Thursday 03/09/2017 by Icculus

NEW HF PODCAST: TWEEZER + DATA = BLISS

[The following is brought to you by the team behind the Helping Friendly Podcast, dot net users @swittersdc, @mdphunk, @rowjimmy, and @brad10s. -charlie]

For many (most?) fans, there’s no greater gift at a Phish show than a "Tweezer." Phish experiences are formed and strengthened based on single performances of this song. Shows can be saved by its appearance, soaring out of the speakers with Superman cape and all.

To do an episode about "Tweezer" seemed at the same time overwhelming and almost unfair, because there’s just so much to it. But our friend and collaborator mdphunk took us deep into "Tweezer," in the most ambitious way possible—with data. This week’s podcast is part 1 where we discuss and listen to versions of "Tweezer" from its debut in 1990 all the way through 2000. We will pick up part 2 in the next month or so.

Building on the great work of everyone at Phish.net over the years, Matt developed an interactive data dashboard that slices and dices "Tweezer" in almost every way you could imagine. See the whole data set here. This is, in our view, every Phish nerd’s dream come true. And we hope that it leads to more collaboration among the community, building this out and possibly doing similar projects with other songs. Please send us feedback and thoughts!

To give you a taste of what’s there: Of course, you can cross-reference the Phish.net "Tweezer" jam chart with this data set to see "Tweezer" jams by year.

Also, have you ever wondered how many Tweezers ended in a complete stop vs. a partial segue vs. a full segue? See below for the answer.

Have you ever wondered which day of the week was most likely to have a "Tweezer?" See the visual below (never miss a Saturday show?).



Did you know Indiana has only been graced with three versions of "Tweezer," while neighbors Ohio and Michigan have had a total of twenty-six (26)? And our personal favorite: what is the most commonly played song right after "Tweezer?" You’ll have to dig into the data set to find out.

We want to reiterate our thanks to the Phish.net community for laying the groundwork and making this project possible. Hope you enjoy, and please send us your thoughts and comments.

You can subscribe to the Helping Friendly Podcast on iTunes, and follow HF Pod on Twitter.

If you liked this blog post, one way you could "like" it is to make a donation to The Mockingbird Foundation, the sponsor of Phish.net. Support music education for children, and you just might change the world.


Comments

, comment by mcgrupp81
mcgrupp81 This is good but I think you could have snuck some qualitative analysis in there as well. I'd be interested in seeing the variance in performance quality. In each tour or year or era, how long would it take before I heard a top notch version? It would be interesting to see that taken to a set or entire show level as well. Granted, most fans probably already have well established ideas on this, but a visual representation like you have done here, would be interesting. You can really get crazy with BI and you could probably spark some debate for the right topic. Kudos for the time you've put into this!
, comment by mdphunk
mdphunk @mcgrupp81 said:
This is good but I think you could have snuck some qualitative analysis in there as well. I'd be interested in seeing the variance in performance quality. In each tour or year or era, how long would it take before I heard a top notch version? It would be interesting to see that taken to a set or entire show level as well. Granted, most fans probably already have well established ideas on this, but a visual representation like you have done here, would be interesting. You can really get crazy with BI and you could probably spark some debate for the right topic. Kudos for the time you've put into this!
Thanks for the feedback! One of the short term goals is to hook the dashboard up to the Phish.net API so it can refresh data in real time. At the same time, we can pull in thew review data and so much more (we are just using basic when/where/how data at the moment). Of course, that data will only be as effective as the subjective nature of reviews, but I agree that it will be a cool dimension to layer in!
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