The Babe Ruth Effect in Venture Capital

“How to hit home runs: I swing as hard as I can, and I try to swing right through the ball… The harder you grip the bat, the more you can swing it through the ball, and the farther the ball will go. I swing big, with everything I’ve got. I hit big or I miss big.”  – Babe Ruth

One of the hardest concepts to internalize for those new to VC is what is known as the “Babe Ruth effect”:

Building a portfolio that can deliver superior performance requires that you evaluate each investment using expected value analysis. What is striking is that the leading thinkers across varied fields — including horse betting, casino gambling, and investing — all emphasize the same point. We call it the Babe Ruth effect: even though Ruth struck out a lot, he was one of baseball’s greatest hitters. — ”The Babe Ruth Effect: Frequency vs Magnitude” [pdf]

The Babe Ruth effect occurs in many categories of investing, but is especially pronounced in VC. As Peter Thiel observes:

Actual [venture capital] returns are incredibly skewed. The more a VC understands this skew pattern, the better the VC. Bad VCs tend to think the dashed line is flat, i.e. that all companies are created equal, and some just fail, spin wheels, or grow. In reality you get a power law distribution.

The Babe Ruth effect is hard to internalize because people are generally predisposed to avoid losses. Behavioral economists have famously demonstrated that people feel a lot worse about losses of a given size than they feel good about gains of the same size. Losing money feels bad, even if it is part of an investment strategy that succeeds in aggregate.

People usually cite anecdotal cases when discussing this topic, because it’s difficult to get access to comprehensive VC performance data. Horsley Bridge, a highly respected investor (Limited Partner) in many VC funds, was kind enough to share with me aggregated, anonymous historical data on the distribution of investment returns across the hundreds of VC funds they’ve invested in since 1985.

As expected, the returns are highly concentrated: about ~6% of investments representing 4.5% of dollars invested generated ~60% of the total returns. Let’s dig into the data a little more to see what separates good VC funds from bad VC funds.

Home runsAs expected, successful funds have more “home run” investments (defined as investments that return >10x):

Screenshot 2015-06-06 11.55.45

(For all the charts shown, the X-axis is the performance of the VC funds: great VC funds are on the right and bad funds are on the left.)

Great funds not only have more home runs, they have home runs of greater magnitude. Here’s a chart that looks at the average performance of the “home run” (>10x) investments:

Screenshot 2015-06-06 11.55.55

The home runs for good funds are around 20x, but the home runs for great funds are almost 70x. As Bill Gurley says: “Venture capital is not even a home run business. It’s a grand slam business.”

Strikeouts: The Y-axis on the this chart is the percentage of investments that lose money:Screen Shot 2015-05-25 at 9.48.04 PMThis is the same chart with the Y-axis weighted by dollars invested per investment:

Screen Shot 2015-05-25 at 9.45.05 PM

As expected, lots of investments lose money. Venture capital is a risky business.

Notice that the curves are U-shaped. It isn’t surprising that the bad funds lose money a lot, or that the good funds lose money less often than the bad funds. What is interesting and perhaps surprising is that the great funds lose money more often than good funds do. The best VCs funds truly do exemplify the Babe Ruth effect: they swing hard, and either hit big or miss big. You can’t have grand slams without a lot of strikeouts.

Exponential curves feel gradual and then sudden

“How did you go bankrupt?”
“Two ways. Gradually, then suddenly.”
― Ernest Hemingway, The Sun Also Rises

The core growth process in the technology business is a mutually reinforcing, multi-step, positive feedback loop between platforms and applications.  This leads to exponential growth curves (Peter Thiel calls them power law curves), which in idealized form look like:

Screen Shot 2015-05-12 at 5.46.11 PM

The most prominent recent example of this was the positive feedback loop between smartphones (iOS and Android phones) and smartphone apps (FB, WhatsApp, etc):

Screen Shot 2015-05-12 at 6.05.30 PM

After the fact, exponential curves look relatively smooth. When you are in the midst of them, however, they feel like they are divided into two stages: gradual and sudden.

Screen Shot 2015-05-12 at 5.48.37 PM

Singularity University calls this the “deception of linear vs exponential growth”:


Today, smartphone growth seems obviously exponential. But just a few years ago many people thought smartphones were growing linearly. Even Mark Zuckerberg underestimated the importance of mobile in the “feels gradual” phase. In 2011 or so, he realized what we were experiencing was actually an exponential curve, and consequently dramatically increased Facebook’s investment in mobile:

Screen Shot 2015-05-12 at 6.19.33 PM

Exponential growth curves in the “feels gradual” phase are deceptive. There are many things happening today in technology that feel gradual and disappointing but will soon feel sudden and amazing.

Improbable: enabling the development of large-scale simulated worlds

Over the past decade, computing resources that were previously available only to large organizations became available to almost anyone. Using cloud-scale development platforms like Amazon Web Services, developers can write software that runs on hundreds or even thousands of servers, and do so relatively cheaply.

But it is still difficult to write software that makes efficient use of this abundant computing. For some projects, like creating websites, there are well-known software architectures that work reasonably well. In other areas, there’s been progress building generalized tools (for example, Hadoop in data processing). For the most part, however, developers need to solve the parallelization problem over and over again for each application they develop. New tools that help them do this are sorely needed.

Today, I am excited to announce that a16z is investing $20M in Improbable, a London-based company that was founded by a group of computer scientists from the University of Cambridge. Improbable’s technology solves the parallelization problem for an important class of problems: anything that can be defined as a set of entities that interact in space. This basically means any problem where you want to build a simulated world. Developers who use Improbable can write code as if it will run on only one machine (using whatever simulation software they prefer, including popular gaming/physics engines like Unity and Unreal), without having to think about parallelization. Improbable automatically distributes their code across hundreds or even thousands of machines, which then work together to create a seamlessly integrated, simulated world.

The Improbable team had to solve multiple hard problems to make this work. Think of their tech as a “spatial operating system”: for every object in the world — a person, a car, a microbe —the system assigns “ownership” of different parts of that entity to various worker programs. As entities move around (according to whatever controls them  — code, humans, real-world sensors) they interact with other entities. Often these interactions happen across machines, so Improbable needs to handle inter-machine messaging. Sometimes entities need to be reassigned to new hardware to load balance. When hardware fails or network conditions degrade, Improbable automatically reassigns the workload and adjusts the network flow. Getting the system to work at scale under real-world conditions is a very hard problem that took the Improbable team years of R&D.

One initial application for the Improbable technology is in gaming. Game developers have been trying to build virtual worlds for decades, but until now those worlds have been relatively small, usually running on only a handful of servers and relying on hacks to create the illusion of scale. With Improbable, developers can now create games with millions of persistent, complex, interacting entities. In addition, they can spend their time inventing game features instead of building back-end systems.

Beyond gaming, Improbable is useful in any field that models complex systems — biology, economics, defense, urban planning, transportation, disease prevention, etc. Think of simulations as the flip side to “big data.” Data science is useful when you already have large data sets. Simulations are useful when you know how parts of the system work and want to generate data about the system as a whole. Simulations are especially well suited for asking hypothetical questions: what would happen to the world if we changed X and Y? How could we change X and Y to get the outcome we want?

Improbable was started three years ago at Cambridge by Herman Narula and Rob Whitehead. They have since built an outstanding team of engineers and computer scientists from companies like Google and top UK computer science programs. They’ve done all of this on a small seed financing, supplemented by customer revenue and research grants. We are thrilled to partner with Improbable on their mission to develop and popularize simulated worlds.

“It all blossomed out of this tiny little seed”

Steve Jobs in 1985:

I felt it the first time when I visited a school. It was third and fourth graders, and they had a whole classroom full of Apple II’s. I spent a few hours there, and I saw these third and fourth graders growing up completely different than I grew up because of this machine.

What hit me about it was that here was this machine that very few people designed — about four in the case of the Apple II — who gave it to some other people who didn’t know how to design it but knew how to make it, to manufacture it. They could make a whole bunch of them. And then they give it some people that didn’t know how to design it or manufacture it, but they knew how to distribute it. And then they gave it to some people that didn’t knew how to design or manufacture or distribute it, but knew how to write software for it.

Gradually this sort of inverse pyramid grew. It finally got into the hands of a lot of people — and it all blossomed out of this tiny little seed.

It seemed like an incredible amount of leverage. It all started with just an idea. Here was this idea, taken through all of these stages, resulting in a classroom full of kids growing up with some insights and fundamentally different experiences which, I thought, might be very beneficial to their lives. Because of this germ of an idea a few years ago.

That’s an incredible feeling to know that you had something to do with it, and to know it can be done, to know that you can plant something in the world and it will grow, and change the world, ever so slightly.

– Steve Jobs brainstorms with NeXT team 1985 (starting at minute 18:24)