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.

Dan Mullen has improved recruiting at MSU and Florida; No reason to believe he won’t get the players Florida needs to compete for championships

Recruiting is a hot topic for college football fans. And for good reason, the acquisition of talent is one key aspect to winning football games. When it comes to Florida coach Dan Mullen, there is a general notion that he is a great coach but so-so recruiter. While he hasn’t recruited an elite class yet, he also hasn’t been in position to. At Mississippi State, it can be very difficult to recruit top players. While that isn’t the case at Florida, he has only been there for one full recruiting cycle.

As always, I like to test talking points and narratives to see not only if they are true or not, but to what degree is there some truth. So I took a look at MSU and Florida’s recruiting before and after Dan Mullen arrived. In each situation, he improved the school’s recruiting measures significantly by the two metrics that matter most- average class talent rating and blue chip (4 and 5 star recruits) percentage.

mullen recruiting before and after

 

In the above chart, you can see that the five years before Mullen arrived at MSU, the average blue chip percentage (BC%) was 9% overall and the talent rating was 80.41. With Dan, it increased to 17% and 85.84. At Florida, previous coach Jim McElwein had an overall BC% of 34% and rating of 88.36. Using Mullen’s first year and current 2019 class (that will assuredly change), he has a BC% of 61% and rating of 90.21. Of note, McElwein had a good class lined up for 2018, but losing a bunch of games (among other things) got him fired.

Being that Mullen elevated MSU to higher levels of recruiting during his time there, and has already done that at Florida, it appears as if he can recruit after all. We can only wait and see if Mullen can reel in top 5 classes consistently at Florida, but he is clearly trending in the right direction so far, and I would expect that trend to continue.

There is no doubt that UF recruited at an extremely high level under Meyer and Muschamp. So how does Dan compare to these guys? Pretty good actually:

coaching2

Meyer had a talent average of 90.69 compared to 90.21 for Mullen and a BC% of 65% to Mullen’s 61%. Champ had a 92.16 and 56% BC%. This is quite telling… Mullen with an admittedly small sample size is in the same territory as the great recent recruiters at UF. You can see also how highly ranked Meyer and Muchamp’s classes were. This probably indicates a general shift in recruiting rating, which is why standardizing scores may be important for analysis (not done here).  At a glance, however, it seems as if Mullen is doing just fine bring talent to Gainesville. And he should probably get some credit for the great classes Meyer had since he was the Offensive Coordinator at the time.

Who is the likely SEC coach of the year (CotY)? Some data for you to decide.

Upon analyzing a teams’ talent level, as defined by the 247 Composite ratings for each team roster and their conference win percentage this year compared to last year (‘turnaround’), I compiled standardized scores for each team. I will revisit the numbers after the regular season and each team has completed their conference schedule, but this is a look at how things are with two weeks left.

coty 1

What you’re looking at here is the list of teams ranked in order of winning percentage compared to roster talent. Kentucky has the highest achievement score with 1.37. The closer a team is to zero, the less of a disparity between results and talent- they won at the expected level for their talent on hand. If a score is between -0.1 and 0.1, then they are doing what they generally should be doing. If a team has a larger positive number, they are winning beyond their talent level. If they have a larger negative number, the wins aren’t matching their respective talent and therefore underachieving.

The other metric considered is turnaround. This is simply this year’s conference winning percentage compared to last years. Did a team improve, and if so, how much? These are two straightforward metrics for analysis that give some statistical empiricism to judging who should be the SEC CotY.

coty 2

Achievement Rankings: 2018 ACC vs 2018 SEC. What would FSU look like in the SEC?

In this analysis, I computed the achievement scores for each team in the ACC and SEC and ranked them. The higher the score, the more ‘over achieving’ a team is; they win more games than their talent level would predict compared to other members of their conference. Most of the table is self-explanatory. The talent score is the 4-year moving average of recruiting talent level according to the Rivals 247 Composite ratings.

sec vs acc

As the tables show, Florida is 5th in the SEC in terms of achievement. They are slightly out-performing their talent level within the conference. As we would suspect, the Noles are doing terribly for the amount of roster talent they have compared to their conference peers.

Things get interesting when I switched the teams. How does Florida compare to the ACC teams and vice versa with FSU in the SEC? I took their numbers and transposed them. Of course, every team within each conference would have different records because FSU would have played Georgia and Florida would have played Clemson, etc. But since that would involve speculation, it has no value. We can, however, take the known figures and simply compare them. So, I did:

fsu to sec

As we can see, Florida would drop to underachieving status in the ACC. This is because the ACC, on average, is less-talented than the SEC on average. The mean talent rating for 2018 SEC teams is 88.24. For the ACC it is 86.34. The conference winning percentage for the SEC is 47% (still games to go- will end up at 50%) and 50% for the ACC. When we add Florida to the ACC and take FSU out, the ACC mean talent level drops to 86.15 (makes sense- Florida has a lower talent score than FSU). With FSU in the SEC, the mean talent score rises to 88.42.

What does this tell us? As bad as FSU is in the ACC, they would be just as bad in the SEC, but it would be slightly more acceptable. FSU has the most talented roster in the ACC (edging out Clemson). However, in the SEC they would be the 3rd most talented team and slightly ahead of LSU. Florida in the ACC would be expected to have a better conference record, as the teams are weaker. Florida has the 5th most talented roster in the SEC, but would have the 2nd most talented roster in the ACC.

User note: The achievement scores are based on standardized values. So when you see Clemson, who is undefeated, with a negative value (indicating underachievement), the number is very close to zero, but it is basically saying that with that much talent disparity, Clemson should be undefeated. Since going undefeated obviously cannot be an underachievement, it essentially means Clemson, like Alabama, is doing what it should be- they are performing at expected levels for talent on hand.