NOLA Blue

Prologue:

Mrohblue's MGoBoard question, "How long before Hoke has UM in BCS game??" has prompted me to post some data I previously compiled for personal satisfaction.  I hope you will find it just as satisfying.

I am sure Michigan can/should pull off 10 wins this upcoming year, and it's not just because the Wolverines nearly pulled off 7 wins in 2008 while utilizing a 3rd string QB lining up behind one previously starting OL, throwing to one previously starting WR or TE, or handing off to one of 2 previously starting RBs; with depth being drawn from a team of less than 70 scholarship players.  (Compare this to the 2011 offensive transition that will feature 4 of 5 OL starters and 4 of their 5 backups, the top 8 WR, top 5 RB, top TE, and the nation's most dangerous QB... all from a top 5 Offense from the year 2010.)

Introduction:

Look to Mr. Rittenberg's list of Big Ten returning starters for 2011 to get a general overview of the Big Ten's experience gap. Our beloved Wolverines are the most experienced group in the Big Ten, returning 20. Of note, the 4 programs on our 2011 schedule who are/will be traditional favorites to finish among the conference's top half include MSU, Iowa, Neb, and OSU. This traditionally difficult block of teams return only 13, 10, 12, and 13 starters, respectively. Also of note, we will have our hands full with ND and NW who return 18 and 17 starters, respectively. (That assumes that Brian Kelly's competitive mean-streak translates to Michael Floyd being magically available for Michigan's first home night-game.)

But when looking at whether a team will be the hammer or the nail, the proverbial hammer's head is the Offensive front 6 (OL and TE) or the Defensive front 7 (DL and LB.) Looking at what Wisconsin's 2010 OL was able to accomplish against a young Michigan DL (especially after they purposefully took Mike Martin out of the game) is a great example of experienced big uglies taking (talented) newbies to task. That Wiscy OL featured 2 Sr, 3 RS-Jr, and a So.

Data:

Who will we hammer this year?

	OL / TE	D Front 7	DL	LB
Michigan	5	5	4	1
WMU	2	4	2	2
Notre D	4	6	3	3
EMU	5	5	3	2
SDSU	5	3	1	2
Minnie	3	7	4	3
NW	5	4	3	1
MSU	2	4	3	1
Purdue	4	5	3	2
Iowa	3	2	1	1
Illini	5	5	3	2
Neb	3	5	3	2
OSU	4	3	2	1

As you can see, no one has a clear experential advantage over Michigan in the trenches this year. Not only does Michigan return more total starters than any of its foes, 10 of our returning starters are trenchmen perfectly balanced across the O and D.

By the Numbers:

The teams who will give our offensive Front 6 a challenge: Notre Dame, Minnesota, Purdue, the Illini, and Nebraska.

The teams to challenge our defensive Front 7:  SDSU, NW and the Illini. Of course, ND, Purdue and OSU all return a respectable 4 out of 6 on the offensive front line.

Note that only ND, Purdue and the Illini are on both of those lists.

Conclusions:

WMU, MSU and Iowa will all be overrun by winged helmets on both sides of the ball. That's RBs stuffed, QBs with no time to breathe, and our RBs unleashing an absolute Denarding. EMU will be able to at least rely on its lines to keep the game closer than it should be, and SDSU brings back some real talent on the offensive side of the ball but a very weak-looking defense - posing the threat of a shoot-out. But those 5 games should be our gimmes. We have a heavy advantage on either O or D against Minnesota, Nebraska, and Ohio State (and are not outgunned on the other side of the ball) = 3 more wins. The Illini return a lot along the lines, but only 3 total starters behind the lines. Win.  Our nastiest games will be Notre Dame, NW, and Purdue who each return solid lines in addition to a solid number of skill players behind the lines.

I am only sweating ND, SDSU, NW and the Nebraska/OSU 2-week finale. Four of those are home games. Beat Notre Dame, and we are on our way to a 10-win season. Lose to them, and our second Big Ten game (Northwestern - away) becomes the lynchpin to making a BCS berth. We need 3 out of 5 of these "sweat games." I like our chances.

**Edit:  According to Scout.com's listing of WMU's returning starters there are 3 returning along the O-line/TE (including our very-own-former Dan O'Neill)and 7 (!) returning along the D-Front-7.  That's a massive difference from ESPN's reporting of 2 and 4.  They are one of the more experienced trench-teams we will see this season... I'm going to have to go back through Scout's analyses and see if there are any other examples of ESPN's poor reporting skills.

 

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(Feel free to skip down to the "discussion" for 7 points that can be taken from this data... but, please make sure to first read the research-question the study was designed to answer.  Also, for those of you interested, the study-design is very clearly detailed; so please, only comment on design issues such as sample size if you have read the rationale... of course, if you disagree with the rationale or believe something to be missing, fire-away.)

Sometimes the benefit of writing in two parts is the inherent advantage of finding out what readers’ thoughts are.  After reviewing the comments on yesterday’s post (re: 2- vs. 3-star) I am left with the clear impression that I did not do a good job of explaining the research-question I was testing, nor the size of the sample and what populations it might represent.  I think the addition of today’s data will clear some of that up, as a few questions pertained to “why did you stop there… why not upper echelon teams?”  Today’s data is from the upper echelon.  But, I will take a moment to clear up the central issues of vagueness from yesterday.

First, the question this research answers… 

The most important part of using research to answer questions is assuring that the research was designed to answer the question being asked.  I was motivated to perform this analysis by the abundant number of times, on MGoBlog and elsewhere, that correlation of stars to draft status, or stars to team performance, has been confused with stars answering to the abilities and potential of individual players.  Among yesterday’s responses, I replied to wolverine1987 regarding this issue.

One question we might seek to answer is this:  “Does a recruit heading to Florida have a greater chance of being drafted than a recruit heading to Northern Illinois?”  There are two ways to answer this question: one is using your knowledge of team performance, which makes it quite obvious that the Gators are more likely to send a player to the NFL; the second is to look at the average number of players drafted from each school over the last 4 years, and assign a probability to the recruit’s chances.  Neither of these require recruiting rankings to be solved; but, yes, recruiting rankings do correlate to both.  However, I do not believe that the majority of football fans are curious as to whether recruits going to one school have a higher chance of being drafted than recruits going to another school...

My belief is that we would each like to know “if Michigan signs that 4-star DT out of Texas, will he be more likely to become an NFL-level contributor to our team than that 3-star DT out of Illinois?”  This question requires the star system to assess which players are actually better; it requires that their assessment of Joe X. as the #8 DT, vs. John Y. who they place as the #36 DT, to actually be a reliable measure of difference between the two, stating players’ talent and potential with measurable validity.  As you may have already figured out, correlating the probability of being drafted to all draftees’ recruiting stars, or correlating stars to teams’ performances does not isolate whether the stars actually correlated to individual players’ talents/potential. 

The probability of being drafted is impacted by talent/potential, coaching, team members, facilities, successfully proving oneself against high-level competition, and potentially even media exposure.  Therefore, to actually test whether stars correlate with draft success for individual players (which would answer the question of whether or not they accurately predict individual players’ abilities,) we have to isolate the probabilities of success within systems in which the levels of coaching, facilities, competition, and media exposure are as similar as possible (identical would be the impossible ideal.)  Otherwise, we are just answering the obvious:  Florida, who has a better coaching staff, better facilities, better competition, and more media exposure sends more players to the NFL on average than N. Illinois.

So, this brings us to sampling…

Obviously, a perfect sample would be finding one school that correlates perfectly to a well-defined group of schools.  The requirements for this perfect situation: 

1. A large enough number of 2-, 3-, 4- and 5-star recruits at this single school to assure that a random distribution would be allowed to approach normal in regards to the success of recruits at this school developing into NFL-caliber players.
2. A large enough number of NFL draftees to assure the same process of normalization is allowed (should there actually be normalization.)  If draft-status occurs at the same rate among 2-, 3-, 4- and 5-stars from this school, then we could say that a random assignment of stars to the same athletes would have produced the same result… this would mean the stars were not predictors of success.
3. A group of schools to compare to, which themselves exactly share the “ideal school’s” traits of coaching, facilities, level of competition, and media exposure. 

Next, for the perfect assessment across all strata of collegiate football, we would have to hope that there are some perfectly segmented groups (4, 5…6?) These groups would be different from each other, but the schools in each group would be perfectly matched to each other.  Then, we could take a random member from each group and assess the behavior of its recruits’ success rates, projecting the results to all players within that segment of schools.

This is not the world we live in.  Hence, the inevitable arguments over sample selection and sample size.  No matter how well designed a study is, there will always be uncertainty that it achieved a fully representative sample.  If you are looking for answers of 100% accuracy, then do not look to science… it will always be chasing perfection, and never reach it.  But, looking at the world from a level of random chance vs. predicted order is useful for evaluating a system which is proclaimed to having found order among chaos (i.e. the star-system makes just such a claim, and must therefore be assessed versus randomness to see if recruiters are actually able to differentiate among the top 0.23% of high-school athletes.)

My sample selection…

For this study, which I broke into two analyses, I wanted to isolate players’ abilities from the impacts of coaching, facilities, etc.  In other words, I wanted to see if players’ development within a homogenous environment responded differently based on stars (as administered by Rivals.)  Hopefully we all agree that a study looking at all 119 teams would not isolate players in a homogenous environment.  Therefore, I am interested in assessing players within semi-homogenous sub-groups that, while not representative of 119 schools (or even 40 for that matter,) will offer a look at player development from within multiple strata of Div 1-FBS football.  Players will be compared to players within the same stratus.  

Because no single recruiting class from any single school produces enough NFL-draftees to allow for the possibility of normalization (should it actually occur,) I was forced to track multiple recruiting classes.  Considering that teams are also dynamic over the course of a decade, and that one of the major influences on a player is the players around him, I could not stretch this tracking too far (players selected in 2002 affect players in 2003 while players in 1996 do not affect them… of course, this is a good thing, because the star-system was not around in 1996.)  So I only allowed it to stretch to 4-years, basically representing a full team cycle (yes, I am aware that 5^th year seniors blur this a bit… so 95% of a team-cycle.)  I also needed to be able to see the draft results for all four years of recruits… thus 2002-5 was the latest 4-year cycle I could consider such that I could cross-reference the 2005-9 drafts; and that also lines up with the first available Rivals data-base.  So, four recruiting classes of 2002-5 compared to the five drafts of 2005-9.  Again, 5^th year seniors have not yet had the chance to be drafted; hopefully I will have time to revisit this data after the 2010 draft results are in.  I do not expect there to be much impact, but there is always the possibility.  (**Edit** I did not include kickers/punters in the counts of either recruits or draftees.)

Most teams did not have enough draftees over this four year period to allow strong normalization (USC the notable exception, with 30 draftees…)  So, it was obvious I would have to analyze small groups of schools…

Next, the first step to accomplishing isolation:  coaching.  As “bjk” so aptly pointed out yesterday, my study was constrained by coaching.  In fact, this was the strongest constraining factor on selection.    One of my concerns regarding the use of 4 recruiting classes, was the issue of environmental stability.  The factor which can most quickly impact the environment of player development is coaching… in other words, facilities take years to build and can’t be wholly replaced simultaneously while a coach takes a day to fire and a month to replace.  So, in order to assure stable environments to compare players within, I added a limitation to selection:  there could be no head-coaching change between 2002 and 2009.  Thus, all players assessed at the school received strongly similar tutelage (not accounting for position-coach changes.)  The one exception I made to this rule was Utah; I would not have felt comfortable that Boise and TCU were a large enough sample, with only 15 draftees… adding Utah’s 8 draftees makes me much more comfortable.  Off the top of my head, this gave me:  Boise, Cal, Florida St, Georgia, Iowa, tUOS (aka Bastards from Columbus,) Oklahoma, Oregon, Penn St, TCU, Texas, (Utah,) USC, and Va Tech.  Could I have spent hours researching every team in the nation?  Yes… but I was happy with a list of 14 schools.

The second step to accomplishing isolation of talent/potential:  facilities.  The 119 teams from Div 1-FBS may be broken into groups according to a perceived difference in facilities.  For example: U of M, Texas, USC, LSU, the University of Ohio State, Oklahoma, Florida State and Georgia all have Mount Olympus-level facilities.  Are they all exactly identical?  Of course not.  But they are similar enough that I feel confident that if I were to lengthen this list to the top 20-24 programs in terms of facilities, and then select 3+ of them to analyze, I would be able to say that the results from those analyses were representative of the total sub-group.  The remaining 99 teams could probably be divided into three more sub-groups… those with exceptional, strong or simply functional facilities.  Of course, the teams at the gray areas are arbitrary (i.e. is there really a difference between #46 and #52 even though they may fall into different sub-groups?)  For my purposes, I split the teams with high coaching-stability into groups that I felt were very similar to each other in terms of facilities…

Mount Olympus:  Florida St, Georgia, Oklahoma, (Oregon,) tUOS, Penn St, Texas, and USC

Exceptional:  Cal, Iowa, Va Tech

Strong:  Boise, TCU, Utah

Functional:  None evaluated.  Doesn’t actually matter, because these schools don’t produce draftees on a regular enough basis to power a study without randomly analyzing 20-30 teams; at which point it would become impossible to suggest all the athletes were in an environment approaching identity.

Remember the mentioning of players influencing players?  Well, that bears out that the supposed strength of recruiting must be considered; and I gave heaviest consideration to the two extremes (5- and 2-stars.)  As we all know, 5-stars are actually a dependable source of high-performers, and before the data came in I had to assume that 2-stars would underperform in comparison to 3- and 4-stars.   With that in mind, we end up with 3 tiers of BCS schools to pair with the BCS crashers:

From the “We collect 5-stars like they’re Gold Teeth” category:  (sorry, all I can imagine is Pete Carrol smiling with a mouth full of bling…)

Florida St, Oklahoma, Texas and USC:  This group signed 40 five-star athletes over four years.  That’s an average of 2.5 top-flight athletes per school… per year!  They also had too few of 2-stars to count.

From the “Hey, we collect 5-stars too” category:

Georgia, Ohio St, and Penn St:  This group could have been highly consistent in terms of recruits, except Joe Pa gives recruiting analysts the big middle finger…  Each school recruited exactly 4 five-stars apiece… for an average of 1 top-flight athlete per year.  Then Joe Pa flipped his $h%t and decided to skip over the 4-stars in favor of an army of 3-stars; and swiftly failed miserably at turning them into NFLers.  Joe Pa was certainly an outlier among my sample of 14 schools… probably correlating to either his status of the inventor of college football or his deteriorating sanity.  God bless him.  But, 2-stars were still far and few between in this group.  (Georgia had 5, tUOS had 6, and Joe Pa took 13… Joe Pa is effectively a recruiting ‘tweener.  His thirteen 2-stars borders on having enough to evaluate; but considering the next group of schools averaged over 30 two-stars each, his collection of one 5-star each year made his pool closer to those of Georgia and tUOS.)

From the “We’d take a 5-star, but normally we have to give their spot to 2-stars” category:

Cal, Iowa, (Oregon) and Va Tech:  This group signed only 9 five-stars total… for an average of just over ½ of a five-star per year for each school.  But, what they lacked in prodigies, they made up for in presumed projects… 154 x 3-stars and 123 x 2-stars.

And finally, the “We’ll see your clutch of 5-stars, raise you none, and then promptly kick you’re a$$ with our army of 2-stars” category.

Our beloved BCS crashers… the destroyers of logic.  Couldn’t they just play along with the rules of our dominance?  These guys, as you have seen, signed less 4-stars than Cal, Iowa, Oregon and Va Tech signed 5-stars.  Nearly their entire squads were made up of 2- and 3-star prospects; with two-stars making up more than 75% of the scholarships.

Why is Oregon in parentheses?  Because they are the only one for whom a decision must be made… by facilities they are elite.  By recruiting composition they fit squarely with Iowa, Cal and Va Tech.  In terms of facilities, I cannot give a quantified answer as to the difference between their facilities and Cal’s or Iowa’s or Va Tech’s.  When faced with a decision for which I have numbers (recruiting composition) vs. the inherent vagueness of determining placement on a facilities list, I have to go with the numbers.  Therefore, Oregon was placed with Cal, Iowa and Va Tech.  Also, you can see that the teams with facilities presumably worthy of Mount Olympus are split into two categories based on 5-star recruiting efficacy.

So, the list of groupings:

#1.  Florida St, Oklahoma, Texas and USC

#2.  Georgia, tUOS and Penn St

#3.  Cal, Iowa, Oregon and Va Tech

#4.  Boise, TCU and Utah

At this point the first question is:  are these groupings homogenous within themselves?  Could I move Cal to any other group and feel like it is a better fit with a balanced consideration of facilities, recruiting, media exposure, etc.?  That process is repeated for each school.  The only school that stands out to me is Florida St; but that is because they have underperformed so badly on the field as of late.  As far as recruiting through 2002-4 they are definitely on par with the top group, facilities match, and so does media exposure... competition?  Well, they don’t really fit any better with Georgia or the Big Tens in that regard.

The second question is:  whom do these groups represent?  Each of these groups are probably representative of only 5-20 teams each.

Group #1 – These schools absorb such a disproportionate amount of 5-star talent that they can only really represent themselves… maybe Florida and Alabama are represented by this list?

Group #2 – These schools probably represent what we would expect to find at the top-tier of the Big Ten, Big 12 and SEC (not including the mega-recruiters from the Big 12 and SEC.)  Perhaps with a renewed parity in the Pac-10, this group would represent the top-tier of the Pac-10 as well (perhaps USC going forward will be better represented by this group… all depends on whether or not they keep up the 5-star recruiting.)  As it stood for the last decade, the top-tier of the PAC-10 pretty much consisted of USC, with an immediate drop-off to a middle tier.

Group #3 – These schools probably represent the top-tiers of the ACC and Big East, and mid-tiers of the Big 10, Big 12,Pac 10 and SEC.

Group #4 – Real tough to say.  On the field these three have proven themselves to be able to take on the top tier of the SEC and Big 12.  Likewise, the #2s and 3s from their conferences have proven conquerors of the mid-tier of the Pac-10 in recent years.  But in terms of facilities and recruiting composition, I think they are more representative of the mid-tier of the ACC and Big East, and lower tiers of the Big 10, Big 12, Pac-10 and SEC.  Perhaps we should just take the data of their players to be an isolated analysis.

For me, I only really care about the University of Michigan… and in that regard, I would consider it most homogenous with the Georgia, tUOS and Penn St grouping.  So, I would expect our 2-stars, 3-stars and 4-stars to behave in a similar fashion. 

Unfortunately, as stated before, it is not feasible to analyze the bottom tier of NCAA football due to a low rate of NFL draftees.

Drum-roll for the Data…  (Boise, TCU and Utah not included today, but I’ll repost their aggregated results under the results section.  Their individual data can be found in yesterday’s post re: 2- vs. 3-stars.)  Data in alphabetical order.  Percentages given with decimals for teams which will be discussed in isolation.

 

Cal	Tot 4	Tot 3	Tot 2
	21	36	27
4-Draft	5
3-Draft		4
2-Draft			3
%	24%	11%	11%

 

Flor. St	Tot 5	Tot 4	Tot 3
	11	47	23
5-Draft	5
4-Draft		9
3-Draft			2
%	45%	19%	9%

 

Georgia	Tot 5	Tot 4	Tot 3	Tot 2
	4	46	35	5
5-Draft	1
4-Draft		7
3-Draft			6
				1
%	25.00%	15.22%	17.14%	20.00%

 

Iowa	Tot 4	Tot 3	Tot 2
	13	37	34
4-Draft	0
3-Draft		3
2-Draft			7
%	0%	8%	21%

 

Ohio St	Tot 5	Tot 4	Tot 3	Tot 2
	4	39	31	6
5-Draft	2
4-Draft		13
3-Draft			8
				1
%	50.00%	33.33%	25.81%	16.67%

 

 

Oklahoma	Tot 5	Tot 4	Tot 3
	8	51	27
5-Draft	2
4-Draft		14
3-Draft			2
%	25%	27%	7%

 

Oregon	Tot 4	Tot 3	Tot 2
	16	42	34
4-Draft	1
3-Draft		4
2-Draft			4
%	6%	10%	12%

 

Penn St	Tot 5	Tot 4	Tot 3	Tot 2
	4	19	39	13
5-Draft	3
4-Draft		5
3-Draft			3
				1
%	75.00%	26.32%	7.69%	7.69%

 

Texas	Tot 5	Tot 4	Tot 3
	8	43	24
5-Draft	5
4-Draft		11
3-Draft			2
%	63%	26%	8%

 

USC	Tot 5	Tot 4	Tot 3
	13	40	23
5-Draft	6
4-Draft		20
3-Draft			4
%	46.15%	50.00%	17.39%

 

 

Va Tech	Tot 4	Tot 3	Tot 2
	17	39	28
4-Draft	3
3-Draft		9
2-Draft			1
%	18%	23%	4%

 

 

Aggregated Results:

BCS Crashers

	Tot 4	Tot 3	Tot 2
	8	50	194
4-Draft	2
3-Draft		4
2-Draft			18
Tot %	25.00%	8.00%	9.28%

 

Cal, Iowa, Oregon and Va Tech

	Tot 5	Tot 4	Tot 3	Tot 2
	9	67	154	123
5-Draft	4
4-Draft		9
3-Draft			20
2-Draft				15
Tot %	44.44%	13.43%	12.99%	12.20%

 

Georgia, tUOS and Penn St

	Tot 5	Tot 4	Tot 3	Tot 2
	12	104	105	24
5 Draft	6
4 Draft		25
3 Draft			17
2 Draft				3
Tot %	50.00%	24.04%	16.19%	12.50%

 

Florida St, Oklahoma, Texas and USC

	Tot 5	Tot 4	Tot 3
	40	191	97
5 Draft	18
4 Draft		55
3 Draft			10
Tot %	45.00%	28.80%	10.31%

 

Discussion

What I take from the data and results…

1.       Five stars can go to any BCS school consistently in the top 50 without fear of missing out on the NFL due to choosing the wrong university.  Although only one of the sub-groups examined had enough 5-stars to be able to draw a reliable probability, the conglomerate 45% draft rate seen at Florida St., Oklahoma, Texas and USC is at least theoretically corroborated by the similar rates seen within the two other groupings of BCS schools (44.4% and 50.0%.)  Therefore, if the recruiting gurus at Rivals believe an athlete to be worthy of five-stars, then that athlete has approximately a 45% chance of eventually being drafted as long as he goes to schools on at least the same level of Cal, Iowa, Oregon, and Va Tech (the “lowest” level I could measure.)  This data cannot be used to assign a probability to teams below this level due to a lack of historical data re: 5-stars at these schools.

2.       On teams with no, or few, 5-stars athletes… there is no difference between the draft rates of 2-, 3- or 4-star athletes in terms of likelihood of developing into NFL-caliber players.  We see this for 2- and 3-stars among BCS crashers (their 4-stars can’t be assessed due to insufficient sample size.)  We also see this for 2-, 3- and 4-stars at Cal, Iowa, Oregon and Va Tech.  Such athletes have a 12.2%, 13.0% and 13.4% chance of being drafted, respectively.

3.       2-star players at BCS schools have just over a 12% chance of eventually being drafted... Important for consideration of walk-ons.  The schools on the level with Cal and Iowa turned in the only sample size of 2-stars large enough to really let normalization play out.  At these schools, the 2-stars were drafted at a rate of 12.20%.  It is interesting to note that at the big-timers (Georgia, tUOS and Penn St) the rate was the same for their smaller sample (12.5% of 24 players.)  The potential of 2-stars at this tier of schools may be even more impressive considering that the 2-star sample in this group was dominated by Joe Pa’s group of 13.  Look at Joe Pa’s track record with 2- and 3-star athletes.  He is getting less out of this group than the coaches at all the other schools measured (exceptions: 3-stars at Oklahoma and 2-stars at Va Tech.)  His rates (7.69% and 7.69%) are even lower than the conglomerate rates of the drafting of 2-stars and 3-stars from the BCS-busters, who have a much lower draft-rate than BCS schools.  This, in addition to his having coached at one school for more than 60 years, is what makes Joe Pa an outlier.  Georgia and Ohio State’s tiny sample sizes of 2-stars means we can only assign hope that 2-stars at a school like Michigan has a 16-20% chance of being drafted.  This brings a renewed sense of importance to the emphasis on a walk-on program… if we could bring in ten 2-star walk-ons each year, there should be 1-2 NFL athletes among them given a span of 12-20% probability.

4.       Five stars behave as a catalyst to their teammates’ development.  As the concentration of 5-stars among a team’s composition is increased, the team’s draft rates are improved for 3- and 4-stars.  The data groupings express a consistent progression of average five-star signings each year: 0, 0.5, 1 or 2.5 per team in the grouping.  Recruiting between 0 and 0.5 five-star athletes each year seems to have no effect on the lower-rated players (2- and 3-stars behaved the same in the environment represented by Boise, TCU and Utah; and 2-, 3- and 4-stars behaved the same in the environment represented by Cal, Iowa, Oregon and Va Tech.)  The lack of effect seen after  recruiting 0.5 of a five-star each year suggests there is a threshold that must be reached before a catalytic effect.  This threshold lies somewhere between 0.5 and 1, given the large bump 4-stars see in their draft rate on the campuses which acquire at least one 5-star per year (bump from 13% to 24%.)  Going from 1 to 2.5 five-stars per year equates to a smaller bump for the 4-stars:  from 24% up to 29%... probably something to do with the “catalysts” beginning to take up too many spots on the starting roster, which diminishes playing time for everyone else even as their play is further improved. 

4-stars are most prone to elevating their play.  This either means there is actually a difference between 4- and 3-stars’ abilities; or it is indicative of coaches following a biased hierarchy in which it is much more difficult for a 3-star to get a whiff of the field.  I don’t know, I have never been on a football team, but my experiences as a collegiate swimmer suggest that there are some interesting psychologies at work as far as how coaches stratify their athletes.

5.       Georgia might be evidence of one style of coaching…  although Georgia is not a sufficiently large population in and of itself to be statistically relevant, it is interesting to note that there is no apparent correlation between stars and NFL-drafting on Mark Richt’s team.  5-stars were drafted at a 25% rate, with 2-stars (!) coming in at the next highest rate of 20%.  (4-stars 15.2% and 3-stars 17.1%.)  So, after 5-stars (who had an unusually low success rate) the correlation between stars and drafting was the exact opposite of what we would expect.  This might be indicative of a coach who gives all comers equal opportunity at promotion and demotion… sound familiar?

6.       As NFL draft-rates for 5-stars increase, the draft-rates for 2-stars plummets.  An interesting case study is looking at Georgia, tUOS and Penn St side by side.  It cannot be taken with more than a grain of salt, because these teams have neither enough 5-stars nor 2-stars to be statistically relevant.  But, for the love of patterns I’m going to charge ahead anyway and present that data here:

 

Georgia	Tot 5	Tot 4	Tot 3	Tot 2
	4	46	35	5
5-Draft	1
4-Draft		7
3-Draft			6
				1
%	25.00%	15.22%	17.14%	20.00%

 

 

Ohio St	Tot 5	Tot 4	Tot 3	Tot 2
	4	39	31	6
5-Draft	2
4-Draft		13
3-Draft			8
				1
%	50.00%	33.33%	25.81%	16.67%

 

 

Penn St	Tot 5	Tot 4	Tot 3	Tot 2
	4	19	39	13
5-Draft	3
4-Draft		5
3-Draft			3
				1
%	75.00%	26.32%	7.69%	7.69%

 

Perhaps this is further evidence of a hierarchy, in which programs with multiple starting positions filled by 5-stars are inherently going to have fewer opportunities for a significant proportion of their 2-stars to try-out in a real-game situation.

7.       USC Case Study.  As I have already mentioned, USC had enough recruits AND draftees to be able to compare recruits within the ranks of 3-, 4- and 5-stars.  This is highly valuable, because it is the only piece of data which is both statistically relevant AND from a completely homogenous setting.  This is where we find out if there is a true difference in the odds of recruits going pro based on their star ratings…

USC	Tot 5	Tot 4	Tot 3
	13	40	23
5-Draft	6
4-Draft		20
3-Draft			4
%	46.15%	50.00%	17.39%

 

Ummm… so yeah.  4-stars and 5-stars are equivalent.  Dag-nab…  this analysis was supposed to settle the debate.  I can only explain this by the fact that apparently there is a second critical threshold of obtaining 5-star recruits (USC grabbed 3.2 per year between 2002-5) beyond which their presence produces a sense of normalcy to which everyone else on the team believes they are part of.  How scary is it facing off against 1 Brandon Graham in practice?  Pretty damn scary… But how scary is it if there were 10 Brandon Grahams?  The only way to deal with the absolute terror would be for players to reset to a new sense of normal and elevate their expectations to what they perceive as a new normal.

 

In conclusion….

Hooray for stepping up emphasis on the walk-on program!  The potential of developing a couple of NFL-caliber athletes each year would be a huge gain from something which is basically scholarship neutral.

And, from now on I will be highly interested in our pursuit of 5-stars.  It is quite evident that their value extends well beyond individual preparation for the NFL; their presence adds to the quality of practice and motivation for everyone around them, and can apparently give people a new sense of normalcy where they go all Neo on the recruiting services and leave the reality of the Matrix.

---This completes a retrospective analysis of 1159 athletes and their ultimate rate of success in entering the draft.

**Edit** - I did not include the information I shared yesterday regarding Kickers/Punters.  They were not included in either the count of recruits or draftees.  Thank you "funkywolve" for pointing that out.

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I’ll post the results first, since I understand some people have jobs and spouses… reasons, methodology, discussion are all below.

In short, I analyzed 4 recruiting classes (2002-5) and the corresponding 5 NFL drafts (2005-9).

Likelihood of being drafted from Boise St, TCU and Utah: 

3-stars 8.00%;  2-stars 9.28%

Likelihood of being drafted from Cal, Iowa, Oregon, Va Tech: 

3-stars 12.99%;  2-stars 12.20%

The rationale for the use of these schools and their separate grouping is given below; grouping involves facilities and initial selection involves coaching stability.  Also, the BCS schools (Cal, Iowa, etc.) will be used again in the soon to be posted analysis of 4-stars vs. 3-stars and the effect of 5-stars on their fellow teammates.

As you can see, there is no difference between a 2-star and 3-star athlete, as far as talent and potential can be measured by draft status.  When placed in the hands of a capable coaching staff (all of the above universities) and a similar environment (BCS with high level of facilities, or BCS crasher with decent facilities) there is no appreciable difference in draft status.  I now beg you all to stop bagging on our 2-star recruits, it not only shows a lack of respect but from this point forward will have to be considered a lack of basic comprehension.  An analysis of 521 athletes ranked with 2- or 3-stars, and the subsequent 57 draftees to come from this pool, has proven the system flawed for 2- and 3-star athletes.  Please feel free to refer anyone who remains ignorant to this fact here, so that they may either argue with the methodology or inform their thought process.

 

Now, to begin…

A few weeks back, I wrote two diaries (here and here) regarding the feasibility of recruiting services being able to dependably differentiate between athletes from the top 0.23% of high-school football players.  (If you don’t need a refresher of the findings, please skip to the next paragraph.)  The first post looked at the fact that the talent pool (number of total high-school football players) has grown each year consecutively since at least 1988, while the total number of active scholarships for football at NCAA Division 1-FBS schools grew at one-third the rate (fluctuating between 9,095 and 10,115 scholarships.)  Scholarships available basically grew by 11%, while the pool of high-school football players grew by 31.6% over the same period.  Thus, we see a shrinking percentile from which schools are drawing their talent (parity, anyone?)  The second post was an analysis of the 2009 NFL draft, looking at the impact of “Rivals’ Top 100” athletes from the 2005 recruiting class on total draftee production.  The results of the 2009 draft showed that teams could be broken down into 4 categories:  those which produced at least 4 draftees, those with 2-3 draftees, and those with less than 2.  Thus, when considering that a random spread of draftees across all Div 1 college teams would result in 2.15 draftees per team, it is easy to see that there are “over-performers,” “average-performers” and “under-performers” as far as draftee-production is considered.  The other factor considered was teams’ acquisition of “Top 100” (Rivals) recruits in 2005:  twenty-three of the nation’s college teams accounted for the signing of 86% of these athletes.  Thus, there was a clear delineation between recruiting rankings:  again “over-performers” and “under-performers.”  The result of comparing these groups:  there was no correlation between being a highly successful recruiter of “Top 100” talent in 2005 and producing NFL draftees in 2009.  This was admittedly a very narrow slice to analyze… and yes, I had admitted that upfront.

So, after reading these diary posts there were many great comments and ideas put forth by the MGoBlog community.  One idea in particular caught my attention:  both “brax” and “4roses” suggested analyzing the correlation between stars and draft-status from among players placed in the same environment.  This would be the only way to extract a correlation that accounted for, and negated, the recruiting services’ currently common practice of bumping up athletes’ star ratings after they commit to big-time BCS schools.  The goal would be to isolate the effect of differences in coaching and training facilities to get a true bearing on recruits’ talent and potential, and whether or not “stars” actually peg innate talent/potential differences.

Right now the central argument of “star” supporters is “hey, look at the rate of drafting for 5 stars, 4 stars, 3 stars and so on…”  True, there is a clear regression at work there.  But, it does not necessarily speak to the star system predicting talent and performance; rather it is very likely that the stars are predicting which schools recruits go to.  There is an obvious difference in facilities among schools, and there is clearly a shuffle of coaches that generally flows in one direction (towards U of M, Florida, Texas, etc.)  This creates a potential gap in player development, and should heavily affect draft-status.  If the administration of “stars” were to be biased toward awarding extra stars to recruits heading to big-time schools, then it would lose its validity as a measure of actual player talent and potential.

So, the analysis…

Idea:  Cross reference players’ star-rating at time of signing day with their ultimate rate of selection to the NFL.  Compare the rates for each star-level within a small sample of schools deemed to be similar.

Time Period:  I retrospectively evaluated the star-ratings of players from four consecutive recruiting classes (2002-5.)  Their draft-status was referenced to five consecutive NFL drafts (2005-9.)

Parameters:  To be included in the 2-star vs. 3-star evaluation, a school has to have had a high-number of both 2 and 3 star recruits through the four recruiting classes; as well as a significant number of draftees over the five year period (in this case, Boise set the low bar with only 7 draftees over that period.)  Also, in considering the pool of 2- and 3-stars, “kickers” were not included in the count.

Sample selection:

The first group is a group of three schools which consistently compete at a high level, without the benefit of BCS caliber facilities, and consistently place players into the NFL.  They also have had stable coaching situations.  For this pool of players, I examined Boise St, TCU, and Utah.  I acknowledge Utah had the least stable coaching situation, considering Urban Meyer’s departure.

The second group comes from the larger pool of BCS schools with high draft-success.  Thus, for teams taken from the BCS level, I had the luxury of being able to use a limiting-requirement of coaching stability; only schools with no head-coaching change between 2002 and 2009 were used.  This reduced the available pool:  Cal, Florida St, Georgia, Iowa, Ohio St, Oklahoma, Oregon, Penn St, Texas, USC, and Va Tech.  For today’s comparison between 2- and 3-star athletes, the list is further pared down to the schools with a high number of 2- and 3-star athletes:  Cal, Iowa, Oregon and Va Tech.  This group is important because they have a high number of 4-star recruits as well; meaning they will be a bridge between the analyses of 2- vs 3- star recruits and 3- vs. 4-star recruits.

Data:

Boise	Tot 4	Tot 3	Tot 2
	1	9	76
4 Draft	0
3 Draft		1
2 Draft			5
%	0%	11%	7%

 

TCU	Tot 4	Tot 3	Tot 2
	5	21	53
4 Draft	1
3 Draft		2
2 Draft			7
%	20%	10%	13%

 

Utah	Tot 4	Tot 3	Tot 2
	2	20	65
4 Draft	1
3 Draft		1
2 Draft			6
%	50%	5%	9%

 

Cal	Tot 4	Tot 3	Tot 2
	21	36	27
4-Draft	5
3-Draft		4
2-Draft			3
%	24%	11%	11%

 

Iowa	Tot 4	Tot 3	Tot 2
	13	37	34
4-Draft	0
3-Draft		3
2-Draft			7
%	0%	8%	21%

 

Oregon	Tot 4	Tot 3	Tot 2
	16	42	34
4-Draft	1
3-Draft		4
2-Draft			4
%	6%	10%	12%

 

Va Tech	Tot 4	Tot 3	Tot 2
	17	39	28
4-Draft	3
3-Draft		9
2-Draft			1
%	18%	23%	4%

 

Results:

BCS Crashers…

	Tot 4	Tot 3	Tot 2
	8	50	194
Draft 4	2
Draft 3		4
Draft 2			18
Tot %	25.00%	8.00%	9.28%

 

4-, 3- and 2-star Analysis of Cal, Iowa, Oregon and Va Tech…

	Tot 4	Tot 3	Tot 2
	67	154	123
4 Draft	9
3 Draft		20
2 Draft			15
Tot %	13.43%	12.99%	12.20%

 

Discussion…

First, resist the temptation of looking at the drafting of 4-stars from the BCS crashers as legitimizing the star-system.  Eight athletes from a four-year recruiting cycle does not bring nearly enough statistical power to make any claims.  Therefore, do as I have done:  ignore them.  In the comparison of 3- and 4-star athletes we will look at a much larger sample size from schools which have enough 4-star recruits and NFL draftees to power a comparison.  So, save those comments for that analysis.

Second, for those of you still reading this, I have included the 4-star data for the BCS teams; so you get an advanced view of the 3- and 4-star analysis.  Pretty crappy reward for your reading efforts?  Yes… but I appreciate those efforts nonetheless.  :^)

Third, a bit of repetition… but with more definition.  As you can see, there is no difference between a 2-star and 3-star athlete, as far as talent and potential can be measured by draft status.  When placed in the hands of a capable coaching staff and a similar environment, there is no appreciable difference in draft status.  We can see that while each school has a fairly large sample of 2- and 3-star recruits to analyze (among the schools considered, 81 per BCS-crasher and 69 per BCS) the number of draftees from an individual school would not allow enough power.  Hence, the need to group the schools.  I left the schools in two separate groupings because of two factors:  one, it allowed a consideration of the effect of different facility levels on player development; secondly, the BCS group will be used as a bridge to the 3- vs. 4-star analysis. 

We can see that the rate of drafting from the middle-echelons of the BCS for 2- and 3-star athletes is approximately 50% higher than the rate of drafting from the top echelon of non-BCS schools (12%-ish vs. 8%-ish.)  I generally relate the BCS crashers’ success to the high-quality of coaching staffs they have assembled and been able to keep.  It would be interesting to see if there is a large difference in facilities at these schools when compared to Cal, Iowa, Oregon and Va Tech.  If the gap is minimal, then the difference in drafting above could be considered to have more to do with media exposure and proven play against higher-performing competition.  It is an interesting question, and probably pretty difficult to investigate.

So, an analysis of 521 athletes ranked with 2- or 3-stars, and the subsequent 57 draftees to come from this pool, has proven the system flawed for 2- and 3-star athletes.  I already made my feelings known at the start of the post, but I will say it again… please stop dismissing our 2-stars.  It is obvious that they are just as likely to be contributors as our 3-stars, and as Michigan Men they deserve our support regardless of contribution.

Go Blue!

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After reading through, and enjoying, many comments on yesterday’s diary post I was driven to give more contemplation to the subject of “stars.”  The first premise derived from yesterday’s data is that a very, very small sliver of high school talent is of the caliber to even be recruited (currently, 0.23% of the talent-pool receives a scholarship to a Div 1-FBS school.)  Moreover, the top 0.1% of the talent pool would now include somewhere between 780 and 1002 high school seniors each year, depending on whether you are of the ilk that 70% or 90% of the top high school football players are seniors (or anywhere in between.)  Either way, this large number of players from within a very tiny sliver of a talent-percentile casts some serious doubt (in my mind) on the ability of scouts to truly differentiate among anyone outside of a football “prodigy.”  See yesterday’s post for a more comprehensive analysis of the “diminishing sliver.”

Among the comments were some great links posted by “Oregon_Alum,” credited to “mejunglechop and others” who had “brought this to the fore.”  The links (below; a couple of which had died before I had the chance to click on them) detail the success of “stars” in correlating to measurable outcomes.  I found the first article, from athlonsports.com, to be very convincing and well-stated.

http://www.athlonsports.com/college-football/16635/recruiting-the-nfl-draft
http://www.sundaymorningqb.com/2008/3/17/71811/4082
http://rivals.yahoo.com/ncaa/football/blog/dr_saturday/post/Hug-your-fri...
http://www.athlonsports.com/college-football/13422/nfl-stars-how-recruit...
http://www.sundaymorningqb.com/2008/1/21/1614/43228

The athlonsports.com article uses an analysis of the 2008 and 2009 NFL drafts to point out that the “stars” a player was previously given by talent scouts directly correlated with the likelihood of being drafted.  According to their data analysis of the 2008 draft, a player given that 5^th star coming out of high school has a 40-48% chance of being drafted in the top 3 rounds; 4-star 9-11%, 3-star 3.6% and 2-star less than 1%.  And, giving more credibility to the college scouts’ “eye,” I found that the NFL scouts had a pretty good eye as well… 7 draftees from 2008 were subsequently selected for the Pro-bowl (I know I don’t need to say it, but that includes our very own Jake Long) with draft-order actually seeming to represent the spread of talent: 4 future Pro-bowlers in the 1^st round, 2 in the second, and 1… well he went undrafted (apparently, even the NFL scouts miss one now and again.)  As a side note, these 7 elite players came from (in draft-order): UofM, Boise St., Tennessee St., East Carolina, Cal, Rutgers and Fresno St… I found that interesting.  Also: 4-star, 2-star, 4-star, 2-star, 5-star, 3-star, and 3-star… maybe the “chip on the shoulder” has a bit of a lasting effect.   I digress…

I cannot argue against “stars” correlating well with an eventual NFL selection, the numbers seem clear.  But, given that I am still leery of scouts’ ability to differentiate between non-prodigal high school players (3-17 players each year might be football prodigies, OK maybe 22… ) from within the top 0.23%, I am left questioning where such a correlation might stem from.  My initial theory is coaching and facilities (aka “player development”) as well as media exposure.

At this point my thought goes something like this…

I am currently studying tuberculosis in Moldova.  It is estimated that 1/3 of patients with an active case of TB will spontaneously resolve without treatment.  In contrast, somewhere between 60-65% of patients who undergo pharmaceutical treatment will be healed (note: that is a very low success rate for treatment, not applicable to those seen in the US… hence why I am here studying the system.)  But, let’s say that the infrastructure is overwhelmed and some people’s diagnoses are missed.  Those people will not be admitted to the facilities which (in this case) would have doubled their odds of survival.

So, in the football context:  a certain percentage of the high-school players (and I am not yet talking about the “prodigies,”) who continue on to play NCAA football, will someday develop into the mold of a potential professional player (likened to the cases of TB which spontaneously resolve.)  This is irrespective of their star-ranking, as well as exposure to coaching and facilities (likened to TB-treatment.)  In other words, there is a specific number of recruits from across the star-spectrum who are “destined” for a slot in future NFL draft; based on future physical and mental development, as well as intelligence, self-motivation and character.  This explains the numerous 2-stars we see drafted from the FBS subdivision each year, as well as the 22 players drafted from outside of the FBS subdivision in 2009.  Obviously, the baseline of “destined-success” is nowhere near 33% of all new college players.  My posit is that a longitudinal study would reveal a baseline of inherent draftability among players, measured by the success of Div 1-FCS players in achieving such outcome.  Granted, there are some great coaches and nice facilities in Div 1-FCS; but, the facilities’ levels could certainly be considered “basic” in comparison with those of Michigan, LSU, USC etc., while successful coaches are continually “poached” through the ranks to end up at… Michigan, LSU, USC etc.  Remember, FCS division players still come from the top 0.5% of high school football talent, and apparently this talent pool is still of high enough quality (in comparison with that of the FBS subdivision) to have accounted for 8.6% of the 2009 NFL draft.

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List of schools from outside of FBS represented in 2009 NFL Draft:

Abilene Christian**			Sam Houston St.
Cal Poly SLO			St. Paul’s College
Furman			Stephen F. Austin
Liberty			Stillman
McNeese St.			Tennessee St.
Monmout			Weber St.
Nebraska-Omaha			West Texas A&M
Nichols St.			W. Illinois
Norfolk St.			Western Ontario
North Dakota St.			William and Mary
Richmond
**Indicates 2 draftees

Perhaps this means that upwards of 20% of the NFL draft is filled with players that develop regardless of coaching and facilities.  8.6% of draftees came from approximately 11,500 FCS athletes, correlating to an expectation of an additional 10.5% to have come from the approximately 14,000 participants in the FBS subdivision.  How is the other 80% filled?

Well, my theory is that the other 80% is first filled by the correctly identified prodigies, with the remainder arriving in a disproportionate manner based on attending schools with the best facilities, the best coaches, and the most media exposure.  So, back to the star-system.  What is it truly correlated with?  Well, first, the talent scouts seem to correctly identify most of the prodigies; the 5-star ranking is sort of a highly sensitive measure with a poor selectivity (identifying 90% of the prodigies with a rate of false-positives hovering around 50%.)  This is why we see that 5-star ratings correlate so well with future success (in this case measured by draft-status.)  Secondly, the star-system is correlated to the student-athlete’s college destination.  As “Noahdb” pointed out (among yesterday’s comments,) most schools are using recruiting services as the preliminary filter.  I wouldn’t doubt that the 4 and 5 star kids, at positions of need, are given the first-looks by the big time schools (enter first source of data bias…)   It is also pretty obvious that the recruiting services freely modify recruits’ star-levels after seeing where the recruit is likely to commit (enter second source of data bias…)  These biases add up to meaning that the major, automatically qualifying BCS schools have classes filled with a disproportionate number of 4 and 5 star athletes (regardless of the actual talent of the athletes.)

If the star system was actually a predictor of success, then we should see that 4 and 5 star athletes are drafted from “big-time” schools at the same rate.  But, when analyzing the 2009 draft by universities the draftees are selected from, we see 4 university categories emerge:  Over performers, Average performers, Underperformers, and the dreaded Underperforming Outliers.

The Over-performers:  split into two categories, those who successfully recruited at least one player from the Rivals’ 2005 Top 100 and those who did not.

School		‘09 Draftees	% of Total            Draftees	2005         Top 100	Rate          Proportion
Alabama		4	1.5625	2	1.28
Clemson		4	1.5625	1	0.64
Georgia		6	2.3438	4	1.706667
Iowa		4	1.5625	5	3.2
LSU		6	2.3438	3	1.28
Maryland		5	1.9531	1	0.512
Ohio St		7	2.7344	3	1.097143
Oklahoma		5	1.9531	8	4.096
Ole Miss		4	1.5625	2	1.28
Oregon		5	1.9531	1	0.512
Penn St		5	1.9531	2	1.024
S. Carolina		7	2.7344	3	1.097143
Texas		4	1.5625	3	1.92
USC		11	4.2969	9	2.094545
Virginia		4	1.5625	1	0.64
Wisconsin		4	1.5625	1	0.64

 

Of note: a randomized distribution of draftees would mean an average of 2.15 draftees taken from each FBS school (assuming none come from FCS, Div II, Canada, etc.)  Therefore, I have considered all teams which supplied 4 or more draftees to be over-performers.  The number of draftees from each is accompanied by the percentage of total draftees represented by that team.  For example, USC supplied 11 draftees, which was 4.3% of the total taken across all 7 rounds of the ’09 draft.  The numbers in the next column indicate the number of Rivals Top 100 recruits signed by each school in 2005; 2005 is the class with the biggest impact on the 2009 draft (although I concede the early attrition of juniors would be expected to affect the stability of this figure.)  The final column is a simple comparison of the % of Top 100 talent acquired in 2005 vs. the % of draftees produced in 2009.  If the top 100 recruits are truly more likely to be drafted, then the teams which acquire the highest % of them should produce a comparably disproportionate number of draftees.  A score above 1 indicates a team which is taking a high level of talent, but not matching that rate with NFL talent.  A score below 1 means the team is producing NFL talent at a greater rate than it is taking in top 100 talent; “0” would be the best possible score, meaning a team is producing NFL talent without the aid of any Top 100 recruits.

Speaking of ratio scores of “0,” the next set of draft Over-performers did so without taking a single top 100 recruit in 2005.

School		‘09 Draftees	% Total            Draftees	2005         Top 100	Rate           Proportion
Cinci		6	0.023438	0	0
Georgia Tech		4	0.015625	0	0
Mizzou		6	0.023438	0	0
N. Carolina		5	0.019531	0	0
Oregon State		8	0.03125	0	0
Pitt		4	0.015625	0	0
Rutgers		5	0.019531	0	0
TCU		5	0.019531	0	0
Texas Tech		4	0.015625	0	0
U Conn		4	0.015625	0	0
Utah		4	0.015625	0	0
Wake		4	0.015625	0	0

 

Analyses of these two groups: 

There were 28 teams in total.  26 are from the automatically qualifying BCS conferences.  The other two are… TCU and Utah.

The first group of 16 teams produced a total of 85 draftees.  That is a rate of 5.3 draftees per team, for a total of 33.2% of all 2009 draftees.  In order to accomplish this feat, these 16 teams (13.4% of Div 1-FBS teams) signed 49% of the top 100 recruits in 2005, according to Rivals.

The second group of 12 teams produced a total of 59 draftees.  That is a rate of 4.9 draftees per team, for a total of 23% of all 2009 draftees.   These 12 teams (10.1% of Div 1-FBS) signed zero top 100 recruits in 2005, according to Rivals.

In other words, the first 16 teams signed an average of 3.1 recruits, each, from the top 100, and produced only 0.4 more draftees, each.  Remember there’s basically a 50% chance of a 5-star being a “prodigy”… the first 16 teams signed 14 of these athletes.  This means that 7 of their draft slots were due to the odds of a 5-star being a prodigy… therefore, their expected non-prodigal rate is 4.9 draftees per team (the same as the other 12 teams in the Over-performers category.)

In order of gross output:  the top performers are USC, Oregon St., Ohio St. tied with S. Carolina, followed by a four-way tie for 5^th between Cinci, Georgia, LSU and Mizzou.  Ordered in terms of their “Rate Proportions:”  Oregon St., Cinci tied with Mizzou, Ohio St. tied with S. Carolina, LSU, Georgia and USC.  Based on this data, if I were looking for a coach I would look at Oregon St. (USC tried and failed,) Cinci (Notre Dame tried and succeeded) and Mizzou.

The Average-performers:  since we would expect 2.15 draftees per team after a random distribution, I considered those teams sending 2-3 draftees to the NFL as average.  I excluded teams which finished with 2-3 draftees but had at least 3 top 100 recruits in 2005 (they fall into the Underperforming Outliers.)

School		‘09 Draftees		2005        Top 100
Abilene Christian		2		0
Arizona		2		0
Arizona St		2		0
Auburn		3		1
Ball St		2		0
Boston College		2		0
BYU		2		0
Cal		2		2
Florida		3		1
Fresno State		2		0
Hawaii		3		0
Illinois		3		1
Louisville		2		0
New Mexico		2		0
NC State		2		2
Purdue		2		1
Rice		2		0
San Jose St		3		0
Southern Miss		2		0
Syracuse		2		0
W Michigan		2		0
West Virginia		3		1

 

Analysis:  There were 22 schools which finished in the “Average” category, 12 are auto-qualifying BCS schools.  There were also 9 Rivals Top 100 athletes among these twelve schools, with 3 five-stars.

The Dreaded Under-performing Outliers:  The schools in this category have the distinction of having excelled in recruiting while performing average or below in producing 2009 draft prospects.

School		‘09 Draftees	% Total             Draftees	2005         Top 100	Rate           Proportion
Florida St		1	0.003906	9	23.04
Miami		1	0.003906	5	12.8
Michigan		2	0.007813	7	8.96
Nebraska		3	0.011719	4	3.413333
Tennessee		1	0.003906	7	17.92
Texas A&M		2	0.007813	3	3.84
Va. Tech		1	0.003906	2	5.12

 

Analysis:  Seven schools, all from among the auto-qualifying BCS conferences.  I named this category as being outliers not because their proportional representation is sufficiently small to be a complete anomaly (5.9% of total Div. 1-FBS schools is a pretty large segment of the total.)  It is because each had its own set of circumstances leading up to the 2009 draft.  Michigan and Tennessee finished with uncharacteristically bad records (for different reasons,) same goes for Texas A&M, and who knows what happened with Florida St…. you would think that 9 recruits from the top 100 who helped piece together a 9-4 season would have been a sure-sell for a few more draft slots than 1.  Maybe Bowden really had lost his knack for coaching.

At any rate, these seven schools accounted for a whopping 37% of the Top 100 Rivals recruits in 2005; 10 of which were five-stars!  Four years later they combined for a horrendous showing at the draft:  11 draftees (4.3% of the total.)  Assuming the “50% of five-stars turn out to be prodigies” theory, 5 of the 11 drafts slots were a given, irrespective of coaches and facilities.  That means these seven programs could take credit for developing only 6 NFL caliber players.  Finally, according to the “rate proportions” Florida St. was the most colossal failure, followed by Tennessee and Miami.  I’ll stop there.

Conclusions:  After viewing three categories, we are left with the knowledge that in the 2009 draft, the 66 auto-qualifying BCS schools (67 with Notre Dame) split into 26 Over-performers, 12 Average, 22 Underperformers, and 7 Underperforming Outliers.  Among the Over-performers, there was no difference in draftee output (after 5-star talent was accounted for) between the group of 16 teams with a large representation (49%) of 2005’s top 100 recruits and the group of 12 teams with none of 2005’s top 100 recruits among them.  The impact of recruiting-stars took another hit when teams accounting for 37% of 2005’s top 100 talent combined for a miserable 4.3% of draftees in 2009.  Finally, 8.6% of the draftees in 2009 came from the Div 1-FCS subdivision.  This gives an initial estimate of 19.1% of draft-slots being accounted for by a baseline of players who will develop into NFL talent irrespective of differences in coaching, facilities and media exposure (and stars allotted to them.)

This has been an analysis of a single draft.  The data from the 2009 draft supports that while “stars” may correlate with draft success, it is likely a correlation due to “stars” predicting the collegiate destination of athletes as opposed to describing a differentiable talent level.  The two sources of bias aforementioned would suggest this possibility, as well as the fact that after accounting for 5-star athletes, the presence of “top 100” talent did not impact the NFL-production of universities with comparable levels of coaching and facilities.  In fact, a large concentration (37%) of “top 100” talent within 7 traditionally successful schools’ 2005 recruiting classes had no positive impact on the 2009 draft results.   I acknowledge the value of 5-star athletes; 50% of them are probably the much searched for football “prodigies” while the rest are just mis-rated and subject to the need for successful development.  I am not convinced that there is a discernible and/or long-term difference between 2-, 3- or 4- star athletes; rather, the appropriate coaching and facilities can turn any of these athletes into future NFL studs.

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As we approach national signing day...

Two trends have been colliding over the last 20 years to create a scenario of increased parity across college football, and diminished value of the “star”-based recruiting rankings.  A look at the “participation” statistics provided by the NCAA, as well as those provided by the NFHS and US Census Bureau reveals a trend of growth at both the NCAA division 1A/FBS level and the high school level.  But, while the number of athletes participating at the FBS level of college football has increased by 11.0% over the 20 year span between 1988 and 2007, high school football participation has increased by approximately 31.6%.  (Note:  I say “approximately” because I was only able to obtain 9 high school data points and used the massively-brainy regression power of MS Excel to extrapolate the missing values… However, it was reported by the NFHS that participation in high-school sports has increased every year for the last 20 years; if anyone is a member of NFHS and would like to share the participation numbers in football from 1988-96, then we could have a more exact look at the growth on a year to year basis.)  Also, a larger impact is the fairly stagnant number of scholarships available, rolling back and forth between 9 and 10 thousand over the last 20 years.

A selection of years (spanning the time-frame:)

NCAA Football                                           High School Football      %Schol/HS
Year        1A Teams      1A Ath  1A Schol.     Year        Athletes                Yearly

1988        105                12,726    9,975         1988           841,900*            0.30%
1991        106                12,513  10,070         1991           882,685*            0.29%
1994        107                11,963    9,095         1994           923,470*            0.25%
1997        111                12,643    9,435         1997           971,335             0.24%
2000        114                13,190    9,690         2000        1 005,040*            0.24%
2003        117                13,711    9,945         2002        1 032,682             0.24%
2006        119                13,984   10,115        2006        1 104,548             0.23%
2007        119                14,131   10,115        2007        1 108,286             0.23%

*extrapolated value

What this suggests is that the portion of the bell-curve from which college coaches are recruiting talent is shrinking.  In 1988, the players selected for scholarships across all of Division 1A football would have approximately represented the top 0.30% of all high-school football players (note: 11-player leagues; yes, I know that Nebraska and Iowa have both had some great success with 9-player league players, etc.  I’m just trying to keep this somewhat simple.)  In 2007, we’re talking the top 0.23% of high-school talent.  Of course, keep in mind that the coaches are restricted to taking only graduating seniors.  Would physical and mental maturation suggest that in any given year, 70-90% of the most-elite high school players are seniors?  Tough argument to back up with numbers…


I suppose we would all agree that there probably exists the equivalent of prodigies within the realm of football talent… I don’t know what percentile you would want to attach to that status.  I would assume that the 99.9th percentile would suffice as the level of being the cream of the crop.  Among all players in 2008/9 this would correlate with the top 1113 high school players; I’m guessing (based on the aforementioned 70-90% conjecture) between 780 and 1002 of them were seniors and part of the 2009 recruiting class.  That is enough players to fill somewhere between 31 and 40 FULL recruiting classes of 25 players.  In other words, if the top 30-40 teams were the sole benefactors of the top 780-1000 players; then all of them would have teams made solely of players from the top 0.1% of high-school talent. 


That's right... that "2 star" athlete that you are sometimes tempted to speak negatively of is most definitely from the 99.5th percentile of high-school football athletes.  Among "academics" that correlates to a performance of 2320 or above on the 2007 SAT; 40 and above on the 2008 MCAT; 175 and above on the 2005-2008 LSAT exams; and somewhere around 780 and above on the GMAT.  If you scored less than that on any of those exams, your performance would mean you are less than a 2-star among your academic peers... ouch.  That's a pretty high-level of expectations.


I should also point out that the impact of this glance at the numbers is based on football talent adhering to a simple bell-curve.  But, what I have not taken into account is the growth of high-school players’ exposure to better coaching:  college summer camps, professional trainers, etc.  If the overall access to such expertise has increased over the last 20 years (as a percentage of high-school players receiving such tutelage) then the bell curve could actually be expected to skew to the right over time (compared to its original shape;) meaning it would be even harder to differentiate between the talent in the “right tail.”


What does this all mean?  Well I take a couple of points from it.  First, if people can truly differentiate between the top couple of hundred players in the nation (let alone the top 40 at each position,) without seeing said players side by side and in the same context, then I greatly admire them and place them in the savant category of Gregory House M.D., one of U of M's greatest fictional graduates.  However, I am a bit cynical as to such an amazing ability existing.  Second, given that there are 119 schools divvying up the top 0.23% of talent each year, I am experiencing a renewed sense of importance regarding coaches and facilities.  These players are all starting from nearly identical positions, where one year of intense studying, conditioning, and skill-building is more than enough to erase any gap between them and their peers (meaning today’s "number 1" can very easily become tomorrow’s bust, and vice versa.)  This points to the third take-home message: chemistry and motivation.  A “chip on the shoulder” of anyone from the top 0.1% of their profession can very quickly become a strong motivator of perfection… there are probably 900 of the top 1000 players who have this “chip.”  Harness this source of motivation with the appropriate team-chemistry... anything is possible.

 
Coaching… check.  Facilities… check.  “Chip”… check.  I like where our class stands.


Data taken from:
http://www.nfhs.org/Participation/
http://www.allcountries.org/uscensus/432_participation_in_high_school_at...
http://www.ncaapublications.com/ProductsDetailView.aspx?sku=PR2009&Aspx