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.

NPR Marketplace: For NBA, courting Steve Ballmer could be a strategic move

NPR Marketplace: For NBA, courting Steve Ballmer could be a strategic move

Going forward, Emory University’s Mike Lewis says the NBA would be psyched to have a deep-pocketed guy own the Clippers.

“You know they are sort of the New York Mets or the Chicago White Sox. They are the second team in that city. I think it’s really attractive to the league to essentially have two really strong franchises in a major city like LA,” he says.

NBA Conference Finals: Spurs & Thunder Dominate Local Twitter Market

Last night, the Oklahoma City Thunder beat the San Antonio Spurs in Game 4 of the Western Conference Finals.  We were interested in examining the Twitter presence of both teams in their respective markets during the game.  Thus, we collected all tweets that included the word “Thunder” originating from the Oklahoma City market and all tweets that included “Spurs” originating from the San Antonio market, that were tweeted during the hours that the game was played.  We then divided the number of collected tweets by the total volume of tweets in the respective markets during the time period of the game.  This essentially gave us the “Twitter Share of Voice” for the Spurs in San Antonio and the Thunder in Oklahoma City.   11.8% of all tweets in Oklahoma City during the game included the term “Thunder”!  9.3% of all tweets in San Antonio included the term “Spurs”.  We performed a similar analysis for all other conference finals games thus far.  The results of the analysis are presented in the chart below.

Local Market Twitter Share NBA Conf Finals 2014

It seems as though tweets that mention the local team in the Western Conference Finals cities tend to have a higher Twitter Share of Voice than the Eastern Confernce Finals cities.  We can also examine the content of the team-related tweets to determine if the sentiment of the tweets is positive, negative or neutral.  The chart below presents the ratio of positive to negative sentiment for the team-related tweets in each market during the playoff games.

Local Market Twitter Sentiment Ratio NBA Conf Finals 2014So far, local market Twitter “happiness” in highest for San Antonio fans during the first game of the series, and for Oklahoma City fans during the third games of the series.  Indiana fans seem to tweeting progressively less about the Pacers, and the positive to negative tweet ratio has been decreasing as the series advances as well.

Manish Tripathi & Mike Lewis, Emory University 2014.

Simulating Kyle Korver’s Amazing Streak

On March 3, 2014, Kyle Korver took five three-points shots against the Portland Trailblazers and missed all of them.  This marked the first time in two years that Korver had played in a regular season game without making at least one three-point shot.  Korver has played in over 790 regular season games in the NBA, but his previous streak for games with at least one three-point shot made was only 28.  Clearly, Korver’s 127 games streak is remarkable, but how likely was it?  One method for understanding the likelihood of the streak is to simulate the chances of Korver making a three-point shot in each of the 127 games.  Given Korver’s long history in the NBA, we can use his career statistics to inform our simulation of the streak.  What follows is the setup and results from our simulation; we also offer potential extensions to this analysis, whose completion is truly a function of the level of data available and the level of effort a researcher wishes to expend.

While we know that Korver hit at least one three-pointer per game during the streak, we’d like to know what was the probability of him hitting at least one three-point shot in each game.  In order to do that, we first need to model the number of three point shots attempted in each game.  We assume that the number of three point shots taken in a game can be modeled using a Poisson regression.  This type of regression is common with count (non-negative integer) data.  We model the number of three point shots attempted in each game as a function of factors such as whether the game is at home or away, minutes played by Korver, the record of the opponent, whether the Hawks are in playoff contention, etc.

Once we have estimated the number of three pointers attempted in a game, we simulate the probability of making at least one three pointer using the binomial distribution.  The binomial distribution provides the probability of k successes over n trials, where the probability of success in each trial is p.  In this context, k is a made three-pointer, n is the number of three point attempts (estimated from the Poisson regression), and p is sampled from a normal distribution based on Korver’s historical three-point percentage mean and standard deviation.  The binomial distribution assumes that the probability of hitting a three point attempt in a game is not connected to if the shooter hit or missed his last three-pointer (there is independence across trials).  We can express the probability of making at least one three-pointer as:

Binomial We then multiply these 127 game probabilities together to compute the overall probability of Korver’s streak.  By taking the product of the individual game probabilities, we are assuming that they are independent.  There have been several arguments for why there is no “hot-hand” while shooting within a given game, thus we don’t feel it is unreasonable to assume that there is no “hot-hand” across games (although the “hot-hand” can be easily incorporated into our model).

Now, we are ready to run our simulation.  For all 127 games of the streak, we simulate the probability of making at least one three-pointer.  We then multiply these simulated probabilities together to obtain the overall probability of the streak.  We perform this exercise 500,000 times to get a better understanding of the simulated overall probability of observing Korver’s streak.  Figure 1 is a plot of these 500,000 simulations of the overall streak.  The average of these simulated probabilities is just 0.0000000003843 (where 1 = 100%)!

Figure 1

Korver Streak

If you are anything like us, your head is already full of criticisms of our approach.  Let us address these criticisms through potential extensions of our simulation.  First, it is possible to relax the independence assumptions (both within and across games) if you believe that there is a “hot-hand” in basketball.  Second, of course, the ideal Poisson model of the number of three-point shots attempted would also employ play by play in-game data, where we would observe the score differential, the time remaining, the defense being played, etc.  These in-game situational factors would help determine if Korver launched a three-pointer.  Such an analysis would require access to in-game data and a great deal of time and resources.

The longest current streak for at least one three pointer made in a game is 51 by Stephen Curry of the Golden State Warriors.  In order to tie Korver’s streak, Curry would have to make at least one three pointer in each of his next 76 games.  We decided to simulate the probability of Curry tying Korver’s streak.  Once again, we estimated the number of three-point attempts for each game using a Poisson regression.  We had to limit the covariates in the regression, since we are projecting into the future.  We also truncated the regression to guarantee that Curry attempted at least one three-pointer per game.  We then used the binomial distribution to simulate the probability of hitting at least one three pointer given the estimated number of three-point attempts.  We took the product of these 76 games to determine the overall probability of Curry tying Korver’s streak.

Figure 2

Curry Streak

Figure 2 is a plot of 500,000 simulations of Curry tying the overall streak.  The average of these simulated probabilities (0.000006281) is more than 15,000 times that of the probability of observing Korver’s streak!  This reflects not only Curry’s prowess as a three point shooter, but it also shows the true exceptionality of Korver’s accomplishment (please take note, Mr. Rovell).

Mike Lewis & Manish Tripathi, Emory University, 2014.

The Best Sports Cities: Boston Wins in a Rout; Twin Cities Better than NY & Chicago

Boston InfographicWe started the Emory Sports Marketing Analytics blog back in March of last year.  Our goal was to bring analytics to the world of sports business.  To put a finishing touch on 2013, we are going to present our rankings of the best and worst sports fans by city.  These rankings are based on our revenue premium model of fan equity and our analyses of social media equity.

Phoenix InfographicFor our rankings, we have divided cities into categories based on how many of the four major sports (NFL, NBA, MLB, & NHL) have franchises representing the city.  This categorization does introduce a bit of oddness since Los Angeles becomes a “three-sport” city.  Another tough issue is how to treat teams like the Packers.  Is Green Bay a one-sport city or is Milwaukee as three-sport city (we decided that we would treat Milwaukee as a three-sport city)?

Today we reveal our rankings of the four-sport cities, and a summary of the best and worst markets in the other categories (one, two, & three-sports cities).  Before the actual rankings, a couple of clarifying comments are in order.  The key to our rankings is that we are looking at fan support after controlling for short term variations in team quality and market characteristics.  Basically we create statistical models of revenues as a function of quality measures like winning percentage and market potential factors like population.  This allows our results to speak how much support fans provide as if market size and winning rates were equal.

The number one team on our four-sport city list is Boston; and it wasn’t even all that close.  All of the Boston teams have impressive fan followings.  The Red Sox ranked 1st in terms of fan equity and 1st in social equity. The Celtics finished 3rd in the NBA in both our fan and social media equity rankings.  The Patriots rank 2nd in fan equity and 3rd in social media equity in the NFL.  The Bruins rank relatively low in fan equity (perhaps because they could price higher), but very high in social media equity.  Number two on the list is Philadelphia.  The Eagles, Phillies and Flyers are all very strong fan bases.  The Sixers are weak within the NBA, but the three other sports carry Philly to a second place finish.

The city in third place is likely going to generate Twitter complaints about how clueless we are, and how academics should stay away from sports.  We rank the Twin Cities of Minneapolis and Saint Paul as having the third most supportive fans among the four-sport cities.  Minneapolis/Saint Paul show great support of the Twins and solid support for the Vikings.  The Wild also do surprisingly well in the NHL.

How could Minnesota finish in front of New York and Chicago?  It’s because these cities don’t do a great job in terms of supporting all their teams.  For example, The Brooklyn Nets perform poorly when market size is considered and the White Sox have very poor support on all metrics.  We can hardly wait for the semi-literate Twitter attacks to commence.

At the bottom of the list we have Phoenix.  We should note that the Suns perform well and finish 7th in terms of fan equity in the NBA.  But beyond that, Phoenix sports are a disaster.  In terms of fan equity, the Diamondbacks finish 26th in MLB, the Cardinals 30th in the NFL and the Coyotes 28th in the NHL.  As we have learned over the past year, it seems that weather and tradition are what creates a strong fan culture.  Perhaps the Phoenix teams overall are too new, and the weather is too warm.

Our other winners and losers are given below with linked infographics that summarize raw data and final rankings.

For the three-sport cities, the overall winner is St. Louis, and the worst fan support occurs in Tampa Bay.

For the two-sport markets, the leader in fan support is NashvilleOakland is at the bottom of the rankings.

For the one-sport cities, Portland leads the way, while Memphis trails the field.

Mike Lewis & Manish Tripathi, Emory University 2014.

Don’t Want to Get Fired? Best and Worst Cities for Firing Professional Coaches

Mike_Shanahan_RedskinsIt’s “Black Monday” in the NFL.  The Vikings, Redskins, Lions, and Bucs have already fired their coaches today, and more firings are possible before the day is done.  There are many variables that can affect the firing of a coach in professional sports.  Of course, three easily observable factors are the performance of the coach (winning percentage, playoff appearances, and championships), the investment by the ownership (team payroll), and the sports league (NFL, MLB, NBA, and NHL).   There are also intangible factors endemic to each city in America and Canada with a professional sports team that can influence the probability of a coach getting fired.

We decided to estimate a logistic regression model that could explain the probability of getting fired as a function of performance, investment by ownership, and professional league affiliation.  We looked at data from all four professional sports leagues over the last twelve years.  We then compared the predicted probability from our model of getting fired with the actual firings in each city.  In theory, cities with intangible characteristics that make it more likely for a coach to get fired would have actual firings at a higher probability than predicted through our model of performance and investment.  We tried several specifications of our model, and these rankings are robust.

Based on our study, the Top 8 Worst Cities (Highest probability for getting fired above predicted) are:

  1. Orlando
  2. San Francisco
  3. Montreal
  4. Sacramento
  5. Milwaukee
  6. Oklahoma City
  7. Jacksonville
  8. Miami

The Top 8 Best Cities (Lowest probability for getting fired below predicted) are:

  1. Winnipeg
  2. Nashville
  3. Salt Lake City
  4. Memphis
  5. Los Angeles
  6. Portland
  7. Buffalo
  8. Minneapolis

It’s interesting to note that the top 8 worst cities does not include big media markets like New York, LA or Chicago, where one might think there is large expectation for winning.

Mike Lewis & Manish Tripathi, Emory University 2013.

St. Louis Tops Rankings of Three Team Cities, Tampa Bay is Last

Our city ranking series continues today with a look at cities with three professional sports teams.  These markets tend to be a bit on the smaller side, but many have significant sports histories.  We also fully admit that we struggled a bit with how to classify several of these markets.  For example, what is the city of Milwaukee?  Is Milwaukee a two sport town with NBA and MLB franchises or should we include the Packers and call it a three sport town?  Having lived in Chicago, it always seemed like all the Wisconsin teams should be lumped together.  Toronto was another decision.  Until now, we have only considered US cities, and avoided one professional team Canadian markets such as Calgary and Edmonton.  So before the complaints begin, please realize that we have made some assumptions about markets.

The table on the right provides our ranking of the eight markets with three professional teams.  According to the data, St. Louis is the best of these markets.  Professor Lewis used to live in St. Louis and the first place ranking was a bit of a surprise to him.  While the Cardinals have an amazing following,  Lewis’ sense was that the Rams and Blues only had average fan bases. The Cardinals do have an exceptional fan base ranking 4th in MLB in both fan equity and social media equity.  The Blues have an above average fan base ranking 14th in the NHL.  The Rams do struggle with a fan equity ranking of 22th in the NFL.  So it really is the Cardinals that elevate St. Louis to the top of the list.

Following St. Louis, we have Toronto ranked 2nd, Milwaukee 3rd and Pittsburgh 4th.  Frankly, we would have predicted Pittsburgh would rank higher.  The issue is that our fan equity metric is based on a “revenue premium” model, and the Steelers don’t seem to price nearly as high as they could.  But, this was a close competition.  Toronto has the best NHL fan base and the Packers and Steelers have devoted followings.

At the bottom of the list we have Tampa Bay.  The Lightning ranked 18th in NHL fan equity.  The Bucs ranked 29th in the NFL and Rays ranked 22nd in MLB.  On a side note, the Atlanta ranking should put to rest any complaints about the Braves relocating.  The Braves have delivered phenomenal quality and have only gained an average fan following.  Add in a history that includes players like Hank Aaron and Dale Murphy, and you would expect that the Braves would have a monster following.  Our expectation is that the move to Cobb County and the building of a mixed use development around the stadium should lead to a stronger fan base in the near future.

Mike Lewis & Manish Tripathi, Emory University 2013.