Florida Gators Blog

2018 Teams with 5-stars and their win %

Here is some data on how all teams who had at least one 5-star player on their rosters faired in 2018 (regular season only).

2018 chart 5 stars.winP

This chart above is neat because you can see the contrast levels between the number of 5-stars and winning percentage clearly. Notre Dame, for instance, had a low number of 5-stars (1) and a high winning percentage (100%).

2018 5 star regression

2018 5 star histogram

2018 5 star regression statistics

The R square (yellow highlight) explains the variance attributed to the number of 5-stars on a team’s roster. The significance (blue highlight) shows the likelihood of the relationship is due to randomness. The Multiple R (not highlighted) shows the correlation between the number of 5-stars on the roster and win %.

Because the data isn’t normally distributed (doesn’t take a bell-shaped curve in the histogram above), quartiles are a preferred method of looking for outliers:

Teams w 5-star No. of 5-Star Win % 5-starsOutlier Win% Outlier
Georgia 14 0.92 Yes No
Alabama 12 1.00 Yes No
Ohio State 11 0.92 Yes No
Clemson 9 1.00 Yes No
USC 8 0.42 No No
FSU 7 0.42 No No
UCLA 5 0.25 No No
LSU 4 0.75 No No
Michigan 4 0.83 No No
Penn State 4 0.75 No No
Oklahoma 3 0.92 No No
Stanford 3 0.64 No No
Texas 2 0.75 No No
Florida 2 0.75 No No
Auburn 2 0.58 No No
Tennessee 2 0.42 No No
Ole Miss 2 0.42 No No
Maryland 2 0.42 No No
ND 1 1.00 No No
Texas A&M 1 0.67 No No
Miami 1 0.58 No No
Washington 1 0.77 No No
Miss State 1 0.67 No No
Arkansas 1 0.17 No No
Pittsburgh 1 0.54 No No
Oklahoma St 1 0.50 No No
West Virginia 1 0.73 No No
Iowa 1 0.67 No No
Houston 1 0.67 No No

 

Recruiting and its statistical success to college football in the only metric that matters – winning.

I find the fan fascination with recruiting fascinating. While you’ll never hear me argue against recruiting’s importance – after all, the coaches put so much emphasis on it and they are the true experts – I also don’t subscribe to the theory that it is all about the Jimmy’s and Joe’s and not the X’s and O’s. I think, based on every detailed analysis I and others have done on recruiting, that coaching is the key factor in winning. Not the only factor, but the number one key.

The purpose of this analysis is not to explain every single variable that contributes to winning (SOS, Coaching, home field, randomness, etc.). The point is to isolate the discussion on recruiting across several dimensions. It is often helpful to isolate a variable in order to understand how it is part of a bigger system.

That being said, recruiting is strongly correlated with winning percentage. I analyzed the direct linear relationship between 57 Power 5 teams since the from 2005 through 2017. I tallied up each year’s recruiting data. Then, I parsed each year for each team out along these dimensions: Number of players in year’s class, 3-star players in class, 4-star players in class, 5-star players in class, Blue Chip percentage (calculated by taking the percentage of 4 and 5-star players relative to all of the players recruited in a class), and the average rating of those players. Next, I averaged each team’s scores across each of those dimensions over the time span. First up, Blue Chip percentage (BCp):

BCp no line

The scatterplot above shows the winning percentage for each team on the vertical (y) axis and the BCp on the horizontal (x) axis. A quick visual of this chart indicates that higher BCp is associated with more winning at the P5 level. It looks as if there is a strong positive linear relationship. Next, I added a fit line to the graph:

BCp w line

In this second chart, the line confirms the initial suspicion: As BCp goes up, winning will go up as well.  The regression equation here shows that if you were to have, say a BCp of 79%, the model would predict you to win 78% of your games (y=0.44+0.43*.79, y= 0.7797). Beyond that, however, the model was statistically significant (p = .000, a= 0.05, R= .699, R2= .488). For the non-stats crowd, these numbers basically mean that there is less than a 1% chance that these findings are due to random chance, and that about 49% of winning percentage experienced in this sample is attributable to BCp and other unknown factors accounting for the other 51%. So, we have a strong positive relationship and we know how much of that relationship is due to BCp. So far, so good.

But, there was something about this chart (look at the first one without the line) that immediately caught my eye- there is an obvious curve in the lower quadrant. This lets us know that BCp and, its relationship with winning, is different for different teams. It looks to me like the strongest correlation occurs when a team is above 50 BCp or so. When we apply smoothing (LOESS), we can see this visually:

loess BCp

Things get loose in the 30- 40 range. They look chaotic to me when BCp drops below 30%:

BCp under 30

When BCp gets low, it only accounts for 15% of winning percentage (in this sample, which is 34 team averages over a 13-year period). Intuitively, this makes sense. How can blue-chip players help you win if you don’t have any? That doesn’t mean you can’t win:

Wisconsin

That little guy way up there is Wisconsin. They’ve won 76% of their games with an average BCp of 17%. Props, Badgers. There’s a flip side to that as well… UCLA has had an average BCp of 50% while winning only 54% of their games on average. I’m sure things will get better with Chip running the show…

A Better Recruiting Metric 

While BCp has a clear and strong relationship to winning percentage, the individual recruit rating (RR) using 247 Composite is even better (R=.722, R2= .522, p=.000, a=0.05). Where the BCp model accounted for 48% of the variance and correlated with winning percentage at 69.9%, average rating accounts for 52.2% of the variance and is positively correlated with winning percentage at 72.2%.  Here is that chart with a LOESS curve applied. loess rating

An Even Better Model

Having looked at recruiting’s relationship to winning percentage along these two dimensions (Blue Chip percentage, and recruit rating), I wanted to look at the variables that comprise these two dimensions. In this attempt, I used multiple linear regression. The dependent variables used are (range averages) number of recruits in the class, 3-stars in class, 4-stars in class, and 5-stars in class. What I found was even better than the previous two simple linear models (all assumptions of the MLR were met).

The correlation is .755, or 75.5% positive, with 54.6% of the variance (adj. R2). The table below shows how each variable scored:

pearsons

All 57 Teams

Here is how all of the teams included stacked up.

all teams regression

Teams that were at or near the line generally performed as one would expect given their average RR. Since that chart is a bit cluttered, here are all the teams in list format:

Team Avg Rating Average W%
USC 0.9372 75%
Ohio State 0.9283 85%
Alabama 0.9198 83%
Florida 0.9166 68%
Texas 0.9165 66%
Florida State 0.9164 71%
Georgia 0.9154 72%
LSU 0.9140 75%
Notre Dame 0.9081 63%
Miami 0.9045 60%
Oklahoma 0.9033 78%
Clemson 0.9023 73%
Michigan 0.9017 61%
Auburn 0.9005 65%
UCLA 0.8972 54%
Penn State 0.8951 71%
Tennessee 0.8939 53%
Texas A&M 0.8928 59%
South Carolina 0.8863 61%
Stanford 0.8858 63%
Oregon 0.8857 74%
Ole Miss 0.8810 50%
California 0.8809 50%
Washington 0.8806 49%
North Carolina 0.8795 52%
Virginia Tech 0.8745 70%
Arkansas 0.8742 55%
Mississippi State 0.8741 55%
Michigan State 0.8736 64%
Iowa 0.8710 60%
Arizona State 0.8709 55%
Wisconsin 0.8706 76%
Virginia 0.8700 40%
Oklahoma State 0.8691 68%
Arizona 0.8687 50%
Baylor 0.8680 52%
Texas Tech 0.8675 59%
Louisville 0.8674 65%
Illinois 0.8670 35%
Missouri 0.8668 62%
West Virginia 0.8666 56%
Georgia Tech 0.8636 58%
Boston College 0.8617 54%
Utah 0.8614 59%
Colorado 0.8611 32%
Oregon State 0.8603 47%
Vanderbilt 0.8598 41%
Minnesota 0.8594 45%
Duke 0.8591 37%
Kansas 0.8586 33%
Iowa State 0.8575 36%
Northwestern 0.8575 57%
Washington State 0.8568 38%
Kansas State 0.8560 58%
Syracuse 0.8556 36%
Indiana 0.8533 37%
Wake Forest 0.8529 46%

 

 

 

 

Florida’s Feleipe Franks performance variance in 2018.

Franks ended the 2018 season playing well. This graphic shows how he did throughout the season relative to his own performance, game by game.

ffchart1

The blue line through the middle is the average for Feleipe across several categories of passer statistics. Games with results below the line were below average (for FF). Used here were attempts, completion percentage, yards, touchdowns, and interceptions. The number of completions wasn’t included, as they would be redundant with the completion percentage. Each category was standardized, with each total standard score summed to give a full picture of how FF did game to game.

fftable1

The column on the far right is Franks’ total score for that game when standardized against his full-season performance. The scores bolded are for games against relatively crappy teams, so it puts some of his performance in context.

Also, no rushing stats are used here- only passing. I would use rushing stats, but since college football counts sacks against the rushing total, it messes it up. Lost yardage to sacks should be a completely different statistic and not count against passing or rushing in my view, but I digress…

 

Breaking down the 2020 recruiting class- March

This is a continuation of last month’s breakdown. To get an understanding, I am tracking the changes in recruiting leans (from 247 composite CB/commits) over the next 12 months to record the amount of fluctuation that occurs in a recruiting season. Here are this month’s tables:

march19RRchanges

50.21

51.plus

The list above depicts the 71 teams that are considered a ‘lean’ by 247 Crystal Ball predictions for at least one of the top rated 1000 players in the 2020 high school football class. Inevitably, someone will argue that it is way too early to predict anything and that the CB forecasts are garbage. I’m not arguing against any of that. I’m interested in how much fluctuation this ‘market’ undergoes throughout the year. This way I’ll know when handwringing (which I don’t do) over a recruit’s decision to play college football is appropriate.

Here is a current look at how the leans favor Florida:

marchGATORSleans

One interesting thing I noticed is that the Gators are trending for 100% of the 5-stars from the state of Florida. There’s only one (Bowman), but whatever. I would think more would be from Florida. Here is how the numbers look by state:

march5star

march4star

popchart

Interesting stuff. To me anyways…

Breaking down the 2020 recruiting class

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:

TOTAL N 5-stars N 4-stars N 3-stars
1000 34 309 657
Tot Leaning home 5-star leaning home 4-star leaning home 3-star leaning away
257 15 129 113
Tot Leaning away 5-star leaning away 4-star leaning away 3-star leaning home
211 17 119 75
% Leaning Home % 5-star leaning home % 4-star leaning home %3-star leaning home
26% 44% 42% 17%
% Leaning Away % 5-star leaning away % 4-star leaning away %3-star leaning away
21% 50% 39% 11%

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:

College lean No. of leans No. 5-star No. 4-star No. 3-star Avg. Rate of lean
UCLA 3 1 2 0 0.9568
Georgia 12 3 7 2 0.9458
Ohio State 27 6 18 3 0.9441
Clemson 17 3 13 1 0.9437
USC 13 3 9 1 0.9368
Notre Dame 12 0 11 1 0.9365
LSU 23 2 19 2 0.9360
Penn State 14 3 9 2 0.9356
Alabama 15 2 10 3 0.9338
Oklahoma 14 1 10 3 0.9298
Stanford 9 1 7 1 0.9289
Florida 18 1 13 4 0.9282
Miami 18 0 14 4 0.9220
Washington 7 1 3 3 0.9219
Texas 15 0 10 5 0.9213
N.C. State 7 1 4 2 0.9185
West Virginia 1 0 1 0 0.9149
Louisville 3 0 2 1 0.9143
Tennessee 8 0 5 3 0.9117
Arizona 2 0 1 1 0.9090
Texas A&M 22 3 8 11 0.9076
Florida State 16 1 9 6 0.9074
South Carolina 12 0 8 4 0.9056
Auburn 10 0 7 3 0.9055
Oregon 11 0 8 3 0.9041
Ole Miss 7 0 4 3 0.9041
UTSA 1 0 1 0 0.9020
Michigan 15 0 10 5 0.8963
Arizona State 2 0 1 1 0.8943
North Carolina 4 0 3 1 0.8921
Wisconsin 7 0 2 5 0.8866
Nebraska 2 0 1 1 0.8863
Mississippi State 8 0 4 4 0.8835
Virginia Tech 5 0 2 3 0.8832
California 4 0 1 3 0.8818
Minnesota 5 0 1 4 0.8795
Fresno State 1 0 0 1 0.8789
Arkansas 10 0 1 9 0.8785
TCU 2 0 1 1 0.8781
Michigan State 10 0 2 8 0.8743
Kentucky 4 0 0 4 0.8736
Duke 3 0 0 3 0.8703
Purdue 3 0 1 2 0.8670
Oregon State 3 0 1 2 0.8668
Maryland 2 0 0 2 0.8667
Northwestern 4 0 0 4 0.8663
Iowa 8 0 1 7 0.8634
Utah 3 0 0 3 0.8626
Oklahoma State 4 0 1 3 0.8608
N/A 532 2 61 469 0.8605
Brigham Young 5 0 1 4 0.8596
Missouri 5 0 0 5 0.8576
Baylor 5 0 0 5 0.8569
Iowa State 5 0 0 5 0.8561
Boston College 6 0 1 5 0.8527
Rutgers 1 0 0 1 0.8527
Texas Tech 3 0 0 3 0.8520
Cincinnati 2 0 0 2 0.8477
Tulsa 1 0 0 1 0.8477
Virginia 2 0 0 2 0.8445
Colorado 1 0 0 1 0.8427
Vanderbilt 2 0 0 2 0.8366
UAB 1 0 0 1 0.8366
UCF 1 0 0 1 0.8333
Wake Forest 1 0 0 1 0.8333
Indiana 1 0 0 1 0.8316

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:

Position Offense Defense Skill Pos Total 5-stars 4-stars 3-stars Avg. rating
SDE No Yes No 54 3 16 35 0.8803
RB Yes No Yes 73 5 24 44 0.8895
ILB No Yes No 35 2 8 25 0.8765
OLB No Yes No 75 2 27 46 0.8824
WR Yes No Yes 162 5 61 96 0.8895
OT Yes No No 98 3 33 62 0.8870
PRO Yes No Yes 46 1 12 33 0.8798
ATH Yes No Yes 85 1 23 61 0.8788
CB No Yes Yes 78 4 21 53 0.8814
OG Yes No No 43 1 11 31 0.8759
DT No Yes No 66 1 28 37 0.8908
WDE No Yes No 38 3 11 24 0.8864
APB Yes No Yes 13 1 3 9 0.8829
DUAL Yes No Yes 33 1 6 26 0.8718
TE Yes No No 34 1 5 28 0.8692
OC Yes No No 10 0 1 9 0.8688
S No Yes Yes 57 0 19 38 0.8766

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:

future gators

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

By State:

2020collegestate

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:

2020homestate

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:

State 2018 Population Players produced Per Capita lean (by 10,000)
Hawaii 1420491 18 78.9
Georgia 10519475 105 100.2
Alabama 4887871 45 108.6
Louisiana 4659978 39 119.5
Tennessee 6770010 41 165.1
Florida 21299325 117 182.0
Wisconsin 1805832 9 200.6
Texas 28701845 139 206.5
Nevada 3034392 11 275.9
Missouri 6126452 22 278.5
Kentucky 4468402 16 279.3
Arizona 7171646 25 286.9
Maryland 6042718 19 318.0
Michigan 9995915 30 333.2
Ohio 11689442 33 354.2
South Carolina 5084127 13 391.1
Oklahoma 3943079 10 394.3
Utah 3161105 8 395.1
Virginia 8517685 21 405.6
California 39557045 88 449.5
North Carolina 10383620 23 451.5
Nebraska 1929268 4 482.3
Washington 7535591 15 502.4
Iowa 3156145 6 526.0
Kansas 2911505 5 582.3
Arkansas 3013825 5 602.8
Indiana 6691878 10 669.2
Pennsylvania 12807060 16 800.4
Minnesota 5611179 7 801.6
Colorado 5695564 7 813.7
Oregon 4190713 5 838.1
Illinois 12741080 15 849.4
Massachusetts 6902149 7 986.0
Connecticut 3572665 3 1190.9

What about the 3-stars? Let’s give them some attention.

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):

3star home state

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

3star destination

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:

departure chart

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:

3s Class Rk School N 3 stars Avg Rating Std. score
1 Oregon 12 0.8792 2.370
2 Auburn 6 0.8760 2.121
3 Clemson 14 0.8749 2.031
4 LSU 7 0.8748 2.027
5 Oklahoma 5 0.8742 1.978
6 Florida 8 0.8736 1.935
7 Georgia 3 0.8723 1.833
8 Michigan 10 0.8722 1.819
9 Nebraska 19 0.8716 1.778
10 Texas 7 0.8696 1.621
11 Washington 7 0.8696 1.619
12 South Carolina 15 0.8673 1.441
13 Tennessee 9 0.8673 1.440
14 Notre Dame 6 0.8667 1.388
15 Florida State 10 0.8660 1.336
16 Arkansas 14 0.8659 1.327
17 Wisconsin 17 0.8651 1.268
18 TCU 18 0.8645 1.221
19 Ohio State 5 0.8640 1.179
20 Texas A&M 11 0.8627 1.079

The Gators sit at 6th 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:

mullenUF

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.

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.