NBA Brand Report 2019

With the 2019-2020 NBA season on the horizon, it’s time to take a look at NBA fandom through an analysis of the team fandom and brands. I do these for the various pro leagues on a near annual basis though the last NBA ranking was 3 years ago.

When thinking about NBA fandom, we need to acknowledge that the NBA goes to market a little bit differently than the other major leagues. In the current era, the NBA has been a star driven league. Over the last 40 years, the NBA’s story has been the tale of Julius Erving, Larry Bird, Magic Johnson, Michael Jordan, Kobe Bryant, LeBron James and Stephen Curry. And maybe Charles Barkley Tim Duncan, Shaquille O’Neal, Kevin Durant and a few others.

Those last two sentences are important, debatable, and maybe enraging. Does Kevin Durant belong in the first group or the second? What about Kareem? How about Karl Malone and John Stockton? My list is off the cuff and subjective but what’s important about those two sentences is that those players (plus or minus a few) represent the story of the NBA going back two generations. In most sports, fan loyalty is about teams first and players second. In the NBA it is often the reverse.

LeBron James is probably the best example. When LeBron was in Cleveland the Cavs were a top brand. A TV ratings draw and an arena filler when the team was on the road. As he moves on the Cavs quickly reverted to being a second tier NBA brand.

The upside of this approach is NBA players can become true cultural icons. This means that the NBA generates a lot of “Free” media. I suspect that the daytime ESPN talk shows pay as much or more attention to the top 5 NBA players as they do to all of MLB. The downside is that the players have more control over the fortunes of the league than in the NFL or MLB.

I should probably be more specific – a small group of NBA players has enormous power over the league. This can create challenges for individual teams and can create issues for the entire league. What happens when a team (Toronto?) loses a star player? How do they recover? Try to talk a free agent into coming to a small market (OKC?)? Good luck. At the league level, is there “face of the league” succession planning? When do you stop emphasizing Michael Jordan? Or even better – how do you decide who to market next? Why is LeBron a bigger star than Kevin Durant?

As we go into this next season the big on-court topic (written before the NBA-China relationship blew up) is the same as the big marketing topic. How does the shift towards player empowerment (or player collusion to circumvent the CBA if you prefer) impact the league?

This is relevant to today’s article because the focus is on analyzing the loyalty and engagement of fans across the NBA. Specifically, I am taking a look at and ranking the NBA teams in terms of fan engagement and loyalty. Player movement is a major part of the story. In the NBA, it is tough to disentangle the loyalty to teams versus loyalty to stars. This past off-season, saw significant player movement towards teams that have not historically been iconic NBA brands.

The Metrics

I’m going to skip the details (no one reads them) but I will say my analysis of team brands is different because it’s based on the statistical analysis of 20 plus years of data on winning, attendance, pricing, population and just about any other factors for which I can collect data. The basic idea is that I look at how teams perform in terms of several marketing performance metrics after controlling for factors such as winning rates and market population.

I evaluate NBA team fandom using three metrics. Fan Equity is based on how teams perform in terms of home revenues. This captures pricing power and attendance. Social Equity is based on team’s reach via Twitter. Road Equity is based on how teams draw on the road.

Each metric has advantages and limits. Fan equity is based on consumer’s willingness to attend and spend. This is the gold standard for measuring consumer engagement as it is based on opening the wallet and taking the time to sit in traffic and make it to the arena. On the downside, this metric does not consider a team’s national following and may be constrained by arena capacity. The Social Media metric has advantages as it can capture out of market fandom and fans who are priced out of an arena. I also think social media following skews younger so it is more of a forward-looking metric. A problem with social media is that social followings are “sticky” in the downward direction. When LeBron moves to a new team, we see a huge jump in the team’s social following. But when he moves again, relatively few fans make the effort to stop following the previous club. Road Equity is based on a combination of national following and willingness to travel. In the NBA this is probably almost all about national following. The weakness of the Road equity metric is that national following may be much more about a star player than the team itself.

To get an overall ranking I combine the three metrics using a weighting scheme that treats the Fan equity measure as twice as important as the other two. Debatable but simple.

The Winners

It has been a few years since I did the NBA rankings and there are some significant changes. The overall winner going into the 2019-2020 season is the Los Angeles Lakers. The Lakers score consistently high across the three metrics. Number 1 in Road Equity and Social Equity and number 2 in Fan Equity. The Lakers are followed by the Warriors, Bulls, Celtics and the Cavs.

The significant changes from the previous ranking are the elevation of the Warriors and the fact that the Knicks are absent from the top 5. The Bulls and Celtics have long been iconic NBA brands. Different histories but similar results. The Bulls are the house that Michael built while the Celtics were built by many.

The Warriors are a great example of how powerful brands are created. Golden State was long a second tier team. Now, after years of Stephen Curry and Kevin Durant winning championships, the Warriors have become a premier brand with a national following of engaged fans. The lesson is that it is not enough to win once. Like the Bulls, brands are built through repeated championships.

The Knicks are ranked 7th on the overall list. The Knicks win the Fan Equity measure but fall short in terms of the Social and Road metrics. I suspect that the control variables do not adequately capture the unique advantages that the Knicks enjoy based on location.

What about the Cavs being ranked 5th? Two quick thoughts. First, I use three years of data to measure current brand equity. This is done because brand equity usually shifts slowly and to average out noise in the data. The problem is that using three years means that the Cavs still benefit from seasons that include a now departed player. The downward stickiness of social media is also an issue in the Cavs results.

The Losers

At the bottom of the list, we have the Washington Wizards, Memphis Grizzlies, Charlotte Hornets, Brooklyn Nets and Detroit Pistons. For this group, it has been different paths to the bottom. Did the Wizards ever recover form the name change from the Bullets? Memphis and Charlotte have never had a history of success. The Pistons are interesting because they also had a period of success at about the same time as the Bulls. Why is the legacy of Isiah Thomas so much less than that of Michael Jordan? Its likely about the scale of winning and maybe something related to the dynamics of Detroit versus Chicago.

 

The Brooklyn Nets are also a curious case. The move from New Jersey to Brooklyn came with a lot of hype. So what happened? First, there was no period of great success to take advantage of the hype. Second, the Nets fall short in comparison to the Knicks (and other major market teams like the Bulls and Lakers). While they play in the same market, the Nets results do not compare to the Knicks on any of the fan metrics.

What is really necessary for a team to move up the rankings is consistent winning. And not just winning in terms of making the playoffs – brand equity seems to be built by winning and contending for championships.

The List

Finally, the overall list. Please enjoy and tell me what you think in the comments. I expect 80% hostility.

 

Click on the logo below to listen to the NBA Fan Rankings podcast episode! You can also listen on iTunes, Spotify, Google Play Music, TuneIn and Stitcher. Don’t forget to rate, review, and subscribe!

 

Fanalytics Podcast: Sales Force Analytics

In this podcast episode, I sit down with Jon Adler, Director of New Membership and Ticket Sales for the Atlanta Hawks. In the first half of the episode, Jon and I talk about the mechanics of selecting and managing a team of entry-level sales professionals. The conversation focuses of using incentives to both motivate employees and to teach effective sales tactics. We also talk a little about applying “Money Ball” techniques to sales force management. This is an important point because the same basic techniques that can be used to predict the performance of a point guard can be used to select a salesperson.

In the second half of the episode, I take a deeper dive into how different techniques and theories can help sales managers. Salesforce management has some real challenges related to forecasting performance and using dynamic incentive schemes to motivate performance. Recognizing some of the underlying complexity can be helpful because it provides a guide to decomposing managerial problems and identifying the best analytic approaches.

Click on the logo below to listen to the episode.

You can find the episode on iTunes, Spotify, SoundCloud, TuneIn, Stitcher, and Google Play Music. Please rate, review, and subscribe!

Fanalytics Video: NBA Off-Season & Draft

Here’s the latest sports headlines on the Fanalytics video this week! Mike shares his thoughts going into the NBA off-season and the draft happening on Thursday.

 

Fanalytics Video: NBA Finals & FIFA Women’s World Cup

This week on the Fanalytics video, we discuss the big story lines happening in the NBA finals and FIFA Women’s World Cup. Thanks for checking out the trending sports stories with us on Monday mornings!

Fanalytics Video: NBA Finals

Fanalytics Podcast: Three-Point Field Goal

This week, Professor Mike Lewis and Emory student Alex Notis examine the three-point field goal (also 3-pointer) in the NBA.

The modern NBA has been transformed by the three-point shot.  Points are up, turnovers are down and NBA rosters are now built to shoot the three.

Some key facts…

When the three-point line was introduced in 1986 only 3% of shots were three-point attempts.

This season, 36% of shots were three pointers.

In this episode, we talk about Alex’s project which looks into trends and outcomes related to the three-point shot.

In the second half of the episode, Professor Lewis takes a step back and talks about the concept of expected value.  Expected value is a key concept in sports analytics. In decisions ranging from taking a three-point shot in the NBA, pulling the goalie in hockey, going for 2 in the NFL, or bunting to move a runner to second in MLB, expected value calculations are the key.

Click logo below to listen to this Fanlaytics episode.

Fanalytics Podcast: 2018 NBA Competitive Balance & Super Stardom

In today’s episode, economist Tom Smith and I talk about the upcoming NBA season.  Specifically, we discuss the trends towards “super” teams comprised of multiple all stars.  The conversation covers everything from Tom’s love of musical theater to how the collective bargaining agreement (the max salary provision) leads to the concentration of all stars in just a few cities.

The NBA has long been more of a star powered league than MLB or the NFL.  It’s an interesting strategy because it means that the NBA often has players that are true popular culture icons.  This provides tremendous marketing benefits.  On the other hand, relying on stars to drive fan interest means that the league is always looking for the next big thing.

Click logo below to listen to this Fanalytics podcast episode.

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

Analytics vs Intuition in Decision-Making Part III: Building Predictive Models of Performance

So far in our series on draft analytics, we have discussed the relative strengths and weaknesses of statistical models relative to human experts, and we have talked about some of the challenges that occur when building databases.  We now turn to questions and issues related to building predictive models of athlete performance.

“What should we predict?” is a deceptively simple question that needs to be answered early and potentially often throughout the modeling process.  Early – because we need to have some idea of what we want to predict before the database can be fully assembled.  Often – because frequently it will be the case that no one metric performance will be ideal.

There is also the question of what “type” of thing should be predicted.  It can be a continuous variable, like how much of something.  Yards gained in football, batting average in baseball or points score in basketball would be examples.  It can also be categorical (e.g. is the player an all-star or not).

A Simple Example

So what to predict?  For now, we will focus on basketball with a few comments directed towards other sports.  We have options.  We can start with something simple like points or rebounds (note that these are continuous quantities – things like points that vary from zero to the high twenties rather than categories like whether a player is a starter or not).  We don’t think these are bad metrics but they do have limitations.  The standard complaint is that these single statistics are too one dimensional.  This is true (by definition, in this case) but there may be occasions when this is a useful analysis.

First, maybe the team seeks a one dimensional player.  The predicted quantity doesn’t need to be points.  Perhaps, there is a desperate need for rebounding or assists.  It’s a team game, and it is legitimate to try and fill a specialist role.  A single measure like points might also be useful because it could be correlated with other good “things” that are of interest to the team.

For a moment, let us assume that we select points per game as the measure to be predicted, and we predict this using all sorts of collegiate statistics (the question of the measures we should use to predict is for next time).   In the equation below, we write what might be the beginning of a forecasting equation.  In this expression, points scored during the rookie season (Points(R)) is to be predicted using points scored in college (Points(C)), collegiate strength of schedule (SOS), an interaction of points scored and strength of schedule (Points(C) X SOS) and potentially other factors.

Points(R)=β0P Points(C)+βSOS SOS+βPS Points(C)×SOS+⋯

The logic of this equation is that points scored rookie year is predictable from college points, level of competition and an adjustment for if the college points were scored against high level competition.  When we take this model to the data via a linear regression procedure we get numerical values for the beta terms.  This gives us a formula that we can use to “score” or predict the performance of a set of prospects.

The preceding is a “toy” specification in that a serious analysis would likely use a greatly expanded specification.  In the next part of our series we will focus on the right side of the equation.  What should be used as explanatory variables and what form these variables should take.

Some questions naturally arise from this discussion…

  • What pro statistics are predictable based on college performance. Maybe scoring doesn’t translate but steals do?
  • Is predicting rookie year scoring appropriate? Should we predict 3rd year scoring to get a better sense of what the player will eventually become?
  • Should the model vary based on position? Are the variables that predict something like scoring or rebounding be the same for guards versus forwards?

Most of these questions are things that should be addressed by further analysis.  One thing that the non-statistically inclined tend not to get is that there is value in looking at multiple models.  It is seldom clear-cut what the model should look like, and it’s rare that one size fits all (same model for point guards and centers?).  And maybe models only work sometimes.  Maybe we can predict pro steals but not points.  One reason why the human experts need to become at least statistically literate is that if they aren’t, the results from that analytics guys either need to be overly simplified or the expert will tend to reject the analytics because the multitude of models is just too complex.

A simple metric like points (or rebounds, or steals, etc…) is inherently limited.  There are a variety of other statistics that could be predicted that better capture the all-round performance of a player or the player’s impact on the team.  But the basic modeling procedure is the same.  We use data on existing pros to estimate a statistical model that predicts the focal metric based on data available about college prospects.

Some other examples of continuous variables we might want to predict…

  1. Player Efficiency

How about something that includes a whole spectrum of player statistics like John Hollinger’s Player Efficiency Rating (PER)?  PER involves a formula that weights points, steals, rebounds assists and other measures by fixed weights (not weights estimated from data as above).  For instance, points are multiplied by 1 while defensive rebounds are worth .3.

There are some issues with PER, such as the formula being structured that even low percentage shooters can increase their efficiency rates by taking more shots.  But the use of multiple types of statistics does provide a more holistic measurement.   In our project with the Dream we used a form of PER adapted to account for some of the data limitations.  In this project some questions were raised whether PER was an appropriate metric for the women’s game or if the weights should be different.

  1. Plus/Minus

Plus/Minus rates are a currently popular metric.  Plus/Minus stats basically measure how a player’s team performs when he or she is on the court.  Plus/Minus is great because it captures the fact that teams play better or worse when a given player is on the court.  But Plus/Minus can also be argued against if substitution patterns are highly correlated.  In our project with the Dream Plus/Minus wasn’t considered simply because we did not have a source.

  1. Minutes played

One metric that we like is simply minutes played.  While this may seem like a primitive metric, it has some nice properties.  The biggest plus is that it reflects the coach’s (a human expert) judgment.  Assuming that the human decision is influenced by production (points, rebounds, etc…) this metric is more of an intuition / analysis hybrid.  On the downside, minutes played are obviously a function of the other players on the team and injuries.

Categories of Success & Probability Models

As noted, the preceding discussion revolves around predicting numerical quantities.  There is also a tradition of placing players into broad categories.  A player that starts for a decade is probably viewed as a great draft pick while someone that doesn’t make a roster is a disaster.  Our goal with “categories” is to predict that probability that each outcome occurs.

This type of approach likely calls for a different class of models.  Rather than use linear regression we would use a probability model.  For example, there is something called an order logistic regression model that we can use to predict the probability of “ordered” career outcomes.  For example, we could predict the probabilities of a player becoming an all-star, a long-term starter, an occasional starter, career backup or a non-contributor with this type of model.  Again, we can make this prediction as a function of the player’s college performance and other available data.

Below we write an equation that captures this.

Pr(Category=j)=f(college stats,physical attributes,etc…)

This equation says that the probability that a player becomes some category “j” is some function of a bunch of observable traits.  We are going to skip the math but these types of models do require a bit “more” than linear regression models (specialized software mostly) and are more complicated to interpret.

A nice feature of probability models is that the predictions are useful for risk assessment.  For example, an ordered logistic model would provide probability estimates for the range of player categories.  A given prospect might have a 5% chance of becoming an all-star, a 60% of becoming a starter and 35% chance of being a career backup.  In contrast, the linear probability models described previously will only produce a “point” estimate.  Something along the lines of a given prospect is predicted to score 6.5 points per game or to grab 4 rebounds per game as a pro.

This is probably a good place to break.  There is much more to come.  Next time we will talk about predicting outliers and then spend some time on the explanatory variables (what we use to predict).  On a side note – this series is going to form the foundation for several sessions of our sports analytics course.  So, if there are any questions we would love to hear them (Tweet us @sportsmktprof).

Click here for Part I

Click here for Part II 

Mike Lewis & Manish Tripathi, Emory University 2015.

Analytics vs Intuition in Decision-Making

Charles Barkley“I’m not worried about Daryl Morey. He’s one of those idiots who believe in analytics.”

Whenever the Houston Rockets do anything good (make the Western Conference Finals) or bad (lose the Western Conference Finals) it’s a sure thing that the preceding Charles Barkley quote about Daryl Morey will be dusted off.  We teach a couple of courses focused on the use of analytics, so these occasions always feel like what a more traditional academic would refer to as a teachable moment.  For us, it’s an occasion to rant on a favorite topic.  The value of data and analytics to business problems is something we think a lot about.  When the business is sports, then this becomes a topic of wide ranging interest.  Before we get into this, one thing to note is that this isn’t going to be a blanket defense of the goodness of analytics.  Sir Charles has a point.

Of course, the reality is that there is probably less distance between the perspectives of Mr. Barkley and Mr. Morey than either party realizes.  The key to the quote and the likelihood that there is a misunderstanding is in the word “believes.”  Belief is a staple of religion, so the quote implies that Daryl Morley is unthinking and just guided by whatever data or statistical analysis is available.  From the other direction, the simplistic interpretation is that Charles Barkley sees no value in data or analysis, and believes that all decisions should be made based on “gut feel.”  These are obviously smart guys so these characterizations undoubtedly don’t reflect reality.

However, the Barkley quote and the notion that decisions are either driven by data analysis or by intuition and gut is a useful starting point for talking about analytics in sports (and other businesses).  As the NBA draft approaches, we are going to discuss some key point related to using analytics to support player decisions.

As a starting point for this series we wanted to discuss the proper use of “analytics” and “intuition” in some general terms.  In regards to analytics, one thing that we have learned from time in the classroom is that statistical analysis and big data are mysterious things to most folks.  The vast majority of the world just isn’t comfortable with building and interpreting statistical models.  And the percentage of people that both really understand statistical models (strengths and limitations) and who also truly understand the underlying domain (be it marketing or sports) is even rarer.

One key truism about statistical models is that they are always incomplete and incorrect.  For example, let’s say that we want to predict college prospects’ success in the NBA.  What this typically boils down to is creating a mathematical equation that relates performance at the college level, physical traits and other factors (personality tests?) to NBA performance.  (For now we will neglect the potential difficulties involved in figuring out the right measure of NBA success, but this is potentially a huge issue.)

In some ways, the analytics game is simple.  We want to relate “information” to pro performance.  Potentially teams can track data on many statistics going back to high school.  These stats may be at the season, game or even play-by-play level.  The challenging part is determining what information to use and what form the data should take.  Assuming we can create the right type of statistical model, we can then identify college players with the right measurable.  On a side note, this is what marketers do all the time – figure out the variables that are correlated with future buying, and then target the best prospects.

Computers are great at this kind of analysis.  Given the necessary data, a computer with the right software will tell us the exact relationship between two pieces of data.  For example, maybe college steal stats are very predictive of professional steal stats, but maybe rebounding in not.  An appropriate statistical analysis will quantify how these relationships work on average.  The computer will give us the facts without bias.  It will also incorporate all the data we give it.

This is what computers, stats, and data are good at.  Summarizing relationships without bias.  But analytics also has its pitfalls.  We will deal with these in detail in later posts, but the big problem is the relative “incompleteness” of models.  Statistical models, and any fancy stat, are by definition limited to what is used in their creation.  While results vary, when predicting individual level results such as player performance statistical models ALWAYS leave a lot unexplained.

And this is where the human element comes in.  Human beings are great at combining multiple factors to determine overall judgments.  Charles Barkley has been watching basketball for decades.  His evaluations likely include his sense of the athlete’s past performances, the athlete’s physical capabilities and the player’s mental approach to the game.  Without much conscious thought an expert like Barkley is condensing a massive amount of diverse information into a summary judgment.  Barkley may automatically incorporate judgments about factors ranging from player work ethic, level of competition, past coaching, obscure physical traits, observations about skills not captured in box scores and myriad other factors along with observable data like points scored into his evaluations.  It’s an overused academic word, but experts like Barkley are great a making holistic judgments.

But experts are people, which means that they are the product of their experiences and prone to biases.  Perhaps Charles Barkley underestimates the value of height or wing-span because he never had the dimensions of a classic power forward, or, maybe not.  It could also be that maybe he overestimates the importance of height and wing span based on some overcompensation.  The point is that he may not get the importance of any given trait exactly right.

To some extent we have two systems for making decisions; Computers that crunch numerical data and people that make heuristic judgments.  Both systems have good traits and both have flaws.  Computers are fast, can process lots of data and unbiased. But they are limited by the design of the models and the conclusions are always incomplete or limited.  Experts can come up with complex and complete evaluations but there is always the issue of bias.

What this whole discussion boils down to is an issue of balance.  In one-off decisions like selecting a player or signing a free agent analytics should not be the complete driver of the decision.  These are evaluations of relatively small sets of players and it’s hard, for a variety of reasons, to create good statistical models.  Since we are usually looking for a complex overall judgment the holistic expert judgments are probably the best way to go.  More generally, in this type of decision making – think about tasks like hiring an executive – analytics should play a supporting role.  But it should play a role.  Neglecting information, especially unbiased information can only be a suboptimal approach.  The trick is that the expert fully understands the analytics and can use the analytics based information to improve decision making.

In the lead up to this year’s NBA draft, we are going to discuss some issues related to player analytics.  As part of this we are going to tell the story of a project focused on draft analytics that we recently partnered on with the Atlanta Dream and members of the Emory women’s basketball team.  We think it’s an interesting story and it provides an opportunity to discuss several data analysis principles relevant to player selection in more detail.  Stay tuned!

 Mike Lewis & Manish Tripathi, Emory University, 2015.