Now that 2019 is in the books, let’s take a look at how 2020 is shaping up. Obviously, class metrics will change considerably. The benefit to charting how things are looking now versus how they shape up a year from now will provide insight as to how much fluctuation occurs, and maybe provide some relief for fans experiencing angst over their school’s recruiting efforts before the classes are signed. I expect lots of change between now and this time next year, but here is how things are shaping up for next year’s class at the moment.

Overall Summary:

The above chart shows the breakdown of how the top 1000 recruits (247 Composite) are leaning. Many aren’t leaning toward a school yet. Out of the 34 five-star recruits, 15 are leaning toward an in-state school while 17 are leaning toward an out-of-state school. The 2019 class saw 65% of the five-stars leave their home state. Right now, that figure is at 50%.

By Team:

The above table is a breakdown of how each school is doing in recruiting among the top 1000 players currently. Florida currently is 16th in terms of average talent rating for recruits committed/leaning toward them with a score of .9282. UCLA has the highest average, but they only have 3 leans.

By Position:

This table breaks down the class by position. There are 73 running backs in the top 1000 players, 5 of which are 5-stars, 24 are 4-stars, and 44 are 3-stars. The overall average rating for the position group is .8895. The skill position designation is subjective. Feel free to let me know if you disagree and why. I will gladly take input on that.

The University of Florida Gators:

This table depicts how Florida’s class is looking now. So far, so good.

By State:

This map shows how many recruits from this sample are leaning/committed to schools in various states. 53 of them are leaning toward Florida teams, with Florida and Miami each having 18, FSU having 16, and UCF having 1.

By Home State:

This map depicts where the top 1000 recruits for 2020 are from. Of note, 3 are from Canada and not depicted here. Florida and Texas are doing a lot of the heavy lifting, however, Georgia is producing some great players. Here is how the per capita breakdown looks:

Every year college football fans go bananas over blue-chip recruits (guilty). However, it isn’t uncommon for those unheralded three-star recruits to turn into a special player. For us Florida fans, we have had a bunch- Antonio Callaway, Jarrad Davis, etc. That aside, the reality is that blue-chip recruits are fairly rare. In 2019 there were a total of 383 blue-chips, that ultimately ended up at 54 different colleges. I took an in-depth look at those, and those findings will be finished sometime soon. But in the middle of that study, I got curious about the three-stars.

I took a sample of 1,225 three-star recruits from the 2019 class. There were more, but that was enough. They were the top-rated group that had committed to a school according to the 247 Composite website. In my blue-chip study, I was looking at overall migration patterns- where do the BCs come from and where do they go, etc. I started by doing the same thing for the 3s. Here is a map of where this sample came from (of note, 5 of them came from Canada, not depicted):

This is a map of where these recruits ended up. This gives you a general idea of where they end up going to school.

As you can see, a lot of Florida’s 3-stars leave the state. 165 (first map) are from Florida, but only 75 (second map) committed to schools in Florida, about 45%. This is a net loss of 3-stars of 55%. Keep in mind those that committed aren’t necessarily from Florida- that is just the gross number.

The overall average for recruits to leave their home state is 66%. 2019 5-stars left their home state 65% of the time, 4-stars 67%, and 3-stars left 67% of the time as well.  However, for the state of Florida, 118 of their 165 3-stars committed to schools out of state- 72%. A bit higher than average. In this metric, Florida ranked number one:

The average rating for the group is .8490. These players committed to 163 different schools. I was also curious as to how good Florida’s 3s rated compared to everyone else. I took the average rating and then standardized each school’s 3s ratings compared to the other schools in the sample. I then removed schools that have 2 or fewer three-stars; schools like Monmouth, Prairie View A&M, Alabama, and Stony Brook. Because they have so few of the 3s, their averages wouldn’t really change, and I wanted to rank the teams by average rating. Here are the top 20 teams for best 3-star recruits:

The Gators sit at 6^th overall. Their 3-star recruits are almost 2 full standard deviations above the mean (“Std. score”). If you’re going to get some three stars, might as well be good ones. Florida’s success recruiting good 3-stars is not an indicator (to me) of recruiting failure. Mullen is doing an excellent job of stocking the roster with blue-chip talent, especially compared to his predecessors:

With Florida landing a top 10 class this year and on track to have an even better class next year, Florida will be just fine on talent. Go Gators.

In this analysis, I was curious about the migratory patterns of the modern blue-chip high school football recruit. Blue-chip is defined as a 4 or 5-star recruit. Using 247 Composite ratings, I analyzed every BC in the 2019 class across several dimensions. It ended up being a large data set because I got carried away. The factors reviewed were:

1. 5-star vs 4-star status
2. Home state
3. State of school committed to
4. School committed to

Then I looked at how many recruits stayed in their home state versus leaving for another state, and each school’s role in that migration. The chart below shows the overall breakdown.

I then looked at how each school contributed to its states’ level of migration. For example, the state of Georgia produced 40 blue-chip recruits (Seven 5-stars and thirty-three 4-stars). 34 of those committed to schools not in Georgia, so they lost 85% of their home state talent. The Georgia Bulldogs, however, have 18 blue-chip high school recruits committed as of this writing. 13 of those come from other states. Their blue-chip poaching percentage is 72%. The chart below shows the findings for every school that has a BC committed.

An important note here. For BCs that are still unsigned, the Crystal Ball percentages were used to include them. So, if a recruit was 51% to University 1 and 49% to University 2, that recruit is included in the data for University 1. I will update after all are signed.

Migration is not just a Florida issue.

Out of the 383 blue-chip recruits for the 2019 class, 255 have committed to or are favored to go to out of state schools, which is 67%. Florida makes up a chunk of that date, with 28 out of 46 (61%) leaving. California (35 out of 47 -74%), Georgia (34 out of 40- 85%), Texas (25 out of 47- 53%), Louisiana (8 out of 15- 53%), and Alabama (9 out of 14- 64%) all have similar issues.

Both 5-stars and 4-stars are equal in terms of migrating away from home, 68% and 66%, respectively.

I won’t disagree with anyone who thinks winning state recruiting battles isn’t important but keeping talent in-state isn’t a common thing in modern times.

Teams with top 20 recruiting classes for 2019 who have the highest poach percentage (number of blue-chip recruits committed who come from out of state relative to BCs from in-state) are:

Schools with the most BC recruits from Florida (either committed or favored by Crystal Ball):

There is a lot more I’m looking at here, and this will be updated after NSD. Check back with me to see what the final numbers look like.

A hot topic on Twitter, at least for a few minutes, was Georgia’s poaching of Florida cornerbacks coach Charleton Warren. Florida coach Dan Mullen responded by bringing back one defensive backs coach Torrian Gray. The general consensus was that Florida upgraded. Taking a look at the numbers from each coach’s one year spent in Gainesville, it appears as if Florida did indeed come out ahead in this series of transactions.

Taking each coach’s statistics at Florida (Gray in 2016, Warren in 2018), I analyzed opponent QB ratings. There are several other metrics that could be included, but I chose this one because I thought it provided a good overall view of how the defensive backs performed. I analyzed how the team performed relative to the previous year. So for Gray, I used 2015 and 2016 statistics. For Warren, I used 2017 and 2018.

I was generally looking to see how much improvement each team showed under the coach compared to the previous year. I then compared the raw data to the national average and to how they stacked up compared to the rest of the SEC. Here’s how it played out:

Coach Warren saw his unit improve from an average opponent passer rating of 130.9 in 2017 to 122.9 in 2018. Overall, Florida ranked 38^th in the nation in 2018 (way up from 68^th in 2017). When standardized against the rest of the SEC, Florida showed the 3^rd best improvement from the previous year. Not at all a bad job by coach Warren.

In coach Gray’s only year in Gainesville, he saw his defensive backs move from 11^th best in the nation in terms of opposing QB rating all the way to number 1. Relative to the rest of the SEC that year, Florida had the second-best improvement behind Arkansas. The Razorbacks, however, had been so horribly horrendous the previous year that even with the excellent improvement, they were still worse than average. Florida, under gray, went from very excellent to the best.

What the numbers in the charts mean:

In columns 3 and 4 (from left), this is the overall opponent QB rating for that year. The columns marked with a ‘z’ is the standardized score for the SEC. The lower the score the better. The improvement score shows the difference from the year the coach was there compared to the previous year. Again, the lower the number, the better. When you look at the two charts, you can see that Florida improved -0.948 standard deviations better under Gray in his one year than they did under Warren in his year with the Gators. Given the findings, I believe the Gators upgraded after all is said and done.

The number of 4-star players on the roster are most strongly correlated with winning percentage for the 2018 teams in the top 20 talent ratings. Here are how some factors correlate to win percentage:

Five-star players: 28.1%

Four-star players: 59.8%

Three-star players: negative 18.2% (the higher the number of 3-stars, the less you win).

Blue-chip percentage: 48.8%

Talent rating: 43.7%

However, when you go one step further and look at the strength of schedule ratings (Sagarin), SOS takes the cake:

SOS: negative 62.8% (the weaker the schedule, the more you win)

Total Blue-chips (4 and 5-stars) 54.4%:

The SEC as the dominant conference in college football is a common theme. There has been a lot of analysis attempting to prove and disprove this widespread notion. This analysis utilizes the 2018 season only, as to avoid any claims of historical biases and to make the argument about which conference is the best right now. I don’t need historical data for that.

This analysis examined each conference across several statistical dimensions, scored the conferences and then ranked them. One caveat- only teams in the top 50 talent rankings, roster-wise, were included. This was done for one primary reason: Time constraints. I have to analyze each, and every roster of every team and I already have the 2018 data for the top 50 most talented teams according to the Composite Rating website. However, there is another benefit. By excluding teams without top 50 roster talent, negative outliers are removed. The analysis is based on averages and outliers can have a heavy impact on averages. So, there’s that.

The dimensions analyzed were:

1. Average ordinal roster ranking.
2. Average number of 5-star recruits
3. Average number of 4-star recruits
4. Average number of 3-star recruits
5. Average roster blue chip (4 & 5-star recruits) percentage.
6. Average roster talent rating (Composite).
7. Average winning percentage (regular season).
8. The average number of blue-chip recruits in a conference.
9. Percentage of teams in the conference among those with top 50 talent roster (included for analysis).
10. S&P+ Strength of schedule average.
11. Dispersion scores.

The dispersion scores measured the amount in variance each conference had among its sample scores. The smaller the number, the more consistent the scores. This is a way of examining the influence of potential outliers or extreme scores.

The table above shows the scores for each conference along each dimension. So, the SEC scored as follows along the 11 dimensions:

• Averaged ranking of roster talent: 20.1
• Average number of 5-stars per team in the sample: 2.9
• Average number of 4-stars per team in the sample: 27.9
• Average number of 3-stars per team in the sample: 47.2
• Average roster blue-chip percentage: 37%
• Average talent rating per roster: 87.9
• Average win percentage per team: 63%
• Average number of blue-chip players per roster: 30.8
• Percentage of teams from conference among the top 50 talented rosters: 100%
• The average strength of schedule (SOS) according to S&P +
• Overall average variance of all dimensions: 5.5

The charts below depict how each of the conferences ranked among the dimensions. This clearly shows the SEC as the dominant conference in 2018. The rankings flow top to bottom, 1 to 5.

The SEC ranks at the top of almost every category and has all 14 teams in the sample. Each of the other conferences scores would be lowered with the inclusion of every team, thus widening the gap between those conferences and the SEC. The Big 12 has the highest winning percentage but also had the fewest teams included in the top 50. While I can’t say for sure, I suspect that if I included all of the Big 12 teams, the overall win % for that conference would go down. I might do that in the future, but until then, this gives you an idea of how 2018 went for each conference.

The data on the 2019 high school class Blue Chip players (4 and 5 star rated players by 247 Composite). A full chart of the players is at the bottom.

By position:

By State:

All Players: