fail, so prior to the 2017 NFL season we developed an analytical model that more accurately predicts the success of NFL rookies. We knew what the answer to the equation was; it was Bo Jackson, Barry Sanders, Calvin Johnson, and Randy Moss; so, we did what all good engineers and modelers do; we reverse-engineered an equation by compiling all of the measurables of former and current fantasy legends to see which attributes they had most in common. Then through thousands of iterations, we weighted each of the model’s attributes until the resulting ratings reflected their fantasy production.

Here are some fantasy busts that were supposed to have a lot of NFL success, along with their draft round and their draft grade in our analytical model; Clyde Edwards-Helaire (1st / 3rd), C.J. Spiller (1st / 3rd), Jahvid Best (1st / 3rd), Bishop Sankey (2nd / 5th), Nelson Agholor (1st / 6th), Devin Funchess (2nd / 5th), Laquon Treadwell (1st / 5th), Devontae Booker (4th / UDFA), Royce Freeman (3rd / 5th), Kerryon Johnson (2nd/ 4th), Kevin White (1st / 2nd), N’Keal Harry (1st / 3rd), Curtis Samuel (2nd / 5th), Derrius Guice (2nd / 5th), and Corey Davis (1st / 5th), just to name a few.

Here are some fantasy studs along with where NFL GM’s drafted them and our analytical draft grade; Derrick Henry (2nd / 1st), Chris Carson (7th / 3rd), Alvin Kamara (3rd / 1st), Nick Chubb (2nd / 1st), Jonathan Taylor (2nd / 1st), Antonio Gibson (3rd / 1st), Elijah Mitchell (6th / 3rd), Chris Godwin (3rd / 1st), DK Metcalf (2nd / 1st), Terry McLaurin (3rd / 1st), George Kittle (5th / 1st), and Mark Andrews (3rd / 1st).

There are some players that we both missed on, such as John Ross (1st / 1st), and Rashaad Penny (1st / 1st). There are also players that we missed on that the GMs did not; such as Bo Scarbrough (7th / 4th). 

Our model takes the guesswork and emotion out of the equation, and provides an objective ranking based on a score calculated from each player’s NFL combine or pro day measurables, their best year of college production, their strength of conference, and the draft capital invested in them by NFL GMs. Different positions need different skills to succeed in the NFL, so our model uses different attributes and weighting factors for each position. For example, we believe that college production is more of an indicator of NFL success for receivers than backs, because backs might have a very good (Sony Michel) or very bad (Cam Akers) offensive line that skews their production. Therefore, the model weights receiver college production higher than running back college production. Football is a big, fast man’s game, but there are other attributes that indicate NFL success. The attributes our model uses include size, speed, explosiveness, quickness, power, college production, strength of conference, and draft capital; as well as offensive line ratings for running backs.

Do our analytical scores mean more than just the resulting ranking? Yes, they do. As any good model should, our “hit rate” increases with the score. Since 2016, a running back first round score of 81+ correlates to a 80% hit rate, meaning the player scored 13+ PPG in 1/2 PPR with a 10 game minimum. During that same time period, the NFL's first round hit rate is 64%.

For wide receivers a hit means they have averaged 12+ PPG in 1/2 PPR over a 10 game minimum. Since 2017, a wide receiver first round score of 81+ correlates to a 73% hit rate, compared to the NFL's 1st round hit rate of 46%. Here's a nugget for you; since 2017 there have been 121 receivers drafted after the third round by the NFL, and only two have had a top 30 fantasy season, which is less than a 2% hit rate. Think long and hard before you use anything but a late round pick on a receiver drafted in rounds 4-7.

We release our annual rookie rankings in early May, the Friday after the NFL draft. You can follow us on Facebook and Twitter, as well subscribe to our blog on this website.