Running the startup miner uncovers great startups almost every day (like Thread Genius, Trove and Lively.) Side note: you can see the updated list of mined, high potential startups here. I started to get curious whether some value could also be extracted a 'meta-data' style analysis.
The output of the miner is the list of startups that were listed on AngelList the previous day per geographic region, I run it for NYC and SF Bay (note: you could also cut the newly listed startups by market, so you could scrape new blockchain, healthcare or AI startups from the previous day.) So now we can explore the raw number of startups listed on AngelList per week and the market composition of these startups. This could help to create a real time awareness of what founders are excited about and potential differences between regions.
To explore this idea, I ran these numbers for the week of 03/05/18 to 03/12/18.
So we see approximately 1.7x number of startups listed in SF Bay than NYC. This seems to be much smaller than common wisdom suggests (given the prominent position SV holds in the tech community.) Indeed this data is mirrored in data from PwC Moneytree. Taking the median of the number of startups and the amount of capital deployed to NYC and SF Bay seed stage startups (since this most closely approximates AngelList startups) over the last 2 years we see a 2.1x number of startups in SV over NYC and a 2.0x in capital deployed.
The market breakdown for the NYC startups is shown below:
It might be hard to make any meaningful observations from this data in isolation . But there are some we can see clearly. Consumer dominates and Blockchain and AI /ML/Data startups are low (based on my prior expectation.) But comparing with SF Bay will be most helpful. The market breakdown for SF Bay area startups for the previous week is shown below.
Here we can see a (nice, somewhat predictable) balance between Enterprise Software and Consumer startups (this may be representative of the "maturity" of SF Bay as a startup ecosystem.) Healthcare and Blockchain seem low and Education surprisingly high.
As common VC wisdom suggests, I think the (ongoing) market examination of these startup ecosystems will be helpful in a contradictory way: the best startups are often tackling markets that are not hot (home sharing, transportation, social etc.) and many are resistant to, and in fact break rigid data structures by definition being highly innovative (which is precisely the point.)
I'm looking forward to continuing this series (with more than just one weeks worth of data!)
 AngelList's UI allows users to write free form text for their market categorization when creating a new startup profile. If it matches a previous tag it autofills but if not a new market tag can be created. This makes it a little difficult to run analytics (some of the best market categories for scraped startups last week include: swimming, USA and livestock options.) So i created my own 'umbrella' market tags to consolidate free form text tags. Disclaimer obviously this could introduce distortion, but it is assumed to be negligible.