Recruiting Ranking and Results in 3-4 Years

#1

pualsline

poakley@safeoak.com
Joined
Oct 18, 2008
Messages
9,432
Likes
371
#1
First Column is Recruiting Rank top is the #1 class down to 15

Second is Jr Years for those Recruits final ranking

Third is Sr. year for Recruits final ranking

View attachment RecruitingMS.xls

I found it interesting that Sr classes were always in the top 15 in recruiting 4 years prior, but twice the Jr. classes did not win the National Title.
 
Last edited:
#5

Dust10

A Drain.
Joined
Jan 31, 2010
Messages
10,269
Likes
4,779
#5
The only thing that sticks out to me is that the top ranked class leads to a top 3 finish 4 out of 6 times their senior year. Everyone else out performs or under performs their rank pretty randomly.
 
#9

RiotVol

Well-Known Member
Joined
Dec 25, 2011
Messages
766
Likes
819
#9
Ok, just re-read what you did, and I'm not entirely sure what you're trying to do.

Are you trying to say that recruiting momentum has a tendency to be cyclical?
 
#17

RiotVol

Well-Known Member
Joined
Dec 25, 2011
Messages
766
Likes
819
#17
Do you guys think that recruiting classes are better reflected 3 or 4 years after recruiting?
Absolutely. That's what I tried to get at by aggregating the past four classes to see how well they predicted win/losses this year.

I would say there's a weak trend, but I wouldn't put money on it in Vegas.

At some point I want to actually get the various numbers for each service and run them against win/loss ratio. I have that for Scout. I think I can get that for Rivals. Maybe not enough data for ESPN or 24/7 (How long have they been around? Not since 2008 to combine all four years).

In theory, the best service should have the best correlation.

On the other hand you could do it on a player by player basis, but you'd have to winnow your sample down or it would take FOREVER to do.
 
#21
Joined
Sep 4, 2007
Messages
4,557
Likes
0
#21
http://data:image/jpeg;base64,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
 
#24

RiotVol

Well-Known Member
Joined
Dec 25, 2011
Messages
766
Likes
819
#24
For those complaining about the format what would you rather the layout be?
Since you're trying to show the relationship between two ordinal variables, I'd try a scatterplot and give the NR value some kind of number so they're not omitted.

Keep crunchin' the numbers, dude. Interesting stuff...
 


VN Store

Sponsors
 

Top