Florida Gators Blog

An in-depth statistical look at the 2019 Blue Chip high school recruits.

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.

migchart1

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.

chart2

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.

chart3

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:

school heat map

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

chartFL

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.

Did Florida upgrade when they acquired Gray after Warren went to Georgia? A statistical breakdown.

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:

warren ranks

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 38th in the nation in 2018 (way up from 68th in 2017). When standardized against the rest of the SEC, Florida showed the 3rd best improvement from the previous year. Not at all a bad job by coach Warren.

gray ranks

In coach Gray’s only year in Gainesville, he saw his defensive backs move from 11th 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.

Factors related to winning percentage in the top 20 most talented college football teams

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%

5star

Four-star players: 59.8%

4stars

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

3stars

Blue-chip percentage: 48.8%

bcp

Talent rating: 43.7%

talent

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)

sos

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

totalbc

A complete look at how the Power 5 conferences compared in 2018.

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.

table1 conference 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.

ranks1

ranks2

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.

Statistical breakdown of the 2019 Blue Chip class

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:

Position Count % by Pos 5 star %5 star Pos 4 star %4 star Pos
WR 48 13% 6 18% 42 12%
CB 39 10% 3 9% 36 10%
OT 33 9% 5 15% 28 8%
S 30 8% 1 3% 29 8%
SDE 28 7% 3 9% 25 7%
DT 27 7% 3 9% 24 7%
WDE 23 6% 2 6% 21 6%
RB 22 6% 2 6% 20 6%
ATH 21 6% 1 3% 20 6%
ILB 21 6% 2 6% 19 5%
OG 20 5% 2 6% 18 5%
OLB 19 5% 1 3% 18 5%
TE 15 4% 0 0% 15 4%
PRO 13 3% 1 3% 12 3%
DUAL 13 3% 0 0% 13 4%
APB 5 1% 0 0% 5 1%
OC 3 1% 2 6% 1 0%

Total BC by pos

5 star by pos

4 star by pos

By State:

State Total BC % of BC 5 star by state 4 star by state % 5 star % 4 star
 CA 47 12% 3 44 9% 13%
 FL 46 12% 5 41 15% 12%
 TX 45 12% 4 41 12% 12%
 GA 40 11% 7 33 21% 10%
 LA 16 4% 4 12 12% 3%
 MS 16 4% 1 15 3% 4%
 NC 14 4% 0 14 0% 4%
 AL 13 3% 1 12 3% 3%
 TN 13 3% 0 13 0% 4%
 OH 12 3% 1 11 3% 3%
 VA 10 3% 1 9 3% 3%
 MI 9 2% 2 7 6% 2%
 NJ 8 2% 1 7 3% 2%
 MD 8 2% 0 8 0% 2%
 KY 8 2% 0 8 0% 2%
 AZ 7 2% 1 6 3% 2%
 IN 6 2% 0 6 0% 2%
 MO 6 2% 0 6 0% 2%
 OK 5 1% 1 4 3% 1%
 HI 5 1% 0 5 0% 1%
 AR 4 1% 0 4 0% 1%
 IL 4 1% 0 4 0% 1%
 CT 4 1% 0 4 0% 1%
 PA 4 1% 0 4 0% 1%
 WV 3 1% 1 2 3% 1%
 SC 3 1% 1 2 3% 1%
 WA 3 1% 0 3 0% 1%
 DC 3 1% 0 3 0% 1%
 IA 3 1% 0 3 0% 1%
 NY 2 1% 0 2 0% 1%
 KS 2 1% 0 2 0% 1%
 MN 2 1% 0 2 0% 1%
 UT 2 1% 0 2 0% 1%
 OR 2 1% 0 2 0% 1%
 RI 1 0% 0 1 0% 0%
 NE 1 0% 0 1 0% 0%
 CO 1 0% 0 1 0% 0%
 DE 1 0% 0 1 0% 0%
 NV 1 0% 0 1 0% 0%
Total BC by state
States with fewer than 3 were omitted from graph.

5 star by state

4 star by state

All Players:

State Player Position Rating Stars
 FL Nolan Smith WDE 0.9994 5
 CA Kayvon Thibodeaux WDE 0.9976 5
 LA Derek Stingley CB 0.9973 5
 OH Zach Harrison SDE 0.9970 5
 FL Trey Sanders RB 0.9969 5
 GA Jadon Haselwood WR 0.9968 5
 CA Bru McCoy ATH 0.9954 5
 OK Daxton Hill S 0.9948 5
 LA Ishmael Sopsher DT 0.9946 5
 WV Darnell Wright OT 0.9944 5
 TX Kenyon Green OT 0.9935 5
 LA John Emery Jr. RB 0.9931 5
 GA Wanya Morris OT 0.9929 5
 MS Nakobe Dean ILB 0.9925 5
 GA Owen Pappoe OLB 0.9922 5
 TX Garrett Wilson WR 0.9922 5
 MI Logan Brown OT 0.9921 5
 SC Zacch Pickens SDE 0.9913 5
 GA Andrew Booth CB 0.9905 5
 FL Evan Neal OT 0.9904 5
 TX Theo Wease WR 0.9900 5
 AL Clay Webb OC 0.9897 5
 AZ Spencer Rattler PRO 0.9886 5
 GA Travon Walker DT 0.9886 5
 VA Brandon Smith ILB 0.9886 5
 MI Devontae Dobbs OG 0.9876 5
 FL Frank Ladson WR 0.9873 5
 NJ Antonio Alfano SDE 0.9867 5
 CA Kyle Ford WR 0.9851 5
 TX DeMarvin Leal DT 0.9850 5
 GA Harry Miller OC 0.9845 5
 LA Kardell Thomas OG 0.9844 5
 FL Akeem Dent CB 0.9842 5
 GA Dominick Blaylock WR 0.9841 5
 AL Pierce Quick OT 0.9827 4
 AL George Pickens WR 0.9825 4
 TX Tyler Johnson OT 0.9815 4
 FL Tyrique Stevenson CB 0.9810 4
 NC Quavaris Crouch ATH 0.9807 4
 CA Chris Steele CB 0.9804 4
 CA Henry To’oto’o OLB 0.9800 4
 CA Zach Charbonnet RB 0.9800 4
 CA Mykael Wright CB 0.9793 4
 TX Marcel Brooks OLB 0.9787 4
 MS Jerrion Ealy RB 0.9787 4
 HI Faatui Tuitele DT 0.9780 4
 AL Bo Nix DUAL 0.9777 4
 IN George Karlaftis SDE 0.9773 4
 GA Chris Hinton DT 0.9760 4
 CA Mase Funa ILB 0.9757 4
 CA Joe Ngata WR 0.9746 4
 AL Amari Kight OT 0.9744 4
 TX Jordan Whittington WR 0.9734 4
 CA Ryan Hilinski PRO 0.9731 4
 TX Brian Williams S 0.9723 4
 CA Sean Rhyan OT 0.9706 4
 FL Khris Bogle WDE 0.9701 4
 TX Baylor Cupp TE 0.9700 4
 TX Trejan Bridges WR 0.9699 4
 AR Hudson Henry TE 0.9692 4
 FL Kaiir Elam CB 0.9690 4
 MI Julian Barnett CB 0.9687 4
 TN Maurice Hampton CB 0.9685 4
 CA De’Gabriel Floyd ILB 0.9682 4
 MS Nathan Pickering DT 0.9679 4
 TX Lewis Cine S 0.9676 4
 TX Demani Richardson S 0.9674 4
 OH Jowon Briggs DT 0.9668 4
 FL Jeremiah Payton WR 0.9655 4
 MD Shane Lee ILB 0.9650 4
 NJ Caedan Wallace OG 0.9649 4
 TX Austin Stogner TE 0.9643 4
 CA Jacob Bandes DT 0.9638 4
 CA Jonah Tauanu’u OT 0.9638 4
 GA Justin Eboigbe SDE 0.9636 4
 MD Nick Cross S 0.9633 4
 FL Jordan Battle S 0.9629 4
 FL William Putnam OG 0.9626 4
 NY Adisa Isaac WDE 0.9625 4
 VA Devyn Ford RB 0.9624 4
 AZ Jake Smith WR 0.9620 4
 CA Jayden Daniels DUAL 0.9615 4
 FL Mark-Antony Richards ATH 0.9614 4
 TX Marquez Beason ATH 0.9613 4
 CA Mycah Pittman WR 0.9611 4
 TX Erick Young CB 0.9609 4
 NC Sam Howell PRO 0.9606 4
 MS Byron Young SDE 0.9593 4
 TX Dylan Wright WR 0.9592 4
 FL Rian Davis OLB 0.9590 4
 TX Jeffery Carter CB 0.9589 4
 WV Doug Nester OG 0.9581 4
 NC Savion Jackson SDE 0.9580 4
 MD DeMarcco Hellams S 0.9576 4
 MS Charles Cross OT 0.9574 4
 MS Jaren Handy SDE 0.9571 4
 TX Elijah Higgins WR 0.9570 4
 CA Austin Jones RB 0.9565 4
 MS Brandon Turnage CB 0.9557 4
 AR Treylon Burks WR 0.9550 4
 IN David Bell WR 0.9543 4
 KS Graham Mertz PRO 0.9535 4
 GA Kyle Hamilton S 0.9530 4
 FL Noah Cain RB 0.9529 4
 MS Charles Moore SDE 0.9526 4
 OH Cade Stover OLB 0.9517 4
 GA Ramel Keyton WR 0.9517 4
 MN Quinn Carroll OT 0.9507 4
 MO Isaiah Williams ATH 0.9502 4
 AL Taulia Tagovailoa PRO 0.9502 4
 LA Trey Palmer WR 0.9495 4
 NJ Ronnie Hickman S 0.9493 4
 GA Kevin Harris WDE 0.9492 4
 LA Donte Starks ILB 0.9492 4
 AL Christian Williams CB 0.9490 4
 FL Brendan Gant S 0.9485 4
 MS Dannis Jackson WR 0.9485 4
 FL Dontae Lucas OG 0.9478 4
 OH Zeke Correll OG 0.9477 4
 FL John Dunmore WR 0.9474 4
 VA Sheridan Jones CB 0.9470 4
 GA Trezmen Marshall ILB 0.9468 4
 GA Zion Puckett S 0.9458 4
 CA Jeremiah Criddell S 0.9454 4
 AR Stacey Wilkins OT 0.9450 4
 TX Arjei Henderson WR 0.9449 4
 LA Bryton Constantin OLB 0.9438 4
 CA Trent McDuffie CB 0.9436 4
 TN Bill Norton SDE 0.9429 4
 LA Devonta Lee ATH 0.9428 4
 CA Jason Rodriguez OT 0.9427 4
 HI Enokk Vimahi OG 0.9427 4
 CA Laiatu Latu WDE 0.9424 4
 TN Lance Wilhoite WR 0.9422 4
 GA Kenyatta Watson II CB 0.9419 4
 FL Keon Zipperer TE 0.9410 4
 MI Mazi Smith DT 0.9407 4
 TX NaNa Osafo-Mensah WDE 0.9407 4
 RI Xavier Truss OT 0.9406 4
 MO Jameson Williams WR 0.9404 4
 NE Nick Henrich ILB 0.9399 4
 LA Tyrion Davis RB 0.9396 4
 KY Stephen Herron Jr. WDE 0.9393 4
 CA Sean Dollars APB 0.9390 4
 LA Christian Harris ILB 0.9386 4
 OH Nolan Rumler OG 0.9384 4
 TN Joseph Anderson SDE 0.9383 4
 GA Joseph Charleston S 0.9381 4
 FL Diwun Black ILB 0.9379 4
 TX Branson Bragg OC 0.9378 4
 GA Tyron Hopper OLB 0.9376 4
 CA Max Williams CB 0.9372 4
 AZ Noa Pola-Gates CB 0.9352 4
 LA Ray Parker OT 0.9352 4
 WA Dylan Morris PRO 0.9346 4
 KY Wandale Robinson APB 0.9340 4
 KY Jacob Lacey DT 0.9324 4
 GA Keiondre Jones OG 0.9318 4
 UT Siaki Ika DT 0.9315 4
 GA King Mwikuta WDE 0.9312 4
 FL Tyler Davis DT 0.9310 4
 GA Jaylen McCollough S 0.9308 4
 IL Trevor Keegan OT 0.9307 4
 GA Trente Jones OT 0.9302 4
 CA Drake Jackson SDE 0.9300 4
 TX EJ Ndoma-Ogar OG 0.9298 4
 AL Mohamoud Diabate OLB 0.9288 4
 CA Isaiah Foskey WDE 0.9285 4
 FL Jaquaze Sorrells DT 0.9284 4
 TX Isaiah Spiller APB 0.9284 4
 TN Eric Gray APB 0.9283 4
 FL Travis Jay CB 0.9278 4
 IN Sampson James RB 0.9268 4
 FL Deyavie Hammond OG 0.9262 4
 DC Joseph Weté WDE 0.9261 4
 CT Taisun Phommachanh DUAL 0.9258 4
 VA Jaden Payoute ATH 0.9253 4
 FL Ge’mon Eaford OLB 0.9252 4
 FL Kenny McIntosh RB 0.9248 4
 NJ John Olmstead OT 0.9246 4
 MS Derick Hall WDE 0.9245 4
 AZ Brayden Liebrock TE 0.9241 4
 TX Jalen Curry WR 0.9240 4
 TN Woodi Washington CB 0.9237 4
 FL Keontra Smith S 0.9235 4
 MN Bryce Benhart OT 0.9231 4
 CA Joshua Pakola SDE 0.9231 4
 TX Kam Brown WR 0.9226 4
 TX Marcus Banks CB 0.9222 4
 FL Braylen Ingraham SDE 0.9221 4
 MO Marcus Washington WR 0.9217 4
 HI Maninoa Tufono ILB 0.9212 4
 FL Anthony Solomon OLB 0.9208 4
 GA Jalen Perry CB 0.9207 4
 MI Lance Dixon OLB 0.9207 4
 GA Ryland Goede TE 0.9206 4
 TX Tyler Owens S 0.9206 4
 NC Nolan Groulx WR 0.9205 4
 MI Anthony Bradford OG 0.9203 4
 FL Jaleel McRae OLB 0.9197 4
 FL Josh Delgado WR 0.9197 4
 CA Isaiah Rutherford CB 0.9195 4
 FL Quashon Fuller SDE 0.9195 4
 MS Jonathan Mingo WR 0.9195 4
 TN Kane Patterson ILB 0.9192 4
 CA Chris Adimora S 0.9191 4
 AZ Ty Robinson SDE 0.9191 4
 NC Anthony Harris S 0.9189 4
 LA Makiya Tongue ATH 0.9186 4
 FL Chez Mellusi RB 0.9182 4
 SC Cameron Smith CB 0.9178 4
 NC C.J. Clark DT 0.9176 4
 VA Salim Turner-Muhammad CB 0.9173 4
 TX Roschon Johnson DUAL 0.9172 4
 NC Osita Ekwonu ILB 0.9171 4
 KY Milton Wright WR 0.9169 4
 UT Puka Nacua WR 0.9165 4
 NC Khafre Brown WR 0.9163 4
 MS De’Monte Russell WDE 0.9161 4
 TN Trey Knox WR 0.9159 4
 FL Avery Huff OLB 0.9155 4
 VA Litchfield Ajavon S 0.9148 4
 CA Daniel Heimuli ILB 0.9148 4
 TX Jalen Catalon S 0.9147 4
 VA Cam’Ron Kelly ATH 0.9145 4
 FL Jahfari Harvey WDE 0.9140 4
 TX Deondrick Glass RB 0.9138 4
 FL Jaden Davis CB 0.9138 4
 PA Andrew Kristofic OT 0.9138 4
 NC Tyus Fields CB 0.9136 4
 LA Lance LeGendre DUAL 0.9132 4
 MD Isaiah Hazel WR 0.9132 4
 TX Marcus Stripling SDE 0.9131 4
 GA Derrian Brown RB 0.9129 4
 GA Warren McClendon OT 0.9128 4
 KY Bryan Hudson OG 0.9127 4
 FL Lloyd Summerall WDE 0.9125 4
 NC Donavon Greene WR 0.9123 4
 OK Demariyon Houston WR 0.9120 4
 GA Steele Chambers ATH 0.9119 4
 TX Braedon Mowry WDE 0.9117 4
 CA Hank Bachmeier PRO 0.9116 4
 GA Kalen DeLoach OLB 0.9114 4
 CA Jude Wolfe TE 0.9112 4
 IL Jirehl Brock RB 0.9112 4
 OK Grayson Boomer TE 0.9108 4
 TN Jackson Lampley OG 0.9107 4
 NC Garrett Shrader DUAL 0.9106 4
 AL DJ Dale DT 0.9095 4
 MD D’Von Ellies DT 0.9092 4
 FL Josh Sanguinetti S 0.9087 4
 GA Jashawn Sheffield ATH 0.9086 4
 AL Paul Tyson PRO 0.9086 4
 CA Darren Jones WR 0.9083 4
 CA Stephon Wright SDE 0.9075 4
 CT Cornelius Johnson WR 0.9074 4
 CA Colby Bowman WR 0.9072 4
 CA Tristan Sinclair OLB 0.9072 4
 AR Darius Thomas OT 0.9071 4
 CO Luke McCaffrey ATH 0.9069 4
 MI Dwan Mathis PRO 0.9065 4
 IN Beau Robbins WDE 0.9065 4
 CT Tyler Rudolph S 0.9063 4
 FL Nay’Quan Wright RB 0.9063 4
 MS John Rhys Plumlee DUAL 0.9062 4
 MI Marvin Grant S 0.9061 4
 LA Devin Bush CB 0.9057 4
 TX David Gbenda ILB 0.9055 4
 GA Ja’Darien Boykin WDE 0.9055 4
 CA Drake London WR 0.9054 4
 OR Michael Johnson Jr. DUAL 0.9052 4
 GA Jaelin Humphries DT 0.9051 4
 AZ Jacob Conover PRO 0.9051 4
 DE Saleem Wormley OG 0.9049 4
 NV Cade McNamara PRO 0.9048 4
 AL Jaydon Hill CB 0.9048 4
 FL Derick Hunter SDE 0.9046 4
 NJ John Metchie WR 0.9045 4
 AL Vonta Bentley ILB 0.9043 4
 CA Keyon Ware-Hudson DT 0.9043 4
 FL Michael Tarquin OT 0.9042 4
 NC Joshua Harris DT 0.9039 4
 NJ Taquan Roberson DUAL 0.9039 4
 TX Daimarqua Foster RB 0.9038 4
 GA Curtis Fann SDE 0.9034 4
 KY Tanner Bowles OG 0.9033 4
 MO Jalani Williams S 0.9032 4
 MS Jarrian Jones S 0.9029 4
 TX Peyton Powell ATH 0.9029 4
 IA Ezra Miller OT 0.9029 4
 TX Hunter Spears DT 0.9028 4
 MO Shammond Cooper ILB 0.9027 4
 TX Josh Ellison DT 0.9026 4
 MD Osita Smith S 0.9025 4
 OH Ryan Jacoby OT 0.9023 4
 CA Asa Turner ATH 0.9022 4
 CA Giles Jackson WR 0.9020 4
 NC Tony Davis CB 0.9019 4
 CA Casey Kline ATH 0.9019 4
 IL Jason Bargy SDE 0.9018 4
 IA Max Duggan DUAL 0.9016 4
 FL Te’Cory Couch CB 0.9011 4
 GA K.J. Wallace CB 0.9009 4
 CA Joey Yellen PRO 0.9006 4
 GA Jaelyn Lay TE 0.9005 4
 PA Andre White Jr. ILB 0.9005 4
 MD William Harrod OT 0.9002 4
 FL Wardrick Wilson OG 0.9000 4
 PA Keaton Ellis CB 0.8999 4
 IN Cameron Williams OLB 0.8999 4
 IL Jahleel Billingsley TE 0.8998 4
 NJ David Ojabo SDE 0.8998 4
 VA Jalon Jones DUAL 0.8996 4
 VA Hakeem Beamon SDE 0.8995 4
 TN Keveon Mullins ATH 0.8990 4
 OH Jestin Jacobs OLB 0.8989 4
 FL Raymond Woodie III S 0.8984 4
 GA J.D. Bertrand OLB 0.8980 4
 NY Jason Blissett DT 0.8980 4
 OR Patrick Herbert TE 0.8978 4
 CA Joshua Calvert ILB 0.8977 4
 NC J.R. Walker ATH 0.8977 4
 PA Joey Porter Jr. CB 0.8975 4
 OH Tommy Eichenberg ILB 0.8966 4
 IN Joe Tippmann OT 0.8963 4
 MS KJ Jefferson DUAL 0.8961 4
 CA Cameron Davis RB 0.8960 4
 KY Jared Casey ILB 0.8958 4
 TX Bobby Wolfe CB 0.8958 4
 HI Julius Buelow OT 0.8958 4
 OH Noah Potter SDE 0.8958 4
 MD Darrian Dalcourt OG 0.8958 4
 OH Steven Faucheux DT 0.8958 4
 GA Jaylin Simpson CB 0.8958 4
 CT Marquis Wilson CB 0.8958 4
 NJ Howard Cross SDE 0.8958 4
 CA Braedin Huffman-Dixon WR 0.8958 4
 TX Javonne Shepherd OT 0.8958 4
 AL Peter Parrish DUAL 0.8954 4
 OK Collin Clay SDE 0.8950 4
 OK Marcus Major RB 0.8946 4
 VA Tayvion Robinson ATH 0.8943 4
 KS Marcus Hicks WDE 0.8942 4
 MI Karsen Barnhart OG 0.8942 4
 TN Zion Logue SDE 0.8941 4
 GA Jackson Lowe TE 0.8940 4
 TX Jamal Morris S 0.8940 4
 LA Reginald Johnson WR 0.8939 4
 TX Jacob Zeno PRO 0.8938 4
 GA Mataio Soli WDE 0.8937 4
 GA Colby Wooden WDE 0.8937 4
 OH Erick All TE 0.8937 4
 KY JJ Weaver SDE 0.8937 4
 WA Nathaniel Kalepo OT 0.8937 4
 CA Ethan Rae TE 0.8937 4
 TX Steven Parker WDE 0.8937 4
 CA Jordan Wilmore APB 0.8935 4
 WV Brenton Strange TE 0.8935 4
 AZ Matthew Pola-Mao DT 0.8934 4
 LA Jordan Clark CB 0.8933 4
 MS Raydarious Jones ATH 0.8932 4
 HI Sama Paama DT 0.8929 4
 SC Jamario Holley WR 0.8926 4
 FL Keshawn King RB 0.8925 4
 DC Keilan Robinson RB 0.8925 4
 CA Kamren Fabiculanan CB 0.8921 4
 GA Rashad Cheney DT 0.8920 4
 GA Jamious Griffin RB 0.8917 4
 TX Tamauzia Brown ATH 0.8917 4
 TN Kristian Williams DT 0.8910 4
 TX Isaiah Hookfin OT 0.8910 4
 DC Quinten Johnson S 0.8909 4
 TX Langston Anderson WR 0.8908 4
 OH Moses Douglass S 0.8906 4
 WA Darien Chase ATH 0.8905 4
 FL Mikel Jones OLB 0.8904 4
 FL Shamar Nash WR 0.8904 4
 IA Tyler Endres OT 0.8903 4
 TN Ani Izuchukwu WDE 0.8902 4
 MO Kyren Williams RB 0.8900 4

How did roster Blue Chip recruit percentage relate to on-field success in 2018 for the college football regular season? A statistical review.

As recruiting season heats up, there is a lot of attention being paid to how teams are able to add blue chip (4-star and 5-star high school players) recruits to their rosters. Just browse the internet and you will find a plethora of correlational analyses touting the importance of blue chip (BC) recruiting. There is no doubt that recruiting is important (after all, the coaches heavily stress recruiting success). To say otherwise is likely naïve and statistically unsupported. What gets missed in many of the conversations (at least in my experience) is the question of how important recruiting is relative to other factors. Where on the scale of ‘not at all’ to ‘all about Jimmys and Joes, not Xs and Os’ does impact of recruiting on winning really lie? Well, that of course varies from team to team, as each has their own situation. But we can explore a sample of talented teams to get a glimpse of the range that matters to most of us; the top 25.

To gain some insight as to the strength of the relationship between recruiting and winning, there are a few ways one could go about it. In this analysis, I look at the relationship between the top 25 most talented teams, as indicated by their Rivals 247 roster composite ratings, and those same teams’ regular season win percentage. Each of these teams’ BC percentage was used to analyze what their 2018 regular season win percentage would be expected to be as compared to the other 24 teams in the sample. To ensure the scope of what is being discussed here is not misunderstood, the purpose is to explore the expected impact on winning if predicted by roster BC%. This is not an exercise in predictive analytics- it is a review of what happened and exploring relative performance on a single, isolated variable that is discussed quite often- BC%. It is not intended to discuss all of the factors that go into winning (coaching, SOS, home field, etc.). It is intended to do just the opposite- flesh out how a top 25 most talented team should perform relative to their peers on the basis of BC roster percentage alone.

Now, to the data. The table below shows each of the teams in the sample. These teams were inclusion was based upon their average roster rating, as previously described. Then, their blue-chip recruit percentage was calculated.

bcp chart

The above list is in alphabetical order of school. In the far-right column, that is how a team performed relative to what their BC% would have predicted. For example, Florida would be expected to win 64% of their 2018 regular season games on the basis of their BC% of 42%. The Gators won 75% of their games, 11% over what would be predicted by BC% alone. The top ‘over performer’ was Notre Dame at +27% with the most ‘under performing’ team being USC at -40%.

bc scatter

The above graph outlines where along a continuum each team would plot. Above the dotted line indicates ‘over performing’, below the line represents the opposite. I included some notable teams for illustration purposes. UCLA, even though their roster wasn’t stacked, still under performed. FSU did terrible.

Here is a scatter plot with each team’s data point entered:

top 50 tabline

Methods:

A simple linear regression was performed to predict overall win percentage of the designated 25 most talented rosters, as determined by an independent scouting service website based on the team’s roster blue-chip percentage (BC%).  A significant regression equation was found (F(1,23) = 10.59, p < .001), with an R² of 0.315. Teams’ predicted win percentage is equal to 0.324 + .756 * X, where X= BC%.

Regular season achievement: The Southeastern Conference.

Now that the regular season is complete, I took a look at how each team fared relative to the talent they have on hand. Taking the composite rating of each SEC team’s roster talent, as calculated by the Rival’s 247 Composite website, I analyzed how each team’s overall win percentage aligned with their talent. Obviously, we would expect teams with more talent to win more than those with less talent. But by isolating the talent as a variable, and then viewing the results of the season, we can see how good of a job each coach has done. To be sure, there is an element of randomness (injuries, etc.). However, this analysis gives you an idea as to how a team performed relative to its talent level.

SEC chart

In the chart above, you can see the regression line through the middle of the chart. This represents the expected value of win percentage (along the vertical, or “Y” axis) is it relates to the 247 Composite Talent average per team (horizontal, or “X” axis). Teams below the line had a lower winning percentage than their roster talent would predict. Teams above the line performed better than their roster talent would predict. And, of course, those teams with scores on or at the line performed as expected. The distance from the line indicates the degree in which a team under-performed or over-performed.

The table below shows the data. Kudos to all the teams that exceeded the expectations. Detention for those that didn’t, especially Tennessee and Auburn.

chart 2 reg season

For our stats friends, the model is significant and has an adjusted R² of .333, which means talent rating accounted for 33.3% of the variance in win percentage, with 66.7% of the results coming from factors other than talent rating. If you care to play around with the numbers (hey, SLR is fun because it is easy), the β = -4.009, ‘Talent’ coefficient (unstandardized) = 0.053. In this model, if you’re Florida for example, with a roster talent of 88.28, you would multiply 88.28 with 0.053 (giving you 4.68) and subtract 4.009 from that, giving you .670. So, you’re expected win percentage with be 67%, which is 8 games. So, Florida out-performed their talent by one game this year. Mullen is good at coaching sports. The table displaying expected win percentage based on talent is below:

win expectancy

Like the Florida example above, you can see how it all plays out for each team in this table. Alabama won one game beyond expectations (so they are above the line in the scatter plot above), while Arkansas lost 4 more games than their roster talent would predict. Of course, a ‘zero’ means they performed at expected levels. Great job this year by Kentucky, winning 3 games over their talent level.