Applying Moneyball and AI to Venture Capital

Connetic Ventures
Oct 11 · 5 min read

How Connetic Ventures is using data and technology to increase odds of increasing returns

Earlier this year, we launched Wendal. Wendal is our AI platform that automates the due diligence process for early-stage companies. You can talk to him here:

Wendal is open to anyone with an internet connection, takes 10-minutes, and allows us to screen 92% of companies that apply… Meaning we focus on 8% of companies that are a good fit for our model, and we are much more likely to write a check too. Wendal is essentially 4–5 MBA educated analysts, except Wendal is self-learning and can recall over a billion data points on command.

Wendal was created out of the need to see more companies due to a lack of sufficient deal flow in our surrounding area. It has evolved over time to include alternative data, proprietary behavioral assessments, machine learning, and more… but that we will save for another post.

We view Venture Capital as the last financial front-tier that will be disrupted by data and technology. Nearly all public market investing is done by computers and eventually so will the private markets.

The argument we hear over and over again (almost exclusively by Venture Capitalists) is:

A machine can never learn all of the intricacies about high-growth companies to make decisions as good as an experienced Venture Capitalist. There are too many human elements like accessing the team, or understanding macro trends or being a deep subject matter expert.

To these arguments we say:

  1. We are not replacing the human element; we are simply complementing it
  • We are using data and technology where the cost of human capital is very high: Sourcing and Screening
  • We also believe the Gut + Data will always beats Gut alone

Prior to Wendal, we used some analytics, however, it ultimately came down to how we felt in our gut. If we had used Wendal from the beginning, we would have been 2x better at avoiding losers and picking winners.

The table is on outcomes from our 60 seed investments to-date. We are using Series A as a barometer of success since the aver
The table is on outcomes from our 60 seed investments to-date. We are using Series A as a barometer of success since the aver
The table is on outcomes from our 60 seed investments to-date. We are using Series A as a barometer of success since the average age of companies in our portfolio is a little over 2 years.

2. Data removes all inherent biases from the vesting equation

  • Since we launched Wendal, we have funding women and minority founders at an 8x higher rate than the venture average (42% vs. 6–8%)
  • We do not think the answer to the disparity in funding is creating “ONLY” funds (even though we need to start somewhere), it is creating a fair and equal funding process which Wendal provides

3. Nearly every venture fund has a deal flow problem — technology is the only way to remove this barrier

  • If you are running a fund without Wendal or another tech-focused solution you are limited your pool of companies to whatever your human and geographical bandwidth can handle
  • During our first 2 years (no Wendal) our first 15 checks were located within Kentucky, Ohio, and bordering states. Of our last 15 checks, we have invested in companies from 11 different states and 3 countries. Investing is about optionality, if you can expand your investment pool you will have better investment options

If you are located ANYWHERE outside of San Francisco, New York City, or Boston you have a deal flow problem in your immediate vicinity.

Don’t believe me? I used Pitchbook to calculate the average VC-backed exits per year. If you are running a venture fund in Wisconsin and only invest in Wisconsin start-ups, you have 1 exit per year on average. Given most funds invest in multiple companies per year, it is going to be very hard to see enough quality deals in your backyard to have enough successful exit to make money.

Now that we have automated both the recruiting and screening process, we are able to source deals from anywhere in the world. For us, we look to make 1.5 investments per month and to get to sufficient deal flow to make 1.5 investments a month we now source deals from all 50 states and 7 countries around the world.

4. Last and most importantly, we are NOT trying to pick Unicorns, we are simply trying to improve our odds of a better return

  • I do believe to pick unicorns or have funds that return 10x+, a machine or data-driven process can’t be involved. You need to concentrate your company in a small number (5–10 companies), be very smart, and a little lucky to hit this kind of homerun

To visualize this, I’m going to use a chart that I recently found on Toptal. Depending on the publication, Venture failures (<1x return) are between 60–65%. The average venture fund return is 2.1x return. So, using the chart below, the average venture fund has returns close to the red circle. 60–65% of companies in their portfolio do not return a profit and the remaining 35–40% of companies deliver a 5–6x return on average.

Average returns for VC Funds is 2.1x

The goal of Connetic Ventures is to use AI and ML to get smarter over time. This should reduce the odds of selecting a loser and increase the odds of selecting winners. Effectively, we are trying to shift the circle down and to the left, ideally ending up somewhere where the yellow circle is.

By leveraging data, if you can get 15% better at picking winners and eliminating failures then your fund can stand to outperform the average by nearly 2x.

So, data and technology investing is not about hitting homeruns — it's about increasing your batting average. Even a small step towards picking better companies can dramatically increase overall fund returns.

What do you think? Are we crazy? Are we smart? Too early to tell?

Let us know. Comment below, follow us on Twitter (@conneticventure) or send us a note at

And if you are a start-up founder or know one…. Send them to

Connetic Ventures

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Connetic Ventures is a data-driven growth VC fund. We use technology to automate the decision-making process to eliminate human bias & improve success.

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