There are a lot of articles about how it is important to successfully recruit talent in a school’s own state. Here is how each state did in terms of Blue-Chip (composite 4 and 5-stars) acquisition.

The percentages in the map and waterfall chart represent the net gain/loss for the number of blue-chips for that state. The totals are outlined in the table below.

Ultimately, 252 (66%) of 382 Blue-Chips left their home state. Texas, Florida, Georgia, and California had the highest levels of loss. To control for population differences, I then standardized the number of Blue-Chips produced and received and subtracted the produced from received to see how the states did proportionally.

When controlling for number of players produced, Florida moves up some and Texas moves up a bunch.

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:

Linebacker:

Defensive Back:

Quarterback:

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.

Expectations:

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.

Takeaway:

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.

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.

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.

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.

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:

5/1/2020

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.