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”.
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.
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…
…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.