Impact of Talent Ratings on 2019 Win % in the SEC by Position Groups

We can all infer that not all position groups impact the outcome of a season equally. This could be due to numbers (5 OL vs 1 or 2 TE) or nature of importance (QB vs long snapper). I’ve already looked at the correlation between talent level and outcome by whole roster and by offense vs defense. So this time, I just broke the same data down a little further by position groups.

Each scatter plot below has a box that shows the correlation and effect size. The correlation is how closely the two variables (2019 win % and average composite talent rating for that team’s position group) are related. The effect size (technically, a coefficient of determination, but that can be interpreted as effect size in regression) essentially shows how reliable that correlation is. Every relationship was statistically significant at 0.06 or lower.


Offensive Line:



Running Back:



Wide Receiver:



Tight End:



Defensive Line:






Defensive Back:




QB was done differently due to sample sizes. When I used the average rating of all QBs on each roster, the outcome was random. In situations like Florida, where the highest rated QB was not the starter, the highest rated was used (So, Feleipe Franks for UF). Of course, this doesn’t make sense on the surface, but I surmised that using the highest ranking individual is a general indicator of how good the QB situation is for a team. If that QB gets beat out by a lower rated player, then the lower rated player may have been undervalued (or the higher rated player was a bust…). Either way though, using the highest rated player at the QB position had some value, so I used it. If you have a better explanation (and I hope you do), feel free to share.



Position Group Effect Table

Here is a table summarizing effect size by position


Surprising to me, is that the LB position group had the strongest correlation to win % for SEC teams in 2019. Keep in mind, this isn’t how important a position group is to winning – this is how well the composite rating of that position correlates to winning. Most of us would agree that effective QB play is key to winning. Here is that output visually in a radar graph:



Expectations scores around zero, whether positive or negative should be considered as meeting the expectations. Here is the table with each team’s total:


Looking at each team’s performance relative to expectations by position group is a neat way to peer into how well the on-field coaching is. It is important to note that teams with higher talent ratings will have harder times surpassing expectations and it will be easier to fail expectations. Not surprisingly, LSU had the best on-field coaching performance. Winning all your games and a Natty should cement that. Relative to their on-roster talent levels, Florida was second only to LSU. Imagine what Dan Mullen could do with elite talent… he’s already not far off with the talent he has.


Dan Mullen is probably the best on-field coach in the SEC. I am skeptical LSU will have anywhere near the success they had last year when they caught lightning in a bottle. I expect Saban and Alabama to return to the top of the West. Georgia can recruit like crazy, but just doesn’t perform up to that talent level. They are a good team, that should be great.

I would focus hand-wringing over recruiting on QB, WR, and LB. The need for an elite QB is a given (though nothing I found here would indicate that, likely because of the small number of rated QBs on a given roster). But if 2020 holds form with 2019, LB roster talent and WR roster talent will more strongly predict win percentage. RB and TE position groups are the weakest predictors. We will see how this holds up in 2020.


Number of 3-stars on roster negatively correlates to wins: SEC 2019

Just playing with some numbers, I found that on-roster 3-star count was pretty strongly correlated with regular-season wins for SEC teams in 2019. I’ve done this in the past, and found that it is a pretty consistent relationship.

3-stars and wins sec 2019

The correlation is 84%, R-squared is 70%, p-value is <.001, and SE is 1.76. Definitely something to this. The relationship between 3-stars and wins is strong than Blue-Chip count and wins, though it is close. This might just be a slightly different way of looking at the same thing, but I found it interesting.

Crystal Ball/Commit Accuracy for the class of 2020 by Month

The graph below shows how accurate Composite Crystal Ball (CB) predictions were relative to a recruit’s ultimate landing spot by month for the 2020 class. This is only for 4-star and 5-star players (composite).



I scraped web data every month and recorded it last year. Then, I just compared the CB % and commits to where a recruit ultimately signed. I’m doing the same for the 2021 class to see if it follows any trend.

Players that commit to a team generally stay committed. This is the same time frame of players who were committed to a team and ended up with that team.


Even as far back as February, 71% of Blue Chip recruits did not (ultimately) flip. We will see if this year is any different.

Tracking College Football Recruiting for 2021: Examining Recruiting’s Volatility

This will be a look at how much recruiting changes over the course of a year. I constructed an algorithm that tracks how each program is doing in terms of recruiting for 2021. I will reveal the full methodology after NSD, but the purpose here is to see how chaotic recruiting in college football is. Each school is assigned a random code. The star-points is each school’s current score based upon the algorithm. The column on the right is that school’s rank for that month. Schools with fewer than 4 points are excluded.

I coded each school (I also did for players and positions, but that is irrelevant here) so I don’t know how each school is doing (yes, I could look it up, but I’m not going to do that until after NSD).

Here is the first list:


school sum_star_points rank_points
m22b8 345 1
d7t22 280 2
u9z14 211 3
r21v4 181 4
f7z2 172 5
v26x18 161 6
r20z2 159 7
c1d19 148 8
e19i12 144 9
l14z25 141 10
d16o5 133 11
k26m12 112 12.5
t25m16 112 12.5
k20i15 97 14.5
d11u16 97 14.5
z22v17 83 16
h4u3 73 17
h1z22 69 18
i24d10 68 19
k18a7 64 20
d17g13 57 21
c20u4 48 23
p16n8 48 23
r5f22 48 23
y5l1 44 25
h15z5 40 26
n12m12 36 27
a19c19 32 28
f18q6 28 31
m10y9 28 31
p24s14 28 31
u10j20 28 31
c26t19 28 31
r5m14 24 34.5
h24h7 24 34.5
l19d7 16 37
n22p23 16 37
x10h10 16 37
g24t21 12 41
k8f6 12 41
l2k21 12 41
o12i11 12 41
s22p11 12 41
q25f10 8 45.5
n1v26 8 45.5
d4t10 8 45.5
o14n6 8 45.5
o17q4 4 48


Visual breakdown of the SEC 2020 NFL Draft

As always, the SEC was well-represented in the NFL draft. The graph below shows how each SEC player was drafted relative to their Composite rating coming out of high school.

The Y-axis is draft points. Each player was given 100 points for the round in which they were drafted and 1 point for order within that round. For example, Joe Burrow was drafted in the first round (100 points) and was selected 1st in that round (1 point) for a total of 101 points. Fewer points obviously means a higher draft pick.

The x-axis is the player’s composite rating. The quadrants are created by averages of both axes. Quadrants are labeled 1 through 4, starting on the upper left and working clockwise from there.


Players in quadrant 1 were not highly rated coming out of high school and were drafted later than the conference average. Players in quadrant 3 were highly rated and drafted earlier. These can be considered accurate outcomes. Quadrants 2 and 4 work in the opposite way. They were either highly rated but drafted later (quadrant 2) or lower-rated and drafted earlier (quadrant 4).


Most of the players (39.7%) fell into Q3, which is good. Overall, the Composite ratings were a pretty good indicator if a player would live up to their draft potential for SEC teams with an accuracy of 63.5%.


To no one’s surprise, LSU was a draft-day winner. They had 14 players drafted and on average went in the 3rd round. Alabama was very impressive.


The SEC had a lot of defensive linemen drafted and drafted highly. Pretty impressive.

The Players

Below is a list of all the SEC players drafted and their relevant info. The distance score is a player’s standardized draft points subtracted from their standardized composite rating. This metric was calculated to see just how far or near a player’s draft position matched his composite rating. A negative score indicates some level of overachieving, a positive score is the opposite (underachieving), and a score close to zero indicates the player pretty much got drafted as expected.




The biggest overachiever was Justin Jefferson of LSU. I’m not discussing the biggest underachiever. I will, however, point out that the biggest underachieving QB was from Georgia.

As always, if you see any errors, just let me know so I can fix them.

Go Gators.

Reviewing SEC 2019 QB Performances: Burrow and Tua were great. Watch out for Kyle Trask next year.

In this analysis, I took the opportunity to look at how the SEC QBs performed in 2019 when controlling for the disparity in games played. As is evident in the table below, of SEC QBs with a minimum of 250 passes, there is a bit of disparity in the number of games played.sec2019rawperformance

Joe Burrow obviously had a fantastic year. But to get a sense of the year each QB had on a scale with each other, I took the average number of games played for these 10 qualifying QBs (12.1) and projected each of their statistical performances over that number of games. For my analysis, I only used the categories in gray in the above table. This is to avoid unnecessary repetitiveness, as completions and attempts comprise the category of “Pct” (completion percentage). Y/Comp uses the completion data, so I kept that.

Below is a look at how each of these QBs projected numbers would look:


I then standardized each of the statistical categories except completions, as I no longer needed that. Of note, standardization worked here because each of the categories had data that were approximately normally distributed. Now, the only categories I was interested in were completion percentage, yards, TDs, Interceptions, and Yards per Completion. The standardized score with color-scaling is below:


To see how each of the QBs did relative to their peers, I simply summed each of the standardized scores to achieve an aggregate score. I then graphed each of these to give a sense of proportion to each performance. Burrow and Tua were on a completely different level overall:

* = standardized performance

Other than Tua and Burrow, only Kyle Trask and Jake Fromm had a net positive rating. Kudos to both. Below are the rankings and aggregate score for each QB:


This analysis serves to highlight the magnitude of the year Burrow had and Tua would likely have had. Furthermore, it shows that Kyle Trask, who started the year as a backup, really did have an outstanding season. To play that well with such limited experience indicates to me that he will potentially have a great year next season. Trask should be considered the SEC’s leading QB going into 2020 in my opinion. Of course, no QB performs in a vacuum, but looking at the 2019 performances from a statistical standpoint is certainly encouraging for Florida fans and possibly the Cincinnati Bengals (Burrow) and Miami Dolphins (Tua).

A Peek at Things on a National Scale:

I also decided to apply the same process to the top ~100 QBs nationally ( Here, I included only the top 25 Power 5 QBs:


This shows us again how impressive Trask was in 2019. He ranked 16th nationally among P5 QBs. Of note, the aggregate score changed because it is based upon the relative national scores instead of the relative SEC scores as in the previous section. Furthermore, this table shows how Tua and Burrow were both dominant at the national level as well. Other takeaways for me were Sam Howell of North Carolina performing so well as a true freshman and Trevor Lawrence being *only* at number 10.

As always, let me know if there are any errors. Go Gators.