Professor IDP’s Approach to Combined Rookie Rankings
Professor IDP’s Approach to Combined Rookie Rankings
What factors into the development of combined rookie rankings? Professor IDP walks us through the six components of his approach.
With free agency behind us, I’m excited to pivot fully to the NFL Draft, which for me starts with creating combined rankings. For all the high-quality content out there on offensive players and IDP, combined rankings are relatively rare. This is surprising given the most recent State of IDP Report demonstrated that only roughly 5% of IDP leagues are IDP-only.
As a result, I’ve been creating my own league-specific combined rankings for roughly a decade, and as my league formats proliferated, I started doing more general rankings. This will mark my third year of sharing these rankings publicly and I’m thrilled to have them as a part of The IDP Show consensus alongside Adam and Bobby. Many of you have known them for years and understand their philosophies and methodologies, so I want to be transparent about my approach so you can understand how I arrived at my conclusions.
In this article, I’ll walk through the six components of my approach, how my rankings have fared the last two years, and the surprising truth I learned while ranking the 2024 IDP rookie class.
#1: Consensus Scout Evaluation (Starting Point)
I do not watch tape or analyze college stats. I let the professionals do this for me. I rely on the fine folks at NFL Mock Draft Database who compile hundreds of data points to create a consensus big board. I then scale these rankings using a 0-100 system to account for the value distribution within the rookie pool based on the overall pool evaluation conducted by Scouts Inc.
#2: Fantasy-Relevant Metrics (Pre-Draft Modifier)
There is some subjectivity here but I look for one to three key data points by position and add or subtract value based on this (i.e. tackling grade for LBs or route running metrics for TEs). I cross-reference multiple (non-fantasy) draft guides to arrive at these evaluations.
#3: Fantasy Positional Capital (Pre-Draft Modifier)
If I only used the criteria described above and below, you would be drafting CB1 from a class near the top of your draft. To make my rankings more practically usable, I make a fantasy VORP-style adjustment and an adjustment based on historical data on past combined drafts (including a substantial IDP penalty).
#4: Draft Capital (Post-Draft Modifier)
Earlier NFL draft picks tend to get more chances at playing time and snap counts are a top predictor of fantasy performance. There is also great data out there on NFL longevity and success based on draft capital which informs the specific weights that I use.
#5: Trade Up (Post-Draft Modifier)
If a team moved up to draft a player, they invested more than the draft capital associated with the pick number, so I make an adjustment relative to what was given up to get the pick/player. NFL GMs are also not immune to the sunk cost fallacy, so players traded up for likely earn additional chances relative to the other players taken around the same point in the draft.
#6: Team Fit (Post-Draft Modifier)
This is admittedly a subjective metric but I use multiple reports of projected post-draft depth charts to determine the likelihood that the player will see the field in Year 1. I also take note of the contract, age, and performance of the presumptive starter as secondary factors.
My Markers of Success for Rookie Rankings
Two of the hallmarks of my fantasy analyses are informed by my career as an organizational researcher. The first, as you’ve just seen, is that I value transparency in my processes. I am used to writing pages on methodology for my scholarly research, so the above is my attempt at keeping it brief!
The second is accountability. I love theory but find even more value in testing and refining theory for practical improvement, which means attempting to quantify successes and failures.
To that end, I have created a running analysis of how my rankings have compared to (correlated with) several markers of success. If it’s been a while (or forever) since you have had a stats course, a correlation ranges between -1 and 1, where a 1 means that there is a perfect positive relationship (every time variable x goes up, so does variable y), a 0 means there is no relationship, and -1 means that there is a perfect negative correlation (every time variable x goes up, variable y goes down).
The metrics I am using to evaluate success are:
Fantasy Points (Season): This is what we all hope for and so it is a crucial benchmark.
Fantasy Points (Per Game): Including PPG acts as a buffer against players who were injured but played well while active.
PFF Grade: Across a sample of 362 rookies from 2022 and 2023, 2023 PFF grades had a .48 correlation with 2023 fantasy points (and fantasy PPG). These grades are therefore valuable data points for identifying under- and over-performers from a fantasy perspective that are due for positive or negative regression towards the mean.
Snaps: The same data above highlights that snaps are correlated with fantasy points at .87 (PPG is .78). Predicting players who will play high numbers of snaps is another (indirect) path to fantasy success.
How My Rankings Have Fared
The full analyses are available here but I have summarized them at a high level in the table below. While it is hard to say what is a “good” versus “bad” correlation without knowing how others have fared, I am generally pleased with my results thus far. Note that these are unadjusted results, so if a top-ranked player was injured early in the season, it would hurt the overall and position-specific correlation between my ranking and fantasy points.
I made one addition to this year’s analysis and separated out results for the top 100 combined players since:
1) This is the length of many of your drafts aside from the degenerate leagues you may (like me) play in and
2) It is not as hard to predict that low or undrafted players with poor evaluations are unlikely to perform well.
My rookie rankings improved in every first-year metric from the 2022 class to the 2023 class. As expected, the correlations got generally weaker with time since there are more intervening variables between when the rankings are created and future performance, but correlations between .21 and .35 between rookie rankings and 2nd-year performance are all statistically significant in a sample this size, meaning the relationships are strong enough to conclude that they are not likely a random occurrence.
Turning to individual IDP positions, I have had the most success thus far in predicting DE output. In 2023, my rankings had a .58 correlation with fantasy points for rookies and .73 for second-year players. In 2022, the correlation between rookie DE overall rank and fantasy points was .67. Across all positions in both years, LB output has been the most difficult to predict (particularly in terms of PFF grades).
Finally, if you look at the data, one group of numbers will stand out to you. My model was horrible at predicting QB performance in the 2022 rookie class. In fact, the worse I had a QB ranked that year, the better they performed.
This is a cautionary tale about putting too much stock in correlations as a metric of success since this effect is due to a very small sample size (6 2022 rookie QBs played at least one snap in 2023) and an extreme outlier named Brock Purdy. If the Purdy data point is deleted the correlation between my rank and fantasy points goes from -.58 to .11!
What I Learned About the 2024 IDP Class
After finishing my 2024 combined rookie rankings (which paid supporters can see here), I realized something: this is the weakest IDP class (pre-draft) that I have evaluated in the past three years at the top end of most positions. In 2022, my highest-rated IDP was Aidan Hutchinson at #15 overall. In 2023, it was Will Anderson, who was similarly positioned at #17 overall.
My top pre-draft rookie IDP this season is Dallas Turner but he only slots in as the #24 overall player. Turner has an identical grade at this point in the process as I had in 2023 to Tyree Wilson and my #24 overall was Myles Murphy. While I still have hope for Wilson and Murphy (and don’t think either landing spot was ideal for 2023 production), I would not recommend investing precious 1st or 2nd round draft capital in IDP yet this year as I have on some occasions in the past. The gap from Turner to the next best IDP is also smaller with four other edge rushers (Laiatu Latu, Chop Robinson, Jared Verse, and Darius Robinson) in a position to jump Turner based on landing spot assuming all five will have great NFL draft capital.
2024 also marks the lowest I have ever initially slotted the top LB. My top pre-draft LB in this year’s class is a tie between Edgerrin Cooper and Payton Wilson at #40 overall (IDP #8). My model does not hate LB; in fact, I adjust all LB scores upward twice based on their actual and perceived fantasy value. But before learning landing spots, it’s difficult for me to get too excited. This is somewhat worse than last season when my pre-draft top LB was Trenton Simpson at #33 overall. The grades for Cooper and Payton are closer to Drew Sanders (overall #46).
In 2022, I had the LB class rated much more strongly. Devin Lloyd was overall #21 and Nakobe Dean occupied the #33 spot but with a much higher grade than the 2024 LBs. The closest 2022 rookie LB to Cooper and Wilson from an internal pre-draft grade perspective was Chad Muma.
The brightest spot in the IDP landscape (relative to past seasons) can be seen in my highest-ever rated CB, Cooper DeJean, who is my overall #42 and IDP #10 (ahead of the top LBs mentioned above). DeJean checks most of the boxes I love in a CB: talented enough to get immediate snaps and high NFL draft capital, a strong tackler, and has upside as a returner. I usually don’t recommend drafting any CB until late rounds as evidenced by my highest-ranked CB in 2023 (Christian Gonzalez) being #55 overall and in 2022 (Sauce Gardner) coming in at #71 overall, but depending on landing spot, I just might buck that trend this year.
Below, you will find a breakdown of IDP in my pre-draft top 25 and 50 each of the past three years. What likely jumps out immediately is the lack of high-end depth in this year’s draft class as is highlighted above. 2024 features the lowest number of IDPs in my overall top 25 (1) and top 50 (14) in the past three years.
While this might seem disheartening to the IDP enthusiast, it creates a variety of useful strategies to play out this fantasy rookie draft season. If you need immediate contributions, this is a great year to trade mid-to-late rookie picks for IDP vets in the peak of rookie fever. If this is a reloading year for you (or at least your IDP side) accumulating picks in the later rounds of deep drafts should allow you to still land quality developmental IDP assets.
I am a huge fan of moving back in rookie drafts, and given the offensive depth and later starting point for IDP in the overall rankings, a mid-2nd and a handful of 3rd-5ths should be able to land you a high-quality offensive player and several of the top IDP (at proper or good value).
Schedule of Topics
Hopefully, you’ve appreciated the transparency and accountability I’ve provided as well as the high-level preview of this year’s rookie class. I’m looking forward to helping you in your rookie drafts and I have plenty more to share in the coming months including:
2024 Top 100 Combined Rookie Rankings (Pre-Draft, Post-Draft, Pre-Season)
A Degenerate Draft Guide (featuring over 200 players)
2nd and 3rd Year Players to Target in Trades
2nd and 3rd Year Players to Fade in Trades
Just like in my regular classes and research, I can only improve with feedback, so if you have any thoughts, suggestions, or criticisms (or want to join me in one of my fantasy leagues) my office hours are always open on Twitter (@ProfessorIDP).
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