This is a guest post by venture capitalist Matt Oguz

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.

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 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.

Baseball image via malinx // Shutterstock

---

HealthBeat 2013 is a new conference showcasing how technology is transforming health care. We'll explore how IT is driving out inefficiencies on the hospital, practice, and patient levels. Check out full event details here, and register here.

---

This is a guest post by Jon Soberg, managing director at Blumberg Capital.

Venture capitalists picking up the Moneyball strategy. It’s a fascinating idea, and I know plenty of my peers are working on algorithmic approaches to better their craft and gain an edge. As I see innumerable pitches that leverage “big data” as a key aspect of a business, and read about all of the ways that big data is changing the world, it seems natural that it will infiltrate the world of venture capital. Whether it works or not remains to be seen.

As someone who has worked at both ends of the spectrum — as a CFA performing public equity analysis, and an early stage investor — this is more than just a passing interest for me. I pay close attention to data, and I firmly believe that people who understand the numbers better than their competitors, will ultimately win. In public equities, some groups have clearly found ways to outperform using algorithms over time periods.

Are algorithms the future of VC?

In VC, we know the stats; a large percentage of companies fail, and returns have lagged over the past several years (although some firms have continued to outperform). Is data analytics the answer as some have suggested? Is it key to success in our industry, which is clearly changing? I have to say no, but will qualify my answer.

At the early stage where I invest, I am often assessing a team with a product that is not yet in the market, with no revenue, and with plans to hire some significant team members within the first year. Small data. There are very, let me stress VERY, few data points. And most of the data points that are there are qualitative, and there is plenty of noise. It hasn’t stopped me from building a few models along the way to see if I can glean some insight, but I can tell you what the models yield — garbage in, garbage out.

One might argue that algorithms can help predict markets, and I believe that could be true, but the size of the market opportunity for most companies is defined not by the actual size of the market, but by the team’s ability to address the market. It is SAM, not TAM. Let’s say a model detects that a market is developing, can the model help select the team that will win in that market? Revenue and growth are tightly coupled with a team’s ability to understand the needs of their customers and to fulfill those needs. It comes down to the team. I have yet to see a model that can tell me that a Mike Lazerow, Jason Goldberg, or Joe Lonsdale are better at figuring out their markets than others, but I can tell you that if you meet with any of them in person, you will know.

Early-stage vs. later-stage investing

That said, I do think they can be helpful in some ways, especially in later stage investments. One of my toughest challenges is assessing opportunity cost. If an algorithm can help me decide which of several investments has the best potential, then it can be valuable. But, in my experience, the value is directly linked to the maturity of the company, as small data becomes bigger data. And the breadth of the comparison set is critical to get a good signal; any bad data in these models can really skew results.

One algorithmic approach that I believe has potential is employed by Correlation Ventures. They analyze investments made by other VC’s using their proprietary algorithm, and join some of the rounds. Just the fact that they are leveraging the work done by other VC’s gives them an inherent advantage in selection — they add another layer to existing analysis.

Data won’t help us find the next Zuckerberg

The combination of human and algorithmic analysis is the best way to use data analytics. But data analytics won’t help us score the home run hits that are absolutely essential. The Moneyball approach simply doesn’t work because hit rates are not high enough to manufacture returns by getting on base. The key is swinging hard every time you step up to the plate — a Facebook home run is like hitting the ball OVER McCovey Cove and getting 10 runs for it.

So when I look at how data analytics will impact VC, do I realistically think algorithms will give me a competitive advantage? No. These are not liquid markets, and there are very few good data points. Will it help with due diligence and adding a few data points for my analysis? Maybe. Will it help identify target markets? Potentially. Will it make me a better investor? Doubtful. Will it help me win deals versus our competitors? No. Will it help raise money? To be determined.

To quote one of my mentors, Wharton Faculty member Dave Pottruck, “Go with your gut, but always crunch the numbers first.” But in the absence of all that much valuable data, VC’s need to rely on old school-hustle, homework, and instinct.

Jon Soberg is a Managing Director at Blumberg Capital, where he invests in early stage companies, specializing in FinTech, SaaS, and eCommerce. Prior to joining Blumberg Capital, Jon has been a serial entrepreneur and senior executive in multiple companies including Ditech, Broadband Digital Group and Adforce, which had a highly successful IPO.  

A CFA Charterholder and adjunct faculty in the Wharton Marketing Department, Jon earned a B.S in Engineering from Harvey Mudd College, an M.S. in Engineering from Northwestern University, and an MBA in Entrepreneurial Management and Marketing from the Wharton School, where he is a Palmer Scholar.

Top Image via Shutterstock

---

HealthBeat 2013 is a new conference showcasing how technology is transforming health care. We'll explore how IT is driving out inefficiencies on the hospital, practice, and patient levels. Check out full event details here, and register here.

---