NFL Draft Prospects | Breaking down the Draft Sharks Rookie Model
Introducing the Draft Sharks Rookie Model
Many people are good at watching college football and “knowing” if a player is good or not.
Is he “bendy”? Does he “have a lot of burst”? Maybe “good vision"?
I am not one of those people. I am, however, good with data. So, let’s talk about some data (and leave the tape breakdowns to the other guys).
One of my first Draft Sharks projects was to build a model combining analytics with film grades to create an all-inclusive approach to rookie prospecting.
Based on research, I knew we would want to break things into three categories:
- production scores
- athletic scores
- film grades
We use these grades and NFL draft capital (4th piece) to generate an overall Overall Model Score.
Production Score (0-1.0)
Most (highly) successful players in the NFL had significant production at the collegiate level. There are obviously outliers every year. But as a whole, good-to-great NFL players had solid college careers.
College production matters
This production score looks at a player's total college output, weighing different factors depending on the position.
It also adjusts for team competition. For example, if a team has a stud running back like Bijan Robinson, then the other RBs in the backfield (Roschon Johnson) get a little credit for a lack of production. The same is true for wide receivers and, to a lesser extent, tight ends. We also use a player’s college team conference to adjust the production score further.
Check out the NFL rookie running backs with the best college production (far right column):
Athleticism Score (0-1.0)
Let's go out on another limb and say that being super athletic is helpful for NFL success. Bold, right?
Being a freak athlete doesn’t hurt
The truth is everyone playing in the NFL is already a top 0.1% athlete, but we want the best of the best. So, we break down their “athleticism” as a function of how they compare to historical NFL athletes at their respective positions.
It is again threshold based and uses NFL Combine numbers as the baseline metrics. If a prospect didn’t participate in the combine, their metrics are estimated using a combination of Pro day or reported values with a correction applied.
Here are the most athletic 2023 NFL rookie QBs (far right column):
You can check out how Anthony Richardson's insane combine and athletic profile stack up in our updated Dynasty Rookie Rankings.
Prospect Film Scores (0-1.0)
Grading rookies is hard. Henry Ruggs and Jerry Juedy were graded better than Justin Jefferson heading into the 2020 NFL draft.
Rookie film is the missing piece
It is difficult, and specific sources often have biases or blind spots. So, I created a model to aggregate grades, normalize them, and weigh them based on historical success to generate a final film score.
The goal was to look for outliers and get an overall view of a rookie’s success or lack thereof on film.
The film model gave the most weight to Pro Football Focus metrics and Lance Zierlein.
Additional sources of player grades were included but to a much lesser extent.
Here are the rookie WRs with the highest film score in the model (far right column):
Agreement
A key metric to analyzing the model outcome is to look at how the production, athleticism, and film grades agree for a rookie. The agreement factor looks at the spread of scores in each category and can be used to identify divisive prospects or prospects with red flags.
It's essential to look at the variance in data to see how much confidence we can have in the final output. If the production and athleticism are there and the film grades match, then there's a better chance we have a strong prospect.
Or a better chance that everyone was wrong and we can sleep better at night knowing we did everything we could.
Sometimes prospects miss. We aim to identify red flags or places we should focus more on certain incoming players.
Disagreement
Disagreement, on the other hand, doesn't necessarily mean you should ignore a player in your rookie draft. Usually, there is a narrative or story about why the categories disagree, and a little more investigation is needed.
Anthony Richardson is a prime example of a rookie with a very low agreement. Richardson had minimal college production, split film grades, and freak athleticism that did not correlate to film or production. But, NFL draft capital trumps all, and he went top 5.
The agreement factor should be used to screen, not remove rookies from contention.
Here are some of the rookies that have the least agreement (far right column):
NFL Draft Capital
The last step was to normalize the scores with NFL draft capital. At a macro level, NFL scouts are good at getting the picks right. Everyone has some misses, but they do a good job as a whole.
I did a study that looked at all players drafted since 1999 and the effectiveness of each draft pick by positional grouping. I was able to assign a value for a draft pick by position and used that to generate the overall scores.
Overall the rookie model has been handy as a baseline to scout these incoming rookies. The functionality to sort incoming rookies by specific categories makes it easy to identify strengths and weaknesses at a glance.
These are not the same as our Dynasty Rookie Rankings or the Superflex Dynasty Rookie Rankings.
But they were part of the ranking process.
Here is the full spreadsheet for the Draft Sharks 2023 Rookie Model Grades!
Ready to Draft?
We put a lot of work into our scouting process and creating rankings.
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