Y Combinator vs TechStars: Whose Companies Are Bringing In More Funding?
techcrunch.com
techcrunch.com
That is the real test, and we've published those numbers:
http://ycombinator.com/nums.html
(The number I quoted in June is now out of date. The combined value of the top 21 companies we've funded is now slightly over $7 billion, and the average value of all the companies we funded up to summer 2010 is thus about $33 million.)
If Airbnb is valued at $1B and if there are 200 YC alums who've raised, that adds $5M to the "average valuation" of each YC startup. (I know those #s are not right but just for purposes of the example).
Those median valuation figures available?
If optimizing for the median outcome is your goal, VC funding is probably not a good plan.
Plus, "median series A returned a loss" - huh? Can you clarify?
If 5 companies have valuations of $5, $10, $15, 20, $1000, the avg is $210 million. The median is $15 million. I'd argue the median is more representative of valuations received than averages. And if you're a startup founder, the median is more useful to gauge the program as that is more likely what your valuation will be near than the average.
New founders might care more about the median, but big investors, not so much.
The median is more representative of what a founder's expected financial return is, but many folks aren't optimizing for that. If they are, you need to look at the off-balance-sheet asset: the six-figure job offer from Google, FB, MS, Yahoo, etc. etc. etc. that is generally available to people of the viable startup-starting caliber.
Since the mean startup return is so driven by a small number of massively successful outliers, it makes economic sense that the median outcome will not look so great. (If startups presented a good chance of being as good as the next best option, plus a small chance of F-U money, nobody would work anywhere else.)
>> If 5 companies have valuations of $5, $10, $15, 20, $1000
A more likely range of valuations out of 10 companies is:
$0, $0, $0, $0, $0, $0, $3M, $11M, $55M, $220MThe YC average is quite decent. The median result is likely to be $0.
If this probability distribution scares you, it is time to rethink startup companies.
Are there any studies that demonstrate the amount of correlation between money-raised and exit valuations?
With that information, it would be easier to judge how informative it is to use money-raised as a surrogate for the true objective criterion.
Yes, many. That's what any study of the returns of venture funds is measuring. And since the returns of the top venture funds are consistently at least positive, we can be fairly confident that $7 billion is a lower bound on exit valuations.
In terms of the analysis, some attempt to normalize the data would have been good.
Also, time-series figures would be more interesting as it would help show which program might be gaining or losing momentum.
In general, the idea of total funding being the best metric is laughable given how a few outliers skew the data.
It would have been interesting to see how quickly companies raise after the programs conclude. In a sense, analyzing by vintage/class would be more useful.
And then to conclude with the following "Y Combinator beat TechStars in many of these metrics, but none of these numbers translate to which (if either) is the best fit for your startup. That’s for you and them to figure out."
If your going to do some data analysis, try to make it actionable/useful and stand by it or take an opinion vs just a shallow attempt at data analysis which you neuter with caveats.
A bit harsh on TS, considering every YC startup automatically raises an extra $150k right away...
http://www.businessweek.com/smallbiz/running_small_business/...
Spreadsheet link: http://goo.gl/fDZB
Young, high growth startups often have irregular revenue and no profit. The early years of Facebook would've shown weak revenue and no profit. Most smart people knew that it had a disproportionate shot at big returns long before it had meaningful revenue.
Also, why do the plot areas go above the maximum axis labels?
But as a fellow data scientist, the choice of bar graphs to represent this compact bit of info really just feels like padding to the article which could really use a bit more analysis.
In any case, I wouldn't read much into the OP.