Starting a business is a dangerous thing.

A larger competitor might undercut your prices. Someone might sue you for patent infringement. Someone else could sue you because your products don't do what you said it would. Or, well, the market may have no interest in what you're selling. According to the U.S. Bureau of Labor Statistics, about half of all businesses fail within five years.

But Thomas Thurston thinks data science could remove a fair amount of the risk. For the past nine years, he's been honing techniques for evaluating business plans statistically rather than intuitively. He calls it business model simulation, and you can think of it as something akin to Moneyball for investors.

He says his simulations correctly predicted that Snapchat, Uber, and Airbnb would be big—and that they're now right about 66 percent of the time when predicting that a company will still exist within five years. When predicating that a company will fail, he adds, they're right 88 percent of the time.

The simulations have proven so successful, Thurston is now using them to make money for himself. He runs a research firm called Growth Science, which sells his predictions to large companies—and applies them to investments he makes as a partner at the venture found Ironstone Group. In the long run, he believes, these simulations could have a rather profound effect on the business world as a whole—because they can steer people away from bad ideas.

"Most businesses fail, and that's not good for people," he says. "People lose their jobs, the economy suffers."

Thomas Thurston. Growth Science.

He admits the models will never be perfect, but thinks that even a model that's only right about 50 percent of the time could help investors and entrepreneurs avoid particularly bad ideas that, to the untrained eye, look like excellent opportunities. If fewer businesses fail, he reasons, the whole economy would be more stable and everyone would benefit.

Thurston isn't alone in applying Moneyball-style data science to investing. Google Ventures takes a data-driven approach, as do funds like Correlation Ventures and Venture Science. But he isn't just using his calculations to make his own bets in the market. Growth Science also helps big corporations on investments, acquisitions and strategy. 3M, for example, uses it to predict the degree of success of new product and services. The idea is to help these companies make informed decisions and avoid having to do mass layoffs. And eventually Thurston thinks it could help small businesses and startups as well.

Banishing Intuition

Thomas Thurston's Top Three Bets

Arcimoto: An electric car company aiming to offer a two-seat vehicle that can travel 130 miles on a single charge at a much lower price than competitors like Tesla and Lift Motors. "Arcimoto is targeting the lowest cost automobile platform to own and operate in the US, with the simplest possible solution," Thurston says.

Color Genomics: A startup building a system designed to help the masses take advantage of genomics. "Color is using computing and data to do this in a much more simple way at a tiny fraction of the cost that's accessible to anyone," Thurston says.

Indow Windows: Swapping out your drafty old windows for new energy efficient ones could save you a bundle in the long-term, but not everyone wants to spend the time and money to retrofit their entire home or office building. Indow Windows offers inserts that can improve efficiency without the cost or hassle of replacing the windows entirely. "Some other startups have tried this, and some of the big guys are trying to respond, but there's a lot more innovation required to pull this off than most people suspect," Thurston says. "In a very short timeframe Indow has zoomed up to become the market leader."

Thurston came up with the idea to simulate business models 2006 while working for Intel Capital, the investment arm of the venerable chip maker. One day he decided to chart Intel's investment history and see if any patterns emerged.

His approach is based on turning various pieces of qualitative information—such as whether a company is a "first mover" or "fast follower" in a market—into quantitative data that he can plug into a spreadsheet. That requires a degree of human judgement, but this also requires a certain amount of rigor or consistency.

"You can't trust the model until you get all the intuition out of it," Thurston says. "The hard part of that is translating the qualification into yes or no questions," he says. "How do you define the market? How do you define first mover?"

Surprise, Surprise

Using this process, he discovered some surprising things—most notably that a company's team is only about 12 percent predictive of a company's success. "You need to find a good team that won't ruin the company, but hiring 'rock stars' isn't that great," he explains. The market the company is entering is far more important than who's running the company.

His work at Intel ended up landed him a Harvard University fellowship thanks to Clayton Christensen, author of influential book The Innovator's Dilemma. After the fellowship, he started Growth Science to fund the further refinement of the process, and bring it to the rest of the world.

To The Masses

Thurston wants Growth Science to advise entrepreneurs—and help people with good ideas find better business models. And although his work has mostly been used by large companies and investors so far, he says, it's beginning to trickle down to the entrepreneurs themselves.

Last year, for instance, Ironstone Group invested in electric car company Arcimoto, but the company barely made the cut. "We liked them, but they were on the edge," explains Thurston. So he tweaked his simulation, and eventually decided the company should go after emerging markets rather than just the U.S. For Arcimoto founder Mark Frohnmayer, that was a crucial piece of advice.

"We have had a strong interest in emerging markets from the beginning, because this is a global problem that we're trying to solve," Frohnmayer says. "But we've been doubling down on the emerging market story in the past year, making sure that we had an offering that would be competitive not just locally but in the world market."

The Problem

Even businesses that Thurston has ultimately turned down for investment purposes, he says, have ended up benefiting. "People will come back to us months later, and say: 'We thought about what you said, and now we're doing something different.'"

But what Thurston would really like to do is help all businesses, not just the ones Ironstone considers investing in. The problem, however, is that Growth Science charges a few thousand dollars to consult these companies because it still take a lot of time to convert a traditional business plan into something the Growth Science team can run through their algorithms. That's still too much for most early stage companies to spend.

One way to make it affordable would be to automate more of the process, and offer it as a web-based service for a low monthly fee—or maybe even for free. And, in fact, Growth Science has already built a beta service that does just that. But there's a catch.

According to Thurston's own model, Growth Science's own chance of survival following its current business model is about 69 percent. Adding the automated service would actually improve its chances, he says. But that would mean risking cannibalizing the already successful business he's built consulting higher-end clients. In short, he has an innovator's dilemma of his own. And that goes to show that there's always risk in change, no matter how reassuring your data models are.