NBA Fan Rankings: 2016 Edition

On an (almost) annual basis I present rankings of fan bases across major professional and collegiate leagues.  Today it is time for the NBA.   First, the winners and losers in this year’s rankings.  At the top of the list we have the Knicks, Lakers and Bulls. This may be the trifecta of who the league would love to have playing at Christmas and in the Finals.  At the bottom we have the Grizzlies, Nets and Hornets.

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Before i get into the details it may be helpful to briefly mention what differentiates these rankings from other analyses of teams and fans. My rankings are driven by statistical models of how teams perform on a variety of marketing metrics.  The key insight is that these models allow us to control for short-run variation in team performance and permanent differences in market potential.  In other words – the analysis uses data to identify engagement or passion (based on attend and spend) beyond what is expected based on how a team is performing and where the team is located.   More details on the methodology can be found here.

spike-lee-knicks

The Winners

This year’s list contains no real surprises.  The top five teams are all major market teams with storied traditions.  The top fan base belongs to the Knicks.   The Lakers, Bulls, Heat and Celtics follow.  The Knicks  highlight how the model works.  While the Knicks might not be winning , Knicks fans still attend and spend.

The number two team on the list (The Lakers) is in much the same situation. A dominant brand with a struggling on-court product.   The Lakers and Clippers are an interesting comparison.  Last season, the Clippers did just a bit better in terms of attendance (100.7% versus 99.7%).  But the Lakers filled their seats with an average ticket price that was substantially higher.  The power of the Laker brand is shown in this comparison because these outcomes occurred in a season where the Clippers won many more games.

Why are the Lakers still the bigger draw?  Is this a star (Kobe) effect?  Probably in part, but fan loyalty is something that evolves over time.  The Lakers have the championships, tradition and therefore the brand loyalty.  It will be interesting to see how much equity is retained long-term if the team is unable to quickly reload.  The shared market makes this an interesting story to watch. I suspect that the Lakers will continue to be the stronger brand for quite a while.

The Losers

At the bottom of the list we have Memphis, Brooklyn and Charlotte.  The interesting one in this group is Brooklyn.  Why do the Nets rank poorly?  It ends up being driven by the relative success of the Knicks versus the Nets.  The Knicks have much more pricing power while the teams operate in basically the same market (we can debate this point).  According to ESPN, the Knicks drew 19,812 fans (100% of capacity) while the Nets filled 83.6% of their building.  The Knicks also command much higher ticket prices.  And while the Nets were worse (21 victories) the Knicks were far from special (32 wins).

What can the teams at the bottom of the list do?  When you go into the data and analyze what drives brand equity the results are intuitive.   Championships, deep playoff runs and consistent playoff appearances are the key to building equity.  easy to understand but tough to accomplish.

And a Draw

An interesting aside in all this is what it means for the league.  The NBA has long been a star and franchise driven league.  In the 1980s it was about the Lakers (Magic) and Celtics (Bird).  In the 1990s it was Michael Jordan and the Bulls.  From there we shifted into Kobe and Lebron.

On one hand, the league might be (even) stronger if the top teams were the Bulls, Knicks and Lakers.  On the other hand, the emergence of Steph Curry and Golden State has the potential to help build another powerful brand.

Some more thoughts…

The Fan Equity metric is just one possible means for assessing fan bases.  In this year’s NFL rankings I reported several more analyses that focus on different market outcomes.  These were social media following, road attendance and win sensitivity (bandwagon fans).  Looking at social following tells us something about the future of the brand as it (broadly) captures fan interest of a younger demographic.  Road Attendance tells us something about national rather than local following.  These analyses also use statistical models to control for market and team performance effects.

Social Equity

Top Social Equity Team: The Lakers

Bottom Social equity: The Nets

Comment: The Lakers are an immensely strong brand on many dimensions.  The Nets are a mid-range brand when you look at raw numbers.  But they suffer when we account for them operating in the NY market.

Road Equity

Top Road Equity: The Lakers

Bottom Road Equity: Portland

Comment: The Lakers dominate.  And as this analysis was done looking at fixed effects across 15 years it is not solely due to Kobe Bryant.  Portland does well locally but is not of much interest nationally.

It is possible to do even more.  We can even look at factors such as win or price sensitivity. Win sensitivity (or bandwagon behavior) tells us whose fans only show up when a team is winning and price sensitivity tells us if a fan base is willing to show up when prices go up.  I’m skipping these latter two analyses today just to avoid overkill (available upon request).  The big message is that we can potentially construct a collection of metrics that provide a fairly comprehensive and deep understanding of each team’s fan base and brand.

Note: I have left one team off the list.  I have decided to stop reporting the local teams (Emory is in Atlanta).  The local teams have all been great to both myself and the Emory community.  This is just a small effort to eliminate some headaches for myself.

Finally… The complete list

City Fan Equity
Boston 5
Charlotte 27
Chicago 3
Cleveland 20
Dallas 15
Denver 11
Detroit 25
GoldenState 16
Houston 7
Indiana 21
LAClips 17
LALakers 2
Memphis 29
Miami 4
Milwaukee 14
Minnesota 22
Brooklyn 28
NewOrleans 24
NYKnicks 1
OKCity 13
Orlando 19
Philadelphia 26
Phoenix 9
Portland 6
Sacramento 10
SanAntonio 12
Toronto 18
Utah 8
Washington 23
 

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.

2014 NBA Fan Quality Part 2: Demanding or Bandwagon Fans?

Note: This summer we are studying the fan quality of various sports leagues.  We have already examined MLBNHL, and College Basketball.  For Part 1 of our NBA study on Fan & Social Equity, please click here.

An analysis we have had fun with this summer involves looking at fan response to winning rates.  This encompasses looking at how different fan bases respond to variations in winning.  If fans only show up when the team wins, does this mean they are bandwagon fans?  Or does it mean that they demand quality?  We report, you decide.

We looked at the last fourteen years of data for our study.  For more details on our methodology, please click here.  Our analysis suggests that the city with the most bandwagon or demanding basketball fans is Detroit.  Pistons fans are followed by 76ers fans and Pacers fans.  At the other end of the spectrum, we have fan bases that either always or never show up, regardless of the team’s fortunes.  The Spurs fan base is the most indifferent to winning (or the most loyal, if you’re a glass half-full type).  New Orleans, Oklahoma City and the Lakers also have fans whose attendance doesn’t seem to have much to do with the team’s success.

2014 NBA Attendance Sensitivity to Wins

This summer we have also looked at the fan bases that are the most and least responsive to ticket prices.  The table below shows the five cheapest (or value-conscious) fans bases and the five that don’t seem to react to prices.

2014 NBA Attendance Sensitivity to Price

New Orleans is an interesting fan base:  indifferent to performance, but the most price sensitive in the league.  We are starting to feel very sorry for 76ers management.  Philadelphia’s basketball fans are the most demanding in terms of winning, but the least willing to pay.  Quite the dilemma!  At the other extreme, we have an interesting collection of teams.  Orlando, Portland and Atlanta also seem to have attendance that is minimally affected by average prices.  It’s an interesting list, because Portland is generally regarded as having passionate fans, while Atlanta is not.

Mike Lewis & Manish Tripathi, Emory University 2014.

NBA Pricing: Teams that Provide the Best Value

Today we are taking a look at pricing in the NBA: according to the Team Market Report’s fan cost index there is a wide range of prices across the league.  Last year, the Knicks had the highest average price at $123.22 while Charlotte’s average was just $29.27.  Rather than compare raw prices our objective is to look at the value provided by teams.

For our first look at value, we created a model of average prices as a function of variables such as team winning percentage, team payroll, metro area population and metro area average income.  This model is used to predict how team and market quality influence ticket prices.  A comparison of actual prices to predicted prices tells us which teams provide the best value.  This is along the lines of looking at the ratio of price to wins but with a bit more sophistication as we also control for factors such as market size and star power.

Astute readers will likely realize that this analysis is somewhat related to our fan equity rankings.  A key assumption of that analysis was that teams price in order to maximize revenue.  Today’s analysis can be interpreted in two ways: teams that price under the market are pricing low either because they are not trying to maximize revenue or because they are mispricing.  For now, we will just say that teams that price below market (according to the model) are providing added-value value.

Over the last 3 years, the top 5 teams in terms of value are the Brooklyn Nets, LA Clippers, Atlanta Hawks, Memphis Grizzlies and Washington Bullets.  These teams provide the best product in terms of winning relative to their market positions.  At the other end of the spectrum are the Knicks, Celtics and Suns seem to be the most overpriced.

Obviously, we have an issue in that the value provided is negatively correlated with brand equity since the Knicks and Celtics are two of the league’s most prominent brands while the Clippers and Hawks are not.  As a further look into pricing we performed an additional analysis, which was similar to the first pricing model but we added social media data (Twitter Follows and Facebook Likes) to the model.  These measures are useful because they are largely independent of owner’s objective functions and observable fan interest is not constrained by prices or capacity.  We also included an interaction between social media success and market size.  We like this model a bit better because it accounts for fan interest and excitement in addition to team and market quality.

When we use this model to compare actual vs. predicted prices we see a few changes.  Now Memphis is the best value followed by Brooklyn, Indianapolis, Charlotte and New Orleans.  Including social media into the model makes the biggest difference in the results for the Bulls and the Lakers.  These teams appear to be underexploiting their brand equity when it comes to pricing.  According to ESPN, both teams have had attendance levels of over 99% of capacity for the past three seasons so it seems that price increases are doable.  Ticket pricing is tough in sports because observable demand is constrained, but it appears that these teams have more pricing power than they realize.  It is also difficult to reach conclusions based on average ticket prices.  As we all know there is considerable heterogeneity in prices based on seat quality.

As always, no analysis is perfect and there are factors that we don’t capture in the market.  For example, perhaps in the case of the Knicks the team has additional pricing power because fans are willing to buy during down cycles in order to insure tickets during winning years.

Mike Lewis & Manish Tripathi, Emory University 2013.

Comment: Clippers Explain Dynamic Pricing

The Clippers’ video description of their dynamic and variable pricing policies seems to be creating a bit of buzz . We agree with other folks that this video is a pretty good description of these pricing techniques.  As an educational tool the video is very effective.

We do have a couple of general observations.  First, taking the straight-forward approach of discussing how market factors lead to increased or decreased demand for certain games is a smart technique.  One of the potential problems of these new pricing systems is simply that they represent a change.  Consumers tend to compare any current offering to some personal or historical reference.  When the current offering is complicated, consumers are very likely to have a negative reaction.

The other thing that the Clippers do well is that they frame the policies in terms of the discounts provided to season ticket holders.  In other words, rather than emphasize the high cost of coveted single games tickets, the focus is placed on the available discounts.  In contrast, think back to the summer when Michigan’s pricing plan quickly became a story of $500 tickets for the ND game.   This is doubly smart since the discounts are linked to season ticket holder status.  In this way, the Clippers are able to provide a “benefit” to their most valuable customers.

Social Media Equity: The NBA

A challenge in evaluating fan bases in professional and college sports is how to adjust for capacity constraints.  Unlike most consumer categories, teams have a limited number of seats to sell.  One way to get around this issue is to look at team revenues.  But this approach also has some strong implicit assumptions in that we must assume that teams are trying to price in a manner that maximizes revenue.

The world of social media provides an opportunity to look at fan base support without worrying about capacity or pricing issues.  To look at NBA teams “social media equity” we collected follows and likes from Twitter and Facebook.  We then created a statistical model that predicts these measures of social media engagement as a function of market size, tweeting activity and team performance for this past season and for the season before that.  We then compared each team’s actual follows and likes against the model predictions.  This method attempts to control for short term fluctuations in winning percentage and market differences.

The top team in terms of social media equity is the LA Lakers.  The Lakers crush the competition both in terms of raw numbers and in our model.  In second place, we have the Miami Heat.  This one is interesting, and we suspect that the Heat results may be a bit misleading.  While the Heat does very well currently it is not possible to separate out how much of the social media equity is driven by the team versus by LeBron.  This is something to watch as we collect more social media data over the next few years.  In third place, we have another non-surprising result in the Celtics.

It is the next three teams that are surprising as Golden State ranks 5th, New Orleans ranks 6th, and Charlotte ranks 4th.  The case of Charlotte illustrates the value of our model based approach.  In absolute terms, Charlotte performs relatively poorly in terms of social media metrics.  However, when we adjust for team performance and market size, the team does fairly well.  This indicates that the Charlotte market has fairly resilient fans, and likely speaks to the potential of the market if a consistent winning team is developed.

At the bottom of the list, the most surprising result is the New York Knicks’ 27th place finish.  This is doubly interesting because when we ranked fan bases in terms of “economic” support, the Knicks were number one.  What these two results imply is that the Knicks’ fan base is economically valuable but not engaged (at least in terms of social media).  The Knicks play in the largest market but have only about 20% of the social media activity of the Lakers.

There were a couple of other interesting findings from this study.  First, the number of Twitter followers was uncorrelated with the number of times a team tweeted.  This suggests that fans follow based purely on their feelings for the teams, rather than the entertainment of following an interesting Tweeter.  We also found a very high correlation between the two social media platforms as the social media equity estimates across the two platforms exceeded 0.91. However, when we looked at the correlation between the social media equity and the economics based fan equity the correlation was just 0.3.  We will leave this disconnect between social media and revenues for a future post.

Mike Lewis & Manish Tripathi, Emory University, 2013.

LeBron Saves the Heat (and the NBA?)

We have seen a number of articles and social media activity speculating about the NBA’s desire to have Miami advance to the NBA finals.  It’s a nervous time for the NBA because the other 3 teams in the conference finals are from “small” markets.  In some ways, the success of small market teams is a welcome outcome as all professional leagues tend to be nervous about large market dominance resulting in competitive imbalance, but the overwhelming short-term concern is obviously about how this situation will impact the television ratings for the final.

We have seen speculation about the impact of having small market teams such as Indiana, San Antonio and Memphis in the finals, but not a great deal of analysis.  To fill this gap, we have developed several statistical models that forecast TV ratings as a function of the characteristics of the two teams who are participating.  As a starting point we collected data on market population, winning percentage, home attendance, pricing, road attendance, and the number of All-star game starters and reserves for each team participating in the NBA finals over the last several years.  In this case, we have only a limited number of data points, so the key to the analysis is in identifying which of the variables are the best predictors.

We tried a great many combinations of the previously listed variables and found that the two best predictors were the sum of the two participants’ home box office revenues and the number of All-star game starters participating in the seriesA model with these two variables yielded an R-squared value of 0.53, and both explanatory variables had t-stats with p values of less than .05.

Our speculation is that combined home revenue captures the market size and fan intensity of the two teams.  This metric seems to be much more effective than population simply because not all large market teams are equivalent draws.  For example, in LA, the Lakers are a more powerful brand than the Clippers, and in New York, the Knicks have dominated the Nets (let’s say the New Jersey Nets to avoid any additional angst from the Brooklyn contingent).

We also found that All-star starters was the right metric rather than total All-stars.  In hindsight, this is also an intuitive finding.  The NBA has long been known as a “Star” driven league.  In fact, if you look back in history, the Michael Jordan era had amazingly high ratings compared to the last decade.  Based on the data, it appears that finals ratings are driven by the number of extremely high profile players.

In the tables below, we report actual ratings for the last six finals and our model’s predictions for the possible NBA Finals matchups.  As expected, the most promising matchup is Miami versus San Antonio.  What are really notable are the predicted ratings for the least promising matchup.  We predict that an Indiana – Memphis matchup would result in an epic failure in terms of ratings.

As a reality check for our prediction, consider the most recent finals matchup of small market teams.  In 2007, San Antonio defeated Cleveland, and the finals achieved a 6.2 rating.  While this number is much higher than our prediction, the San Antonio and Cleveland series had a significant advantage relative to an Indiana-Memphis matchup.  The difference was that the Cleveland and San Antonio series featured LeBron James and a Tim Duncan still close to his prime.  These types of stars would be sorely lacking in a Memphis – Indiana series.  This is, however, not a criticism of the Grizzlies or the Pacers, but more an indictment of how the NBA markets itself.  The NBA’s practice of emphasizing a few marquis players means that ratings will suffer when teams without these high brand-equity players make the finals.

The other problem for the NBA is that fans also understand the league’s dilemma.  This means that a meaningful percentage of fans believe that the NBA clearly prefers a series that features Miami.  This is a significant problem if fans believe that marketing considerations influence outcomes.

Mike Lewis & Manish Tripathi, Emory University 2013.