The secret algorithm one VC firm uses to pick entrepreneurs


Credit: Sony Pictures Digital, Inc.

Statistics and big data took over baseball scouting some years ago, with the rise of Moneyball. More recently, those tactics have spread to the political world, with presidential candidates using big data to maximize their vote, and Nate Silver using algorithms to correctly predict outcomes in all 50 states.

Now we’re doing it, too. Instead of relying on the gut instincts, punditry and armchair quarterbacking that VCs are notorious for, our firm is pattern replicating to decide which entrepreneurs to fund.

Here are the components of a secret algorithm I developed that we use to score and rate potential investments with entrepreneurs.

1. We look at (and score) what you read

What content has been uploaded into your cerebral cortex via apps, websites, PDFs, books and other miscellaneous educational all matter to your eventual success outcome.What books have your founder team read and what have you posted in your social media feeds? If they’ve spent time reading Technology Ventures, Gear Up, Four Steps to an Epiphany and tweeting knowledge nuggets – quoting chapter and verse – that could lift their entrepreneurial success score via our proprietary algorithm. Posting to Twitter your snowboarding pictures from Lake Tahoe might say something else.

Chris Sacca has been quoted as saying he goes right to Twitter and reads the last 50 tweets before he even considers taking a meeting. In our new VC algorithm, you can scrape and evaluate multiple social media streams for what founders have read, uploaded, integrated, and executed. Quality is great, but we are really looking for the quantity of entrepreneurial material comprehended.

2. Age of cellphone number and time of first daily phone call

The age of an entrepreneur’s cellphone number reveals so much: their relative stability, how old they are, whether their number was a jettisoned friends-and-family program or an imported landline number. We get the age of cellphone number through a process called cellphone underwriting, which reveals all of the above and more, and which is perfectly legal but secretive enough that I won’t reveal how it works. At the end of the day, we’re looking for entrepreneurs who are young, stable, middle class, and who have the support of family and friends networks — and the age of the cellphone number tells us all those things.

So, I take the approximate age of a founder’s cellphone number and then during due diligence, I get a stat: The average time of their first phone call in the day. If you and your entrepreneur team are making and taking calls at 6:30 a.m PT, you’re probably talking with people back East and there’s a 50-50 shot you’re making north of a million in revenues.

So revealing data like this, that we used to W.A.G. (wild ass guess), we can now find out by using our firm’s make-shift cellphone underwriting API, which hooks in with Verizon and AT&T – the two main carriers of the iPhone. (And by “makeshift API,” I mean have our associate do it manually.)

3. How Othman Laraki are they?

Othman Laraki is a tale that is told inside of the VC community. He sold something big to Twitter. He has degrees from both MIT and Stanford, but also has a ton of street smarts. As an example of his street smarts, he squatted in 2,000 square feet of office space at Stanford’s Engineering building. That duality of street smarts coupled with academic smarts is a critical component of my firm’s algorithm.

A fund that is now underwater used to troll for deals in the basement of the CS lab at Stanford without taking into consideration the street-smart component. They failed to realize that you can’t just replicate what other people already did super long ago — you have to innovate a quarter step. That team of VCs is now begging for LP money in the pay-for-play conference known as “Venture Alpha.” (Spoiler alert: They will not make it to their next fund.)

We don’t talk about the data inputs for this street-smart metric but as a hint: Augie Garrido once said, “Question authority but follow the rules.” We are looking for entrepreneurs who question the status quo and tip-toe the fine line, but still firmly understand rules and existing hierarchies.

4. Stanford University founder team formula

If you reverse engineer the biggest exits, you can see that the whales in every fund’s portfolio had two or three founders from Stanford, with an odd-ball founder from some other school tossed into the mix. YouNoodle released public data on this observation. Our algorithm does not say “just Stanford.” It does say two or three founders from a good school with at least one in the litter that is not exactly like the others – think Cal, NYU, CMU, Illinois or MIT in a pinch.

Recently, Mark Suster, blogged about the phenomenon of overly homogenous founder teams. He argues that if teams are too similar where “all founders even have the same phone,” the founders will likely follow versus lead.

Remmy Oxley is the pseudonym of a Silicon Valley VC. Follow him on Twitter @RemmyOxley. 


Anthony Decena

So if you’re up at 6am talking with people back east, there is a 50-50 chance that your revenue is on the high side of a million? That has to be the dumbest thing I’ve ever heard in my life.


The author broke his own rule: (from his twitter) ‘never go full douchebag.’

Andrew Shannon

Of the 59 comments, it seems like 90% are negative – which surprises me. Bringing quantifiable metrics into the VC equation isn’t necessarily bad, this anonymous guy didn’t say these were the exclusive basis for his decisions… just data points his firm uses. Besides, evaluating an entrepreneur’s street smarts, extrapolating what they read, and finding complementary teams isn’t novel – he’s just describing the fund’s methodology for doing so. I’ll admit, the cell phone is fishy at best – but seriously… not bad enough for the flack being dished.


Lame observations, social metric investing gets Tea Cup drippy returns. The fellow ought to go back to school and learn more about VALUE and not the age of one’s phone number, nor the school one went to, as claimed investment litmus test criteria.


I wonder how Alexander Graham Bell or Thomas Edison ever managed to get off the ground without a cell phone or without an electric lamp? Did either of these geniuses go to university, or college? Had they had either tool, or graduated from the best universities, would they have done so? Would they have succeeded in today’s world? Are modern electronic tools absolutely essential to entrepreneurship today? Isn’t the key to entrepreneurship, innovation, and isn’t it founded on invention, and isn’t it, in turn, founded on a vision of what is not, but could be? Again, what about Jobs and Gates, to what extent did they succeed because of Angels or VCs, having dropped out of college and begun their business adventures in garages? How much energy and time should an entrepreneur invest in these helpers?

Jason Shurb

With handheld sized drones that start at $99 and are military grade. It will be interesting how we can repurpose this tech to help those with disabilities to view locations around the house with a computer. This is a great advancement in security.

Jason Shurb

With spy drones that start at $99 and are military grade. It will be interesting how we can repurpose this tech to help those with disabilities to view locations around the house with a computer. This is a great advancement in security.


What algorithm was used by Wall Street? hmmm……..Sounds like these people are the same people that need a decision tree in order to decide if they should go to the restroom.


Let’s be honest kids, those with the money make the rules in this game. And as one of a group of four who took VC money many years ago (in the late ’90s), we quickly learned that the game changes constantly, rules are re-written and if you take money from these people, be prepared to sell your first born, and in our case, sell out your team. At the end of the day, there were two of the original four left standing (I guess you could say I was one of the fortunate two – on which side of the equation I’m not saying), but needless to say it was a messy bloodbath. The top tier VC brought in a new team and it got even messier and nastier from there. There was a sweet exit for all of us which pretty much has us all set for life, but would I do it again – knowing what I now know? Not so sure, after watching people I thought were ethical and spoke about values like integrity, throw their values down the crapper (love that word – pretty much sums it up) just in the name of money. You want to take VC money? Just be prepared to live with the consequences.


Cell phone data = infringement of privacy lawsuit? Why not just go through the trash too???


Snooping on people’s cell phone data – infringement of privacy lawsuit in the making? Why not go the whole hog and just trawl through their garbage??


The most useful nugget of information in this article, is that I know have a new TLA in my lexicon: WAG (which is probably what they do anyhow)

Jeff Higgins


Very entertaining. However, I lead a company that performs analytics on company workforce groups/jobs/divisions/management and, far be it for a startup CEO to lecture a VC but if the shoe fits… First your data elements are what we would call biased selection, which traditional statistics/analytics methodology says not to do. Clearly you are looking for leading or predictive indicators of future entrepreneurial success but what you have done is, instead of building a predictive algorithm. You have quantified, to use a phrase from “Moneyball”, ‘the player who has the ugly girlfriend’, therefore they have no confidence, therefore they are not a good draft pick. You are doing the same with #1, #2, #3 (#4 has validity but that is not new). Selecting only data that supports gut belief and conventional/experiential wisdom sounds good but you have lost before you started. With such a model you will find what you have always found and get what you always got, because you have not opened up the analysis to new possibilities. By the way, the phone data could be good if used properly and first call is not it. Rather track total calls, the time span during the day from first to last call and the geography of all calls. This could be further enhanced with zip code or geo-metro data in the US but also globally and when combined with social media (twitter postings, blogs etc.) and email activity, reveal a much larger picture of total activitiy i.e. productivity. Then add weekend and holiday activity and stir and you just might find some really interesting stuff. By the way, just to validate, wouldn’t your model exclude Steve Jobs, (didn’t read the right books), Bill Gates and Mark Zuckerberg (dropped out of school) as well as all late night inventors/workers etc.. Better luck with your next model algorithm.


Mark Gavagan

The first vetting point presumes a person Tweets everything of substance they read…

Spencer Cooley

Didn’t know that recognizing existing hierarchies was an entrepreneurial attribute. I would imagine that an entrepreneur would want to destroy existing hierarchies.

Or-Tal Kiriati

It’s always interesting to get the insights of the other party. You may not like how this VC is thinking, but that is how they are thinking. VCs by perception are meant to take high risks for a chance to get wild profits. But it makes sense to take a calculated risk and each VC is doing its own calculation. I’ve recently attended a talk by one serious American VC, considered seed investors, and he said they were looking for venture with traction, preferably in the hundreds of thousands and up. This is not risky nor is it early. Traction costs money! Where are the bold VCs that would invest in the pre-traction startups?
So I am not sure what sort of investments this VC is doing, but if after doing their own filtering they make early seed investments in pre traction startups, then at least some startups get that so needed support.
Older entrepreneurs, entrepreneurs who didn’t study in a top school, who may own an old mobile phone, and might not have the time to read books etc – will simply have to look for a VC that uses a different set of filters.
Lucky for us ere are still some around…

Klim Teetle

This is the dumbest approach I’ve ever heard or seen, but hey, that’s what the top 5 VCs in the valley generate 90% of the returns of the entire asset class, and bozos like this are wht make the asset class negative overall.

judy shapiro

The truth of how ventures is as quirky as this post suggests.

But it misses one crucial point – true quality always rises. A venture with a great idea and a team capable of executing will win the day – even if they fit the cell phone/ social/ age/ training/ gender algorithm. Creating a business is a leap of faith and by definition algorithms are limited and limiting.

I am not to be limited by such formulas They are irrelevant.


This sounds awfully naive and misguided. You’re straddling between idealism and elitism. I know several accomplished serial entrepreneurs in Silicon Valley who are neither young, Twitter fiends, Facebook users and who didn’t go to Stanford or a “top” school. And no offense to Chris Sacca, but I wouldn’t make any policies around his habits.

Raj Shankar

Very interesting and contrarian thoughts compared to what is widely believed as VC investing logic. But it does reinforce one underlying message: “who you are” is more important than “what you are developing”. Not sure if it is a great concept – but exceptions are allowed!


I’m amazed that Om Malik would allow this person to write such a post, its easy to see that the author is putting this anon guest post out there as bait to see what type of responses he/she gets (to illicit a reaction). Is GigaOm really this desperate for readers?

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