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Player mentions

Trey Benson
RB · ARI · #general

...And second, it's good, but it's not perfect. Yes, it faded JJ McCarthy and Trey Benson in 2024, but it also thought a little too highly of Ben Sinnott (as many o...

Ben Sinnott
TE · WAS · #general

...Carthy and Trey Benson in 2024, but it also thought a little too highly of Ben Sinnott (as many of us did). The 2025 class is looking better so far - it faded Ka...

Kaleb Johnson
RB · PIT · #general

...tt (as many of us did). The 2025 class is looking better so far - it faded Kaleb Johnson and Travis Hunter while boosting Harold Fannin and Tyler Shough, and no on...

Harold Fannin
TE · CLE · #general

...ng better so far - it faded Kaleb Johnson and Travis Hunter while boosting Harold Fannin and Tyler Shough, and no one appears to have been terribly under- or over-...

Travis Hunter
WR · JAX · #general

...did). The 2025 class is looking better so far - it faded Kaleb Johnson and Travis Hunter while boosting Harold Fannin and Tyler Shough, and no one appears to have...

Tyler Shough
QB · NO · #general

...it faded Kaleb Johnson and Travis Hunter while boosting Harold Fannin and Tyler Shough, and no one appears to have been terribly under- or over-rated (yet). Now...

Elijah Sarratt
WR · BAL · #general

...urst WR - 1.69 - 21%, 59%, 87% Zavion Thomas WR - 1.67 - 20%, 59%, 87% Elijah Sarratt WR - 1.67 - 20%, 59%, 87% De'Zhaun Stribling WR - 1.66 - 20%, 59%, 87%...

Fernando Mendoza
QB · LV · #general

...100% Sam Roush TE - 2.88 - 76%, 98%, 100% More Confident Than Not Fernando Mendoza QB - 2.71 - 65%, 95%, 99% Carnell Tate WR - 2.63 - 61%, 95%, 99% Jordy...

Carson Beck
QB · ARI · #general

...WR - 1.67 - 20%, 59%, 87% De'Zhaun Stribling WR - 1.66 - 20%, 59%, 87% Carson Beck QB - 1.6 - 19%, 55%, 85% Malachi Fields WR - 1.52 - 16%, 51%, 83% Jona...

Germie Bernard
WR · PIT · #general

...Tate WR - 2.63 - 61%, 95%, 99% Jordyn Tyson WR - 2.51 - 54%, 91%, 97% Germie Bernard WR - 2.49 - 54%, 90%, 96% Omar Cooper Jr. WR - 2.48 - 53%, 90%, 96% Ty...

Ty Simpson
QB · LAR · #injuryupdate

...rd WR - 2.49 - 54%, 90%, 96% Omar Cooper Jr. WR - 2.48 - 53%, 90%, 96% Ty Simpson QB - 2.44 - 51%, 88%, 97% Their Chances Aren't Bad KC Concepcion WR...

Omar Cooper
WR · NYJ · #general

...son WR - 2.51 - 54%, 91%, 97% Germie Bernard WR - 2.49 - 54%, 90%, 96% Omar Cooper Jr. WR - 2.48 - 53%, 90%, 96% Ty Simpson QB - 2.44 - 51%, 88%, 97% Th...

Max Klare
TE · LAR · #general

...iams WR - 2.06 - 39%, 78%, 94% Denzel Boston WR - 2.06 - 36%, 74%, 92% Max Klare TE - 1.99 - 34%, 71%, 90% Oscar Delp TE - 1.88 - 29%, 68%, 88% The Be...

Carnell Tate
WR · TEN · #general

...More Confident Than Not Fernando Mendoza QB - 2.71 - 65%, 95%, 99% Carnell Tate WR - 2.63 - 61%, 95%, 99% Jordyn Tyson WR - 2.51 - 54%, 91%, 97% Germi...

Jordyn Tyson
WR · NO · #general

...ndoza QB - 2.71 - 65%, 95%, 99% Carnell Tate WR - 2.63 - 61%, 95%, 99% Jordyn Tyson WR - 2.51 - 54%, 91%, 97% Germie Bernard WR - 2.49 - 54%, 90%, 96% Oma...

Malachi Fields
WR · NYG · #general

...ribling WR - 1.66 - 20%, 59%, 87% Carson Beck QB - 1.6 - 19%, 55%, 85% Malachi Fields WR - 1.52 - 16%, 51%, 83% Jonah Coleman RB - 1.47 - 16%, 48%, 82% Zach...

Jadarian Price
RB · SEA · #injuryupdate

...adiq TE - 3.27 - 95%, 100%, 100% Makai Lemon WR - 3.19 - 91%, 100%, 100% Jadarian Price RB - 3.16 - 90%, 100%, 100% A Pair of [Likely] Starting Tight Ends E...

Jeremiyah Love
RB · ARI · #breakoutcandidate

...arter, 96% backup or better, 100% depth role or better The Upper Crust Jeremiyah Love RB - 3.31 - 95%, 100%, 100% Kenyon Sadiq TE - 3.27 - 95%, 100%, 100% Mak...

Makai Lemon
WR · PHI · #general

...ove RB - 3.31 - 95%, 100%, 100% Kenyon Sadiq TE - 3.27 - 95%, 100%, 100% Makai Lemon WR - 3.19 - 91%, 100%, 100% Jadarian Price RB - 3.16 - 90%, 100%, 100%...

Reggie Virgil
WR · ARI · #general

...R - 1.38 - 14%, 44%, 79% Mike Washington Jr. RB - 1.38 - 14%, 44%, 79% Reggie Virgil WR - 1.38 - 14%, 44%, 79% Malik Benson WR - 1.29 - 12%, 40%, 76% Cade...

KC Concepcion
WR · CLE · #injuryupdate

...96% Ty Simpson QB - 2.44 - 51%, 88%, 97% Their Chances Aren't Bad KC Concepcion WR - 2.34 - 47%, 85%, 96% Nate Boerkircher TE - 2.28 - 44%, 83%, 95% A...

Nate Boerkircher
TE · JAX · #injuryupdate

...% Their Chances Aren't Bad KC Concepcion WR - 2.34 - 47%, 85%, 96% Nate Boerkircher TE - 2.28 - 44%, 83%, 95% Antonio Williams WR - 2.06 - 39%, 78%, 94% D...

Cade Klubnik
QB · NYJ · #general

...irgil WR - 1.38 - 14%, 44%, 79% Malik Benson WR - 1.29 - 12%, 40%, 76% Cade Klubnik QB - 1.29 - 12%, 40%, 76% Bryce Lance WR - 1.23 - 10%, 38%, 75% Cole P...

Mike Washington
RB · LV · #general

...ridon TE - 1.45 - 15%, 48%, 81% Kendrick Law WR - 1.38 - 14%, 44%, 79% Mike Washington Jr. RB - 1.38 - 14%, 44%, 79% Reggie Virgil WR - 1.38 - 14%, 44%, 79%...

Article text

I never gave my data model an actual name, so "Expected Value Adjustment Model" is something I made up on the spot to capture the gist of it in 4 words. Here's the short version first, for those who want to skip to the rankings at the end and don't care about the fluff: Your probability of succeeding in the NFL declines as the number of your draft pick increases, but that rule is not absolute. By looking at certain qualitative and quantitative data from scouting reports and measurables, you can adjust that initial expected value, and from players with similar adjusted EVs you can also derive probabilities of certain achievement levels in the NFL. Now here's the long version, which you're either reading because you're genuinely interested in unlocking the secrets of dynasty or you skipped to the rankings and thought "wow that was unexpected" so you needed to know more. As a bit of background, I'm fairly new to dynasty, but I've been playing redraft a long time. My 2QB dynasty league started up for Puka Nacua's rookie year, when I took him in the 18th round. That same year, I also grabbed Nico Collins in the 15th round and Kyren Williams off of wavers. In week 2, I traded Garoppolo (who was benched shortly thereafter) for a tight end named Trey McBride. It likely goes without saying that I had a stellar year and won the championship. Heading into that offseason, I had a question: Was I simply incredibly lucky? Or was I recognizing a statistically measurable pattern in finding underrated players? To start investigating, I chose to focus on NFL success as opposed to fantasy scoring expectations. In drafting dynasty players, my priority is to pick guys who are going to have longevity in starting lineups. So I created five tiers of NFL success: 4: Superstars, likely future hall-of-famers. 3: Starters - namely players who spent all or a good majority of their careers as a QB1, RB1/1Bs, TE1, or WR1/2, but may not be enshrined in Canton. 2: Backups - players who spent all or most of their career as the "next man up." 1: Depth - players who hung around at the bottom of rosters for at least several years, occasionally filling in or having a specialty role. 0: Bust - players who spent no more than a couple years on a roster, most of whom never even saw the field in a regular-season game. Then, I assembled a list of traits, skills, and measurables that are frequently cited in pre-draft scouting reports (plus the level of college competition they faced) - a separate list for QBs, RBs, WRs, and TEs. Then, I mined the reports of every single player at those positions drafted into the NFL since 2015 to figure out which traits/skills/measurables were highlighted or criticized for each player. If you take the draft capital spent on all of those players and graph it against their NFL success values, you get a polynomial relationship with an r-squared value of 0.38 - a weak correlation, which is unsurprising. But that polynomial relationship of course has an equation, and that equation can be used to calculate a player's "Expected Value" on the NFL tier scale - and that's my starting point. Next, I take all of those traits/skills/etc. and plug them into a regression model alongside their expected value as inputs, with their success tier as the dependent variable. The result is that each variable is assigned a coefficient by the model - the larger the coefficient, the larger the impact that variable has in determining success. Some of those variables have s…