Category: The Great Irishman Challenge – Theaters vs Streaming?

How The Irishman Lost $280 Million: The Great Irishman Challenge Part IV – The Results

(For the last few weeks, I’ve been debuting a series of articles answering a question posed to me by The Ankler’s Richard Rushfield: Will The Irishman Make Any Money? It’s a great question because it gets as so many of the challenges of the business of streaming video. Read the rest here, here, here and here.)

The biggest uncertainty in The Great Irishman Project was figuring out how well the film did with viewers in the first place. I was all set today to parse Nielsen’s estimates from last week, but doing so meant tons of estimating based on a very limited data set. So I waited.

Specifically, I had a suspicion that Netflix would feel forced to tell us something. They couldn’t let Nielsen drive the narrative for their most high profile picture of the year. Sure enough, we got the results today, as Ted Sarandos spoke at the UBS Media conference:

26.4 million subscribers watched 70% of The Irishman in its first week.
40 million are projected to watch in the first 28 days.

Huzzaw! Now we can be a lot more confident in our estimates.

Here’s today’s plan. First, I’ll given you the “Bottom Line, Up Front”. The results and my model. Second, I’ll discuss a few specific estimates and inputs I still had to make. Third, I’ll answer what I assume will be the most commonly asked questions or criticisms of my model. In Q&A format.

(Also, look for my write-up in The Ankler if you’re subscribed.)

Bottom Line, Up Front: Netflix will lose $280 million The Irishman

As I wrote in Part I, the goal was to make a scorecard, and here it is:

IMAGE 20 - Irishman Profiitability

For the full model, here you go:

Image 21 - Irishman Full Model

In other words, if this were a big budget tentpole from Disney or Warner Bros–whose flops have extremely public numbers–I think Netflix would have to write down the costs for “only” getting 40 million viewers in the first four weeks. This film was extremely expensive, and it’s already decaying fairly rapidly in viewership. Even with a bump from a Best Picture nomination, Netflix will lose money on this investment.

The Model Details

Even having built the model ahead of time, I had to make some assumptions. Here they are.

Determining US versus International Split

One of the big assumptions of my model right now is that international viewership is much less valuable than US viewership. I do this based on their reporter lower international “Average Revenue Per User” and higher churn rate overseas (from what I’ve been told/researched). As a result, the more US customers for a film (for now) the better it is financially for Netflix.

We have two data points to triangulate the split for The Irishman. First, we can look at historical box office trends of mobster films. According to all the films listed as “Mafia” in The-Numbers database, about 61% of box office comes from domestic versus international box office. For example, a film like American Hustle did $150 million in the US/Canada vs $107 million in the rest of the world. Black Mass from 2015 was even more weighted to the US: $62 million vs $36 million rest of world. (The Departed did $132 million US to $157 million rest of world.) This would imply viewership was skewed to the US.

Second, Nielsen provided their estimates that 13.2 million people watched The Irishman during its first five days of release. That’s almost exactly half of the 70% completion Netflix claims. If we assume this would increase with two more days viewership, again we get closer to 55-60% of viewership being in US/domestic.

I decided to use 62.5% US viewership for Netflix. This is pretty beneficial to Netflix, but makes sense. In all, since US viewers pay more on average for longer, this change benefits Netflix.

Changes to Best Picture Bump

My initial model assumed 25% more folks would watch on Netflix if The Irishman is nominated for Best Picture. I decided to bump this up to 25% first window revenue, since that’s a more accurate reflection of the box office bump. (That’s also a benefit to Netflix’s bottom line.)

Adding in Box Office?

I wanted to add in box office revenue, but Netflix hasn’t released any since this film wasn’t released in the traditional theatrical system. (Netflix rented out theaters and then collected the revenue themselves.) Given the limited number of theaters, I think leaving this out won’t drastically impact the bottom line. 

Frequently Asked Questions

I imagine a lot of folks have a lot of questions about this analysis. Let’s try to answer what I imagine are the most common.

What is the best case for Netflix?

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The Great Irishman Challenge – Appendix: How Confident Am I?

(For the last few weeks, I’ve been debuting a series of articles answering a question posed to me by The Ankler’s Richard Rushfield: Will The Irishman Make Any Money? It’s a great question because it gets as so many of the challenges of the business of streaming video. Read the rest here, here, here and here.)

On Twitter, TV journalist and friend of the website Rick Ellis of All Your Screens pointed out that my model is likely not to be accurate:

You know what, he’s right!

I don’t have leaked Netflix internal accounting and data. This means that I have to make a lot of assumptions to build my models. The more assumptions in an estimate, the more sources for error. While estimates are naturally more accurate than forecasts, they’re still estimates. Meaning they are not an exact science.

Before I publish my results for The Irishman—my next article for those waiting—it’s worth probing what type of analysis this Great Irishman Project” is. As I’ve been thinking about it, I have three analogies to explain what I’m trying to do here, ranging from a weak to strong.

Analogy 1: The Scientific Method

This is the gold standard. Arguably, the very principle that has driven human progress for the last 500 years.

And it doesn’t apply here.

I know, I know. It would be great if it did. But to apply the scientific method means starting with a hypothesis, then gathering data to prove or disprove it. That’s not my approach at all. I’m building models based on my experience and data. Which are “scientific”, but I can’t “test” them on Netflix’s releases.

The streamers specifically have reams and reams of data. They can set theories and test whether they are correct on marketing, user experience and sometimes content. (However, they often overhype their data and stretch the limits of statistical accuracy.) Unfortunately, for yours truly, Netflix and Disney and other companies will never tell me if I am right or even super close with my models, so I can’t ever test my hypotheses.

Since we’re talking scientific method, it’s also worth pointing out that lots of data analysis at technology, consulting and really all companies is “faux scientific method” too. At their best, companies can set out hypotheses, and run tests to see what works best for their customers. At their worst, companies just collect a ton of data and use the data that supports their preexisting beliefs. That’s not the scientific method. (I can’t count the number of times I’ve seen a boss parsing a quad chart for connections that are noise, not signal.)

Analogy 2: Sports

One of my inspirations for this website was the boom in top notch sports writing. Specifically, the top tier analysts. Folks like Zach Lowe or Kevin Arnovitz on basketball, Bill Barnwell in football, Pete Zayas for the Lakers, or Chris Harris on fantasy football. These folks don’t just report the news, but analyze the what and how of sports. They provide deeper insights than just the box score, whether they use scouting, film breakdown or analytics.

I’ve tried to fill the same need in entertainment business. This industry is filled with a phenomenal amount of great reporters, but not the rigorous analysis I needed when I worked in entertainment. (There’s also a lot of great investor-side analysts, but they usually charge an arm and a leg for their work. Their focus is also less on strategy and more on stock price. Which is great for their clients, but not quite what entertainment professionals need.)

There are two huge differences between sports analysis and entertainment strategy analysis, though. First, I don’t have nearly as much data. For sports, even casual observers can now get advanced analytics, from Basketball Reference to NBA.com to Cleaning the Glass. Second, we don’t get the “results” beyond quarterly earnings reports, and even those can be woefully incomplete for streaming. Imagine how much tougher Zach Lowe’s life would be if he didn’t know the scores to games!

This analogy gets a lot closer to what I’m trying to do. Instead of breaking down how a play is run, I’m breaking down how film finances work. Then, I’m applying the data we do have to that model. Then I’m pulling insights for what that can tell us about entertainment strategy. If I had more data, I could pull even more insights.

To find the perfect analogy, we need a field where making predictions is key, but data is sparse. Well…

Analogy 3: Military Intelligence

In war you have to make predictions about what your enemy is thinking, planning and eventually doing. But the amount of information is usually extremely sparse. Even if you have tons of information, you are terrified that it’s all planted by the enemy in misdirection campaigns. Inevitably, an intelligence officer has to make assumption after assumption to project an enemy’s course of action.

As I laid out in my recent series, I used to be an intelligence office in the Army, making these types of predictions. I’m used to uncertainty when forecasting enemy actions.

That’s not a bad analogy either for companies in competitive fields. While every company professes to not care what any other company is doing, they’re lying. (All of Hollywood is obsessed with Netflix, even if they say they aren’t. And yes, Netflix is obsessed with Disney+ right now, even if they deny it.) Understanding whether your competitor is making hits or duds, burning cash or wallowing in it, and how they’re doing it, is a key piece of information for making decisions.

Most companies have blind spots into every other company the same way my models have blindspots into Netflix or Disney’s specific performance.

Just because our models have inaccuracies doesn’t mean we shouldn’t make them. And doesn’t mean they aren’t useful. If you waited in war until you have perfect information, you’d be plagued by indecision. That’s really what I’m doing here: I’m providing my competitive analysis of streaming video, starting with Netflix, at the same level of accuracy I’d try if I were doing this for insurgents in Afghanistan or working in a strategic planning group in a streaming company. Even though I could be wrong, I don’t have a choice because we need these assumptions to improve our estimates.

And yes, as a bonus, these models will eventually inform my larger series on an “Intelligence Preparation of the Battlefield”. Essentially, the models will tell us a lot about how can and can’t make money in streaming video, and how that can impact all of Hollywood. But that’s for a future article.

The Great Irishman Challenge – The Specific Assumptions for The Irishman Part III

(For the last few weeks, I’ve been debuting a series of articles answering a question posed to me by The Ankler’s Richard Rushfield: Will The Irishman Make Any Money? It’s a great question because it gets as so many of the challenges of the business of streaming video. Read the rest here, here, here and here.)

Tomorrow, we start to get data for the Great Irishman Challenge. Well, we don’t, but it will hit screens everywhere as Netflix releases it and presumably places it front and center on everyone’s Netflix homepage. One of our goals with this project is to set our criteria ahead of time, this way we aren’t back-fitting the results to our preconceived notions about Netflix. 

Now that we have our models for valuing film explained and re-explained, it’s time to fill in the specifics. Today, I’m going to lay out what we know about The Irishman before it launches. I’ll update some assumptions on the model and revenue streams from some feedback. Then I’ll describe two key inputs for the streaming model—Customer Lifetime Value and Attribution of Subscribers. Plus, I’ll touch on how I plan to triangulate popularity after The Irishman launches, which may evolve as we get more data or potential partners. Finally, I’ll talk about the benefits for this model and how I plan to draw insights from it in December.

Assumption 1: Production Budget

Discussing with Richard, we think this is high. Super high. A pretty good summary of this is Jeff Sneider’s take on his Collider podcast a few months back (episode 11 specifically at minute 54):

“I’ve seen [The Irishman budget] figures from $125, $140, $150, $160, $175, $200 million for The Irishman. If you go on Deadline you can read an article that says the budget is $140 and then two hours later another writer is under a completely different impression and says it’s $200 million. No one is on the same page on the budget for this film. And let me tell you what that means. It means the budget is way f***ing higher than any of you are imagining.”

Gosh, that type of cynicism about PR efforts exactly matches my own. If you hear tons of different numbers that can’t seem to decide how much something cost, well the likeliest option is that it was WAY WAY WAY more. At a minimum, this is a $200 million dollar film. And I’m going to do some scenario modeling up to even $300 million. Which means I’ll split the difference and call it a $250 million dollar movie.

Is this ridiculous? Not so much when you think about it. Consider, what does it cost to get Martin Scorsese, Robert De Niro, Al Pacino and Joe Pesci (out of retirement) on a film set? Especially if you’re buying out all the backend, which Netflix had to do since there aren’t any second window revenue opportunities here. (Which is cool too because it simplifies my model.) If I told you between those four it cost $100 million in talent costs, would you blink an eye? Is $150 million too high? I’m assuming $125 million in talent costs.

Then we can add in the extra production costs. This was a very long shoot. (I saw 300 days somewhere.) And then it was as VFX-intensive as some Marvel movies due to the de-aging process, which also required extra work because initial versions didn’t work. (Sneider lays out this situation with great details in his podcast.) This film required a VFX push to get finished in time for launch at the New York film festival, meaning it ran up tons of overtime. Does that sound like $125 million in costs? Absolutely. If not more.

Assumption 2: Marketing Budget

The Irishman will have two marketing budgets. First, the initial roll out. I looked for estimates online and didn’t find a ton. That said, I’ve seen billboards, online ads and even commercial spots. Which screams definitely something, but less than a franchise tentpole roll out. I’d say it’s probably between $50-$100 million, and since I went high with the production budget, I’ll go low here.

(Also, to echo Richard Rushfield’s “see something; say something” if you know a better number for the marketing budget, shoot me a line.)

Then we have the Oscar budget, which is a little bit harder to disentangle. Already, Netflix has started their awards campaigning, but has specifically tied many of their films together, from A Marriage Story to The Irishman. We know from Richard’s reporting that Netflix likely spent over $50 million on Roma’s Oscar campaign last year. They look likely to beat that again. The question is, will they spend $50 million just on The Irishman, or split it with A Marriage Story? Both are getting rave reviews, and I think Netflix is desperate for a Best Picture win. I’m going to end up calling it about $40 million for The Irishman alone.

Assumption 3: Profit Sharing and other revenue streams

We have a few categories here, so let’s run through them.

Library Value? Yep. 

I added library value, assuming the retention model is the equivalent of the theatrical window. Meaning it sets the “price” of the film. Then, we can our theatrical financial model to value library windows. Meaning, the “value” to Netflix for the film after the initial release. To provide an example, Bird Box got most its viewership in the first month, but folks will keep watching it out on Netflix for years. That has a value, which is the “library” value. Using my theatrical model, I’m assuming library value of 25% of first window value. (Specifically, the 10% “digital” revenue for theatrical films is about 25% of the the free, cable, syndicated TV, pay TV and digital second window buckets.)

Box Office Bump? Yep.

If a Netflix film wins a Best Picture, or even gets nominated, that will result in boost in viewership. I’ve seen that for past film and TV series for major awards series. For most Netflix films, this isn’t worth a line in the model, but for this one it is. In this case, I’ll use a 25% threshold of the initial window for the Best Picture bump. This will have a “halo” effect on the library window as well.

Second Windows? None.

Since Netflix films are exclusive to the streamer, every other potential window from home entertainment to licensing to cable channels is a zero in my model. This simplifies our model.

Merchandise? None.

Mobster films don’t really sell a lot of merchandise. Especially brand new films without fan bases or cultural cachet. 

Distribution Fees? None.

Since there aren’t second window or merchandise revenue to shield from profit participation, I don’t need to model any Netflix distribution or marketing fees.

Talent participation? Some.

Initially, I didn’t have any profit sharing, but then I got a note that Netflix for super-duper-huge stars did put a bonus system in place for feature films. Basically, if you hit a certain viewership level, you get a 20% bonus in your paycheck. So I’ve updated the model with that assumption in place, assuming that 80 million views (or “one Bird Box”) is the threshold.

Assumption 4: Calculate CLV

It’s crucial have to a good estimate for customer lifetime value. These are calculated fairly often by other people (see estimates here, here, or here). My difference is I don’t factor in content costs because I’m trying to value the content itself. If I did factor them in, I’d be double counting content costs, and that’s a huge “no-no” in accounting.

So here are my inputs for CLV. First, blended average price per month comes from Netflix’s 10Ks. Customer retention estimates come from various sources, including Second Measure. Crucially, though, I have a much lower rate for international because I’ve heard the churn machine is very high overseas. Finally, I use other estimates of Netflix’s marketing spend for customer acquisition costs. All this leads us to:

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The Great Irishman Challenge – How to Calculate the Straight-to-Streaming Film Profitability? Part II

(For the last few weeks, I’ve been debuting a series of articles answering a question posed to me by The Ankler’s Richard Rushfield: Will The Irishman Make Any Money? It’s a great question because it gets as so many of the challenges of the business of streaming video. Read the rest here, here, here and here.)

On Monday, I explained the grand plan of Richard Rushfield and I plan to estimate the value of future Netflix films, starting with The Irishman, out earlier this month in limited theatrical release, but coming to the world’s biggest streamer next Wednesday. For traditionally released theatrical films with normal second windows, we have a robust model we can employ. 

What about streaming only? Well, that’s where it gets tricky.

A lot of folks do some back of the envelope math for this, and this can be a useful way to look at the problem of valuing streaming. Take Richard’s approach from a few weeks, back looking at Disney films that had been in theaters after 3 weeks (when Netflix pulls them from streaming). Of all the films, Disney earned roughly $310 million after 3 weeks of theatrical distribution. That’s the equivalent, Richard noted, of 4.3 million customers subscribing for 12 months on Disney+. If that number seems big, it should be, which shows the value of theatrical releases for studios.

Could we just take that approach and just apply it to The Irishman? Unfortunately, it has some flaws, mainly double counting subscribers. We need a different method to employ in “The Great Irishman Challenge”. Unlike traditionally released films, in steaming there are a few ways to value a given title’s performance, and each method has its own pros and cons, ranging from crippling to merely difficult to over come. 

Which I’ll (re)explain today, along with describing which ones are fine, which ones are incorrect, and which ones I prefer. At the end, I’ll explain which one we’re using.

Four Ways to Value Streaming Video and One Way NOT To

I’ve previously valued streaming video in two articles. First, back in January, I looked at Disney’s decision to keep theatrical windows for Star Wars films. Second, back in May, I explained streaming video models in order to put a value to HBO’s Game of Thrones. Today’s article will explain all the models from those two articles and add a new method I figured out how to calculate last month. (I had employed this method at a previous employer, but needed a key piece of data, as you’ll see.)

For each of these methods, I’m going to assume that Netflix had a feature film that was seen by 40 million subscribers in the first 28 days, divided evenly between the US and international. The film cost $115 to make and Netflix spent $50 million to market it. As for box office? Let’s say it had $120 million in the US and $80 million globally.

Sub-Optimal Method #1: Multiply Customers by Month by Price

This is the most common method of “back of the envelope” valuations I’ve seen for Netflix films. Usually, you hear folks do a version of this on podcasts, and I’ve seen it for Lord of The Rings on Amazon a few different times. Also, you could do “customer years” the way Richard did above. Here’s how the model for this approach would look:

IMAGE 7 By Viewers per Month

The problems? First, this is one-quarter of Netflix’s subscriber base attributed to a film in one month, which would probably be one-third of their active users. In other words, if Netflix had two other properties getting similar ratings, then every other film released that month would “financially” be a net loser. Second, this approach doesn’t account for “customer lifetime value”, which is really the better approach to valuing customers, versus the one month or 12 month view. Third, this approach doesn’t distinguish between films and TV series total hours of viewing (because it is just subscribers) so it’s tilted towards films, which are shorter and easier to finish.

Still, you can use this to ballpark how long a film would need to make its money back. It’s just sub-optimal because of double counting.

Sub-Optimal Method #2: Attribute Customers by Usage

One of the interesting ways to look at content is to think about what percentage of viewership a title makes up of all the viewership on your platform. If 10% of all hours watched are your platform are Friends, that has to mean something. The challenge is knowing how much people actually watch on Netflix. Netflix has helpfully told us twice (twice!) that they stream about 100 million hours daily in the United States. That means I can calculate potential usage of a TV series or film! That would look like this:

IMAGE 8 by Usage

The problems? First, getting the usage data is really tough. For films, we’d have a pretty easy time, but for TV series, we really don’t know how many people watch how many episodes. And getting usage numbers for Amazon or Hulu may be nearly impossible. Second, it also doesn’t factor in “customer lifetime value”. Third, it over-weights TV library content because there is just a lot more to watch, hence it’s “usage” is much higher, if you can get that viewership data. 

Still, you can use this to compare the usage of various shows and movies. It’s just sub-optimal because it’s tilted to shows with much longer runs.

The Bad Method: Multiplying Subscribers by Customer Lifetime Value

I’ve seen this mentioned in places, though running them down is tough on Twitter. So this may be a strawman, but it’s worth pointing out in case it hits anyone to do. Twice, I’ve criticized valuation methods because they don’t use customer lifetime value. You may be tempted, then to just take the number of subscribers and multiply by CLV instead. Like this:

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The Great Irishman Challenge – How to Calculate Feature Film Profitability? Part I

(For the last few weeks, I’ve been debuting a series of articles answering a question posed to me by The Ankler’s Richard Rushfield: Will The Irishman Make Any Money? It’s a great question because it gets as so many of the challenges of the business of streaming video. Read the rest here, here, here and here.)

Chatting with the esteemed Richard Rushfield a few months back—we share sensibilities on Hollywood and the (hashtag) streaming wars—he pitched me a straight forward question. Could we build a model that can answer this deceptively simple challenge:

Did The Irishman make money for Netflix?

It’s a good question because the buzz for The Irishman from critics has been so positive. From what I can tell—based on “film Twitter” reactions—this would be the greatest film ever made by man, except that with this masterpiece Martin Scorsese has elevated from mere mortal to filmmaking demigod.

It would be cool to know if Netflix made any money off it.

Which is pretty tough. I mean, we don’t even know the ratings for Netflix films…how can we determine if they are profitable? It will be hard, but to quote a famous president, we write these articles not because they are easy, but because they are hard.

So you know what? Richard and I are taking the law into our own hands. Yeah, we paint houses, with financial models and data hacks! 

Later this week, in The Ankler newslettersubscribe here for the must read newsletter—Richard will explain our purpose, reasoning and goals to start this project early. Today, I’m going start explaining how we’ll develop a “Feature Film Profitability Score”. In previous articles, I’ve pretty much built the models needed for this analysis. Now, I’m just combining them with a little special sauce. 

Moreover, we’re doing all this ahead of time. We’re not judging The Irishman based on preconceived notions, but based on its actual performance. Moreover, once we build this capability, we can leverage it for future releases on many streaming platforms.

Here’s what today’s article will explain:

– The specific profitability score we’re creating.
– The four models of film release in the streaming era.
– A quick review of the traditional film model.
– Some notes on competing theatrical film models.

The “bottom line up front” is that combining my methods for valuing theatrically-released films and streaming video, we can make a model of success depending on either box office results or streaming popularity. While the last seems unknown, using some publicly available data—mainly Google Trends, potentially other third party survey data, or even Netflix datecdotes—we can make guesses on popularity.

The Goal: A “Feature Film Profitability Score”

At the end of the day, the goal is to keep this project simple. So Richard asked if I could boil this down to one (1!) number for every film—streaming or theatrical—that determines, “How profitable was this?”

Well, I failed, but I have this down to 2 numbers. Let me explain why. The obvious start is that a film can make a lot of money. This is good. Making nearly $2 billion dollars on Avengers: Endgame, Avatar, or Titanic matters. That’s a lot of money. 

But you don’t just want raw totals. If it costs $1 billion to make $1.5 billion, that’s not as good of a value for investors as making a film for $200 million that makes $700 million. Same raw total, but one required less up front capital. This is a quick definition of ROI, by the way. The Joker is currently the ROI golden child of the trades. The all-time ROI club is films such as Blair Witch, Paranormal Activity, or Saw that still fill the dreams of indie horror producers everywhere. 

If you wanted a quad chart of success, you could see this:

IMAGE 1 Profitability Quad ChartEssentially, films in the upper right are living the dream. Films in the lower right made a lot of money, but not a great return on investment. Films in the upper left made some money (they aren’t all negative), but had great ROI, meaning they were likely cheap but just not as big as some other films. And don’t be in the lower left—though most films are—which means you aren’t making money period. The majority of films in the current climate end up there. Combining these two numbers—with other metrics I’ll explain—brings us to this scorecard we’ll give The Irishman:

Ankler Image - Feature Film Profitability ScoreBeside the two promised numbers, I have four “breakeven numbers” for streaming films in particular. That’s because “breakeven” is easy for feature films (make more money than you cost), but with streaming the challenge is “what is making money”. I’ll explain those in the last section, but before we get there, we have to explain why I needed to build a new model in the first place.

The Four Models of Film Distribution in the Streaming Era

It’s no surprise that film distribution is changing. And commonly, we say, “Hey Netflix is skipping theaters.” That’s decision number one: to go to theaters or not; Netflix opts not; Amazon (formerly) and traditional studios opt in. Financial modeling wise, that’s an easy decision to calculate.

The tougher part to keep track of—and it is neglected in the media coverage—is the second window and beyond distribution plan. (I’m calling everything from home entertainment to Pay Per View to TVOD/EST to linear licensing to streaming licensing “second windows” for simplicity.) See, a new streamer like Apple is going to put its movies in theaters, but then—from what I understand—release it to Apple TV+ directly, exclusively and forever. Amazon too from now on. In other words, all these windows get condensed into this one:

IMAGE 3 - Traditional Second Window vs StreamingThe cool thing is that all the companies I think of make these two choices, meaning we have only need four models for films:

IMAGE 4 - Future of feature Film dist(Two quad charts in one article? Probably my favorite article of the year. Well, after this one.)

The one variable is Apple TV+. I believe they are doing streaming only, but haven’t confirmed yet. With that understanding, let’s build our models. I’ll need a model for theatrical and streaming only to evaluate the Irishman.

My “Traditional” Theatrical Model 

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