What Schools Recruit the Best, the Worst and Perhaps a Bit too Well?

For the final entry in our college basketball recruiting series we have taken a look at how well different schools recruited for the period from 2002 to 2011.This is the culmination of our other analyses that looked at factors that are expected to affect recruiting such as a team’s fan base support and ability to convert recruiting hauls into draft picks.  In this last entry we take a look at how schools recruit versus how we would expect schools to recruit.

What do we mean by “expect schools to recruit?”  Basically, our premise is that recruits are interested in playing for teams that have supportive fan bases, play in high profile conferences, are successful on the court, have significant financial resources, produce NBA players and have storied histories.  Our analysis begins with a model that predicts recruiting results (we use Rivals recruiting points as the dependent variable) as a function of these factors (revenues, last season winning rates, previous NCAA tourney appearances, previous final fours, recruit conversion into draft picks, conference, etc…).

We then compare a school’s actual recruiting results with the model’s prediction for each year in the data.  We then look at the ten year average of the difference between the actual and the predicted results (the residuals) to classify schools as over and underachievers. Because our results have the potential to stir up emotions, before we get into specific results we should make a couple of points clear.  First, the meanings of over achieving and under achieving recruiting results can be interpreted in multiple ways.  One interpretation is that schools (and coaches) that “over” achieve do a great job in attracting recruits.  However, given that the model controls for factors such as winning rates, being on the list of over achievers can also imply that the school underachieves on the court with the given talent.  Likewise, at the bottom of the list, “under” achieving can be interpreted as either lousy recruiting or an ability to get the most out of recruits.

The Top 10 list for the high majors is led by Texas at number one (I can almost hear Texas fans saying that this proves that Rick Barnes is a poor game coach), UCONN at 2, Florida at 3, Villanova at 4 and Memphis at 5.  Duke was number 10.

At the very bottom of the list of high majors we have Boston College, Houston and Arkansas.  In the cases of Boston College and Arkansas, these are fascinating results.  These schools regularly make the tournament and win games.  They just don’t seem to be able to draw elite recruits.  If I am a college AD looking for a new coach, I would take a close look at the coaches at these schools.  Perhaps these are coaches that if surrounded by super start recruiters could build elite programs.

While we aren’t going to spend much time on the mid majors in this analysis, our analysis did yield one very interesting finding for this group.  The school at the very bottom of the list is Butler.  Again, this is a result that can be spun in either direction.  Perhaps Brad Stevens is truly a basketball savant who can succeed with any players.  Alternatively, maybe schools like Illinois and UCLA dodged a bullet because Stevens would not have been able to recruit at the high major level.

Finally, maybe the most interesting element of our analysis is that we are able to identify recruiting results that are statistically unlikely.  If we agree that our model captures the key drivers of recruiting (expenditures, revenues, past success, current success, conference affiliation, conversion of recruits to NBA picks, etc…) then exceptional recruiting hauls should be a bit troubling.  These unusual results mean that either a given coach or program have a “specialness” not included in the model.  We will let readers speculate as to what this “specialness” might be. Our list includes three programs: Kentucky, Texas and Villanova.

The Kentucky results are especially dramatic.  Our calculations (which are a bit of the back of the envelope variety) suggest that the probability of Kentucky’s results occurring by chance is just 1%.  But again, we do acknowledge that there may be something special about this program that our model doesn’t capture.  However, we should also note that we do not find a similar “specialness” for schools such as North Carolina, Kansas, Duke and UCLA.  And to take things just a step farther if we just look at John Calipari’s results across Memphis and Kentucky our estimated probability of his recruiting results is less than .1%.  As before we acknowledge that we may be omitted a variable or two that captures coach Calipari’s recruiting gifts, but our model doesn’t identify other high powered recruiters such as Thad Matta, Bill Self or Coach K as outliers.

Mike Lewis & Manish Tripathi, Emory University 2013

Converting High School Talent into NBA Draft Picks: Ranking the ACC

The NBA Draft can be a time for college basketball fans to cheer about the “success” of their basketball program.  Kentucky, Duke, North Carolina, and Kansas fans can boast about the number of alums currently in the NBA.  This year, ESPN is taking that discussion one step farther by describing the quality of NBA players produced, and ranking the “NBA Pedigree” of colleges.

Our take is a bit different as we will examine the process of taking high school talent and converting it into NBA draft picks. In other words, we want to understand how efficient are colleges at transforming their available high school talent into NBA draft picks? Today, we launch our NBA draft series by ranking the schools in the ACC based on their ability to convert talent into draft picks.

The initial approach is fairly simple.  Each year, (almost) every basketball program has an incoming freshman class.  The players in the class have been evaluated by several national recruiting/ranking companies (e.g. Rivals, Scout, etc…).  In theory, these evaluations provide a measure of the player’s talent or quality*.  Each year, we also observe which players get drafted by the NBA.  Thus, we can measure conversion rates over time for each college.  Conversion rates may be indicative of the school’s ability to coach-up talent, to identify talent, or to invest in players.  These rates may also depend on the talent composition of all of the players on the team.  This last factor is particularly important from a recruiting standpoint.  Should players flock to places that other highly ranked players have selected?  Should they look for places where they have a higher probability of getting on the court quickly? Next week we will present a statistical analysis (logistic regression) that includes multiple factors (quality of other recruits, team winning rates, tournament success, investment in the basketball program, etc…). But for now we will just present simple statistics related to school’s ability to produce output (NBA draft picks) as a function of input (quality of recruits).

Our first set of rankings is for the ACC.  At the top of the list we have Boston College and Georgia Tech.  Boston College has done a good job of converting low-ranked talent into NBA picks (in this time period they had two three-star players and a non-rated player drafted).  Georgia Tech, on the other hand, has converted all of its five-star recruits, and several of its four-star recruits.  A result that may at first glance seem surprising is the placement of UNC and Duke.  However, upon reflection these results make a good deal of sense.  When players choose these “blue blood” programs they face stiff competition for playing time from both current and future teammates.

Here are some questions you probably have about our methodology:

What time period does this represent?

We examined recruiting classes from 2002 to 2011 (this represents the year of graduation from high school).  While the chart above ranks the ACC, we compiled data for over 300 Division 1 colleges (over 12,000 players).

How did you compute the conversion rate?

The conversion rate for each school is defined as (Sum of draft picks for the 2002-2011 recruiting classes)/(Weighted Recruiting Talent).  Weighted Recruiting Talent is determined by summing the recruiting “points” for each class.  These “points” are computed by weighting each recruit by the overall population average probability of being drafted for recruits at that corresponding talent level.  We are using ratings data from Rivals.com.  The weights for each “type” of recruit were 0.51 for each five star recruit, 0.13 for each four star, 0.03 for each three star, 0.008 for each two star, and 0.004 for each not ranked.  

Second-round picks often don’t even make the team.  What if you only considered first round picks?

We have also computed the rates using first round picks only, please see the table below.

NEXT: RANKING THE BIG 10

*We can already hear our friends at Duke explaining how players are rated more highly by services just because they are being recruited by Duke.  We acknowledge that it is very difficult to get a true measure of a high school player’s ability.  However, we also believe that over the last ten years, given all of the media exposure for high school athletes, this problem has attenuated.

Mike Lewis & Manish Tripathi, Emory University 2013.