The Changing Baseline of Composite College Football Recruits

The recruiting rankings, which are led by the Composite rating service, are likely evolving their analytical techniques. If this is true, then there should be some identifiable changes in output over time. I took a look at the top 1000 recruits from 2005 through 2020 and averaged their ratings. I then standardized the ratings to see if any significant movement is occurring over time.


The above chart shows the raw scores for each year. It is easy to see there was a fairly sharp upward trend in average ratings from 2005 to 2009. From there, it looks like things have leveled off for the most part.


When I standardized the scores, we can see just how sharp the rise was. It also looks like things have generally trended upward since 2017. Because there is some rise, it may be wise to take the baseline changes into considerations when comparing classes over time. That being said, the effect does not look to be too dramatic.


The above table shows the raw and standardized scores. The numbers in the bright green boxes are the group average and standard deviations. All in all, the top 1000 average out fairly consistently, though there is definitely an overall upward trend.

When we look at this more deeply and parse out by positions, the trends stay the same. allpositions

In this table, we can easily see that with the exception of center and fullback, all position groups are trending toward higher average ratings since 2009. Something must’ve happened in 2009 that lead to a change in how recruits were rated. Every look so far at the data shows a sharp climb from that point on. In looking at the positions combined into position groups, we see the trend continue.

The table below shows the average rating for top 1000 recruits by position group. The offensive skill group consists of QB, RB, APB, WR, and TE. The others are self-explanatory.


When we standardize these scores and apply heat-mapping, the contrast is again clear- from 2009 on there was a sharp rise.


Since there was a clear delineation in ratings from 2009 on, I charted the data without years prior to that. There’s a bit of variance in that time. 2018 and 2019 were both pretty high above average for the group, and 2011 far below. So far 2020 appears to be reverting to the mean.


The key takeaway is that when comparing class ratings over time, it might be a good idea to control for rating inflation. This is easy to do by simply standardizing each year’s data and moving forward with standardized values rather than raw values.


Revisiting Stars and All American Achievement

I’ve heard/read that 5-star recruits are much more likely to be All-Americans in college football than other recruits of lower star designation. I don’t doubt that has been historically true, but I was curious as to whether that still stands true today. Short answer, it does.

I took roughly the top 1000 rated recruits (Composite) from 2006 through 2017 and compiled their recruit rating and star designation. I then pulled the All-American teams from 2010 through 2018 from

Out of 11,843 recruits, here is what I got:

Stars 5 4 3
Count 397 3432 8014
Percent 3% 29% 68%

So we see that 5-stars make up 3% of this sample, 4-stars 29%, and 3-Stars 68%.

Then I tallied up the counts for All-Americans. Kickers and Punters were removed, as they can’t be ranked higher than 3-stars in the composite rankings for some silly reason.

Stars 5 4 3 2 Total
Count 36 65 68 48 217
Percent 17% 30% 31% 22% 100%

We can see here that, relative to the percentage of the sample, 5-stars are over-represented, 4-stars are consistent and 3-stars are under-represented. 2-stars are probably under-represented, but since my comparison population didn’t include 2-stars, I don’t know what percentage of all players they represent.

Here is how the All-Americans breakdown by position and stars:


When looking at relative success rates, 5-stars come out ahead. Out of the 397 5-stars drawn, 36 of them were All-Americans. But wait! What if a player was All-American more than once? Well, that happened 15 times. One was a punter (Tom Hackett), so that record was removed. Out of the 14 remaining players that were 2x All-Americans, 2 were 5-stars, 4 were 4-stars, 7 were 3-stars and one was a 2-star*. Position-wise, there were 2 WR, 3, LB, 2 RB, 3 OL, 3 DL, and 1 DB.

Adjusted for duplicates, the table now looks like this:


Stars 5 4 3 2 Total
Count 34 61 61 47 203
Percent 17% 30% 30% 23% 100%


As we can see, removing duplicates doesn’t change the takeaway. If you breakdown the relative success rates for players from the sample, here is what you get:

Stars 5 4 3
Representation 9% 2% 1%

9% of the 5-stars from the sample went on to be All-Americans. By far the most. So, yea. 5-stars are generally more successful if you agree that being an All-American is a metric for success.

Breaking down the size differences among college football recruits by star rankings

As part of the data gathered on one of the previous analyses, I ended up with some information on the size differences between the 3 categories of star-rankings for college football recruits. The sample size is 4,964 and covers the top 1000 or so recruits from 2015 to 2020.

counts table

height table

weight table

The grey columns on the right of the height and weight tables are the standard deviations. The height and weight scores in the table are heat-mapped- darker green is a higher score, yellow is lower. All in all, it is easy to see that higher ranked kids are usually bigger in almost every position group.

State Matters: College Football Recruit ratings

I analyzed the Composite recruiting data from 2015 to the current 2020 rankings across several dimensions: Position, Height, Weight, Home State, and Star-ranking. Punters, kickers, and fullbacks were excluded because punters and kickers cannot have more than 3-stars and there are very few fullbacks, so why bother with them. Incomplete records or those with missing data were removed. The sample size was sufficiently large (N= 4964) to not be impacted by removals. Also, each year was analyzed for the top 1000 recruits, so not all the 3-stars were included. *Data from the 2019 class was corrupted, so only the top 50 players were included from that class- I will update once I fix the issue.

Here are some insights I found.

First, the talent, if you go by the composite rankings, is certainly not evenly distributed among the states. I took some of the states with varying total numbers and applied some heat mapping. I included states with differing number of recruits as to allow for the contrast. I then mapped each segment by state.






We can easily see that, in this sample, California and Georgia are overrepresented in terms of 5-star players. Cali has 10% of the overall number of top players, but 14% of the 5-stars. Georgia is the same. Georgia, however, has only 9% of the 4-stars, while Cali has 11%.  This is even more marked given Georgia’s overall population (credit: Wikipedia):


So, are Cali and Georgia kids bigger than the other state’s ‘croots? Nope.



When standardizing the average weight for each position by state, Cali and Jawja aren’t necessarily putting out heavier players. I was too lazy at this point to do the same for height. Maybe I’ll add that later. But the red squares are incidents in which the average for that group is lower than the overall group average (all the states). Yellow is above the average. Out of the 17 position groups, Cali kids were lighter than average in 13 of them. Georgia kids were lighter in 7 of them.  Yes, this is just weight, and that certainly isn’t a measure of ability. But it can be considered as a measure of physical development to some degree.

Is there a bias for these states’ recruits? I don’t know, but there is certainly some level is disproportionality for some reason.

So, kid… you want to be a 5-star recruit?


Great. Then don’t play these positions:

Safety (7)

Athlete (6)

Dual-threat QB (6)

Inside Linebacker (5)

Guard (4)

All-Purpose Back (3)

Center (2)

Tight End (1)

Spanning from 2015 to the 2020 class, I looked at 4964 recruits generally made up from the top 1000 for each year (some were dropped for being kickers, punters, and fullbacks, and if the data was incomplete/missing on a player). These positions only had the number in parentheses designated as 5-stars.

Do play these positions:

Offensive Tackle (26)

Wide Receiver (23)

Defensive Tackle (21)

Cornerback (20)

Running Back (19)

Strong-side Defensive End (15)

Outside Linebacker (14)

Weak-side Defensive End (14)

Pro-Style QB (10)

Also, if you play:

RB      DT       ILB      OLB    SDE            WR      OT       CB       ATH            WDE   PRO    OG APB

Then be taller and heavier than everyone else. Each of these positions 5-stars averaged higher heights and weights than 4 and 3 stars.

If you are a CB or OC, then you need to be significantly (greater than 1 standard deviation) heavier than your peers. If you’re a tight end, you need to be significantly taller and heavier (though this may not be true since only 1 tight end is the 5-star sample size, but whatever).

Looking at Strength of Schedule: An Analysis of the Early Top 10

There are a number of pre-season rankings for the 2019 college football season that released their post-spring rankings. I was curious as to which teams, based on one of these rankings ( was looking at the toughest schedule, at least as it appears now.

Team Rank
Alabama 1
Clemson 2
Georgia 3
Oklahoma 4
Ohio State 5
Texas 6
Florida 8
Michigan 9
Notre Dame 10
Texas A&M 11
Penn State 12
Oregon 13
Washington 14
Mississippi State 15
Auburn 16
Army 17
Washington State 18
Syracuse 19
Stanford 20
Wisconsin 21
UCF 22
Iowa State 23
Northwestern 24
Nebraska 25

I then assigned a value to each of the top 25 teams in the Sporting News’ rankings. This point value was simply inverse to their ranking (the 25th ranked team was worth 1 point to play, the top-ranked team is worth 25 points to play). Then, I took the top ten teams and looked at their upcoming schedule and tallied up the number of points their opponents are worth. Any opponent not ranked in the current top 25 is worth zero points. Charting the totals in a histogram shows that the point system is approximately normally distributed (i.e., similar to a bell-shaped curve).


To understand the degree of difficult beyond ordinal rankings, I standardized each teams’ overall point total relative to the other top ten teams. What we end up with is a pretty close look at how tough a schedule each of these top ten teams has compared to each other.


The outcome shows that LSU is looking at a very tough schedule, while Clemson has a cake-walk ahead of them. The column on the far right displays each teams’ overall difficulty in terms of standard deviations from the group (top ten teams) average.


I considered penalizing teams for home games and non-P5 opponents, but it wouldn’t change the outcome, so I chose not to complicate the analysis. Furthermore, if any of these teams are to make the playoffs, how they do against top 25 competition will be key.

A 5-star bias? A case study.

247 sports, a college football recruiting heavyweight, just released their top 25 running backs list for the 2019 college football season. They also included 10 ‘Honorable Mentions’.  Here is the list (Honorable mentions in blue, all tied for 26th rank):

Rank Player Team HS Year HS Rating HS Stars
26 Stevie Scott Indiana 2018 0.8402 3
26 Brian Robinson Alabama 2017 0.9361 4
26 Isaiah Bowser Northwestern 2018 0.8639 3
26 Larry Rountree III Missouri 2017 0.8435 3
26 Kylin Hill  Mississippi State 2017 0.9184 4
26 Jordan Cronkrite USF 2015 0.8853 3
26 Anthony McFarland Maryland 2017 0.9537 4
26 Master Teague Ohio State 2018 0.9132 4
26 Chuba Hubbard Oklahoma State 2017 0.8868 3
26 Trey Sermon Oklahoma   2017 0.9232 4
25 Zack Moss Utah 2016 0.8389 3
24 Spencer Brown UAB 2017 0.758 2
23 Pooka Williams Kansas 2018 0.9055 4
22 Darrynton Evans Appalachian State 2015 0.7519 2
21 Joshua Kelley UCLA 2015 0.7667 2
20 Rakeem Boyd Arkansas 2018 0.8467 3
19 Ben LeMay Charlotte 2016 0.8273 3
18 Lamical Perine Florida 2016 0.8699 3
17 Salvon Ahmed Washington 2017 0.9476 4
16 Ricky Slade Penn State 2018 0.9853 5
15 Cam Akers FSU 2017 0.9984 5
14 CJ Verdell Oregon 2017 0.8752 3
13 Ke’Shawn Vaughn Vanderbilt 2015 0.8953 4
12 Michael Warren II Cincinnati 2017 0.8707 3
11 J.J. Taylor Arizona 2016 0.8396 3
10 Kennedy Brooks Oklahoma 2017 0.9159 4
9 Jermar Jefferson Oregon State 2018 0.8619 3
8 Greg McCrae UCF 2016 0.8135 3
7 Najee Harris Alabama 2017 0.9984 5
6 J.K. Dobbins Ohio State 2017 0.9791 4
5 D’Andre Swift Georgia 2017 0.9838 5
4 AJ Dillon Boston College 2017 0.8803 3
3 Eno Benjamin Arizona State 2017 0.94 4
2 Jonathan Taylor Wisconsin 2017 0.8854 3
1 Travis Etienne Clemson 2017 0.9171 4

There’s nothing on the list that I really care to disagree with. As a Gator fan, I would Perine number one, but there may be some bias occurring there. I certainly think he is better than 18th, but I digress…


The 2018 statistics were taken for each player on the list and analyzed. Of note, only the stats for those players who were in the top 290 in performance last year were included (because this is how many were available at my source). In the above table, the production for each of the players on 247’s list is displayed. There were four 5-star players on the list and three 2-stars, comprising 11% and 8 % of the list, respectively. The key statistic here is where each of the star categories averaged their rank on the list. The 5-stars averaged the 11th (10.8) overall ranking and the 2-stars averaged 22nd (22.3).  4-stars were ranked evenly with 3-stars on average, 16.9 to 17.0.

When we take each of these categories and ranking them from 1 to 4 (1 being the highest), here is what we get:


The 5-star running backs averaged the best ranking, number of receptions, and receiving touchdowns. However, they were last in rushing yards, rushing touchdowns, plays from scrimmage, total yards, and total touchdowns. Overall, their production was barely better than that of the 2-stars (2.8 to 2.9) on average across the categories.

For the statistics folks, the difference between the 2-star averages in terms of where they are ranked on the list and that of the 5-star averages is statistically significant:

An independent-samples t-test was conducted to compare the rank of 2-star and 5-star players. Given a violation of Levene’s test for homogeneity of variances, (F= 32.775, p = .002), a t-test not assuming homogeneous variances was calculated. There was a significant difference in the scores for the 2-stars (M=22.33, SD=1.528) and 5-stars (M=10.75, SD=5.560) conditions; t(3.58)= 3.436, p= 0.02. The size of this effect (d = 2.84), as indexed by Cohen’s (1988) coefficient d was found to exceed the convention for a large effect size (d = 0.80).

These results suggest that 5-star players were ranked significantly higher on this list than were 2-star players. But did the production between the two groups warrant the disparity in the ranking?

An independent-samples t-test indicated that total yards were not significantly higher for
5-stars (M = 995.66, SD = 304.93) than for 2-stars (M = 1319.0, SD = 102.22), t(4) = 1.741,
p = .157, d = 0.35. Equal variances were assumed. 

Ricky Slade from Penn State was not included in the statistical comparison of yards because he wasn’t among the top 290 performers last year. His low production scores would’ve drug the 5-stars overall average down. (Did he redshirt last year?).

So why the disparity? 5-star running backs are given the highest ranking, but overall had the lowest production (if you included Slade) of each of the groups? I think there was certainly a bias for those players in effect in this particular ranking. When we look at each player’s high school year and average them out per star ranking, we can see the 5-stars are typically newer. This goes down the line:

Stars Avg HS yr
5 2017.3
4 2017
3 2016.8
2 2015.7

As you can see, the lower the star ranking, the more likely the player was to have been in college longer. This players ranking was likely on potential and subjective opinion.

Disclaimer because some college football fans get upset about everything:

This is just a case study of one list that was put out by 247 sports. Their list may be complete garbage and invalid in every way, not representative of the population, etc. I know. The point here was to take a micro-level look at the potential bias that occurs when sports reports, journalists, etc do these rankings.

Yes, the 5-star players play against tougher competition than the typical 2-star. Yes, that matters. But they don’t play against tougher competition than the 4-stars and most of the 3-stars.

Also: Alabama (Brian Robinson and Najee Harris) and Ohio State (Master Teague and JK Dobbins) have 2 players each on the list. Florida has 2 players it recruited on the list (LaMical Perine and Jordan Cronkite), but Cronkite transferred (now as USF).