2015 NBA Draft Efficiency

Last night, the NBA held its annual draft.  The NBA draft is often a time for colleges to extol the success of their programs based on the number of draft picks they have produced.  Fans and programs seem to be primarily focused on the output of the draft.  Our take is a bit different, as we 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 present our third annual ranking of schools based on their ability to convert talent into NBA draft picks.

Our 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?  A few years ago, we conducted a statistical analysis (logistic regression) that included multiple factors (quality of other recruits, team winning rates, tournament success, investment in the basketball program, etc…).  But today, 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).

For our analysis, we only focused on first round draft picks, since second round picks often don’t make the NBA.  We also only considered schools that had at least two first round draft picks in past six years.  Here are our rankings:

NBA First Round Draft Efficiency 2010-2015Colorado may be a surprise at the top of the list.  However, they have converted two three-star players into first round NBA draft picks in the last six years.  This is impressive since less than 1.5% of three-star players become first round draft picks.  Kentucky also stands out because while they do attract a lot of great HS talent, they have done an amazing job of converting that talent into a massive number of 1st round draft picks.

Here are some questions you probably have about our methodology:

What time period does this represent?

We examined recruiting classes from 2006 to 2014 (this represents the year of graduation from high school), and NBA drafts from 2010 to 2015.  We compiled data for over 300 Division 1 colleges.

How did you compute the conversion rate?

The conversion rate for each school is defined as (Sum of draft picks for the 2010-2015 NBA Drafts)/(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 trying to control for the fact that a five-star recruit is much more likely to get drafted than a four or three-star recruit.  We are using ratings data from Rivals.com.  We index the conversion rate for the top school at 100.

Mike Lewis & Manish Tripathi, Emory University 2015

Analytics vs Intuition in Decision Making Part IV: Outliers

We have been talking about developing predictive models for tasks like evaluating draft prospects.  Last time we focused on the question of what to predict.  For drafting college prospects, this amounts to predicting things like rookie year performance measures.  In statistical parlance, this is the dependent or the Y variables.  We did this in the context of basketball and talked broadly about linear models that deliver point estimates and probability models that give the likelihood of various categories of outcomes.

Before we move to the other side of the equation and talk about the “what” and the “how” of working with the explanatory or X variables, we wanted to take a quick diversion and discuss predicting draft outliers.  What we mean by outliers is the identification of players that significantly over or under perform relative to their draft position.  In the NFL, we can think of this as the how to avoid Ryan Leaf with the second overall pick and grab Tom Brady before the sixth round problem.

In our last installment, we focused on predicting performance regardless of when a player is picked.  In some ways, this is a major omission.  All the teams in a draft are trying to make the right choices.  This means that what we are really trying to do is to exploit the biases of our competitors to get more value with our picks.

There are a variety of ways to address this problem, but for today we will focus on a relatively simple two-step approach.  The key to this approach is to create a dependent variable that indicates that a player over-performs relative to their draft position. And then try and understand if there is data that is systematically related to these over and under performing picks.

For illustrative purposes, let us assume that our key performance metric is rookie year player efficiency (PER(R)).  If teams draft rationally and efficiently (and PER is the right metric), then there should be a strong linkage between rookie year PER and draft position in the historical record.  Perhaps we estimate the following equation:

PER(R) = B0 + BDPDraftPosition + …

where PER(R) is rookie year efficiency and draft position is the order the player is selected.  In this “model” we expect that when we estimate the model that BDP will be negative since as draft position increases we would expect lower rookie year performance.  As always in these simple illustrations, the proposed model is too simple.  Maybe we need a quadratic term or some other nonlinear transformation of the explanatory variable (draft position).  But we are keeping it simple to focus on the ideas.

The second step would then be to calculate how specific players deviate from their predicted performance based on draft position.  A measure of over or under performance could then be computed by taking the difference between the players actual PER(R) and the predicted PER(R) based on draft position.

DraftPremium = PER(R) – PER(R)

Draft Premium (or deficit) would then be the dependent variable in an additional analysis.  For example, we might theorize that teams overweight the value of the most recent season.   In this case the analysts might specify the following equation.

DraftPremium = B0 + BPPER(4) + BDIFF(PER(4) – PER(3)) + …

This expression explains the over (or under) performance (DraftPremium) based on PER in the player’s senior season (PER(4)) and the change in PER between the 3rd and 4th seasons.  If the statistical model yielded a negative value for BDIFF it would suggest that players with dramatic improvements tended to be a bit of a fluke.  We might also include physical traits or level of play (Europe versus the ACC?).  Again, we will call these empirical questions that must be answer by spending (a lot of) time with the data.

We could also define “booms” or “busts” based on the degree of deviation from the predicted PER.  For example, we might label players in the top 15% of over performers to be “booms” and players in the bottom 15% to be “busts”.  We could then use a probability model like a binary probit to predict the likelihood of boom or bust.

Boom / Bust methodologies can be an important and specialized tool.  For instance, a team drafting in the top five might want to statistically assess the risk of taking a player with a minimal track record (1 year wonders, high school preps, European players, etc…).   Alternatively, when drafting in late rounds maybe it’s worth it to pick high risk players with high upsides.  The key point about using statistical models is that words like risk and upside can now be quantified.

For those following the entire series it is worth noting that we are doing something very different in this “outlier” analysis compared to the previous “predictive” analyses.  Before, we wanted to “predict” the future based on currently available data.  Today we have shifted to trying to find ‘value” by identifying the biases of other decision makers.

Mike Lewis & Manish Tripathi, Emory University 2015.

For Part 1 Click Here

For Part 2 Click Here

For Part 3 Clicke Here

Impact of NBA Draft Day on Social Media Following

Social Media is of course a popular medium for athletes to build their brand.  Two popular platforms are Twitter and Instagram.   I tracked the Twitter and Instagram followers for the top 100 draft prospects in the weeks leading up to the draft, and the morning after the draft.   The chart below presents the growth in followers for the lottery picks.

Akash Lottery

It is interesting to see how the following of second-round picks of the teams that had lottery picks as well was affected by the draft.  The chart below documents the social media presence of some of these players.

Akash Non LotteryNote: Gary Harris should have 35,265 Twitter followers on June 13

Guest Entry By Akash Mishra, 2014.

2014 NBA Draft Efficiency

Last night, the NBA held its annual draft.  The NBA draft is often a time for colleges to extol the success of their programs based on the number of draft picks they have produced.  Fans and programs seem to be primarily focused on the output of the draft.  Our take is a bit different, as we 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 present our second annual ranking of schools based on their ability to convert talent into draft picks.

Our 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?  Last year, we conducted a statistical analysis (logistic regression) that included multiple factors (quality of other recruits, team winning rates, tournament success, investment in the basketball program, etc…).  But today, 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).

NBA 2014 Full Draft Efficiency

Here are some questions you probably have about our methodology:

What time period does this represent?

We examined recruiting classes from 2002 to 2013 (this represents the year of graduation from high school), and NBA drafts from 2006 to 2014.  We compiled data for over 300 Division 1 colleges (over 15,000 players).

How did you compute the conversion rate?

The conversion rate for each school is defined as (Sum of draft picks for the 2006-2014 NBA Drafts)/(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 trying to control for the fact that a five-star recruit is much more likely to get drafted than a four or three-star recruit.  We are using ratings data from Rivals.com.  We index the conversion rate for the top school at 100.

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.

NBA 2-14 First Round Efficiency

Mike Lewis & Manish Tripathi, Emory University 2014.

*Once again, 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 eight years, given all of the media exposure for high school athletes, this problem has attenuated.

College Basketball Recruiting and the NBA Draft: Data, Theory and Statistical Models

Over the last week or so we have presented data on school’s success in developing high school recruits into NBA draft picks.  What we have presented thus far is raw data summarized at the school level.  These results provide offseason wins and bragging rights for some fan bases and losses for others.  One of our favorite responses came from a University of Wisconsin blogger who made a link between our brand equity study and the draft efficiency results.  In the Wisconsin case, the combination of high fan equity combined with low draft efficiency is something that should give fans (and athletic directors?) something to think about.

But while summarized data is great, there are some limitations.  The biggest limitation is that the data limits our ability to draw deeper insights.  We know that Boston College players develop better than Duke players (adjusted for recruiting rankings) or that Purdue players have more success than Indiana players, but we don’t know why?  With respect to college basketball recruiting, one question that is of interest to us is how does the composition of a recruiting class impact the likelihood that a given recruit is successful in developing into a draftable player.  Our starting theory for our analysis was that players would have better chances to make the pros (controlling for the player’s individual talent) when their teammates were less highly regarded.  The theory is that less talented teammates would result in a player seeing more playing time, and being more of a focus of the offense.  In our earlier analysis of NFL draft efficiency, we found evidence for this theory being true.

In general, what we do on the website is use theory to design statistical models and then take these models to data.  When we did this for the college basketball draft efficiency data, we got some surprises.  For this analysis we used a tool called logistic regression.  Logistic regression is useful when we are trying to predict yes/no type events.  In this case we were interested in predicting the probability that a recruit of some quality level (5-Star, 4-Star, 3-Star or other) is drafted.  Our theory would suggest that having more 5-Star players would reduce the probability of any given player being drafted.

For the statistical analysis, we began by predicting whether a player was drafted based on the composition of the team, the school’s expenditures on the team, the team’s historical success and other factors.  What we found was that for 4 and 5-Star players the best predictor was the number of other 5-Star players on the team.  We tried a variety of specifications and used some extra tools such as Factor Analysis, and this general result that draft efficiency is positively correlated with recruiting success was robust.  For the 3-Star player, the best predictor was the school’s level of investment. Very few of the variables we included in the model were significant.

While we didn’t get what we expected, we did get some interesting results.  For the elite high school recruit, our results do suggest that it is better to go to a blue blood program.  Given the lack of significance of variables related to exposure, such as whether the team participated in the NCAA tournament, our conjecture is that these results suggest that better teammates equates to more competition in practice and for playing time, and it is this competition that is the key to developing NBA playersThis result would suggest that the highly recruited athlete is doing the right thing by choosing Kentucky, Kansas or North Carolina.

The other interesting take-away from the results is the lack of significant variables and the overall fit of the model.  In this case, it appears that we are missing a big part of the story.  While our model results tell us about the “average” importance of team composition, it doesn’t tell us about the talent developing ability of specific schools and coaches.

Our model results can be used to evaluate individual schools.  To do so, we use our statistical model to predict the draft efficiency of each school (based on historical recruiting results, investment in the program, conference affiliation, historical successes, etc.) and compare this to the actual draft efficiency.  When we do this comparison, we get some thought-provoking results.  The overall “winner” of this analysis was Georgia Tech.  During our ten year study period, Georgia Tech had four 5-Star recruits and twelve 4-Star recruits.  All of the 5-Star recruits and a quarter of the 4-Star recruits were drafted.  Other high scoring schools included Ohio State, Kentucky and UCLA.  Perhaps the most interesting result we can extract from this analysis is which schools struggle to convert talent into NBA players: out of the 68 BCS schools evaluated, Duke finished at 51 and Michigan State at 61.  In the case of Michigan State, only two of the six 5-Star recruits were drafted.  Even worse, none of the twelve 4-Star recruits were drafted.  So while Tom Izzo and Mike Krzyzewski are great coaches when it comes to tournament success, a high school recruit may want to think twice before choosing these schools.

Mike Lewis & Manish Tripathi, Emory University 2013.

Nevada & BYU Best at Converting Talent into NBA Draft Picks: Ranking the Best of the Rest (Non-BCS)

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors.  We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the Non-BCS Conferences (The Best of the “Mid-Majors”).    The chart below lists our efficiency rankings (for more details on our methodology, please click here).  The University of Nevada Wolfpack were the leaders in converting talent into NBA draft picks.  The Wolfpack were followed closely by BYU.  It should be noted that there was a minimum threshold of recruiting talent over the ten year study that was needed to be considered for this analysis.

Nevada and BYU not only are on top of the “Best of the Non-BCS” ranking, but they are also the two best teams in the country overall based on this talent conversion metric. Also, although Colorado State and North Texas are at the bottom of this top 10, their conversion rates would put them near the top of any of the BCS conference rankings.  Finally, Gonzaga and Memphis are not on this list, despite producing 3 and 9 draft picks, respectively, during the period of this study.  This is due to when we control for the amount of talent that was recruited to these schools, their conversion rates are less than stellar.

In the period of the study, Nevada did not have any 5-Star recruits in its basketball program.   Nevada had 50% of its 4-Star recruits, 17% of its 3-Star recruits, 14% of its 2-Star recruits, and 6% of its non-rated recruits drafted into the NBA.  This is incredible given that the national overall average for getting drafted was 13% for 4-Star recruits, 3% for 3-Star recruits, 0.8% for 2-Star recruits, and 0.4% for non-rated recruits!

Similar to Nevada, BYU did very well in converting lower-ranked talent.  BYU had 14% of its 3-Star recruits drafted into the NBA.  Remarkably, BYU had 13% of its non-ranked players drafted; this is almost 33 times better than the overall national average!

PREVIOUS POST: RANKING THE BIG EAST

South Florida & Marquette Best at Converting Talent into NBA Draft Picks: Ranking the Big East

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the Big East.    The chart below lists our efficiency rankings for the Big East (for more details on our methodology, please click here).  The University of South Florida (USF) was the leader in the Big East in converting talent into NBA draft picks.  The Bulls were followed by Marquette and then Connecticut.

 

In the period of our study, USF had no 5-Star or 4-Star recruits at all.  However, 9.5% of 3-Star recruits at USF were drafted into the NBA (The overall national draft rate for 3-Star recruits during this period was 3%).

Marquette performed better than traditional Big East powers UConn, Syracuse, and Georgetown in the period of our study.  This is largely due to 13% of 3-Star recruits and 14% of non-ranked recruits from Marquette being drafted.  This is incredible considering that the national draft rate for 3-Star recruits was 3%, the rate for non-ranked recruits was 0.4%! While Georgetown and Syracuse were both slightly above average with respect to their 5-Star recruit drafting rates, they were both below the national average for being drafted with respect to their 4-Star recruits.  This is potentially problematic, as 4-Star recruits reflect a large portion of the recruiting classes for both schools.

PREVIOUS POST: RANKING THE SEC

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Vanderbilt & Florida Best at Converting Talent into NBA Draft Picks: Ranking the SEC

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the SEC.    The chart below lists our efficiency rankings for the SEC (for more details on our methodology, please click here).  Vanderbilt was the leader in the SEC in converting talent into NBA draft picks.  The Commodores were followed by Florida and then traditional power Kentucky.  To all of our friends in Lexington, we realize that Coach Calipari has done an excellent job in producing NBA draft picks.  Our analysis covers the recruiting classes of 2002 to 2011, and thus Calipari only comes in at the tail-end of the sample.  We are trying to look at long-term trends.  It is quite likely that if we only looked at the Calipari era, Kentucky would be on top.

In the period of our study, 14.3% of 3-Star recruits at Vanderbilt were drafted into the NBA (The overall national draft rate for 3-Star recruits during this period was 3%).  The Commodores only had one 5-Star recruit during the time-frame of our study, and that 5-Star recruit was drafted.  Thus, Vandy was able to effectively convert the limited high-level of talent that it recruited, and it was able to transform lower-ranked talent into NBA material at a rate far above the national average.

During the time period of our study, Kentucky and Florida had 32% and 21% of their overall recruits drafted, respectively.  This puts both schools in the top 10 in the country for overall percentage of recruits drafted.  While Kentucky had 72% of their 5-Star recruits drafted (the national average was 51%), they did not do as well with lower-rated recruits as compared to Florida.  Florida had 26% of their 4-Star recruits drafted (the national average was 13%), and also had 3-Star and non-rated recruits drafted during the time period of our study.

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Iowa State & Kansas Best at Converting Talent into NBA Draft Picks: Ranking the Big-12

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the Big-12.  The chart below lists our efficiency rankings for the Big-12 (for more details on our methodology, please click here).  Iowa State was the clear leader in the Big-12 in converting talent into NBA draft picks.  The Cyclones were followed by traditional power Kansas and then Texas.

In the period of our study, 15% of 2-Star recruits and 13% of non-rated recruits at Iowa State were drafted into the NBA.  This is very impressive given the overall national draft rates: 0.8% for 2-Star recruits and 0.4% for non-rated recruits!  Furthermore, two 3-Star recruits were drafted from Iowa State.  Iowa State did a remarkable job of converting its available talent into NBA draft picks.

Perennial power Kansas finished second in the rankings.  Kansas had an overwhelming 30% of its overall recruits drafted into the NBA.  The Jayhawks also had 39% of its 4-Star recruits drafted (compared to the 13% national 4-Star average).  Third place Texas had 66% of its 5-Star recruits drafted (compared to the 51% national 5-Star average).

PREVIOUS POST: RANKING THE PAC-12

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Washington & USC Best at Converting Talent into NBA Draft Picks: Ranking the PAC-12

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the PAC-12.  The chart below lists our efficiency rankings for the PAC-12 (for more details on our methodology, please click here).  The University of Washington was the clear leader in the PAC-12 in converting talent into NBA draft picks.  The Huskies were followed by USC and Cal.  Traditional power UCLA finished 5th.

In the period of our study, Washington produced nine draft picks, and 22% of the overall recruits for UW were drafted into the NBA.  66% of 5-Star recruits, 31% of 4-Star recruits, and 12.5% of 3-Star recruits from UW were drafted.  This is truly remarkable given the overall national draft rates: 51% for 5-Star, 13% for 4-Star and 3% for 3-Star!

USC finished second in the PAC-12 rankings.  The Trojans had 29% of their 4-Star recruits drafted, and had two 3-Star recruits drafted in the first round.  Cal finishing third potentially speaks to the importance of the head coach in the efficiency rankings.  Mike Montgomery, the head coach for Cal, was at Stanford in the late 1990s and early 2000s, when the Cardinal enjoyed an excellent talent to NBA draft pick conversion rate.  Current head coach Johnny Dawkins has produced a grand total of 0 draft picks for the Cardinal from his recruiting classes.

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