Category: Ideas

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.

Applying “VORP” to the Entertainment Industry: A Step by Step Guide

Think about your team right now. Either the people reporting to you or your peers. The people sitting around you in your cubicle or open office desk farm. The ones who should be working, but are probably reading the internet, like you are right now.

How many of them could you “replace” and see your team improve?

How many are just average?

How many are delivering LeBron James-esque over-performance?

Yesterday, I explained “value over replacement player”, a concept from sports that compares all all players to the average to determine how much value they add to the organization. And how rare they are, hence how much compensation they can demand. Unfortunately, as I also wrote yesterday, VORP isn’t commonly used in business. Most managers don’t look around at their peers and direct reports and judge them in “value over replacement” terms. They especially don’t measure performance that concretely.

That should change!

Today I’ll explain why. I’ll even provide some principles that lay the out basics for how to apply this to your team. And then I’ll provide some examples. From the entertainment business.

Before we get to the good stuff, we have to explain the difficulty with this whole enterprise.

VORP: Why not business?

Let’s start with one very simple answer, and then dig into the details:

Value Over Replacement is hard!

That’s it. It takes a lot of work, requires a lot of numbers to do it well, which requires a lot of thinking and analyzing, and then a lot of feelings could get hurt. But let’s get into some specifics.

First, there is a goal or “value” problem.

Teams applying advance metrics start with “value”. Ideally every part of every organization would know right off the top of their heads how they create value. In value based terms. But while most people could speak generally about value, very few could define it in concrete terms. Even fewer could tell you how well they did last year. To put this in sports terms, most teams (and even companies) couldn’t tell you their record from last year.

Each team’s goals should be aligned with the company’s mission. Since most businesses are pretty awful with goal setting (and accountability) you have a problem from the start. At its core, it’s really easy to account for “value over replacement” when you have objective numbers like runs scored and assists generated like in sports; it’s much harder when it is about running a team with an undefined goal.

(This is why most companies and analysts default to stock price, since it is arguably the goal, but mainly it is the easiest thing to track. Even profits/cash flow require digging into financial statements.)

I could pick on numerous traditional teams in movie studios. I’ll try to stick with just one for these examples: business affairs (BA). (For those who don’t know, the BA folks are responsible for negotiating with agents and managers for the deals that make up TV shows or films. All the deals too, including actors, writers, directors, producers and sometimes production staff. Sometimes they also do “Legal Affairs” making them BALAs!) I don’t mean to pick on BA, but they make a great example, because they usually have a clear mission and they have a defined skill set/experience (law school).

A BA team adds value in one of two ways: they either negotiate cost effective deals, or they close a lot of deals quickly. The balance between these two can change per company. Some companies—like Netflix and other streamers that aren’t constrained by budgets—care more about getting all the deals they want or the speed to close a deal. Other studios could pinch pennies, like say Viacom or Lionsgate.

But many (most?) studios never clearly define the goals for this team. Sometimes they want deals closed quickly, sometimes they want to save money, sometimes they want to get projects. The heads of BA usually don’t push on this either, since not having goals makes it easier to succeed. Overall, they definitely don’t define how the BA teams add value. In the gap, the BA teams just work really hard.

Second there is a data problem.

Or problems. Take the fact that even if teams do have clear goals, they have way fewer metrics measuring their progress towards these goals.

Read More

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 Myths of CBS…Debunked!

A few years back, I was at a party—more like a family get together—and the subject turned to TV. Everyone at the party started raving about the latest The Big Bang Theory. Then raving about other CBS shows. As an effete, Millennial, west coast liberal, with New York values, I joked about it with my brother. We don’t watch any CBS shows!

Well, that’s not quite true. I currently watch Life in Pieces. I used to watch The Good Wife. My brother watches The Amazing Race.

Hmm.

I was stereotyping. I took some data points and anecdotes about CBS from my experience—both personal and professional—and drew broad, generalized conclusions. Like most people in my social circle, I don’t watch The Big Bang Theory. So I stereotype the people who do, along with the people who watch NCIS (formerly CSI). In fact, very few coastal liberals brag about watching CBS. TV has become a cultural identifier, especially peak TV. We judge other people by the TV shows they watch.

Critics do this too. Well, especially critics.

If it ended there, in judgy cultural wars, fine. But I work in entertainment and media in a business capacity. These stereotypes inevitably infect my thinking. They infect all of our thinking. I’m saying “our” in the “we work in the entertainment industry” sense.

In business, you make decisions. You do that based on data, both good and bad. Stereotypes are bad data, and they’re a lot more common than uncommon. If you use stereotypes to make decisions, you’re likely making bad—sorry, “sub-optimal”—decisions.

On Monday, recapping the end of the Moonves era, I laid out a series of stereotypes about CBS. Broadly, Moonves made shows for “middle America”—meaning rural, white and not coastal—that were popular, but not “good” in a critical sense. That’s the general consensus. Today, I’m going to look at the data today because I wanted an excuse to reexamine these stereotypes I’ve carried for so many years.

Caution 1: I’m going to primarily use Nielsen data for today’s post. I used Nielsen data in past research projects at my former company, but I don’t currently have a Nielsen subscription. This means I’m relying on websites that do, that also publish their results. This makes it tougher to interrogate the data. Further, I wasn’t a Nielsen ratings expert by any means. (I was focused on streaming data, you know?)

Caution 2: This is also going to be a lot of selective data pulling. I’m not setting out provide a definitive answer. Instead, I want to pull just enough data to make you question your own assumptions and stereotypes.

Myth 1: CBS was popular with middle America, meaning not the coasts or not the cities.

If you think middle America, you think the middle of the country, not New York and Los Angeles. Fortunately, New York and Los Angeles are large enough markets that Nielsen could tell us how well shows performed in those specific geographies. Unfortunately, as I mentioned above, I don’t have a Nielsen account.

Here’s what I did find. Joe Adalian of Vulture used Comcast Xfinity data to pull the most popular shows by city. Here are three cities as an example (but seriously read the whole article):

Chart 1 City View 3

Source: Xfinity viewing data via Indiewire

The first lesson is that different cities do have different tastes, and likely these differ even further from rural tastes. America isn’t some uniform blob. Obviously. That’s what makes this a great country.

But…and here’s the huge but…notice that The Big Bang Theory is just popular. It made every city list except one (out of 16). Blue Bloods made a bunch more. (The data is from 2016.) My guess is CBS would do pretty well in the top 25 and top 50 lists.

The lesson is that sure CBS “over-indexes” in the middle of the country. But CBS still has a lot of fans in cities. And in all the states. That’s what blockbusters do. Besides Game of Thrones, The Walking Dead, and, I presume, Stranger Things, CBS is the closest thing to blockbusters in TV.

Myth 2: Middle America means old people.

CBS is an aging dinosaur and no one who is under 50 watches the channel.

That’s the stereotype. While it is true a lot of cord cutters are young people, a lot of cord cutters are also older people. Another stereotype for another article. But just because CBS over-indexes on older viewers, which it does, doesn’t mean that no young people watch the network. That’s a fallacy. For this data point, I used Michael Schneider’s summary of TV network performance from 2017. Here’s the broadcast channels:table-2.jpg

Source: Nielsen via Indiewire

It isn’t that CBS under-indexes on younger viewers—it has roughly the same as ABC and Fox—but that it has such an over-index on older viewers. More young people watch CBS than watch any cable channel on average.

Again the lesson isn’t that CBS doesn’t favor older viewers or favor rural areas versus cities. But it’s much too simplistic to say CBS is only older viewers, which is the stereotype. We need to be careful moving from a “trend” to to “no one” or “never”. That’s when evaluating data turns into stereotypes. (And bad decisions.) A lot of young people still watch CBS, not zero.

Myth 3: Middle America means white people.

Don’t get me wrong: I’m not setting out to prove that CBS is the most popular TV network for viewers of diverse backgrounds such as African-Americans or Latinos. I don’t think I could prove that because it isn’t true. But is the converse true, that no African-Americans watch CBS, which is the stereotype?

No. Here’s from Nielsen directly, their top 10s of a given week, broken down by demographic:Table 3

Source: Nielsen

Do you see differences in viewing habits? Yep. Only four shows overlap between the two lists. That said, a CBS show makes the cut for African-Americans, and I bet if we saw the top 25 or top 50 we’d see some other CBS shows make the list. Yes, CBS skews older and whiter, but it isn’t a monolithic blob. It’s heterogeneous, like America.

Myth 4: CBS has underperformed financially.

Okay, this isn’t a widely repeated myth, but it is the analysis I read in two critiques of Moonves, one by Richard Rushfield in Vanity Fair (which I said you should read on Monday) and one by Joe Nocera in Bloomberg. Both articles cited CBS stock price lack of stock growth as evidence of Moonves’ failure as a CEO. Nocera used a pretty blunt headline for this, “Moonves was not a good CEO”. Here’s their evidence in two charts:

Pic 4

Source: Bloomberg.

I have two responses to this. First, yes, the stock price has been flat. That said, if you have faith that the stock market is a good predictor of future performance in particular (meaning for individual stocks) then you have a lot more faith in the market than I do. (Also, if you pick and choose dates on the stock market, you can rig the outcome.)

Moreover, when judging firms, I hate just using one metric. This comes from my unwavering belief in “the balanced scorecard” approach to most problems. If you just focus on stock price, you’ll get executives focused on inflating that. If I had to pick one metric above all else, though, I’d pick cash instead of stock price. Specifically free cash flow. So here’s a comparison of the CBS Corporation and, oh say, Netflix in the terms of free cash flow:table-4.jpg

Source: MarketWatch.com and Annual Reports

I’d love to include other broadcast channels such as Fox, NBC and ABC, but they’re so encumbered by their large conglomerates it would be too tough to untangle. (And I didn’t do this analysis, I relied on others, either the company’s own annual reports or MarketWatch.) Either way, to call CBS a financial disaster is disingenuous at best and flat wrong at worst. It generated at least $3 billion for shareholders in the last three years, whereas the main tech giant in tech lost at least $4 billion, and plans to potentially double that number this year.

But this myth isn’t really about the numbers, but the narrative. Let’s get to that.

Myth 5: CBS is old broadcast, not new tech.

This accusation was leveled by Rushfield, Nocera, and I’d add most importantly, by Rich Greenfield, the most quoted analyst in entertainment. Here’s the money paragraph from the Nocera article, citing Greenfield:

There were no larger ideas — no sense that Moonves had a plan for competing in a future where Netflix has size CBS can’t match (130 million subscribers), HBO has content it can’t match (“Game of Thrones”), and AT&T-Time Warner has revenue it can’t match ($158 billion vs. $14 billion). Nor was there any inkling that he might invest for the future if it meant taking a short-term hit to earnings, something Netflix does as a matter of course. Rich Greenfield, the BTIG analyst who has been a rare Wall Street voice critical of the CBS chief executive, says that Moonves has long preferred to “focus on short-term cheerleading actions versus real long-term strategy.” Greenfield is right.

First, saying CBS didn’t have a strategy is my pet peeve. Clearly they had a strategy to generate about a billion in cash each year. You may not like it; you may not be able to define it, but they had a strategy. If you want to criticize someone’s strategy, define it first, then criticize it. Otherwise you’re building a straw-man.

Second, wait, it doesn’t have the content? That’s Nocera’s second point, but honestly, CBS makes more popular series than HBO, so that’s just not factually accurate. Both NCIS and The Big Bang Theory have viewership comparable to Game of Thrones. It also took a huge swing with Star Trek: Discovery.

Third, size isn’t a strategy. Ask GE. Conglomeration goes in waves, as I predict this wave of consolidation will do. (Also, I hate industry consolidation. Bad for consumers, good for stock prices. More in future articles.)

Fourth, it’s all moot because of the broadcast channels, CBS was the most forward looking. Alone among the broadcast channels, CBS had an independent streaming platform.

Disney still doesn’t have a plan for ABC with streaming, NBC has been trying to figure out a digital strategy since Comcast acquired them—and they have so many stakeholders they still haven’t figured it out, though they are hinting in recent interviews they have—and who knows what Fox’ plan is now that Disney is buying almost all of 21st Century Fox except for the broadcast.

So it can’t be about the tech. What really bugs Nocera/Greenfield about CBS?

That CBS won’t burn cash to grab market share.

Really, that’s what separates CBS from Netflix. They could have taken the $1 billion in free cash flow and made say 40 additional shows and put them on their streaming service, and poof cash gone. (Or ten shows at Netflix/Amazon Prime/Video/Studios prices.) Amortize over long enough it may not even hit the net profit line.

But Wall Street would have crushed them with that approach. Only Netflix gets away with that in today’s stock market. If you’re criticizing CBS for having a flat stock price, what would you have done if the stock price had tanked?

To sum up, was CBS the best streaming platform? No. Was it the most dinosaur-ish of the broadcast channels? No. It was somewhere in the middle, in that it was actually small enough to be able to launch CBS All-Access, even if it was late to the streaming party compared to Netflix, Hulu and Amazon.

Myth 6: CBS makes bad TV shows

Listen, I’d love to find an absolute ton of links with critics saying this, but I think this sentiment is, if anything, more popular in quiet discussions at entertainment shindigs than it is something said out loud. In the entertainment press you don’t want to burn too many bridges or future places of employment. The best summations were Todd Van der Werff’s three articles on the subject from 20152017, recapping each year’s upfront.

The problem is “bad” is just so darn subjective. So we need to find a way to prove this. I have two definitions that get semi-objective: awards and critical acclaim (which is usually the forerunner to awards). For the last time, and fifteenth time this article, I’m not setting out to prove that CBS is the best at making award winning shows—it clearly is not—but that it hasn’t completely struck out. (This is probably the most “accurate” myth.)

Awards

Reviewing the Emmy nominees for drama and comedy (the Golden Globes aren’t a real award show) since Moonves took over in 1995, CBS popped up regularly. Not the most, but not the least. In comedies, Everybody Loves Raymond won twice, Two and a Half Men was nominated, along with How I met Your Mother and The Big Bang Theory. The Good Wife was one of the few broadcast dramas nominated for several years.

In smaller categories, David Letterman won for talk show until Jon Stewart took a stranglehold. (Colbert and James Corben have both been nominated recently.) The Amazing Race, though, had a similar stranglehold on the reality-competition award for years.

Critical Acclaim

Okay, I’m not going to fight this battle. Most critics hated everything on CBS. This stereotype is accurate that critics just hate on CBS.

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.

slide021.jpg

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.

slide031-e1536791810207.jpg

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.)

slide041.jpg

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.

slide051.jpg

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:

slide131.jpg

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:

slide151.jpg

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.

slide161.jpg

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!

The Most Important Shape in Entertainment Part I: Distributions Explained!

You want to know a secret? The underlying secret to all media and entertainment? The peak behind the curtain that explains all you see in film, TV, music and more?

Here it is.

“Logarithmically distributed returns.”

Once you learn it you can’t forget it. Like how to do a magic trick, which is what I call it, my magic trick for the business of entertainment. I didn’t discover logarithmic distributions. I first read it in Vogel’s Entertainment Industry Economics, the wonk bible of entertainment financial analysis. (Figure 4.8 in chapter 4 if you’re really curious.) I also assume it’s the theoretical underpinning of Anita Elberse’s Blockbusters, which I haven’t read. (Her book is one of those books that has been on my “to read list” for years.

Unfortunately, I can’t just show you that logarithmic distribution under girds all of entertainment. As important as the “logarithm” part of the statement is, the “distribution” part is even more crucial. I don’t want to gloss over that. The value comes in not just seeing one chart, but seeing the value of distributions as a tool.

Today, I’m going to teach you about distributions. What they are and why you need them. This is a mini-statistics lesson to pair with my other mini-statistics lesson on why you can’t use data to pick TV series. I won’t use any equations, because they’re boring, but I’ll show you what the distributions look like. Then, tomorrow I’ll show you the ubiquity of logarithmic distribution.

(As I recommended before, go pick up The Cartoon Guide to Statistics for the best reader on statistics. Learn them in a weekend. It’s way better than this very useful, but very technical Wikipedia page.)

Before we get to the “what” of distributions, let’s get to the “why”.

Read More

A Quick Addendum to Theme 2: Making it More Complicated

When I wrote “Theme 2: It’s Not Value Capture, It’s Value Creation” last week, I made things seem really simple. Probably too simple.Value Creation ChartThat said, I hold to my core point: most businesses could benefit by pulling out that chart and answering three simple questions,

“What price do we charge customers?”
“What is their willingness to pay (WTP)?”
“What are our costs?”

Then they could ask the forward looking questions: “How can we raise the willingness to pay for customers?” or “How can we lower our costs?” In short, how do we create a competitive advantage derived by creating value, not capturing it?

Real life, unfortunately, is never that simple. That simple chart gets complicated. Really quickly. Here are some ways.

The entire value chain

I kept the chart and examples from the last post relatively simple. I only used one buyer and one seller. But this transaction is repeated down the chain. I pay the store for the beer, the store pays the beer distributor, the beer distributor pays the beer producer and the beer producer pays it’s suppliers of water, hops and aluminum. Each stage has it’s own version of this chart.

Value Chain

This applies to film: the production company pays the talent (who pays a piece to their agent), the studio pays the production company, the distributors pay the studio (theaters, tv networks, streaming platforms) and the distributors collect the money from the customers.

One time transactions versus relationships

Of course, I don’t just go into the story to buy beer once, I go in regularly. (Not that often. Well, maybe.) For customers, regular trips like this can develop habits or a sensitivity to the changes in the price. So I could choose to measure the WTP/Price/Costs as one time events, or over the course of a month, or over a year or even longer. That’s a great way to make something simple complicated.

For example, say you lower the price of a good, which causes a customer to buy it more frequently or larger quantities. In other words, this chart looks like a single transaction, where profits went down, but they would go up with increased iterations. Of course, a customer could just stock up on items and store them, which means you did lose value, but the customer gained in consumer surplus. This is an age old challenge in “consumer packaged goods” that can offer regular discounts.  Like I said, it gets complicated quickly.

This biggest ramification for this for entertainment is evaluating subscription services. Analyzing MoviePass last week, I focused on the per month value chain. Arguably, MoviePass could consider their relationships annually, so they look at it on that basis. Maybe any given month is a bad deal, but over a year it saves you money. Or take HBO, I subscribe for a year, usually, but the biggest TV show by far that I devour is Game of Thrones. Is a year subscription worth that one show? Maybe, so being too lazy to aggressively cancel isn’t that bad of a deal, overall.

Distributions of people

I hate averages. Telling me the average almost never tells me anything useful about a data set. Take height: most men are five foot eight inches tall. Is everyone clustered around that point, or are there outliers? (Maybe an excellent explainer on this next week.)

Same with movie box office grosses. Chart it next to height and they look completely different. One is logarithmic and one is normally distributed.

So the value creation chart is basically the averages, especially for WTP. To extend the beer analogy, some people would pay a lot for a very bitter IPA, other people would pay a little more, many wouldn’t pay anything. And even among the people who would pay for it they have different values attached to the IPA. You can’t really summarize this as one number, though that’s exactly what I did.

When in doubt, use distributions, even with value creation. Understand who gains the most and try to emphasize that, but don’t stop with the averages.

You can’t measure parts of the chain

Especially “willingness to pay”, which is an imaginary value. How do you measure imaginary? Well you have to guess, and there are complicated and often unreliable ways to do that. (The worst way? Ask someone what they would pay for something. That never works.) The most reliable way is a conjoint analysis, but even that can get unwieldy with too small a sample size.

Streaming services are bedeviled by this problem, especially when they have to figure out what consumers actually love on their platform. Is it Stranger Things? Or GLOW? Or both, in some combination? Or is it actually the Disney movies, but the other shows are filler? That’s an epically tough problem to sort out.

Costs can be tricky

The “costs of goods sold” can be difficult to allocate. Especially for support functions that don’t directly tie to a good. Allocating the value correctly can be the difference—in a big conglomeration—between profitability or loss. Right now, content costs and how companies allocate those costs versus the prices customers pay is the biggest accounting/economics/finance question in the industry. Getting that answer right could determine he future of entertainment, for good or ill.