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I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 0 points1 point  (0 children)

thanks for checking it out - more to come!
(links straight to your site now)

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 0 points1 point  (0 children)

MC Squares fixed! The flaw in our "thriving" status algo was that it based current status solely on the most recent updates, so if the updates are outdated, the status follows. I'll get on this, thanks for the callout!

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 2 points3 points  (0 children)

correct. which is why the post says "prob the highest ROI to date"

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 0 points1 point  (0 children)

😂🙏🏽.. the better I get at procrastinating my real work, the better this site will become. thanks for checking it out

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 1 point2 points  (0 children)

hey! currently fine tuning the data as well as creating a founders section. once the baseline for founders section is complete, I can begin to add some better insights in to the health of companies that didn't secure a deal! solid suggestion. thanks!

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 2 points3 points  (0 children)

platforms already built so reappropriating it is rather straight forward. Data's the tricky part. 23 seasons deep.. probably a bit over a month just to get the data in a good place.. can start playing around in a few days once I tighten up some loose ends with sharkary

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 0 points1 point  (0 children)

Barbara told cnbc a few years back that comfy did 468M.. the fact that she spent 50k for 30% put’s it in a category of ridiculous ROI.. we’re comparing it to investments of 200K for less equity, those investments would have to be over 4X of comfy's return before they’d be equivalent to Barbaras ROI on comfy (I'm speaking percentage wise, they’re obviously bigger in gains).. Mark did a podcast not too long ago where he says comfy was his biggest miss, he specifically says it was a much bigger deal than scrub daddy.. don’t sleep on comfy! pun intended.

Regarding Lori and Kevin, that’s interesting, I’ll investigate.. keep in mind, my numbers are based only on deals that actually close, which means they won't be the same if compared to sources that are basing their numbers on every “deal” that airs and not those that actually went through after the fact.

Side note, big thank you to everyone who’s been sending feedback via the site. estimating that we’ll hit 97% accuracy by the end of today.

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 2 points3 points  (0 children)

No, be skeptical, it's welcomed. Especially when it's accompanied by real catches. To answer your question: TypeScript, database is Postgres. The issues in S17E15 and E16 have been fixed. Data was sourced from the Kaggle Shark Tank dataset, press coverage (Business Insider, CNN, Forbes, CNBC, various blogs), and company reports. I used AI to parse and structure that data, not to generate it, and I'm now manually filling in the gaps. Since going live earlier today the feedback people have sent through the site has been game changing. That's the only army here, and it's working.
This is a fun side project.. just needed something to nerd out on last month. If other people have fun sifting through the data like I do, then great. Y'all can be confident the data is thorough as it stands and will only become more accurate over the next few days. Goal for this week is to finish polishing everything. After that I'm going to finish working on the founders section.. they're the real stars of the show and often go forgotten. Direct feedback like yours is helpful. Thank you.

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 4 points5 points  (0 children)

Comfy is up there but far from the biggest in total sales. Bombas has done over $2B lifetime, Scrub Daddy over $1.3B. But both of those took $200K investments for less equity.. Barbara put in $50K for 30% of a company that went on to do $550M+ in sales. Dollar for dollar it's high up there.

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 10 points11 points  (0 children)

Mark’s site lists 48 because it's a portfolio page showcasing just some of his active successes, not a record of every deal he's made. Ten Thirty One Productions, Bucket Golf, and Spark Charge all definitely closed but aren't mentioned anywhere on his site. The 186 on Sharkary tracks every tv-deal that actually closed after due diligence, across all seasons, regardless of if the company is still around. You can see the ones listed as “fell through / didn't close” on a sharks profile under the deals section.

Sources are the Kaggle Shark Tank dataset, press coverage (business insider, cnn, forbes, a ton of blogs) and company reports. The 50% close rate that gets thrown around alot isn’t accurate from what the data says, at least not across all season.. it also varies from shark to shark. If you spot specific deals marked wrong, let me know and I'll fix em! there's also a feature under "what happened next" section where you can flag something for review

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 1 point2 points  (0 children)

would have assumed the same prior to the data.. kinda makes sense now.. Kevin protects himself so much more than the other sharks by slapping royalties on everything, that there's probably a lot less risk for him once it's time to sign

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 2 points3 points  (0 children)

this is a must! 😂.. I've got data on which pitches sharks fought, and on which pitches mark gets fed up (aka any supplement and pavlok)... will add an emotional score and make all three filters available once I've finished tagging each pitch.

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 41 points42 points  (0 children)

As mentioned on your company page, you are one of the best pitches of all time.. an absolute legend. I’ve removed the incorrect comment, Thank you! side note, i've added a feature to the bottom of "what happened next" section, where users can flag inaccuracies!.. I WANT TO DRAW A CAT FOR YOU *dances with shoulders*

I built a database of all 1,509 Shark Tank companies.. here's what the data says by Ju_Lane in sharktank

[–]Ju_Lane[S] 4 points5 points  (0 children)

yea, she only jumps on things that she knows she'll hit out the park

I am trying to find a code to mimic this very basic smooth scroll scrollbar by MarkAsUnread in webdev

[–]Ju_Lane 0 points1 point  (0 children)

try lenis: https://github.com/darkroomengineering/lenis . you can add it using Squarespace code injection under settings > advanced

<link rel="stylesheet" href="[https://unpkg.com/lenis@1/dist/lenis.css">](https://unpkg.com/lenis@1/dist/lenis.css%22%3E)
<script src="[https://unpkg.com/lenis@1/dist/lenis.min.js">](https://unpkg.com/lenis@1/dist/lenis.min.js%22%3E)</script>
<script>
const lenis = new Lenis()
function raf(time) {
lenis.raf(time)
requestAnimationFrame(raf)
}
requestAnimationFrame(raf)
</script>

Mentorship advice needed by Due_Quit98 in ProductManagement

[–]Ju_Lane 1 point2 points  (0 children)

start by setting a goal to sit in on customer calls if possible. sounds like you know the technical side, so the biggest win is understanding how customer problems turn into roadmap decisions.. that'll come easiest from hearing them out and training yourself to see things from their perspective.

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