Moneyball: Finding the Elusive Unicorn
Moneyball — a concept coined by Billy Beane and inspiration behind the Brad Pitt movie of the same name — is simple. Leveraging statistical analysis, investors can buy assets that are undervalued and sell assets that are overvalued. Traditional VCs typically investing with a wide, all-encompassing approach, considering some amalgamation of a company’s vision, product market fit, capacity for execution, and team dynamics, and “gut feeling” of course suffer from their own cognitive biases. Which is why Moneyball funding is appealing in its purported bias-free, data-driven model of selection.
Can Computer Data Predict the Next Unicorn?
I recently spoke at the Premoney SF 2016 Conference on a panel titled “VC Moneyball: Can Computer Data Really Predict the Next Unicorn?”. We discussed whether it is possible to simply “look at the facts” and make investment decisions. And while in theory it may be, doing so may not be the best investment option. First when it comes to data, you have two main types: proprietary and public. Public data is easily accessible — it simply must come from a reliable source. The best option for VCs is a VC-targeted source such as Mattermark or other dependable authority. Many VC associates spend precious time duplicating public data research that has already been conducted by companies such as Mattermark. It is much more efficient to simply purchase preexisting data outright.
In contrast to public data, much proprietary data is dependent on trust. For some companies — for example, certain consumer apps like Uber — obtaining proprietary data is harder, because it may not be available early on. The best proprietary data sourcing comes from backchannel sourcing, personal relationships that provide access to proprietary deals, and in-person conversations The best strategy is a vertical approach combining a reliable public data source and in-person proprietary data.
Another issue, too, is a lack of data. If you are investing solely based on a snapshot on data, it is more suitable to invest in a business that’s stable and established, or within a domain like CPG that naturally lends itself to visibility through standard and early metrics tracking. Only for late-stage companies or companies within specific domains will you truly have enough data to look back on and analyze. Information for early-stage companies especially within emerging fields is typically scarce, and privy to a trained investor’s or operator’s eye. Playing moneyball is still taking a risk — often a bigger one than with traditional investing. That’s because moneyball, ultimately, is still a game. It’s trading, selling, and swapping numbers to leverage the most profit.
Investing Money Doesn’t Make You an Investor
But here’s where moneyballing falls short: it cannot account for human nature. Finding the next unicorn requires a contextual understanding of the ecosystem and market landscape surrounding a startup and deep familiarity with the tech stack driving changes within the domain of investment. It’s the result of factoring in dozens or even hundreds of different elements and details into one conclusion. It’s a science, but also an art, and it’s not easy. That’s why early stage investors pour their souls into researching, refining, polishing, and constantly improving their understanding of companies.
That is to say, simply laying down money doesn’t make you an investor. When you play moneyball, you’re an analyst. An investor, in contrast, goes above and beyond to do their research, learn about the companies they invest in and the people behind them, and tap into the potential buried in each. An investor is truly invested in his or her assets. As an investor, you’re trying to do more than just invest capital; you’re trying to help build the company.
Of course, data-driven analysis can be extremely beneficial. Used in conjunction with traditional VC methods, it can help drive smart investments. Our panel discussion at the PreMoney conference reminded me of one thing, though: that inescapable human element. While it often seems like we are swimming in a sea of numbers, it is important to remember the drive behind the growth, and the time-honed experience it takes to train, guide and scale great founders and companies.