The Moneyball strategy is the future for venture capital firms?

QDOTS imagesCAKXSY1K 8*** Note To Readers: I know, I know … some of you will read this with a seasoned sense of skeptisim … but more often than not, with enough data points and with “financial analysis tools” the approach to picking ‘winners and losers’ when it comes to new business ventures … becomes less and less dependent upon ‘ye old spin the bottle’ approach! Enjoy … “Live Long and Prosper!”  BWH

January 10, 2013 4:44 PM
Matt  Oguz


“Money Ball”

Traditional venture capital invests with a “gut” feeling approach, and as  VentureBeat’s Christina Farr  recently  put it, “Relying on gut feeling simply isn’t good enough anymore”.

Investors suffer from a number of cognitive biases. The biggest, most  powerful and most dangerous bias in Silicon Valley today is called the “herd  mentality” or “bandwagon effect.” It’s difficult to oppose the general  consensus.

As investors, this and other cognitive biases skew our decision-making  process every day. How do you get around it? We believe that the answer lies in  mathematics. If we can work with models that are built to protect us from human  biases, guide us through the turbulent waters of high-risk investing and  incorporate factors of safety, that would be a better option than swinging for  the fences to make up for losses.

Most people in traditional VC, including  Blumberg Ventures’ Jon Soberg would make the claim that there’s very little  data to work with. In a recent post in VentureBeat, Soberg comes up with a  heuristic statistic to prove that “most investments fail.”

I would argue that historical data is available. When you actually take a  deeper look at the numbers, you’ll find some definitive patterns. The returns  actually resemble a log-normal or log-levy distribution, not a normal  distribution. We actually have a strong grasp of what the return data looks  like, and do not need to accept the widely-held belief that most startups  fail.

Screen shot 2013-01-10 at 4.03.53 PM

There are a lot of data points available, but you need to know where to look.  At first look, it may seem like late-stage investments are safer bets than  early-stage investments. But looking back over the previous decade, we  discovered in our research that the risk of failure is about the same! A 49  percent failure rate in early rounds yielded a 2.8x money multiple versus a 45  percent failure rate and 1.3x multiple in later-stage rounds.

Traditional investors claim that the key to success is finding the next  Zuckerberg. In my view, this is the very reason why traditional VC firms fail to  deliver results. It shouldn’t be about discovering the next Facebook. It’s about  positioning yourself to find it. You don’t do that by swinging hard every single  time. Look at any legendary investor, Warren Buffett for instance, and you’ll  see that they don’t swing at every ball, but rather follow mathematical  investment models that incorporate appropriate factors of safety.

The claim that VC’s need to rely on old school-hustle, homework, and instinct  is also simply wrong because the VC’s that follow this mindset couldn’t deliver  sufficient returns, and are struggling to raise their next funds.

Let’s get into a little more detail. There are three key activities in  venture investing: Deciding which startup(s) to invest in, how much to invest,  and how to construct the portfolio.

Using decision models, and some of these models have been widely-used over  the last 20 years in a number of areas such as medicine and engineering, we  can:

  • Establish a bias-free, data driven selection model.
  • Optimize investment sizes per company.
  • Optimize investment portfolio of companies.

Without revealing too much about our research, I can say that we use  proprietary variations of models already used elsewhere, such as Multiple  Criteria Decision Analysis (MCDA), Kelly Criterion, and the Markowitz portfolio  theory. These theories must be modified for the characteristics of the venture  capital business, and we’ve attempted to do that, and filed patents on them  while we were at it. Our MCDA matrix has elements similar to those used in the “Startup Genome” project.

The “Moneyball” approach to venture capital forces us to work harder and  smarter to overcome the cognitive limitations instead of “the best gut-feeling  pickers.” Traditional VC takes way too much credit for successes, and doesn’t  accept its failures.

We should look at successes and failures as data points to improve our math  to get to our goal: to deliver superior returns to our limited partners.

Matt-OguzMatt  Oguz is a founding partner of Palo Alto Venture Science, a firm that brings a  data-driven approach to VC. He has been an angel investor in early-stage  startups since 2005, and specializes in e-commerce, analytics, behavioral  economics and decision sciences.

Prior to this, he built big data solutions for a number of Fortune  500 companies such as Dow Corning, Coca Cola and General Electric.


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