First Round 10 Year Project

Rule 1 - Uber had no female founders.

2 - Travis and Garrett were "older" founders.

3 - Neither went to a "top school".

4 - Neither worked for one of the name brand companies listed.

5 - Both were repeat founders. If included, FRC's investments in repeat founders would likely perform much better than first-time founders.

8 - Uber is based in the Bay Area. If included, FRC's investments in "Big Tech Hubs" would likely perform much better than outside of tech hubs.

A couple of the other rules might also not apply to Uber (don't have enough data to assess).

On the whole this is a well-intentioned exercise but I wonder if the exclusion of Uber doesn't lead to wrong conclusions.

e.g. on comparing companies that do better. You could have a data set of 150 companies whose exit performance (or current value) looks something like this (numbers in millions):

5000,1000,500,400,200,200,100,50,50,30,30,20,20,20,15,15,15,15,0,0,0,0,0,0,0 (repeated 6x to get 150 data points)

Now compare that against a data set that is those exact numbers divided in half:

2500,500,250,200,100,100,50,25,25,15,15,10,10,10,7.5,7.5,7.5,7.5,0,0,0,0,0,0,0 (repeated 6x to get 150 data points)

If you compare these data sets with a Two Sample T-Test you have to go down to 91% confidence to get a significant result (http://www.evanmiller.org/ab-testing/t-test.html#!307.2/986....)

That may not sound that bad, but now add a super-unicorn to each one of those data sets, a $20B exit. Now the differences aren't even significant at 80% confidence.

e.g. in Item 7 about technical co-founders: "consumer companies with at least one technical co-founder underperform completely non-technical teams by 31%"

Lets say that First Round has 150 consumer businesses and we're just going to look at a binary outcome of something like "valued over $50m". Now lets say that 100 of these consumer companies have technical co-founders and 50 are completely non-technical. Say 40% of the non-technical teams are "successful" by the $50m metric. That means that 30.5% of the technical teams are successful (if they are doing 31% worse by the numbers in the article since 40%/1.31 = 30.5%). That's not a significant result at 80% confidence (http://www.evanmiller.org/ab-testing/chi-squared.html#!31/10...)

I understand why they published the piece and think it will get a lot of reads, but really wish I could read a version with statistically relevant insights instead.

[1] https://en.wikipedia.org/wiki/Data_dredging

Female founders outperforming male teams: My hunch would be that the bar for women to get funded (at least historically) has been higher than men so the female led start-ups would be a better calibre of company. Related, since this is based on investment performance, could it be that the female founders received smaller initial investments so performing on par with male teams would make the ROI look better?

Halo effect: This to me would indicate that we shouldn't be encouraging fresh college graduates to work at start-ups and instead get experience at a more mature company. I wonder how much tenure they had at their halo company prior to founding the start-up and how it ties with the average age of founding.

Solo founders perform worse: I wonder what happens if you frame this from the point of view of the founder. If the solo founder had a $100 return and the team had a $260 (160% better) return; assuming equal dilution and equal division between founders, solo founder get's $100, a two founder team get $130 each (30% better), a three founder team gets $85 (15% worse).

Next big thing from anywhere: Also interesting, I'd like to see how this varies by referral source. Do companies referred by other investors perform better than non-investor referrals (or can other investors pick companies better than social connections).

For example:

Ivy League School and working at a prestigious company? You don't get either of those by being a slacker.

Younger team, woman co-founder and more than one founder? You better believe there is going to be more pressure to prove yourself and not sell early or give up (vs. being a single founder or an older proven founder).

Standing out from the crowd at demo day or getting noticed out of all the noise of social media? That takes some dedication. I guarantee that the people who did get noticed that way didn't just send one email or one tweet. They were hustling their idea hard.

Great read though. I loved the point that startups don't have to come from SF or NYC to be successful!

For instance:

"The results were stark: Teams with more than one founder outperformed solo founders by a whopping 163% and solo founders' seed valuations were 25% less than teams with more than one founder."

How many of the 300 investments in their portfolio were solo founders? 10?

Solo founders are rare, and it's often harder to raise money as a solo founder. That means less companies have solo founders to begin with.

Source: I am a solo founder

I think that probably explains the "no tech cofounders do better" bias in Consumer; the bar is probably higher there.

I'd love to see an inverted analysis of this effect, ie. which schools had the best indication of success. Pre-deciding to look at their definition of "top schools" is probably only seeing part of the picture.

Technical co-founder, enterprise product: +230

Elite school: +220

ex AMZN, AAPL, FB, GOOG, MSFT, TWTR employee: +160

Female founder: +128

Discovered investment via non-traditional VC channel: +58

Technical co-founder, consumer product: +31

Team average age under 25: +30

Solo founder: -163

The biggest problem is that "performs better" is thrown around a lot but never defined. My hunch is that performance is measured as a return on investment. For instance if First Round invested in a company at a $5M valuation and the company is now worth $100M, that's a 20x return. If they had invested at a $10M valuation, the return would only be 10x. So I suspect in their eyes the "performance" of the first investment is better. Could be wrong, but my point is there's no way to know.

Without knowing that it's hard not to look at their conclusions such as young founders do better and repeat founders cost more with a large helping of grains of salt.

A young founder is less likely to be a repeat founder. Therefore these founders will cost less, per the conclusion they reached. And if performance is measured as ROI then they will "perform" better even if underlying talent or company growth is constant.

So it looks as though there's just a lot of observational data without much insight.

How come? If the valuation for Team is 25% more than a Solo founder is clearly better than a Team.

A team is 2+ founders. Which means your shares are divided by 2+. Solo is clearly winning here even if the total valuation is less.

I'm not convinced about the age conclusion: depending on which statistics you focus on, you either conclude that 25 is best or 32 is best:

Founding teams with an average age under 25 (when we invested) perform nearly 30% above average [...] for our top 10 investments the average age was 31.9

But to be fair, at the bottom of the page, they say that they are not trying to claim any statistical significance... just trying to look at their own data to gain some insights. It doesn't sound like they expect any of this to be taken too seriously.

The majority of their team consists of white guy ivy leaguers who have a penchant for funding their younger counterparts. No offense to first round as that is how the VC market looks and resembles

Until that goes away the diversity issue still stands.

Everything else is secondary to finding the home runs. Even if you multiply all those advantages together you get:

1.63 * 1.3 * 2.2 * 1.6 * 1.5 * 1.63 * 2.3 * 1.58 = 66

So if you manage to somehow get every single one of those attributes at max in a company you'll get roughly 66x the valuation or performance or whatever versus a company/team with none of them.

Of course if you go for all those things you'll probably only get one deal per year.

This isn't terribly meaningful.

What is the value of performing an analysis if the results don't help you to improve future decision making?