Tag: Carousel

NBA-to-Entertainment Company Translator: Part II “The Western Conference”

In the heyday of Grantland, they featured a piece from the good people at Men in Blazers to develop an “NBA to English Premiere League” translator. It helped novices to soccer pick a team in the most popular sports league in the world. It worked so well, I adopted Chelsea as my premiere league squad based off this little comparison to the Lakers:

“Your winning tradition has been soiled by an arrogance which, real or imagined, has caused you to be roundly despised across the league. You have a young coach attempting to gain the respect of a veteran squad, led by a soft Spanish big man and an aging Kobe, who could be any one of Chelsea’s graying superstars — John Terry, Frank Lampard, or Didier Drogba — attempting to substitute experience for pace.”

In 2011, that made a lot of sense. So if you want to pick an NBA team based off where you work, or want to invest based off your favorite NBA team, well I have you covered.

On to the Western Conference. The one with all the stars, all the hits, all the buzz. The “Bestern” Conference. Of course, they still have some teams near the bottom, just not as many.

Western Conference

Sacramento Kings – Spectrum

Let’s just pull the band aid off this wound: the Sacramento Kings are the worst team in the NBA (and have been since the Lakers beat them fair and square in the early 2000s) and Spectrum is just the worst. Honestly, if someone loves “Spectrum” (previously Time-Warner Cable) send me a message.

I’ll wait. Just like a Spectrum customer on hold trying to cancel.

So to “rebrand” Time-Warner became Spectrum a few years back. They said it was because of a merger, but mainly it was to hide from their past. The Kings changed from the Royals because they moved cities, and wanted to hide from their past.

Also, like T-Mobile failing to merge with AT&T, Time-Warner Cable was almost purchased by Comcast, and instead was purchased by Charter Communications. Those set of moves are the NBA equivalent of drafting Boogie Cousins and Willie Cauley-Stein because they were “buddies”, while trading a lot of future draft picks to Boston.

(Yes, I know Spectrum co-owns the Lakers channel. They still are awful.)

Phoenix Suns – AMC Networks

The Phoenix Suns in the 2000s were the flashiest thing in basketball. The “7 seconds or less” teams featured passing & shooting, running & gunning, and won the hearts of NBA pundits, the equivalent of critics. They set the template for pace & space all that would come in contemporary basketball.

AMC Networks won the hearts of critics repeatedly over the same time frame. Breaking Bad, Mad Men, Better Call Saul and even more obscure shows (Halt and Catch Fire; everything on Sundance TV) were the cultural equivalent of Steve Nash, Joe Johnson and Andre Stoudemire. (Nash is Breaking Bad; Shawn Marion is Mad Men; Amare Stoudamire is the rest of the obscure shows, cause he’s career ended too soon and so do they.)

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NBA-to-Entertainment Company Translator: Part I “The Eastern Conference”

Basketball is back!

And the town of glitz and glamour, the home of showtime—Hollywood—is back!

The stars aligned this off-season and the Lakers lured the biggest star in basketball, possibly the world (if you’re an American and ignore soccer), to the greatest franchise in sports history, the Los Angeles Lakers!

Here’s The Hollywood Reporter basking in the glory of LeBron James:

THR LeBron Cover

If you can’t tell, I’m a Lakers fan. At one point, celebrating the arrival of LeBron, I even compared LeBron joining the Lakers to The Walt Disney Company being able to acquire not just Marvel, but Pixar and Lucasfilm too.

Hmm.

That would make the Lakers “The Walt Disney Company” of NBA franchises. That sounds like an analogy. And a gimmick to write 6,000 words mashing together my love of NBA basketball with media & entertainment. That’s right, thousands of words over the next 3 articles celebrating the return of the NBA, giving every NBA team its partner in the world of entertainment (and occasionally media, tech and communications).

Ground Rules & Explanations

Like all things I do, this is a scientific and data-heavy enterprise. Supremely scientific. Yep, I used mounds of data from customer viewing behavior to financial performance to textual analysis of social media posts, Wikipedia pages and financial reports to develop a multi-variable complex regression that fed into a neural network that provided a clustered, nearest neighbor, that I modified via a random forest tree to make the optimal NBA-to-Entertainment analogies.

(Or I just made it up.)

Okay, an actual rule: I allowed myself to use both the conglomerates (Viacom, Disney, Comcast-NBC Universal, AT&T-Warner and others) and their subsidiaries, if the subsidiaries were significantly famous. So ESPN and Lucasfilm are a part of Disney, but they get their own teams, in addition to Disney getting its own team.

Second rule, I tried to use all the “entertainment” companies including conglomerates, studios, broadcast and cable groups before moving on to tech, print media and social media.

Third rule, I organized by conference in order of “power ranking”, which was a blend of ESPN, The Ringer, Zach Lowe and my preseason list of the best teams.

Fourth rule, have fun!

Eastern Conference

We’re starting with the Eastern Conference, that in days of yore we called the “Leastern Conference” since it’s talent paled so much in comparison to the West.

(Actually, it still pales in comparison.)

So we’ll start with the worst-er conference which means the bad movie studios (Paramount, Sony) and providers (cable companies, cellular and satellite companies). Speaking of which, our first translation:

Atlanta Hawks – Sprint
Orlando Magic – T-Mobile

Sprint and T-Mobile are trying to merge together to make a competitive cellular company. If you combined the Hawks and Magic, you (might) have a competitive NBA team. On their own? Sprint and T-Mobile would remain in 3rd and 4th place in cellular and The Hawks and Magic will be lucky to make it to 30 wins.

These analogies work individually too. Like Sprint, the Hawks have a long legacy with a lot of name changes. They started out as one of the original 8 NBA teams, were originally called the “Tri-City Blackhawks”, and possess a tradition that has been good, but never really great. (The Hawks last championship was in the 1950s.) I mean the best “move” Sprint made in the last two decades was luring Paul “Can you hear me now?” from Verizon, which is the cellular equivalent of the Trae Young trade last summer.

T-Mobile is the closest thing to an “expansion team” in the cellular game, like Orlando which was an expansion team in 1989. T-Mobile is also a Germany company trying to merge with a Japanese owned cellular company, which is geographically as confused as putting a basketball team in Orlando. (While Seattle still has approximately zero NBA teams.) Recently, T-Mobile has tried to sell itself, while failing and settling for merging with Sprint. The Magic had an all-NBA guard in Victor Oladipo, but traded him for nothing (basically), and now have a team of all power forwards. That matches.

New York Knicks – MGM

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The Most Important Shape in Entertainment Part III: The Examples

(This is Part III for a multi-part series on “Logarithmic Distribution of Returns”. Read Part I HERE and Part II HERE.)

I come across the flaw of averages in reporting quite a bit. Take my article on MoviePass. The CEO said in an interview with The Indicator that the “average MoviePass customer sees 1.7 movies per month”.

If you read my articles from a few weeks back explaining distributions—and I know you read all 3,000 words—that average of “1.7” is virtually meaningless. He could have told us what the distribution looked like, but didn’t. And probably for good reason. (Impending bankruptcy.)

Since he won’t tell us, here are my guesses:

Chart 1 MoviePass

I would call this a “Log-ish” distribution. First, it’s not a continuous range. With MoviePass, they had discrete scenarios. You see one movie or two movies, but not 2.5. Also, my guess is more people use the service in a given month then let it sit idle, which keeps this from being a true log distribution. I also put an artificial cap at 10 films. That said, the behavior in general will have power-law results. (Some very small number of people will see an order of magnitude more movies over a year, literally 100 in some reported cases.)

(If these numbers were true—and I have no reason to expect them to be—then MoviePass would lose, on average, $5 per month per customer, on average. Given they had 3 million customers when I got my 1.7 number, this would put losses at 15 million per month. Since their CEO said that they were losing 21 million per month, my gut says that tickets were more expensive than my model, mainly because they were over-indexing on coastal users. Also, if the subscribers went up to 4 million, I’d be about perfect.)

Still, I found a Logarithmic Distribution in a random place. (Said in the voice of Rhianna to the tune of “found love in a hopeless place”.) When I started this three part series, I called the Logarithmic Distribution of Returns the “most important shape” in entertainment. I said it applied EVERYWHERE, not just to movies.

Well today, I’ll show you the everywhere. I’ll be blunt with you, I want to convince you of two things:

1. This is the reality of returns in every field of entertainment.
2. The average sucks (or is “sub-optimal”) at describing this reality.

Data Notes and Cautions

Some cautions on data, as always. Why do I always talk about the data itself? Like why provide this critique of my data? Because NO ONE else does on the internet. You should always be as informed, especially when coming with numbers, so when I use data I want you to know what I do and don’t have, what I can and can’t prove.

Caution 1: I’ve seen this in more places than I can share.

I worked at a streaming company, but that data is confidential so I can’t share it. In addition, I’ve done deep dives into other parts of entertainment, but sometimes I can’t find the charts I’d made, or they were on other computers. So that’s a bummer.

Caution 2: I’m limited by available data.

In many cases, I don’t have access to the database that has all the information. To really show a log-distribution, you need all the data, not just slivers. Instead, I have to rely on what I can find—the good graces of the internet—which is usually top ten, top 25 or top 50 lists, which isn’t good enough. We can still extrapolate using some logic, but if I had access to the database itself, it would all look more logarithmic.

Caution 3: I plan to update this over time.

This post has taken a lot of research, which takes time. At the same time, I promised this three weeks ago. So to manage both priorities, my goal is to post this today, then update it over time as I find more examples and/or think of more.

On to the examples.

Video

Or “filmed entertainment”. Any marriage of visual recording with audio usually performs like our logarithmic returns. But let’s start with our example from last time.

More Movies/Films

As a reminder of a perfect logarithmic distribution, here’s box office returns in 2017.Chart 2 Movies Again

In my second article, I showed how this distribution applied to multiple genres of films. Well, I recently looked at this for another genre of films. And guess what? We got the same distribution. In this case, I looked at war films.Chart 3 War Films

Source: Box Office MoJo

TV Ratings by Series

Of course, you could argue that maybe theatrical box office skews the performance of video. So let’s turn to the other primary form of video, TV. Let’s start with traditional broadcast TV. Deadline had a summary of the ratings for broadcast channels in 2017 with the top ratings by series. Unfortunately, it doesn’t look as great as I wanted:

Chart 4 Broadcast Ratings

Source: Nielsen, via Deadline

What went wrong? Well that’s “broadcast” TV. In fact, that’s broadcast “prime time” TV. People with cable (or broadcast) can watch a lot of other types of shows: daytime programming, syndicated shows and cable. Oh, all the cable.

In a future update, my goal is to expand this table. (Trust me I’ve google the internet for a while and this is the biggest hold up to me posting today.) If I had access to Nielsen, I could do make the table pretty quickly. Instead, they only provide “Top 10s” and I can only find prime time broadcast on publicly available sites. (I made this chart for work before with Nielsen data.)

So I’m not off to a great start (though trust me, if you add cable above it looks logarithmic), but I have two other TV options to show.

TV Channels Viewership

Of course, we could also look at “TV Channels” as their own distinct entities. Do we get the same type of performance? I hadn’t initially thought of this, but stumbled across ratings by network when I was looking for data in my “CBS Myths Debunked” article. Here you go:

Chart 5 TV Networks by Viewers

Source: IndieWire

TV Subscriber Fees

Thinking of channels got me to think of another way to measure the value of TV channels, by the amount cable companies have to pay in “subscriber fees”. I don’t have time to explain sub fees now, but just know they were the straw that stirred the drink for the last few decades in cable. I had some old data from 2012 listing cable sub fees and here you go:

Chart 6 - Cable Networks by SubYou could look for logarithmic distribution in “total subscribers” in cable, but you won’t find it. There is a cap on the number of households that can subscribe to a cable channel, which nears the total number of households at 100 million-ish. As a result, when cable channels hit that upper limit, they used fees to capture the extra value.

Streaming

So Netflix, Amazon, Hulu and the rest don’t share ratings data. So no charts here. But I’ve seen the data for one of the streamers, made the charts, and let me assure you this: this law absolutely applies. The most popular shows on a streaming platform are multiples bigger than the vast majority that come, go and are forgotten. If anything, given the larger sizes of the platforms, the effects of the log-distribution are more pronounced.

Speaking of size of libraries, let’s head to the largest library of video on the internet.

Youtube

Know this: if you search for information on the number of views by video, you find a lot of articles on “Gangnam Style”. Which I’m not saying to be negative, just pointing out.

Search hard enough, and I did, and I found the key insight here. This long, information article on a website called the The Art of Troubleshooting, where he used some scraping and R to pull the data on the video views. I took a screenshot of his “log-normal distribution” of video views. (In other words, he converted the logarithmic distribution into “logs” to show the normal distribution. It’s the same thing, it just looks different because the scale is in log.)

Here’s the picture and another link to his site.

Chart 7 - Youtube Log Normal

Source: Art of Troubleshooting

The insight with Youtube makes sense: “Despacito” and previously “Gangnam Style” have literally billions of views. Yet, since anyone can make a video, the vast, vast majority have 0-100 views. This effect continues with channels as well, as measured via subscribers, sort of like how I measured both by show and channel above. This article on Vox has some of the statistics showing how big the biggest stars are. For example, PewDiePie is way out front, but most people don’t have any subscribers to their channel.

Youtube is definitely winner-take-all and the distribution holds. Here’s a chart showing the top 250 channels by sub. Look at the trend:

Chart 9 - Top 250 Channels by Sub

Source: TwinWord

If we turned this into a histogram and expanded it out, we’d get our log distribution.

Social Media & The Internet

As the Youtube example shows, as the sample size grows, the effects of the power-law get amplified. Moreover, with the internet, the data is a bit easier to come across. And it makes the power-law distribution even starker.

Social Media

Let’s start with Twitter. Do the number of followers someone has follow a power law?Chart 10 Twitter Followers

Source: StatisticsBlog.com

According to this website, yep. And again this makes sense: Rinaldo has tens of millions of followers while most people are in the hundreds and bots have hardly any. This other article says that over 90% of people have less than 100 followers, which makes sense. Let’s head to Facebook. In this case, the number of friends someone has is NOT power-law, since it isn’t really consumer facing. But, the number of likes something has does follow this law:Chart 11 Facebook Followers

Source: A ScribD article via Quora

In the future, I could look at both measurements of fandom (subscribers, followers, etc) or popularity of individual posts (likes, shares, etc) on multiple other social platforms and you get the same effect each time. That’s what going viral is.

Internet

One last part of this which is how the internet started: old fashioned webpages. Do certain cites have multiples more viewers? Of course.

Chart 12 Top News Sites Statista

Source: Top News Sites via Statista

That comes from Statista, who only covered news websites. You can go to Alexa and see another list of top websites, all in the hundreds of millions of monthly visitors. Yet, according to this one website, there are 1.89 billion websites. That’s definitely power law distribution. This random paper online backs this up.

Next Time

So that’s five pages, 12 charts, and 7 or 8 different categories of entertainment (film, war films, TV shows, TV channels, Twitter, Facebook and the internet).

But I’m not done, just done for today. In my next update, I’ll try to tackle music—there are two more databases I don’t have access to—and other more unique/weird subsets like toys, comic books, sports and theme parks.

About Me…My biography

I’m a huge believer in “data”. I’ve noticed, though, that sometimes this bias towards data is interpreted as a sole focus on data in databases. Or it’s interpreted as a bias against case studies, or, more specifically, anecdotes.

Here’s the thing: anecdotes are both powerful and awful at the same time. On the “awful” side, a lot of anecdotes are used to refute rigorous data. Something like, “I know you have all this data, but Seinfeld tested poorly!” (It’s always that Seinfeld example.)

That said, anecdotes, or observations of human behavior (in simpler terms, “examples”) can be used as a starting point to form a hypothesis, which we can test with data. So starts with anecdotes, then move to data.

Take my personal behavior when I surf the web. Whenever I come across a new website, one of the first things I do is click on the “About Me” tab to find out, “Who is this person?” (If I’m being crude and I disagree with the person, it’d be more along the lines of “Who is this f-wording joker?”) If the website is poorly made or looks like Russian trolls made it, looking for the “About” page will usually reveal them to be a fraud.

This is an anecdote about personal behavior. But it gives a clue that some readers like to know who they are reading. If I pulled a lot of website data, I’d bet the “About Me” page on many websites top ten most visited pages. It could even be a success metric: getting lots to clicks on “About Me” as a sign people are new and want to learn more about what they are reading.

Since I don’t have the data to run the above experiment, I’m just going to make the hypothesis that I should have a better biography/“About Me” page than my current one, which is non-existent. Before today, I had two tabs, content and contact me. And even the contact me may not have worked before month three.

The challenge is providing a biography so you know my bonafides without giving the game away. If I give away enough hints, then surely someone will do enough Linked-In stalking and my identity will be revealed. (Or I’ll slip up. On the internet, no one knows if you’re a dog, but they’ll know you’re named Fido.)

So here goes. I posted this bio today, and I’m putting it up in an article today for everyone to read.

I’m an entertainment executive who has spent the last few years at the intersection of content, technology and business. I’ve spent the 2010s working in media and entertainment companies. Well, entertainment companies, both as an employee, intern and consultant. These companies have run the gamut from giant studio conglomerates to a streaming company (and one of the big ones) to independent production companies, in both television and film.

I’ve held roles ranging from strategic planning to business development. What does this mean? Well it’s different at every company, but I’ve drawn a lot of analysis from huge data sets and put these into PowerPoints for senior executives to pore over. And hopefully make decisions. I’ve also looked for “revenue generating” opportunities, which means building business plans, evaluating content plans/offering and negotiating deals. I enjoy making Excel spreadsheets too and poring over data for insights.

I’ve been fortunate to cover a lot of fun stuff touching on a bunch of different areas.

Before that I went to a top tier business school and specialized in the business of entertainment. I also took as many classes as possible where “numbers” were involved, only topped by the number of classes where “entertainment” was involved. I graduated at the top of my class. Not near the top, the very top. As a result, I was was asked by multiple professors to TA their classes for them and share my knowledge. I also one several other awards.

Well before that I went to UCLA as an undergraduate. I graduated and worked in a really demanding field that provided me the skills to go to business school. I also developed a love of writing and I’ve been published on many different websites and traditional newspapers under my actual name.

The Most Important Thing Business Can Learn from Sports: Value Over Replacement Player Explained!

Do sports provide good examples or case studies for the oft discussed topic of “leadership”? If we want to excel at leadership, should we study coaches?

On the surface, it would seem so. We cite coaches in particular for leadership all the time in the press. Having gone to UCLA, I was indoctrinated early on to believe in Coach John Wooden, and his pyramid of success, as the touchstones for good leadership. You can find books doing this. So many books. (At least five by my count.)

You can even hire current coaches to speak to your executive team or corporate board about leadership. Nick Saban charges $50-100K to provide this service.

Honestly, isn’t that all kind of nonsense?

Coaches have one of the easiest leadership roles of all leadership roles. All management/leadership is tough, including coaching, but there is a scale, and coaches have it a lot easier than others. They have players who rely—literally are utterly dependent—on their coach for their very future. Sure, a lot of coaches fail—most in fact; sometimes spectacularly—but that’s because sports is a zero sum game: the number of wins equals the number of losses each year.

Yet, I love studying sports for lessons. Love it. There are tons of principles and tactics ane best practics that apply to business in general. It’s just that the most beneficial parts are probably the hardest to figure out and apply.

To take just one example, I think many, many knowledge industries could benefit from the type of discipline embodied by the concept of “practice”. Consider this: college football coaches “only” have their players for 20 hours every week. The goal is to not waste a minute of that time to maximize their training, and hence output on the field on Saturdays. Do you as a manager have the same discipline with your team? Or do you have almost no idea how your team spends their time? (Except when they’re in a meetings with you, of course.)

If this sounds dictatorial, I get it. Imagine your manager tracking every minute of every day you spend in the office. If you chafe at the idea of planning your schedule down to the minute, then don’t bring Nick Saban to your board meeting to talk leadership. Read this article and you understand this is EXACTLY what he does.

So if you won’t use Saban’s single best tool to manage his players, why do you care what he has to say about “leadership”? John Wooden practiced a similar level of control. Coaches are great leaders, but they also control their players with a level of tyranny unheard of in most of corporate America.

(The article in Fortune I quote above cites “leadership” in the title, while again never noting that no CEO would manage his executives with this level of control. I’d add, most knowledge workers would loathe this type of control over their own schedules, but recommend it often for minimum wage factory, retail or warehouse workers, who don’t have a say. Sigh.)

Today, isn’t about complaining about misapplied lessons in sports, though I loved getting that rant off my chest. Instead, it’s celebrating something from sports that would be tremendous if we applied it to office work and corporations broadly. The single biggest insight, in my opinion, from sports statistics (I hate the term “advanced analytics”, since I don’t know what simple analytics are) that most managers should use is:

Value Over Replacement Player

VORP and its cousin WAR (wins above replacement)—from basketball and baseball respectively—are two holistic measurements that take multiple statistical variables and combine them into one measurement. The goal is to get a single measurement that is best correlated with predicting future performance. The value comes from applying that single number to evaluate all potential players against each other; hence, it directly compares everyone to the average.

Today, I want to explain what “value over replacement” is, how it works, and the principles behind it. Tomorrow, I’ll try to apply it to everyone’s favorite industry, entertainment.

The Origins of “Value Over”

In Hollywood, there are really two camps when it comes to sports: either you follow sports super closely, or you hate it, with little in between. For those who hate it, here’s a brief summary on how analytics started in sports, which birthed us WAR/VORP.

Analytics started in baseball in the 1980s by many people, but was popularized by arguably one man, Bill James. He wrote an influential publication that created new statistics for baseball that he believed better captured the influence of various players and strategies. For instance, they tried to downplay the role of batting average. He called this sabermetrics after the organization he founded. James was followed by some other people, notably Billy Beane, general manager of the Oakland As (popularized by Michael Lewis in Moneyball) and Nate Silver, now of the election website FiveThirtyEight.com.

This deeper dive into statistics started in baseball for arguably two reasons. First, baseball is the most “statistical” sport. It has a huge amount of data and a lot of people poring over that data to compare current players to the past. By playing 180 games and tracking most everything that happens during every play, your sample size goes way up. Second, a lot of people play fantasy baseball, which is taking all that data and trying to apply it to fake sports. This was how Silver entered the analytics movement.

The critical challenge for these sabermetricians was to move beyond simple counting statistics and try determine, holistically, how much each stat at the end of they contributed to wins. This led to statistics in baseball like “win shares”—from Bill James—and the one I mentioned above “wins above replacement player”—author unknown, as far as I can find.

As I wrote in my post on “Has Hollywood been Moneyballed?”, baseball is a team sport that cares about winning. Like all team sports. The best way to do this is to have the best players, in general. With more and more people scouring data and drawing conclusions, eventually the baseball teams realized these people could help them win more games. This arguably started with the Oakland A’s and their manager Billy Beane, which was immortalized in the early 2000s, by Michael Lewis in Moneyball, and the idea of using data in sports took off like a rocket. (And people like me wrote articles about it then and now.)

Oh, I guess there is a third reason that explains the explosion of “analytics” in sports. Computing power and storage has increased exponentially from the 1980s to 2010s, making statistics easier for everyone. This has caused the amount of statistics in general just to increase.

The next most data heavy sport is basketball, so the next “holistic” metric popped up there, Value Over Replacement Player, or VORP. (The timing of this little history may be slightly off, but that’s fine. It’s close enough for the internet.) That said, the “democratization” of sports data—and the fact that a lot of geeks/nerds also love sports and fantasy sports—meant that basketball doesn’t just have one “holistic measurement” but several, all of which get to the same point, including PER, VORP, Win Shares, Real Plus/Minus and Box Plus/Minus. All are attempts to summarize a player’s value in one number that can be compared to every other player.

So that’s where it comes from—a single statistic in multiple sports to define value—what is it?

Value Over: How it Works

Let use an example to show how the concept works. I’m going to stick to basketball, because it’s the sport I know best.

Let’s say Player X scores 8 points per game. The key question for a team looking to acquire him is, “Well, how valuable is that?” Let’s go to the numbers. In the NBA last season, 540 players played at least a minute of NBA game time. (I’m using Basketball Reference for my statistics here.) Here’s how many points they scored per game:

Slide1You’ll notice it isn’t quite logarithmically distributed, and as I’ll hopefully finish next week, everything in media and entertainment is logarithmically distributed. That includes sports. The trouble here is that “per game” statistics technically combine two metrics: games played with points scored. If you just focus on points scored…
https://www.basketball-reference.com/leagues/NBA_2018_totals.html

Slide2

That’s better. Logarithmic distribution rules the day again!

Let’s try to understand these two charts. A player scoring 8 points per game is roughly right in the middle of the NBA. The mean average of points per game is 6.6 and the median is 8. In this case, the median gets closer to what we mean by “average” when it comes to points per game.

But you know I hate averages, which is why I put the distribution charts up first. The distributions are arguably the most useful way to look at this since we can quickly see how many players score how many points in various buckets. Combining players who score 6-8 points per game and 8-10 points, we see that about 150 players are in this range, or roughly a quarter of all players who played in the NBA last year.

So now we ask: is scoring 8 points per game valuable? Not really. Or in other words, it is about exactly average, which is how we should compensate Player X. Basically, scoring 8 points per game is very common. Even if a player played all 82 games averaging 8 points, he’d only move up to the 65 percentile in scoring, meaning 35% of players scored more than him. In other words we can find a replacement for that player easily.

What about Player Y—who I’ll call LeBron for short—who scores 27.5 points per game? Well, only two other players score more than that per game. In other words, this hypothetical LeBron fellow is extremely rare and hence extremely valuable. (Welcome to Los Angeles!)

LeBron is a good example, because he doesn’t just score points: he passes and rebounds too. If we’re trying to capture value, we need to value those activities too. In basketball you can count rebounds, assists, steals and the shooting percentage on a variety of shots. Add all this up—with a lot of other calculations and adjustments—and get closer to calculating the “value over replacement” for any individual player.

I’d add, even if you aren’t using a specific metric like WAR or VORP, Moneyball or analytics-minded or sabermetrics-minded general managers like Daryl Morey or Billy Beane think about players in these terms. You either take points, runs, wins or “value” and think, “how much higher is this player’s performance than the average player, who I can find easily?”

Value over Replacement: Complications

That’s the concept, but it quickly gets complicated.

The first part is the challenge to gather all the data. This seems easy (grab all the box score) but even the box score only captures so much. In recent years, teams have begun collecting “movement data” on the basketball court, baseball diamond and football field. This means tracking the movement of the ball and players, which is a lot of data, but allows you to track speed of players, yards run, where shots occurred and how far baseballs traveled. Lots and lots and lots of data.

The downside with this new data is the sample size is limited in years. Take blocks in basketball. We didn’t track blocks in the 1970s, so we don’t know how many times Kareem Abdul-Jabbar blocked his opponents. Same with sacks in the NFL. This means our data sets are limited by the years we collected the data. Moreover, in basketball, for example, a lot of the box score statistics are weighted to the offensive end (points, rebounds, assists, shooting percentages) versus the defense (mainly steals, blocks). This applies to baseball too. This is an example of how what we measure—which is sometimes what is easiest to collect—could could skew our perspective.

Then, once we have the data we have to judge how to weight it. I really like “box plus/minus” as a tool to judge players, and Basketball Reference has a great explainer for how they developed it. Read that explainer and you’ll discover it’s really complicated. It involved a lot of regression analysis and a large sample size. Then using that analysis to weight each statistic. Then testing its predictive power on another half of the data set. That’s a process that requires a lot of art and a lot of science.

Finally, one piece of data that is particularly tough to assess is the context of the data. In sports, this can mean the performance of the entire team and teammates. Going back to my player named “LeBron”, being on a team with a guy who can score 27 points a game and dish out 8 assists is extremely beneficial. It could be the case in basketball—and it is—that playing with fellow all star players makes your numbers go up as a result. This is just one example of how “situation” can improve your position.

If being on a good team is valuable with good players, this could apply to what “league” you play in. It’s easier to be great in college football than professional football. It’s easier to be great in the triple A baseball than the major leagues. So applying the statistics of one level to the next can be difficult. Context matters, and you have to account for that.

Applying to the Business of Entertainment

That seems like a simple proposal and concept. So why hasn’t this genius concept made it to business? Well, that will take another article to explain. Tune in tomorrow.

The Most Important Shape in Entertainment Part II: Logarithmically Distributed Returns

My dad didn’t like the ending of Empire Strikes Back. His felt that it didn’t finish the story, it left off with a, “See you next movie!” conclusion. That irritated him. He hasn’t seen Avengers: Infinity War yet, so you know he won’t like that.

My article yesterday probably did sort of the same thing to the audience. I come up with this big conclusion—the logarithmic distribution—but then barely touch on it.

Well, since we’re already talking about the movies, we might as use that as the ur-example of my magic trick, “Logarithmically distributed returns”. I first learned this law by analyzing movie performance, and it’s my best tool for teaching it to others. But I’m not just going to show you this phenomena, I’m going to show you it multiple ways, in multiple categories. Then I’ll explain the biggest statistical mistake I’ve seen when forecasting box office performance.

Logarithmically Distributed Returns…What is it?

Let’s start with the last word. What I’m describing today is the “output” of most entertainment or media processes. So my examples are about the “result” or the “y-value” or the “dependent variable”, to describe it in three different statistical terms.

In other words, performance. This means how well something does. Box office for movies. Ratings for TV. Sales for music. Attendance for theme parks. No matter what the format, the success (or very frequent failure) is logarithmically distributed.

What does logarithmically distributed mean? Essentially, orders of magnitude. The returns don’t grow on a geometric scale, they grow on an exponential scale. This means that the highest example can be in the billions while the smallest can be in the dollars. That’s a difference in magnitude of 9 zeroes.

The most common summation of this is the “Pareto principle”, who coined the term about “power law” distribution. Roughly speaking, Pareto is summarized by the 80-20 rule, or 20 percent of the inputs deliver 80% of the returns. And like any mathematics/statistics topic, there are obviously a ton of variations on this law and specifics that I’m not going to get into.

(For those who are curious, inputs have their own distributions, but aren’t as reliably distributed as outputs. A topic for the future.)

Logarithmically Distributed Returns Visualized: Feature Films in 2017

Enough talk about what it is, let’s use an example. I went to Box Office Mojo and pulled all the films from 2017 that grossed greater than $0 in theaters. I didn’t adjust for year and pulled everything, no matter how small. The result was 740 movies released. Oh, and I only pulled domestic gross.

I’m going to show you the data two ways to help you visualize it. First, is the less accurate way, but I love it because it shows scale. This is all 740 movies plotted from lowest to highest, with the y-value as the domestic gross in dollars.

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Source: Box Office Mojo.

I love how smooth the curve looks. But the true measure of the data is the “histogram”, where you count the number of examples per category. I set up the categories myself at $25 million dollar in intervals, starting from zero.

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Source: Box Office Mojo.

Most people don’t realize how many films are written, produced and even released every year. Like I said, last year was over 700. So let’s add a threshold of $1 million dollars at the box office to our list. If I had production budget estimates, I’d sort by that, but the result gets you to the same place. (The reason for using production budget is that when you scan that “almost grossed $1 million threshold”, you see some legitimate films such as Patti Cake$ and Last Flag Flying, from Fox Searchlight and Lionsgate/Amazon Studios respectively. Those films cost a lot more than $1 million to make.)

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Source: Box Office Mojo.

All the charts show the same story in different ways: there are hundreds of films that made less than $1 million at the box office, around 150 that did less than $25 million (many of which probably lost money), a range of movies in the middle and then a few monsters (Star Wars: The Last Jedi, Wonder Woman, Jumanji and Beauty and the Beast).

I think I can hear some of you insisting that I give you the “counting statistics”. You still want to know the average, right? Well here they are, for all 740 films. I mainly did this because I’m going to use them in the next section.

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How Logarithmic Distributions Differ from Other Distributions

Perhaps the best way to describe the logarithmic distribution is to show how it isn’t other distributions. In other words, to show how inadequately the normal distribution and uniform distribution capture the performance of feature films.

Let’s start with the uniform distribution. The idea that, “Hey, a movie can gross anywhere between $600 million dollars (Star Wars) and $0, and every where in between.” What if we had an equally likely chance of that? In decision-making, the human brain often defaults to uniform distributions when assessing possibilities, so this isn’t completely academic. Here’s how that would look:slide061.jpg

If only this were how to finance movies! The industry would green light a lot more movies. But it isn’t, only a few films hit that rarefied air of $200 million plus dollars.

What about the normal distribution? I tried to chart this, using our mean of $15 million and standard deviation of $50 million. Unfortunately, that gives us a lot of “sub-zero” grosses, which I just cut off at zero. The problem with the normal distribution is it makes misses as rare as hits. That just isn’t the case. Also, the odds of a giant hit become astronomical in a normal distribution. In this case, a hit like Star Wars: The Last Jedi would be 10+ standard deviations form the mean, meaning it has a 1 in a million chance. Obviously, hits like that happen every year, so more like 1 in 200.slide071-e1536792385695.jpg

Let’s put them all on the same chart, to really show how logarithmic distribution of returns just looks different.slide081.jpg

Source: Box Office Mojo

This chart shows how quickly the results drop off in reality compared to other hypothetical distributions. If someone tells you Hollywood isn’t normal, show them this chart and say, “You’re sure right!”

Variations on the Initial Theme

I might still have skeptics in the crowd.

Maybe, they’d say, I just got lucky. That distributed returns happen to be power-law-based for the year 2017, but this lesson doesn’t really apply to other parts of film. Well, that would be wrong.

Spoiler alert: no matter how you slice the inputs, you get the same result.

First, I could expand the number of years I’m using. I happen to have box office gross from a project I did that covers 2012-2014. Here’s that chart.Slide09

Source: SNL Kagan

Here’s the next fun trick: the distribution of returns still applies for sub-categories. Take horror, which I looked at a couple of months back. Here are all the horror movies going back to the Exorcist, according to Box Office MoJo. Specifically, “Horror-R-rated”, which is 504 films:

Slide10

Source: Box Office Mojo

The rule still holds! In this case, there has been one monster horror film—It—then some other smaller ones. Of course, I could hold all the box office and adjust them for into 2018 grosses. Does that change the picture? No, if anything it amplifies it. In this case, The Exorcist did $1 billion in adjusted US gross, and The Amityville Horror did $319 million. But for those increases, a lot of other smaller films drop down even more, especially recent films.

slide111.jpgI’ve done this for a ton of different genres. Superhero movies. Foreign films. And it always holds. The only caution is that sometimes the “ceiling” of the range gets compacted.

What about sorting by something else? Say, rating? Do R-movies have more hits versus PG-13 or PG? Fortunately, my 2012-2014 data set has ratings. First, know that G, NC-17 and Not Rater just don’t have a lot of examples (only 45) so I deleted them from this analysis. Here are the other three, in line chart form:slide121.jpg

Source: SNL Kagan

As we can see, for R, it holds. For PG-13, it holds. For PG, it looks like it holds, but honestly since we only have 39 examples, it doesn’t show as clearly. Increase sample size and we’re going to see this.

You could do this analysis setting for production budget and studio and even types of studios. As long as the input is independent, it holds.

Two Examples Where This Works Less Well

Listen, I believe in being up front with my data analysis. Even though this is a magic trick, I’m not trying to hide or obscure data that doesn’t make my case as well. That’s why I left PG rated movies in above, even though it’s the least logarithmic looking line in my analysis.

So in my experience, have I come across sub-sets of movies where my rule/law/observation doesn’t hold? Absolutely, so I’ll share those with you next. To clarify, it’s not that my magic trick fails, it is that the floor disappears. So look at this chart, from my series on Lucasfilm:

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Source: Box Office Mojo

These is my data set of “franchises” that included Star Wars, Marvel, DC, X-Men, Harry Potter, Lord of the Rings, Indiana Jones and Transformers. As you can see, those films just don’t have flops. The “floor” is about 200 million in domestic box office, with only 14% of all films dropping below that. So it isn’t logarithmic on one end. I actually think my timeline of films by box office, with their names, shows this floor pretty clearly over time:

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Source: Box Office Mojo

My rule doesn’t hold—this is important—when I sort by another output, not by an input. In other words, I’m sorting by the result.

A franchise is a series of films made off a successful first film. In other words, it is sorting by “success” of the first franchise film. Many aspiring franchises therefore didn’t make my data set. Four examples off the top of my head that I did not include, from three different genres: The Golden Compass, Battleship, The Lone Ranger and John Carter from Mars. If I included all aspiring franchises, the list would have looked more exponential Also, this data set is small, only 50 movies.

What about that huge data set I just pulled to look at Oscar grosses? Well, I haven’t even histogrammed that yet, so I don’t know what it looks like. So we’ll see. Again, though, this is in a way a “success” metric in that these are all “good” films. Obviously, a lot of films at the bottom of our list—meaning getting sub $1, $10 and $25 million grosses—were just bad, so no one saw them. With the Academy Awards, we’ve deliberately sorted that out.

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Source: Box Office Mojo

The rule holds! Mostly. Now, with adjusted gross we do see a bit of a floor. Historically, a best picture film tended to get more than $50 million in domestic box office. But with both Oscars and Franchise Films, we can see that “super-hits” are still rare, but present.

Final Lesson: This is Why Linear Regression Doesn’t Work in Entertainment.

I have one final lesson for the data heads in the crowd.

Let’s say you’re an aspiring business school student who hopes to go into entertainment. Or you’re a junior financial analyst. Or a statistician diving into entertainment. (Three real world examples I’ve encountered.) You’re given a mess of data on the performance of feature films at the box office. And you want to draw some conclusions.

Well now that we know how our data is distributed—logarithmically—we should come to one clear conclusion: linear regression WILL NOT WORK!

It’s really just right there in the name. Linear regression works on things that have linear growth, and our things have exponential growth, which throws off all conclusions. The work around is that you can convert our data points to logarithms, and then have a “log-normal” distribution, which gets you closer to accuracy. (Though, as I wrote here, you still have a sample size problem.) In general, as well, since you have so few examples of success—the long tail at the right—you just can’t draw statistically meaningful conclusions.

Conclusion – What’s Next?

Well, I didn’t say this was a law of media and entertainment because it applies to feature films. I said it applies to everything. And it does.

But that’s for our next installment and another dozen or so tables and charts!

Disney-Lucasfilm Deal Part VII – Merchandise

(This is Part VII of a multi-part series answering the question: “How Much Money Did Disney Make on the Lucasfilm deal?” Previous sections are here:

Part I: Introduction & “The Time Value of Money Explained”
Appendix: Feature Film Finances Explained!
Part II: Star Wars Movie Revenue So Far
Part III: The Economics of Blockbusters
Part IV: Movie Revenue – Modeling the Scenarios
Part V: The Analysis! Implications, Takeaways and Cautions about Projected Revenue
Part VI: Disney-Lucasfilm Deal – The Television!)

In business school, as I said in my first article in this series, I was super bullish The Walt Disney Company. The Lucasfilm acquisition followed on the heels of the Pixar and Marvel acquisitions—which were already doing well—and at the time ESPN was a cash juggernaut. Strategically, they’d made a series of great decisions.

Still, those moves, while good, weren’t the core reason why Disney has succeeded so much over the last forty or so years. I believed then, and still do now, that Disney is one of the few movie studios that has a business model derived from a distinct competitive advantage. As others have written about, this competitive advantage goes back to drawings by Walt Disney in the 1950s.

Slide57

Basically, while having a great content is at the center of the plan, they develop and reinforce their relationship with customers through everything else. Or, to be cynical they make their money off everything else. Walt Disney created an iconic character in Mickey, then another in Snow White, then another in Cinderella, and so on to start. Then Walt Disney (the person and the company) would monetize the characters through music and books and comics and eventually television. Then they pioneered the concept of theme parks. Michael Eisner took this approach and applied it to home entertainment and acquiring TV networks.

When I was in b-school, I took the famous chart and summarized it in economic terms thusly:

Slide58

This is the simplest description of supply and demand in the marketplace, the core model at the heart of economics. Basically, along any curve, you maximize your price and quantity sold to yield the highest profit. I’ll cover this more when I write an article on “Transaction Business Models Explained!” (the sequel to my two articles on subscriptions) but for movies you basically can only charge the same price per movie ticket, regardless of movie. As a result, to maximize revenue you need to maximize customers, and hence Hollywood makes blockbusters.

Most studios stop there. But not Disney. They aren’t just selling movie tickets, they’re selling merchandise on top of that. And then, for the piece de resistance, they sell theme park admissions (and in park up-sales) in an experience they own outright. Other studios do this, but nobody does it as well as consistently as Disney.

In my adventures after business school, I’ve only become more convinced that Disney knows its business model, knows its competitive advantage and makes moves to sustain that model. They may be the only movie studio, er, “giant media conglomerate” that has a competitive advantage. To continue our series on Lucasfilm, I’m going to add in the rest of those boxes going up, starting with merchandise.

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