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The seeding is set, and March Madness is officially upon us. For the teams competing in this year’s Kaggle March Machine Learning Mania, it’s T minus two days until forecasts are locked in— and the same is probably true for your office pool, as well.

As Bradley Carlin describes, in his 1996 paper The American Statistician, there is nothing new about this exercise unto itself.

Perhaps the oldest inferential problem related to sports statistics is that of predicting the ultimate winner of some event, based on whatever information is available concerning the various competitors.

But, these days, from building your…

Given the political status quo, it seems like there’s no ‘right’ way to prepare for the time ahead. And, since the future feels like one giant Wild Card, I’ve been revisiting my hot takes on history (e.g. lessons gleaned while reading The Plantagenets: The Warrior Kings and Queens Who Made England by Dan Jones) for advice. Plus, it’s nice to know this isn’t the first time civilization has dealt with leaders who could be outrageously menacing (see King John re. eyes and tongues of priests in Lesson 4).

What a mess life could be in the Plantagenet empire! The best…

My town police log has kept me riveted ever since I learned to read. I’ve sent snippets to friends over the years, but decided to point out some recent gems on Twitter last week, which I’m sharing below (for posterity’s sake).

Type 1. People who call and want nothing done

While I’m sure there are circumstances in which it’s good to have things “on the record,” for the life of me, I cannot think of how that would apply here. Is he planning on entering this information into evidence in a forthcoming judicial proceeding?

A non-rhetorical question.

Obviously this post has the advantage of being somewhat self-answering. However, if you’re in the mood for answering another question, I’d love some feedback re. my last post: “What’s the point of the unicorn analogy?”

Unicorns, Narwhals & the Existential Crisis of Data Scientists

If you’ve been on the internet of late, chances are you’ve happened upon a reference to data scientists as “unicorns.” Unicorns (of course) are mythical creatures— meaning they do not exist. They are not rare— they are imaginary. And so, when I see something like the image below (excerpted from DataCamp’s otherwise charming infographic outlining the various roles in the data science industry), I can’t help but to feel a bit puzzled.

“Data Scientist: As Rare As Unicorns”

I’m not so literally minded as to miss the point of the analogy entirely. As an “emerging” data *something*, I’m acutely aware of how vast the skill set…

Contrary to my current actions (writing this post), I’m a Medium “user” primarily in the reading-sense of the word. That being said, every now and then I read a post that inspires me to respond, and, in so doing, “publish” something of mine own. I’m a data nerd by day, so it’s near-impossible for me to resist clicking on the Stats button — thus, I have discovered a few responses (read: “comments” in popular web parlance) with precisely zero reads. An extra-large Medium fail, if ever there was one.

I’m not looking for accolades, or recommendations (see Chaz Hutton’s tongue-in-cheek…

Mara Averick

Non-profit #datanerd, #civictech enthusiast, #NBA stats junkie, using #data4good (and/or dominating fantasy sports), less popular half of @batpigandme

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