izyda

Great points. I think #2 (reliability) and #3 (reputation)are really key here.

I would argue that there is a caveat to #1. Agreed that a MVP for a Bloomberg replacement wouldn't work unless you had everything Bloomberg had and more (in which case, it's hardly an MVP and good luck). That said, I think an MVP for a niche fin-tech data product that Bloomberg does not have would work if the sales pitch is that I use your product in addition to Bloomberg. If you offer me an edge by providing data (or analysis) others don't have, an MVP would be fine. Heck, some traders/funds would pay for just a csv file if it contained unique (but directly useful) financial data/analysis (an obvious example that may have had potential 5 years ago was sentiment, although now everyone and their dog is offering that and it's not entirely clear there's direct value to it). I think the very fact Bloomberg can look like its "from the 1980s" just reflects the needs of the market it serves. Wall Street doesn't put great value on pretty interfaces, ease of use, or anything other than direct value add to their revenue generation process (ie. investing, pricing, selling financial products, etc.). Tell me how your product makes me more money today. I think any fin-tech startup needs to be driven by that philosophy if they want to succeed, I don't see how you'd convince a trader that the fact your app offers a nice interface or interesting but questionably actionable analysis is any real advantage if the other guy offers analysis or data that is directly applicable.

Another thought: while disrupting pricing data would be very difficult (given the speed + accuracy required + relationship with exchanges), what about other financial data like fundamentals/accounting/relationships data? That data is free - it's all on the SEC's EDGAR (and it's far more than just financial documents, there's plenty of data on individuals too). Would Fact Set or Capital IQ, for instance, have more competition if the SEC had API/machine readable data? There has been some effort through XBRL to do this - although the percentage of filing actually available in that format and the lack of consistency in the format (in some cases, there's at least several hundred 'tags' for the same or similar financial line item) makes any large scale data processing from EDGAR a massive undertaking.

As a stats undergrad who's quite interested in this and has an active project going in the space, I think the main question here is what exactly are we trying to predict? Is the sole purpose of analytics to "rate this player relative to others"?

I think one of the things slowing down hockey analytics is the focus on finding a "new Corsi". In Baseball, given the game is segmented and the people who score points do so in a single action by themselves, it makes sense to have one metric that measures how much a players scores/their value. In hockey, maybe other types of analysis would be more accurate. What about predicting scoring rates (for/against)? Or the probability of winning particular games? Or simulation models of game outcomes? None of these things will give you a +/- type rating of a particular player - but they could be more accurate and potentially just as useful.

Nice. It would be interesting to see more visualizations or summary statistics that show what type of phrases, topics, or other features of a Kickstarter campaign make it most likely to get funded.