THIS has been updated with a major adjustment to the development scale. Most of the updated text is in red.
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 our Texas Longhorns (and any other team) in building a championship team.
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.
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Now let's continue.
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.
Note: the factor in the equation has been amended to make the development factor 10 X bigger than it was before. All other data have been adjusted and a scale for the adjusted index redefined.
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 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.
Note: With the redefinition of the Development Score my scale and how the Adjusted score has now changed.
The 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
The top 11 projected for the NFL Draft by PFF have development scores as high as 1.0973 for Jayden Daniels. Daniels played in 3418 snaps over five VERY productive years. There was a reason he won the Heisman.
Check out this article to see how the Longhorns headed to the draft have developed and how they compare to that top 11 group. (It's Premium right now, so sign up.)
You will see the modified adjusted ratings as I share the recruiting/transfer, development, and adjusted ratings for the entire Longhorns roster.
There are many Texas 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.
There are actually two players that have negative development scores. They have played below the 50 score. When you see this, I think you understand at least one of the transfer moves that was made. I will also say this. The yearly PFF grades change for each player based on performance. There are several players grading out in the 50s as a freshman or sophomore that are in the 70s as a senior- Christian Jones is an example of that.
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 change here is my revised scale to help you draw to comparison to something more familiar.
Note Revised- How to treat transfer players is probably the most challenging aspect of the adjusted rating right now.
Check out my discussion of all the transfer 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):
Behind draft eligible All- Conference Jaylan Ford after four years
Behind fourth year Barryn Sorrell
Behind second year Anthony Hill
Slightly behind third year Justice Finkley
On par with third year Ethan Burke
Ahead of third year J'Mond Tapp
I think that sets up the day one play expectation of Colin Simmons fairly accurately from what we know now.
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.
So, there is the preamble describing the system. You can refer to the Texas Longhorns Roster Index to see all of the content on the horizon.
Now let's discuss the DATA.
The Adjusted Rating Data
Let me start with this data dump for you. Here is the big picture.
The weighting is on snap count in the above. As we all know, the Longhorns have a strong group that has played a lot of snaps headed to the NFL draft.
The transfers out as you will see in that article have some good players leaving; but overall, they did not play many snaps in 2023. The transfers out include several players that have developed quite a bit (some of that before getting to UT as they are now on their second transfer.)
The transfers in are on average higher rated than the transfers out. They have as many and a little more productive snaps as a whole.
For the rest of this, we will focus mostly on the core roster that remain from the 2023 Longhorns.
Here is the current Texas Longhorns returning roster before we add the recruits and the 2024 transfer players- the burnt orange line above. The players headed for the NFL draft and leaving to the transfer portal are also not in this group.
PLEASE let me know if you see something I have missed. Check it out all the way to the end for a special fun fact.
Here are the 57 returning non- special teams scholarship players sorted by class (last column) and then adjusted rating (purple column). The classes are color coded.
I should explain the class nomenclature that I am using.
Majors- 53RS Sr means
he is playing in his 5th year (2020-2024)
he has 3 previous years of non-redshirted experience (2021-2023)
he has had a red shirt season (2020)
he will be classified as a senior this year.
Here is the special teams group.
Only St, Louis, Stone, and Kern are on scholarship. The 0.7799 for Auburn is a placeholder I added.
Let's look are the core roster by position and then adjusted rating.
We will use this look to start to build my depth chart ahead. You can also see which positions have starter quality or depth challenges.
Notice the lack of relative development at TE, LB, and Edge. This definitely gives a context to the addition of Blackshire and Niblack from the transfer portal.
Let's also look at the list sorted by development.
Majors, Ewers, Banks, and Barron are all four ahead of the great development that Worthy, Jones, and Sweat had before their 2023 seasons. You heard it here first. They are about to rule the NCAA. Kidding, or am I?
All four of these players have played so much and so well already, that all have a chance to hit at least 0.6 development score. Ewers COULD hit 0.7000.
Hook 'Em!