Month: June 2018

Weekly News Round Up – 29 June 2018

Welcome back to another week of my read on the most important story of the week and some other reads or listens to keep you informed on the business of entertainment.

Most Important Story of the Week – Box Office is Strong in 2018

As I wrote after the Solo: A Star Wars Story opening, I don’t follow weekly box office updates too closely. Or more precisely, I don’t consider them the “most important story of the week” most weeks since there is a lot of noise. Instead, I recommend waiting to judge the box office until we have a large enough sample size to draw a conclusion.

Which we had this week in this Variety article analyzing the box office of the first six months. Yeah, six months is a good time to sit back and observe the trends. So far, driven a lot by the surprise monster hit of Black Panther, the unsurprising Avengers: Infinity War performance and solid openings for Deadpool 2, Incredibles 2 and Jurassic World: Fallen Kingdom, box office is up.

The one question, which I’ll reference in a few seconds, is the “MoviePass” of it all. Is MoviePass bumping up attendance by offering artificially lower prices? As the podcast below says, MoviePass claims to sell 5% of all box office tickets in the US. If the MoviePass effect disappears–if it is real and does disappear–could that hurt box office?

Other Contenders for Most Important Story

First, the Justice Department signed off on the Disney-Fox merger if Disney spins off Fox’ Regional Sports Networks. Again, we’ve covered this deal before, but this step does make the merger immensely more likely. (And as the above article on box office highlights, combined the movie studio would have 48.5% of box office this year, which seems…high.)

Second, another social media platform launched more original video. This time Instagram. I want to shrug mainly because everyone making original TV and we don’t have any real metrics to judge success. Which is a topic for a future article. But this does mean more potential capital flowing into Hollywood.

Listen of the Week

Take a listen to The Indicator discussing the implications of MoviePass’ business model. I think MoviePass is one of the more fascinating stories out there, but it remains to see how big of an impact will it have. (Consider this the fill-in for AMC announcing their own subscription service.)

In addition to a business consultant, the good folks at The Indicator interviewed the CEO of MoviePass, Mitch Lowe. This isn’t necessarily a bad thing–CEOs obviously have a ton of knowledge about the company they’re talking about–but it is a red flag on reliability. CEOs and PR folks are well trained in phrasing everything to pass SEC scrutiny, but presenting the best possible case about their company. So you have to have your eagle eyes to spot misleading data.

And I found one glaring one. The CEO of Movie Pass happily passes along this tidbit: the average MoviePass attendee only sees 1.7 movies per month. As a result, MoviePass is confident they can make money with some additional revenue by the end of the year.

But can we take even that “1.7 movies per month” number at face value? Is that the median or mean average? Wait, which month is it from? Is it a rolling average or the number from last month? Or–and this is where it gets potentially shady–was it from a month selected because it looks the best?

He also said at some point that they are “fast approaching 3 million” subscribers. Again, you could take that a lot of ways from they have 3 million currently paying subscribers or they have huge customer churn (or will) when all the annual subscriptions end.

The lesson? Listen to CEOs, but try to hear what they’re leaving out.

An Update to an Old idea

In my first article, I wrote a sentence that critics have bemoaned the number of franchises, sequels and blockbusters going back to when I first started reading the newspaper. But I couldn’t find a lot of historical examples since the internet isn’t great about searching the pre-internet age.

But Sean Fennessey helped me out with this article in The Ringer laying out the sheer volume of sequels coming out. This headline in particular captures the feeling of so many critics: “The Summer of Sequels No One Asked for (or Even Thought Possible)”. He later said,

“It is the first in a series of movies arriving in coming months appearing out of no evident desire, without the breathless anticipation that the studios have churned out for bigger, louder franchises. They’re crypto-franchises, ginned up without anything better to do.

Disney-Lucasfilm Deal Part V: Movie Revenue – The Analysis! Performance, Implications and Cautions

(This is Part V 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! Performance, Implications, and Cautions)

Have you ever been on a long hike? Your friends tell you, “Let’s go on this beautiful four mile hike to the top of a mountain with a scenic overview.” So you say yes. You go. It’s long, but you keep trudging.

Midway, you start to count your steps, anything to avoid thinking about the burning in your legs. You imagine the joys of finishing. It just keeps going.

Finally, mercifully, you finish and you’re at the top of the hill. Man, the view is worth it.

Gorgeous.

Then it hits you: you have another four miles to get back down.

It was four miles each way.

That’s a test of fortitude. I bring this up because this article just keeps going. Every time I think I’m finished, well I find another fascinating principle to explain. The “movies” portion alone has kept me busy for four parts and a bonus appendix. (And maybe a bonus article next week on this topic.)

Last time, I explained how I built 8 scenarios forecasting the future of Lucasfilm’s film slate. Previously, I explained how feature film economics work, explained my financial estimates for the first four Star Wars, and explained the economics of blockbusters, which developed a set of “comps” for franchise blockbusters. I brought them together in my 8 scenarios. Today, we’re bringing it home to analyze the results of my model. (See the links at the top.)

Really, this is the fun part. The hard work was building the model and getting some good data to make it accurate. That was the hike up the hill. Let’s gaze out at that gorgeous view, to drive the above analogy firmly into the ground.

Overall Performance

The best way to summarize our 8 scenarios is to calculate the net profits for each outcome. (In my model so far, gross profits are revenues minus costs, and net profits then subtract talent participation.) I’ve discounted them all back to 2012 acquisition dollars since that is when Disney first acquired Lucasfilm:Slide 33Those are a lot of big numbers, so another way to look at it is in percentage terms of the acquisition price ($4.05 billion with a “b”). Here’s that:Slide 34Wow. So in 5 scenarios, Disney makes all its money back with just this line of business alone. Further, even if they only achieve “Blockbuster Average” at the box office, they can even have production issues and still get 94% of the initial price. At worst, with production issues and a franchise fatigue, they still make back 82% of the initial deal price ($3.3 billion in adjusted dollars).

Still, I can hear you, “Entertainment Strategy Guy, just give us a single number! Tell us the average!”

In scenario modeling, the “expected value” is the closest thing you get to an overall “average”. To give that to you, though, I need some probabilities. These scenarios aren’t equally likely. It’s more likely that Lucasfilm has production issues then it is they ramp up to 14 films per year. It’s also more unlikely that “Star Wars is Star Wars” at the box office, as opposed to franchise fatigue or just general underperformance.

So here is how I would rate the probabilities of each scenario. And to simplify things, I gave each scenario it’s own name to make it easier to refer to. And it’s more fun. So how likely are each scenario?Slide 35Here’s the thing: I don’t have great rationale for the production side of the equation. I had started with MCU-style at 20%, but given the vague post-Solo rumors, Lucasfilm put the spin-off movies on hold. That makes me think they will strive for the one film per year rate. For the performance, I think the odds that franchise fatigue truly sets in is about as likely the high case where Star Wars defies box office gravity forever, especially if Lucasfilm determinedly releases 1 film per year. (Remember, before this, Lucasfilm had a 15 and then 10 year gap in films.)

I used my judgment for these probabilities, but I could tweak them if I found better data to make the forecasts. So with the probabilities of our two inputs, we could calculate the probability of each scenario.

Slide 36

The odds of “Gangbusters”, the scenario where “Star Wars is Star Wars” at the box office while moving to two films per year is only 2%. That makes sense; each is unlikely. On the other hand, the idea that franchise fatigue sets in and they have production issues (“Worst Case”) is 5%, which feels right.

In the downside, in “Burn Out” or “Sub-Optimal” Disney doesn’t make their money back. On the other hand, Lucasfilm makes money in the “Base Case”, “Make Money”, “Make More Money” and really cashes in with “Gangbusters”. They also make money in “Missed Opportunity”, though it contains a good lesson that production issues can really leave money on the table.

With our probabilities and the returns, we can now calculate the “expected value” of Star Wars movie revenue going forward. To take this all the way back to the introduction from yesterday, I can’t tell I “know” how well the films will perform from here on out. (Again, that quote.) But I can tell you this is what I expect just the movie portion of the deal to be worth, in financial terms: $4.3 billion in 2012 dollars from 2015-2028. In other words, I expect the movie net profits, after costs and talent participation, just the movie revenue, without toy merchandise sales either, to account for 106.8% of the value of this deal through 2028. In just one line of business, Disney made its money back.

Slide 37

It does strike me that my “base case” scenario is fairly close to the expected value (only off by about $40 million, or less than 1%. So was it a waste to build all the scenarios? No. Those numbers represent two different things, though it does give me confidence in using the base case in an “average total model” when I finish all the lines of business.

Implications

Overall performance isn’t the only thing we can learn from this model. The great thing about a scenario model like this is we can learn some things from it (assuming our math is right, the data is accurate and representative and our assumptions are reliable):

“Franchise blockbusters” have a low downside.

This was a conclusion from “Part III”, but bears repeating. Blockbusters based off preexisting movie series can usually put up something of an opening weekend and make some money. This doesn’t apply to brand new blockbusters, who can lose a whole lot more when they completely flop (I’m looking at you, John Carter of Mars and The Lone Ranger. I wonder what studio made those?)

For franchises—movies in a series based off preexisting IP, for lack of a better definition—the downside really is limited. For Lucasfilm, you can see why they’ll keep making Star Wars films: it’s a low downside risk. (If you’re a Star Wars fan upset at Disney making so many films, well this is your explanation.)

We can quantify this per film with our “expected value” chart as well. Here is the net profits from the green light model per comparable level. Now we can multiple these by expected probabilities:Slide 38For a franchise blockbuster, like Star Wars or Marvel or Harry Potter, the studio can expect $392 million in expected revenue if it performs like past films. Now they will hardly ever get exactly that, but in a portfolio of films this is what they can expect to earn. (For Star Wars fans who think Disney is driving the franchise into the ground, this is your financial explanation of why.)

2020 is a big year for the model

I put most of Star Wars: Episodes 9’s revenue in this year because it will come out in December of 2019, so Disney won’t collect the cash until 2020. Combined with an Indiana Jones 5—if it stays on track—then the total revenue in 2020 is $2.7 billion. That’s a big year. Combined with a 2019 or 2020 release of a new TV series and Lucasfilm has a lot going on.

The time value of money starts to have a strong effect.

The time value of money has a real effect. Take the $2.7 billion in 2020 dollars. Well, discounted back to 2012 dollars, it’s only worth $1.6 billion.

Assuming Rian Johnson’s first new Star Wars movie comes out in 2021 (the year after Indiana Jones or the Christmas of 2020), if you booked all the profit and loss the next year (2022), well you would only earn $0.50 on every dollar in 2012 dollar terms. Again, not that Disney wouldn’t collect each dollar, but if you’re evaluating the deal in 2012 terms, ten years down the line a dollar is only worth half in 2022 what it was worth in 2012 when discounted at 8%. The easiest way to understand the time value of money (for me) is to multiply the money earned by the discounted rate, as shown here:

Slide 39That’s why literally more than half the value of this deal in terms of movie revenue was baked in when the first four films came out, again when evaluating in 2012 terms. Essentially, huge box office returns from the first three films locked in 63% of the adjusted price. A huge Episode 9 and big Indiana Jones 5 (which I assume in half of the scenarios) yields another 23% of the initial price. By 2028, if the series performs on blockbuster average, the last seven movies only make 20% of the initial price of the deal. Here is how that looks by scenario:

Slide 40In terms of our initial question, it basically says that Disney would have to do a lot wrong with the franchise to NOT make money at this point. It’s possible, just much less likely.

Cautions & Criticisms

I’m not perfect, so naturally I can look at my model and give some critiques. Let’s keep these in mind so we don’t take this model as 100% gospel truth.

Be ready to update your priors…especially with small sample size.

If it seems like everyone is over-reacting to Solo’s disappointing box office, well I disagree. Essentially, I think Solo: A Star Wars Story caused a lot of people, myself included to “adjust their priors” to use Bayesian/Nate Silver-ish talk. We assumed Star Wars films didn’t have flop potential when they clearly did. Solo: A Star Wars Story changed my preconceived floor of box office performance for this franchise. I changed my whole model based off that event. That’s worth the conversation.

And it should provide a warning. Do I have other prior assumptions I haven’t captured in this model? I tried to call out as much as possible, but it’s always the concern. Off the top of my head, a big potential swing in value is the probabilities assigned to each scenario. If instead of my probabilities you just assigned an equal likelihood, then Disney would earn $4.6 billion, or 115% of the initial price.

(Of course, there is always the fact that my model might not match the actual performance. I did a little searching for research on other projections of Star Wars revenue/profits and I’ve found a range of numbers. So again, I went with my best judgement.)

This current expected value isn’t the expected value at the time this deal was signed.

Don’t mistake the current estimate of value ($4.3 billion in 2012 dollars, 106% of initial price) for the expected value at the time this deal was closed.

In 2012, at the time Disney signed this deal, a new trilogy could make over $5 billion in total box office, or it could have made “only” a billion dollars. Or somewhere in between. That’s immensely reasonable. Now, Lucasfilm and Kathleen Kennedy hired the right director for The Force Awakens and that didn’t happen.

Evaluating the deal midstream, we get to look back with hindsight and look forward with our forecasts. But that doesn’t mean the performance for the last five years was guaranteed by any means.

Is “franchise blockbusters” the right data set?

This is the toughest part of the process and I’ve seen really smart people make really big mistakes when it comes to finding the right data set of movies. So I could see quibbles with my data set of “franchise blockbusters”. The worry is I biased the data set positively for Star Wars.

As I wrote before, my notable omissions were Jurassic Park, Star Trek, Fast and Furious, and James Bond. The last two I have no qualms leaving out since they don’t have the kid appeal of Star Wars or Marvel. Same with Star Trek overall. The toughest was Jurassic Park, which would mean Jurassic World, so it’s one movie, but a “super-hit”. Again, I’m fine with this, but I’m monitoring those franchises for future data analysis. (If you’re curious, if we had added all the omitted franchises, it would have pulled down the average for hits and super-hits.)

There is no “terminal value”.

In other words, why did I stop modeling at 2028? Short answer: uncertainty. After ten years, models lose almost all their predictive value.

But won’t these films keep being made on into the future? Don’t I need to account for that? Yes. And I will, when I put it all together. I plan to do that for this deal, but not until the last step when I do it for all the business lines at one time.

Final Questions

Please, can I see the unadjusted gross profits?

Sure, since you asked nicely. Again, I don’t think we should lead with this because they are misleading. The raw numbers just look better because they look bigger. (I’m losing clicks I know by not leading with this in the headline. But fine, here they are: First, the total revenue from 2013-2028, unadjusted:Slide 41Do you learn as much? Again, I don’t think so because I think our brains translate that money into current price/value and not the true scaled price. But again it looks huge.

What is the current value of Star Wars movie revenue?

That’s a good question. First, I’m going to give you the unadjusted revenue for the next ten years. To show you how it can mislead you:

Slide 42

I put the expected value below it. So you could look at this and say, “Look, Lucasfilm is still worth $4.2 billion! In just movies!” But again, let’s discount for the time value of money. But instead of discounting to 2012, we need to discount it to the current value.

Slide 43

In this case, we see the revenue is “only” worth $2.5 billion for the next ten years. Still really not bad, but not as well as the initial deal did. Again, a lot of that is drive by the fact that most scenarios only have one or two “super-hits” whereas Disney came out and delivered three of those in a row.

Where do we go from here?

Back to the analogy. We just finished admiring a gorgeous view of financial prosperity. And we learned some things. But now we need to walk back down the hill.

The movies portion has long been the centerpiece of this deal from a financial standpoint. At least, that’s what I expected when I started on the model and remain convinced by after all this work.

But Disney is so much more than just movies. Toys. TV. Theme parks. Our walk down this hill to arrive at our final conclusion needs to analyze each of those lines of business. And I’ll do that next.

Disney-Lucasfilm Deal Part IV: Movie Revenue – Modeling the Scenarios

(This is Part IV 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! Performance, Implications, and Cautions)

Let’s talk about confidence.

When I built my film financial model, explaining it here, and after I reviewed the economics of blockbusters, I did the next logical thing: I built a financial model forecasting the performance of Star Wars films for the next ten years. I called Star Wars: Episode 9 (the next film in the Star Wars franchise being directed by JJ Abrams, Episode 9 from here on out) a “super-hit” and called the next Indiana Jones film a “hit”. (Indiana Jones 5 from here on out.) I decided that no movie from Lucasfilm would be a flop. This is Star Wars after all.

(Again, this was March.)

I liked my decisiveness. I felt confident. Then Solo: A Star Wars Story came out and proved that even a Star Wars movie could flop. (And it’s floppiness mirrored exactly what I set out for “flop” performance, with one analyst forecasting losses nearly exactly what I had forecast.)

So I began to question my confidence in that initial “single model”.

Then I slapped myself in the forehead. And said, “Duh, I need to model multiple scenarios.”

Which is how today’s article came about. See, any one model can be wrong. But multiple models that capture the uncertainty properly can be more accurate, on average. They better describe the range of outcomes and hence improve our confidence.

As they gaze towards 2028, two things will determine how much money Disney makes off the Lucasfilm acquisition, one of which they can control, the other which they can’t: how many films Disney makes and how well those films perform at the box office. These two variables gives us the solid foundation of a scenario plan. Using these discrete scenarios, we can chart out essentially the 90% confidence interval for how well this franchise will do. The columns are the best through worst case production outcomes. The rows are how well Star Wars continues to perform. It would look something like this:Slide 24Today I’m building out that simple table. But as simple as it looks, it will have a lot of calculations driving it. Basically, the film financial model I built here, the specific Star Wars models I built here and the performance of franchise blockbusters I analyzed here can now be combined.

Building a Scenario Model of Future Lucasfilm Movies: Scheduling

Let’s start with the production side of the equation. This is what the studio (mostly) controls. The studio decides what movies to make, how to develop them creatively and then produces them. Disney could keep making one film per year or decrease to one film every three years (the old Star Wars average) or increase to multiple films per year. Hmm.

Read More

Why You Can’t Use Data to Predict Hit TV Series Either

A few weeks back, I explained why “small sample size” dooms any effort to use big data to predict box office performance of feature films. But what about TV shows? What about streaming services? Can’t they use advanced algorithms to predict success there?

Nope.

As “No, Seriously, Why Don’t You Use Data to Make Movies?” explained in  a “mini-statistics lesson” how small sample size and multiple variables combine to make forecasting very inaccurate in movies. Today, I want to take the lessons of that article and apply it to making TV shows in the streaming era.

Here are the key reasons why “big data” can’t solve making hit TV shows.

1. It’s also data poor environment.

To start, TV has long had fewer data points than feature films. Only recently did the number of scripted TV seasons pass feature films (depending how you count it). Currently, there are over 500 scripted TV series per year in the US. As I wrote last time, that’s still a small sample size.

2. It’s even smaller when you factor in returning series.

Most new “seasons” aren’t brand new, they’re returning seasons of TV series that have been on for several years. That kills your forecasting model.

Take Game of Thrones season 8. Yes, you could call “season 8” a unique data point to study. But with TV shows, to have an accurate model, you’d need to introduce a categorical variable, “has had a previously successful season”. The answer for Game of Thrones for that categorical variables is “Yes!” In other words, it’s super easy to predict that subsequent seasons of Game of Thrones and The Walking Dead will have high ratings because their previous seasons had high ratings. (Though not always.)

The challenge is predicting successful new shows, and that data set is much much smaller than the 400 or so scripted seasons produced every year.

3. The number of categorical variables for a TV show at “pitch” is near infinite.

When a TV show is being pitched or is at the script stage, it has a huge number of categorical variables still in flux. Each of these could influence the final independent variable, which is viewership (depending on if you’re a network or streaming platform you could define this multiple ways).

Everything from the director who ultimately directs the first episode or the acting talent who signs on to the story plan for season one could impact the ratings. Even variables most studios don’t care about like “who is the production manager?” or “can the showrunner manage a room of people?” are categorical variables that could affect the final outcome. Without a large sample size, it’s just tough to predict anything. (And some of them are super hard or very, very difficult to quantify.)

Many good or great scripts or TV pitches become bad TV series. For a lot of reasons that don’t have to do with the script. This is why “algorithms” can’t predict things with high confidence. This explanation also definitely applies to feature films, though I didn’t mention it last time.

4. Most pitches/scripts/pilots will never get made. Hence no “dependent variable”.

Most claims to use advanced metrics or analytics or data to pick TV series utterly discount this key fact. Sure you get thousands of pitches and scripts to read, but they don’t become TV series. Replace “dependent variable” with “performance” and you see the challenge. You have three scripts, and you pick one to become a pilot. The other two scripts don’t get made into to TV shows. So can we use them in our equation for forecast success? No, because they don’t have the same dependent variable to allow us to use them as data. All you can say is you didn’t make them into TV shows. But that’s not a data point.

5. Finally, most of the time, you can only control your own decisions.

The best way to control a data-driven process is to own all the data. And for a TV studio or streaming service, that means understanding all the decisions that went into making a TV show. So, if you don’t make it yourself, well, you can’t really understand what decisions were made. So for a streaming service, that “n” is very, very small.

So let’s use Netflix as an example. They made what, eighty TV shows to date? (Not counting the international productions, that again are their own categorical variables.) So the maximum for their sample size is eighty. Break it down even further by separating kids shows from adult shows and previous IP versus new IP and then you can break it down by genre. You see where I am going with this. The “n” is dwindling rapidly.

What about all the customer viewing data they had from the TV shows on their platform? Well, it doesn’t give Netflix that much of an advantage of traditional networks. Even if traditional networks don’t have Netflix streaming data specifically, they have Nielsen TV viewing data and box office data. Netflix uses that data too.

Which isn’t to say Netflix doesn’t have tons of data and doesn’t use it a lot. But they don’t use it to “pick TV shows” they use it broadly. The “data analysis” that Netflix does is pretty simple: it sees what is popular with its user base. So do traditional TV networks and studios. And what has Netflix learned? People like broad based comedies and dramas featuring crime and/or police. It also knows some people like quirky comedies and some others like arty-shows. (Netflix’ key advantage is it just pays a lot more for the same shows with less opportunity to monetize. That’s a problem for another post.)

So is Netflix is “using data” to decide on TV shows? Yes, but it isn’t that much better than the rest of the industry. Do they have an algorithm that tells them which shows will do well on their platform? Yes, but it is wrong a lot of the time.

Weekly News Round Up – 22 June 2018

Enjoy this week’s updates. A little calmer than last week!

The Most Important Story of the Week – Disney Increased Its Fox Bid to $70+ Billion

So I think I mentioned it before, but if you’re enjoying my long “analysis” article on the Disney-Acquisition of Lucasfilm, you’ll love a sequel coming in a few months: “Who Won the Deal, Rupert or Bob? Analyzing the Disney merger/acquisition of 21st Century Fox”. I started it a few months back, and back then the deal was only worth $50 or so billion (with a b) dollars.

Then a judge cleared the way for the AT&T-Time Warner merger, the topic of last week’s update. With ostensibly the path clear for distributors to acquire content creators, Comcast put in a bid on 21st Century Fox (though Comcast itself proved the government wouldn’t stop these deals six years ago). Not wanting to lose, Disney increased their bid.

Honestly, the higher price both makes sense and will likely cause the winner to lose money on the deal. How can both things be true? On the one hand, when I had done my analysis comparing Time-Warner to 21st Century Fox, the difference in value seemed more tied to market capitalization than the value of the existing assets, especially the value of those assets under Disney’s management. (I’ll write about stock prices at some point and whether they reflect economic reality. They do and don’t.)

The downside is paying too much for the underlying assets. Which can absolutely happen in a bidding war and is called in economics the “winner’s curse”. It’s not just an economic theory: when you have multiple bidders on assets with uncertain value, you increase the risk that someone pays too much and it happens all the time. For the winner here, the margin for error in the acquisition has shrunk to almost nothing.

There is one clear winner, though: Rupert Murdoch makes more money either way.

Long Read of the Week – MGM’s $260 Million Payout: Making Sense of CEO Gary Barber’s Eye-Popping Exit

This isn’t the longest long read I’ll ever recommend, but it’s worth it for executives at the top of corporations to really understand the dynamics of this industry. Read Stephen Galloway in the Hollywood Reporter on the $260 million being paid to MGM’s ex-CEO Gary Barber here. Put in complicated terms to put a shine on it: entertainment conglomerates are currently and have always paid top executives well due to market conditions. Put in layman’s terms, top studios bosses get paid a ton, and it’s cause of all the other guys.

I would love to say, “On one hand I get this” but honestly I still don’t. Being a development executive is one of the most in demand jobs in America. There are thousands of qualified applicants. Same with aspiring CEO types. So why are salaries so inflated? And why do they go to executives who aren’t truly revolutionary? As the long read shows, many times these exorbitantly paid people are paid even more to leave.

(It’s also interesting that in this case it wasn’t so much for firing someone for incompetence, but because of a threatened hostile takeover. So it’s not quite the same thing.)

Lots of News with No News

Man, I guess the theme of today’s update is reflecting on future articles. Especially, my long form ones. Well, in another great long form article in progress, I’m going to compare Amazon’s Lord of the Rings and HBO’s Game of Thrones. Trust me, you’ll like it.

So I read a lot of news about HBO and Amazon Video/Studios/Prime, including this one about Jennifer Salke’s new approach to Amazon Studios/Prime Video. My takeaway is she has a ton of relationships so is taking a lot more pitches and hands-on approach then the previous head of the studio. Coincidentally, while reading I saw this article about Warner Bros. new approach to the DC cinematic universe. (As a fan boy I tend to read anything about comics too.)

In both cases I see the same general story that appears in the pages of the trades every few months: a talented and self-confident executive is taking it on their own shoulders to turn around development, and hence the finances, of a movie studio. Ironically, these same stories were written about their predecessors. So, that’s all to say, these types of stories offer a lot of news without a lot of actual news.

Data of the Week

And my final bugaboo, Netflix! Hat tip to BGR for this article from the Exstreamist where they polled how many Netflix users are sharing passwords. In short, a lot of people don’t pay for Netflix and share passwords. This is unlike traditional TV or cable, and honestly, and I’m rare in this opinion, I think this is a bad plan by Netflix. But more on that in future posts.

Disney-Lucasfilm Deal Part III: Movie Revenue – The Economics of Blockbusters

(This is Part III 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: The Television!
Part VII: Licensing (Merchandise, Like Toys, Books, Comics, Video Games and Stuff)
Part VIII: The Theme Parks Make The Rest of the Money)

A peak behind the curtain into how long I’ve been writing this series. I started sketching thoughts on the Lucasfilm acquisition back in February, then started writing this article in April. I’m still finishing it in June. Fortunately, this extended timeline managed to teach me a rare lesson in humility about forecasting.

See, when I started writing, Solo: A Star Wars Story was still three months away. At the time, I needed to estimate for its box office performance to calculate the movie revenue. So I searched for projections. In early March, I found forecasts for Solo’s US opening north of $150-170 million, ending in a total US run of $350-$475 million. At the time, we all assumed Solo would be a sure thing at the box office. So I put that in my model. So I put into the model that Solo would do a bit less then Rogue One, at about $800 million in total worldwide box office.

By May, the estimates had lowered but still projected an opening weekend of $150-170 million. Days before the launch, the estimates had dropped even further, to $130-$150 million. The estimates continued to decay even as the weekend progressed, as this updated article from Deadline shows. Ultimately, Solo ended up with an opening weekend of $101 million domestic on track for about $350-360 million worldwide. (The March estimates had it at $350 for just the domestic market.)

Using the tracking data above, I see a “floor” domestically of about $300 million for Solo.

There’s a very famous quote about what people do and don’t know about movies. I won’t repeat it here, but I will agree that forecasting box office returns (and hence all revenue for a filmed project) is incredibly difficult. Even a movie as closely watched as Solo: A Star Wars Story missed most of the “ranges” offered by box office projections. My initial assumptions about Solo were way too confident which meant my entire analysis was off.

So we need to go into today’s post with a little humility and our eyes open. Last time, I asked a few questions:

Lucasfilm and Disney are doing well, but how well? More precisely, how much cash did they make on these movies? And how well do we think they will do going forward? Could they start losing money?

I answered the first two questions (doing really well; $3.7 billion in unadjusted gross profits or $2.5 billion in 2012 adjusted terms) in the last article. That was the easy stuff; what I called, “What we know” last time since I just had to plug the results into my model with some sleuthing.

Now comes the hard part. We have to estimate how future films will do and then guess about the rest of the potential slate. Which I’ll try to do this week. But first, I need to illustrate how hard this is…

Difficulty in Forecasting Box Office

To show the difficulty, let’s start with the difference between Solo: A Star Wars Story and The Force Awakens. One grossed $2 billion dollars at the worldwide box office. The other did under $400 million. That’s a gap of more than $1.5 billion dollars. That gap also roughly equates to how how large our confidence interval could be when projecting box office for films that aren’t coming out for two years (or longer). To capture that, look at this chart I made (using approximate numbers):

Slide 15a

I put in these numbers as placeholder estimates. What they are are the fictional “90% confidence interval” for The Force Awakens. In other words, at greenlight, you could have guessed that a new Star Wars movie would do $2.4 billion in total box office or $400 million, and you’d be right, 90% of the time.

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Has Hollywood been “Moneyballed”?

No.

That’s a quick article! Just one word and you can continue on to elsewhere in your internet with too many articles to read.

Oh, you’re still here. Well, I do have some more thoughts, so let’s keep going. Since this website is still gaining steam, I haven’t had time to dig into my thoughts on the entertainment press. The “trades” if you will.

I love the trades. They provide tons of great insight to this industry. That said, like much of the media in general, they can get repetitive. Here’s an example (from an upcoming long, long article on this topic) from back in November: Deadline reported that Amazon Studios had closed a deal for a Lord of the Rings TV show. Then every other outlet reported the exact same thing. The article on The Hollywood Reporter as it was on Variety as it was on The Ringer or A.V. Club. It goes on. I hate that.

That’s why I loved the article by Derek Thompson in The Atlantic called, “Why Amazon Just Spent a Fortune to Turn Lord of the Rings into a TV Show”. Thompson took a stand and made an argument, unlike much of the other coverage. What he said was that this LOTR deal showed the limits to Amazon’s previous strategy, and now they were moving into blockbusters. I liked how he describes their shift in strategies.

Then he added this sub-title, “There is no “moneyball” for media. In entertainment, overkill is underrated.”

Why, oh, why did he have to bring “moneyball” into it?

Long time readers—or new readers who’ve gone back to read every article (it’s not that hard)—know that my first and second articles both said I’m here to help Hollywood “make better decisions”. I love talking about decision-making (especially when bolstered by data). Hollywood and the press coverage do it the other way. They obsess about data and algorithm (ignoring the decision-making).

Naturally then, I’m a fan of “moneyball” even though I didn’t use that term in that first article. Unfortunately, like its cousins, analytics and data, “moneyball”–the term derived from Michael Lewis’ excellent book on the subject–is horribly over-used and abused. Most people use “moneyball” to mean “using data” or in Hollywood’s case, using “algorithms”.

Sigh.

So I’m going to define what a “moneyball” approach to any field would look like. Then I’m going to re-analyze Amazon Studios/Video/Prime as Thompson did to determine if they were using a “moneyball” approach to content selection. As we’ll see, it turns out that Amazon has actually embraced data by moving away from smaller, critically-acclaimed projects. Basically, the opposite of Derek Thompson’s hypothesis.

A “Moneyball” Approach to…Anything?

Let’s start by synthesizing the ideas of “moneyball”. I used three books about decision-making: Moneyball, The Undoing Project and The Signal and the Noise. (I’m going to differentiate the book from the philosophy of moneyball by italicizing the title and putting the philosophy in quotes.) You could add other books like Thinking Fast and Slow or Superforecasting, but those three provide the core. And really those other books amplify the lessons in the first three books. (I would also appreciate any recommendations for books on decision-making you think should be included!)

I’ll add, there isn’t an official “moneyball” manifesto, so these are my opinions. What I’m doing is trying to summarize “moneyball” into a process, impacted by my own opinions on decision-making. For instance, the financial theory of maximizing “Net Present Value” is basically undergirding all of those books, but I’m not going to recommend a finance textbook right now or you’ll all run away. (The introduction to The Undoing Project contains probably the best summary of the ideas.)

The Principles of “moneyball”

Make better decisions

The whole point of moneyball is to make better decisions. The implicit assumption of why you have data is to help you make decisions. In baseball this is about which players to sign, trade or draft. In elections, it’s about where and how to spend campaign money. In finance, it’s about which equities or bonds or companies to buy or sell. And when to buy or sell all things. Those are concrete decisions.

Make better decisions towards a goal

Sometimes this is very clear. Billy Beane is trading baseball players to get “wins”. “Wins” in baseball are the currency of success. He makes decisions to increase the odds they get more wins, which translates to success. However, as was noted in most reviews, Billy Beane had an extremely limited budget…

Making better decisions towards a goal within a budget

That’s why so many people who brought the revolution to sports came from finance or investment banking. They were already used to making decisions with a limited amount of capital to invest. If you raise a $100 million dollar venture capital fund from investors, you can’t get more (unless you borrow more, but then you have to pay interest, so just go with it for now). You have $100 million to invest. It also changes how you phrase decisions from “is this a good deal?” to “is this a good deal for X amount of money?” The answer for many deals could be yes to the first, but no to the second. Price or cost is always an issue. This is also why both the Oakland A’s—a cash poor team—and the Red Sox—a very wealthy team—could both pursue “moneyball” strategies and be correct, and have totally different approaches.

Make better decisions by using data

This is the part that most people know about “moneyball” and where most people stop. They assume that all you need is data and, wham, you’re doing moneyball. But I put it fourth because without the decisions towards a goals in a constrained budget, the data doesn’t matter. You can use data a lot, but switch goals regularly, so the data won’t help you. The key difference is that the data is used to make better decisions. Then tested to keep having the most accurate data to make the best decisions. Data is key to making the whole thing run, but the purpose of the data is to make decisions which lead to the desired outcome.

Test conventional wisdom

Of course, once you start using data, you naturally see how many decisions are justified not with data but conventional wisdom. And it’s what really what separates a moneyball approach from a traditional approach. (And the part that generates the most anger.) “Moneyball” doesn’t take anything for granted. Before you make a decision towards a goal within your budget, you ask why. The sterling example of this is Billy Beane hated how his scouts referred to players as “looking good in a uniform”. As soon as he started looking skeptically at his decision-making process, he understood there is no way to explain why looking good in a uniform is correlated with “wins”. And it wasn’t.

Getting rid of uncorrelated data

Really testing conventional wisdom is a subset of this. Basically, conventional wisdom is the largest area of uncorrelated data. But it deserves it’s own section. Once you start using data to make decisions towards a goal within a defined budget, then you throw out all that data that isn’t correlated with success. In The Undoing Project, Daryl Morey, the general manager of the Houston Rockets—read the opening chapter here on Slate—describes how they’ve tested “player interviews” during the draft process, and it isn’t correlated with success. Basically, it usually adds bad data (personal opinion/bias) into the process. And bad data leads to worse decisions.

Exploiting Inefficiencies

I wasn’t going to include, but felt I should. After “using data” or mistaking tactics for strategy, this is the most common summary of “moneyball”. Basically, Billy Beane exploited market inefficiencies and Lewis, in his introduction to The Undoing Project, mentions exploiting inefficiencies multiple times. So I included it. But the reason I put it so low is that inefficiencies aren’t the driver of “moneyball” but the natural result. If you value things correlated with success, and the market doesn’t value them properly, then, yeah you’ll exploit it. Really it’s a matter of what comes first. I’d say that once you’re focused on decision-making with data, you’ll find inefficiencies. But you don’t start trying to find inefficiencies.

Track your predictions

This point and the next one really comes from Nate Silver, but I lump them in together because you see it in the moneyball sports examples like Daryl Morey and Billy Beane. One of the keys in making-decisions is to be super honest with the decisions you make. And the only way to do that is to track your decisions (and predictions) to see how often you were right or wrong. Then you can analyze where your process was wrong or analyze…

Think Probabilistically

…where the probabilities just went against you. This is another huge point from Silver and he just reemphasized it after the 2016 Presidential election. He gave Trump a 30% chance of winning, and Trump won. Silver’s point is that 30% is a big number. 3 out of 10 happens a lot. So even with the best process, you can be wrong a lot of the time in big bets. Silver comes from a poker background where even the best hand well-played can sometimes only have a 70% chance to win. That’s probabilistic decision-making.

So is Hollywood “moneyballed”?

I don’t know.

Well, not most of it. Netflix definitely uses data. And Amazon has said they do too. And Legendary was the subject of an HBR article called “Moneyball for Movie Studios?”. But as I said above, just because you have data doesn’t mean you’re following the example of the Oakland As, Boston Red Sox and Houston Rockets in following “moneyball”.

So let’s try to answer Derek Thompson’s Atlantic question: did Amazon turn away from a “moneyball” strategy?

I don’t know.

Thompson made the classic “moneyball” mistake which is to take a tactic employed by Billy Beane and call that “moneyball”. He equated Billy Beane buying low-cost, high-upside players instead of paying out huge contracts to superstars with Amazon’s approach of going after lower budget award winning shows. But the Boston Red Sox had a “moneyball” approach to baseball (paying huge salaries to stars) and won a World Series. The difference between them and Oakland was they had a lot more money (budget) to make their decisions.

So is Amazon Studios using a moneyball approach? Frankly, I’d have to interview the key decision-makers and review their slide decks. Only by asking the specific questions could see if data is driving the decisions or justifying them post-hoc. Sometimes really in-depth articles provide a lot of clues (take this New Yorker article on STX for example) or you’ll be the subject of an HBR case study (which has tons of interviews to write it), but short of that it’s nearly impossible to tell.

In entertainment, like baseball, making smaller movies or TV shows can be moneyball. Think Blumhouse. Or making big blockbusters could be moneyball. Think Disney or Legendary. I know the numbers behind both those tactics and I could justify them (again towards the success metric within their budgets).

The true lesson of Moneyball and The Undoing Project and The Process in Philadelphia and The Signal and the Noise, is that moneyball is unpleasant. People hate it. People still disparage Billy Beane, question Daryl Morey, make fun of Nate Silver and Sam Hinkie was straight-up fired. When you test their conventional wisdom and it’s wrong, the conventional thinkers get upset. Like really mad. That’s the true moral of the story in all the above scenarios.

Hollywood is a place that avoids unpleasantness. It’s a people-driven industry (it’s who you know after all) and moneyball would hurt too many feelings. Uncorrelated data (like winning awards) or conventional wisdom (hiring established showrunners) or binary analysis (in pilot season most shows are described as “great” or “bombs”) thrives in Hollywood. It’s a creative, people-driven field, which makes it as un-moneyball as you get. Sure the distribution method (streaming video!) is changing, but the decision-making behind it remains mostly the same.

Of course, that leaves the field ripe for true “moneyball” disruption. We’ll see who does it.