Uncover the groundbreaking College Football Talent Development Adjusted Rating system, revealing how genetics and environment shape players' success.
Note, this is an update to the series we published early this year.
In psychology, folks often discuss the role of genetics and environment on shaping a person's personality and even their future. Similarly, both talent acquisition (recruiting, transfers) and development are key to a college football team in building a championship program, and this is my adjusted rating.
I would like to share my model for accessing a program's true talent level based on several factors:
Recruiting rankings
Playing time as measured by snaps
The QUALITY of play during that playing time
The key to this exercise will be to answer the impossible question one player at a time. What makes a college player great - innate talent (genetics) or development (environment)? My adjusted rating system will show a way to utilize both.
This will be a foundational article to explain my system which I think you will REALLY like. This model uses data that is only available to premium members of Pro Football Focus. I am only going to show data for one player to explain how the system works, and you should subscribe to see the additional details. I am going to focus more on the results and how I use that data.
Defining my Talent and Development Adjusted Rating.
From this point forward I will be labeling the result here my adjusted rating. This is how I calculate what I call a player's adjusted rating.
Recruit- Pull the 247 Sports composite recruiting ranking
Transfer- Pull the 247 Sports transfer ranking
For each season
Total Offense/ Defense Snaps- For Texas these went up to 985 snaps in 2024
Offense/ Defense PFF Grade - For Texas these grades ranged from 43.9 to 91.7 in 2024. A vast majority of the scores are 55-75.
Calculate a yearly development
Development = (Snaps * (PFF Grade - 50)) / 100,000
I picked a baseline of 50 to reflect that a player develops more as he plays and the better he plays. If one plays terribly (below a 50 PFF grade would reflect that), he might actually regress, and it may become clear he is not going to develop.
This Development score is scaled to a level that is similar to the Sports 247 recruiting rating as shown below. The yearly development ratings can be added together to have a total development score.
The adjusted rating is the sum of the recruiting/ transfer ranking and the yearly development ratings.
Example - Let's pick one of the 2024 Longhorns player that has actually developed the most in his time as a Texas player - Jake Majors.
Recruiting - 2020 rating - 0.9218- mid 4 star
Development
2020 - 147 snaps - 62.6 PFF rating - 0.0185
2021 - 792 snaps - 65.6 PFF rating - 0.1236
2022 - 860 snaps - 63.7 PFF rating - 0.1178
2023 - 908 snaps - 70.7 PFF rating - 0.1880
Total - 2707 snaps - 66.5 PFF rating - 0.4478 - this is the development score
Adjusted Rating - 1.3697 - high 4- star
This is the value of this system. Majors has played a LOT -39% more than any other 2024 Longhorns player. He has grown a lot with those 2707 snaps and in his fifth year he will be an even better player than he has been.
I have wanted to quantify a system to define how good is a player right now. As I developed this scale, I have worked on getting something I think fits a majority of my perception, based on statistics, of how good the Longhorns players are. This could then be applied to other rosters too.
Note: With the redefinition of the Development Score my scale and how the Adjusted score has now changed.
The career development scores will typically range up to less than 0.6
Christian Jones- 0.5672 on 3219 snaps
T'Vondre Sweat- 0.5595 on 1858 snaps
Xavier Worthy- 0.5083 on 2271 snaps
Let me calibrate you further with some of the NFL Early Round picks you might find interesting. For these players I am going to give you two numbers- the pre and post 2023 season Adjusted Ratings.
#1 Caleb Williams - QB - 1.6224 - 1.9291
#2 Jayden Daniels - QB - 1.6835 - 1.9973
#3 Drake Maye - QB - 1.4114 - 1.7805
#4 Marvin Harrison Jr. - WR - 1.2882 - 1.5471
#5 Joe Alt - OT - 1.4342 - 1.7243
#6 Malik Nabers - WR - 1.1690 - 1.4723
#9 Rome Odunze - WR - 1.2109 - 1.5746
#11 Olunuliwa Fashanu - OT - 1.0172 - 1.2251
#13 Brock Bowers - TE - 1.5701 - 1.7490
#16 Byron Murphy - DL - 1.0880 - 1.2680
#28 Xavier Worthy - WR - 1.3221 - 1.4795
#38 T'Vondre Sweat - DL - 1.2232 - 1.4330
#46 Jonathon Brooks - RB - 0.9234 - 1.1092
#52 Adonai Mitchell - WR - 1.0715 - 1.2659
All of these players had a development score added in their last college year of 0.15- 0.37. Remember that range.
How do the 2024 Longhorns matchup right now? Here the top 25 Adjusted Ratings Longhorns as they entered the 2024 season.
Quinn Ewers - 1.4426 - QB - RS Jr.
Kelvin Banks - 1.4165 - LT - Jr.
Jake Majors - 1.3697 - C - RS Sr.
Jahdae Barron - 1.2956 - CB/Star - RS Sr.
Jermayne Lole - 1.2623 - DT - RS Sr.
Trey Moore - 1.2392- Edge - RS Jr.
Andrew Mukuba - 1.2196 - S - Sr
Gavin Holmes - 1.1970 - CB - RS Sr.
Alfred Collins - 1.1952 - DT - RS Sr.
Jay'Vion Cole - 1.1734 - CB - Jr.
Isaiah Bond - 1.1708 - WR - Jr.
Silas Bolden - 1.1522 - WR - RS Sr.
DJ Campbell - 1.1261 - RG - Jr.
Malik Muhammad - 1.1162 - CB - So.
Barryn Sorrell - 1.0923 - Edge - Sr.
CJ Baxter -1.0791 - RB - So.
Matthew Golden - 1.0765 - WR - Jr.
Hayden Conner - 1.0715 - LG/C - Sr.
Derek Williams Jr. - 1.0510 - S - So.
Vernon Broughton - 1.0461 - DT - RS Sr.
Anthony Hill Jr. - 1.0452 - LB - So.
David Gbenda - 1.0403 - LB - RS Sr.
Johntay Cook - 1.0117 - WR - So.
Justice Finkley - 1.0099 - Edge - Jr.
Arch Manning - 1.0034 - QB - RS Fr.
There are many players who have VERY little development because they:
haven't played much
played mediocrely (close to a 50 PFF score)
Some of these players may surprise you.
What is interesting is that different coaching staffs may have different development scales. If one wanted, they COULD use this as a measure of coaching effectiveness.
With this scale even the top five-star recruiting rating freshman are rated as 3/3.5 star adjusted rating players.
I described the application of the Adjusted Rating transfers here.
One of the things I like about this scale is that it equates the day one play quality of say a consensus five-star prospect like Colin Simmons (0.9932) as a 3.5- star adjusted rating equivalent. This would rank him (in order) for the Edge players:
Behind fourth year transfer Trey Moore (1.2392)
Behind fourth year Barryn Sorrell (1.0923)
Behind third year Justice Finkley (1.0099)
On par with third year Ethan Burke (0.9934)
Ahead of second year Colton Vasek (0.9492) - had almost no playing time in 2023
Ahead of third year J'Mond Tapp (0.9144) - who had marginal development. This could explain why he transferred.
Ahead of second year Billy Walton (0.8907) - who had nearly no playing time nor development. This could explain why he transferred.
I think that sets up the day one play expectation of Colin Simmons fairly accurately from what we know now. The fun will be to monitor how a player like Simmons develops vs. the other ahead of him and if he gets the PT to develop
This system is intended to get towards a best answer. As with any similar algorithm, it will NOT be precise nor even 100% accurate. There are many assumptions embedded in this that are clearly wrong including:
The recruiting ranking baseline was accurate. This would be impossible to be 100% true when the recruiting services are evaluating high school athletes to develop their rankings and those players are changing more in those years than probably at any other time of their life.
Development works the same for everyone- This clearly is not true and I have many tweaks to the development model calculation I want to explore including possibly scaling/ ratioing the development score on the recruiting score.
I will say that even with these obvious challenges, a system like this can highlight position area that are strengths and weaknesses. It can help quantify how the overall team is improving.
My assumption is that college directors of personnel have something like this they are using with probably their own internal grading. The goal here is to get you immersed in this concept before I start applying it rapidly in several articles to come. s of each player
Here is our full Adjusted Ratings and Texas Depth Chart update series
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