5 ms·

If you're curious about the algorithms used, hover over the column headers. Click any of the rows to see the data sources and individual stats.

RaSoJo 14y ago
Really neat app and useful idea for tracking companies. But what is the logic behind the formula used for the fast_growth metric? I see that the formula is: log(day14_sum_of_metrics/day1_sum_of_metrics) - 2*sqrt(1/day1_sum_of_metrics + 1/day_14_sum_of_metrics) Is this a standard metric? Would be great if someone could share the theory behind the math..
- bufferout 14y ago
  Snip from someone who is smarter then me at these things... Assuming that the first measurement is x likes, the second is y likes, Then the estimate of the natural logarithm of the growth is given by log(y / x) with an error estimate of sqrt(1 / x + 1 / y) But since you are interested in the conservative estimate of the growth, you should use something like ~ 5% confidence interval. So I would recommend ranking your dataset using the folllowing function. log(y / x) - 2 * sqrt(1 / x + 1 / y) For example: growth from 1 to 10 will get the score of 0.2 growth from 100 to 400 will get the score of 1.16 growth from 10000 to 15000 will get the score of 0.38 One of the important properties of this estimator will be that the growth from say 10000 to 100000 will be ranked higher than the grown from 1000 to 10000, which in turn will be ranked higher than the grown from 100 to 1000 etc...
  - RaSoJo 14y ago
    Thanks a ton for sharing. My basics on this front is embarrassingly weak. Coincidentally there is another post on HN's front page right now on Data Science resources: http://news.ycombinator.com/item?id=4930965 http://news.ycombinator.com/item?id=4930965 Matt's teacher's statement on the lack of knowledge on Linear Algebra was: ‘How can you make cheese if you don’t know where milk comes from!? Its plain, common ordinary horse sense!’ That hit home hard :( On another note, Googling for: log(y / x) - 2 * sqrt(1 / x + 1 / y) throws up one heck of an awesome graph
aymeric 14y ago
I think your algorithm puts too much weight on StumbleUpon "recommendations" which are really StumbleUpon "views". I was happy but surprised to find my startup http://taskarmy.com http://taskarmy.com in the list, and having a closer look at the metrics taken in account, it seems that it is because of the StumbleUpon stats.
- collypops 14y ago
  Sorry if I mislead you. This is not my project. I just thought it was HN-worthy.
- bufferout 14y ago
  Fair point- I've tried not to make any arbitrary decisions on how the data is used but I'll take a look at what the results look like sans stumbleupon.
  - bufferout 14y ago
    Top 20 when I remove the StumbleUpon metric: [lifx.co] => 97.942429714246 [kickfolio.com] => 89.598662998513 [shebusiness.com] => 84.413930181291 [netcomber.com] => 83.758506325309 [readershop.com.au] => 71.445369888231 [nameterrific.com] => 71.375673413085 [bugcrowd.com] => 71.328010040444 [shop2.com] => 71.228983543186 [serviceseeking.com.au] => 68.308687033257 [righttoknow.org.au] => 67.654116949018 [theiconic.com.au] => 67.28062258823 [kogan.com] => 67.173702738231 [retailmenot.com] => 65.963267045507 [startlocal.com.au] => 65.922497275844 [social-medicine.org] => 65.833298346458 [kaggle.com] => 65.616138632997 [manageflitter.com] => 65.551656760889 [freelancer.com] => 65.455719109403 [harris.com] => 65.150296026706 [freelanceswitch.com] => 64.742373047548 Doesn't feel like a substantial shift (good).

RaSoJo 14y ago

Really neat app and useful idea for tracking companies. But what is the logic behind the formula used for the fast_growth metric? I see that the formula is: log(day14_sum_of_metrics/day1_sum_of_metrics) - 2*sqrt(1/day1_sum_of_metrics + 1/day_14_sum_of_metrics) Is this a standard metric? Would be great if someone could share the theory behind the math..

bufferout 14y ago
Snip from someone who is smarter then me at these things... Assuming that the first measurement is x likes, the second is y likes, Then the estimate of the natural logarithm of the growth is given by log(y / x) with an error estimate of sqrt(1 / x + 1 / y) But since you are interested in the conservative estimate of the growth, you should use something like ~ 5% confidence interval. So I would recommend ranking your dataset using the folllowing function. log(y / x) - 2 * sqrt(1 / x + 1 / y) For example: growth from 1 to 10 will get the score of 0.2 growth from 100 to 400 will get the score of 1.16 growth from 10000 to 15000 will get the score of 0.38 One of the important properties of this estimator will be that the growth from say 10000 to 100000 will be ranked higher than the grown from 1000 to 10000, which in turn will be ranked higher than the grown from 100 to 1000 etc...
- RaSoJo 14y ago
  Thanks a ton for sharing. My basics on this front is embarrassingly weak. Coincidentally there is another post on HN's front page right now on Data Science resources: http://news.ycombinator.com/item?id=4930965 http://news.ycombinator.com/item?id=4930965 Matt's teacher's statement on the lack of knowledge on Linear Algebra was: ‘How can you make cheese if you don’t know where milk comes from!? Its plain, common ordinary horse sense!’ That hit home hard :( On another note, Googling for: log(y / x) - 2 * sqrt(1 / x + 1 / y) throws up one heck of an awesome graph

aymeric 14y ago

I think your algorithm puts too much weight on StumbleUpon "recommendations" which are really StumbleUpon "views". I was happy but surprised to find my startup http://taskarmy.com http://taskarmy.com in the list, and having a closer look at the metrics taken in account, it seems that it is because of the StumbleUpon stats.

collypops 14y ago
Sorry if I mislead you. This is not my project. I just thought it was HN-worthy.
bufferout 14y ago
Fair point- I've tried not to make any arbitrary decisions on how the data is used but I'll take a look at what the results look like sans stumbleupon.
- bufferout 14y ago
  Top 20 when I remove the StumbleUpon metric: [lifx.co] => 97.942429714246 [kickfolio.com] => 89.598662998513 [shebusiness.com] => 84.413930181291 [netcomber.com] => 83.758506325309 [readershop.com.au] => 71.445369888231 [nameterrific.com] => 71.375673413085 [bugcrowd.com] => 71.328010040444 [shop2.com] => 71.228983543186 [serviceseeking.com.au] => 68.308687033257 [righttoknow.org.au] => 67.654116949018 [theiconic.com.au] => 67.28062258823 [kogan.com] => 67.173702738231 [retailmenot.com] => 65.963267045507 [startlocal.com.au] => 65.922497275844 [social-medicine.org] => 65.833298346458 [kaggle.com] => 65.616138632997 [manageflitter.com] => 65.551656760889 [freelancer.com] => 65.455719109403 [harris.com] => 65.150296026706 [freelanceswitch.com] => 64.742373047548 Doesn't feel like a substantial shift (good).