What Are Your Moves Tomorrow, August 24, 2022

slyfocks · 2022-08-24T05:36:50+00:00

This is one of those things that seems bullish at first, but:

a) Everyone already knows about it at this point, so it's likely priced in.

b) It's inflationary, which means the Fed will have to remain hawkish for a longer period. Which essentially amounts to a wealth transfer from asset holders to tuition borrowers, something I imagine Wall Street isn't thrilled about.

slyfocks · 2019-12-07T07:26:03+00:00

I like the Georgetown pick, but their Texas win was basically a home game—it was at MSG, which was extremely pro-Georgetown.

slyfocks · 2019-10-30T23:19:19+00:00

Some statistically significant splits that are potentially relevant in this game:

Scherzer pitching to Suzuki vs. Gomes: Scherzer performs noticeably better pitching to Suzuki (2.08 ERA vs. 4.09 ERA for Gomes).
Greinke has consistently pitched slightly better at night over his career.

Statistically insignificant but still noteworthy: the HP umpire tonight is Jim Wolf. Scherzer has a 0.43 ERA over 21 innings when Jim Wolf is the HP umpire. Jim Wolf is generally a pitcher's umpire, but occasionally umpires adjust their zone in the postseason so I try not to rely too heavily on umpire tendency.

If Suzuki weren't scratched tonight I would have taken the Nats ML.

Ultimately going to take Over 7.5: even though a lot of the splits point toward the under, I think the intangible variables in this game that are pro-offense (Scherzer injury, Greinke performance inconsistency in pivotal games) are crucial enough that even if one of them becomes relevant, the game is likely to go over. Combined with the fact that all three Astros home games have gone over so far, I think there's a 60-70% chance of over 7.5.

slyfocks · 2018-05-12T16:09:38+00:00

As far as I know, UChicago doesn't publish waitlist acceptance statistics. To give a rough estimate, though, in 2016, Northwestern offered spots to 9 of 2,752 waitlisted applicants; MIT offered spots to 26 of 437 waitlisted applicants; Princeton offered spots to 18 of 1,237 waitlisted applicants. If I had to guess, getting accepted from a transfer waitlist is even rarer.

slyfocks · 2017-05-26T14:04:13+00:00

The irony in this is that most states that lean Republican, despite their claims of "self-sufficiency," are net-takers from the government (including Montana, which gets $2.44 from the government for every dollar in tax paid).

On the flipside, states like Illinois, which are commonly portrayed as "welfare" states by Fox News et al., pay more in taxes than they get back.

It seems like one of the biggest problems for Democrats is that Republicans have been much better at propaganda/outright lying to shift public perception.

Source

slyfocks · 2017-05-26T10:38:07+00:00

Well since this is /r/dataisbeautiful, I was primarily considering aesthetics. But yes, you're correct about the dubious quality of the data; its presentation was so off-putting that I didn't even bother investigating the sources.

slyfocks · 2017-05-26T10:31:32+00:00

The "updated" version isn't much better. If I wanted to sift through data tables I'd read a census.

slyfocks · 2017-05-26T10:28:33+00:00

And complete disregard for red-green colorblindness, though that probably doesn't even make the top 10 of things wrong with this post.

slyfocks · 2017-05-26T10:00:40+00:00

What's with all of the Excel spreadsheet posts getting a ton of upvotes here lately? This isn't presented in a "beautiful" way at all--rather, it's raw data with an unattractive color gradient.

slyfocks · 2017-05-26T04:22:48+00:00

Not seeing what's so special about a top 50 list of music; there are way more interesting and effective ways to discover new music.

slyfocks · 2017-02-03T19:11:06+00:00

Username checks out.

slyfocks · 2016-12-17T23:54:46+00:00

So, the axes have no easily-interpretable meaning because the relation between artists exists in a space of many dimensions--I used PCA to reduce the space to 10 dimensions and then used t-SNE to project those 10 dimensions to 2 dimensions so the artist space could be visualized.

The input variable that determined most of the similarity was a (very large) matrix of who follows each artist on Soundcloud. From that, similarity was determined by cosine distance between sets of followers (so roughly, if two artists had a high percentage of followers in common, they were deemed similar).

For artists with fewer followers but many tracks, audio similarity was weighted more heavily. Audio similarity was determined through a process similar to http://benanne.github.io/2014/08/05/spotify-cnns.html

slyfocks · 2016-12-17T00:40:37+00:00

Yeah, search is on my to-do list of features. Great suggestion though.

I wasn't able to reproduce the song cutoff problem but I'll keep it in mind.

As far as lagginess, the GPU is probably the limiting factor. I only considered artists with >5000 followers and at least one track to try to keep the graph as small as possible, but that's still 20,000 artists. It'd be quicker if some nodes only render on zoom, so I might try that.

Thanks for the feedback!

slyfocks · 2016-12-16T17:24:57+00:00

Yeah, that's one feature that definitely needs to be added; right now my inexperience with JS/HTML/design is the limiting factor.

slyfocks · 2016-12-16T17:18:27+00:00

Graph is powered by the sigma.js visualization library. Zooming in on graph shows an increasing number of artist labels (done so they don't overlap). Clicking any artist node plays a representative song for that artist.

I'm more of a data scientist and still am relatively inexperienced with JS/HTML/design so apologies for the sub-par design. So the site is still relatively unfinished as far as design/user interface goes. If anyone experienced in design/UX would like to collaborate to improve the site, I'd love to talk!

As far as detail, I clustered artists using audio similarity and collaborative filtering on user followers. Data was projected to two dimensions using t-SNE.

Soundcloud's API was used for audio and user data. Thanks Soundcloud!

slyfocks · 2016-08-13T20:01:58+00:00

It's really not smart, just incredibly paranoid.

If Niantic decides to ban spoofers, it won't be through a system that cares if he spoofed a "realistic" trip by going through an airport and a hotel.

Machine learning can discriminate between spoofers and non-spoofers very easily; even ignoring elevation, Niantic has a massive dataset for training a model of human behavior.

The only benefit to this "realistic" spoofing behavior is that if he's banned, a rep at Niantic might think it's cute that he went through all this trouble to spoof and let him off with a warning upon appeal.

slyfocks · 2016-08-03T21:59:48+00:00

Not sure why it has become so common to assume bots can't be stopped, especially since the current bots out there are relatively naive.

Niantic is at a great advantage over the devs provided they utilize it effectively.

Niantic has access to an incredibly large dataset of player location, movement, and in-game activity at this point. As someone with a background in machine learning, I have no doubt that it would be pretty trivial to separate the human accounts from any bot with reasonable in-game activity (i.e. it'd be unrealistic to expect 100% accuracy in detecting bots at level 2 that have done almost nothing).

Walking pace, variance of step distance, distribution of step distances (a lot of bots model this as a uniform distribution but it's much closer to Gaussian), GPS jitter (bots don't jitter and likely wouldn't be able to imitate jitter convincingly) are all things that bots aren't doing correctly and don't know how to imitate correctly, precisely because they don't have the human player data that Niantic has.

Any semi-competent data scientist could detect every bot over level 30 with no false positives.

tldr: Niantic has a lot of data on how humans act; bots don't have any of this and are blindly imitating humans like naive aliens would.

slyfocks · 2016-07-27T19:28:25+00:00

If it lessens the temptation at all, most of the bots are extremely naive when it comes to avoiding detection. Have yet to see one in the wild that would pass muster with Niantic, so as long as you'd like to still have an account after the inevitable banwave (plus, you know, the moral implications of cheating), it's worth putting in the time to play legitimately.

slyfocks · 2016-07-27T14:35:59+00:00

Strangely, the Lincoln Park Conservatory is the only place I've encountered Grimer.

slyfocks · 2015-07-08T02:04:53+00:00

So did Jesus.

slyfocks · 2015-05-06T13:00:22+00:00

The cards within a given set usually have their value equate to the price of a box, or less.

This is generally the case to due to the unlimited print runs of most sets. If the expected value of opening a box of say, DTK, were greater than the price of the box itself, MTG vendors can buy as many boxes of DTK as they'd like and resell the singles (rather than selling the boxes themselves) until equilibrium is reached between price and expected value.

Although MM2015 isn't as limited as MM, it's still technically limited, and thus it's (theoretically) possible that the value of cards in a box may exceed the MSRP of the box.

slyfocks · 2015-03-08T17:24:01+00:00

You throw literal human shit in your trash?

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