Background

My dad recently came to me with an interesting proposition - cracking a combination lock where the first and last numbers are (probably) known. He has a lock where the first and last digits are 6 and 4, respectively, but is looking to determine the middle two digits and hopefully crack the overall combination.

My first thought was to simply generate all the possibilities of numbers - a relatively trivial task, easy but long. This, however, wouldn’t have any sort of predictive aspect at all - when randomly generated, the probability of a 6004 would be the same as a 6474.

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In an effort to determine the league’s best rim protectors, I’ve put together a statistic I’d like to call RIMD: a weighted efficiency statistic that seeks to measure how effectively players can defend shots at the rim, giving preference to those who contest a large volume of shots each game.

With the basic NBA Advanced Stats for rim protection in hand (looking at all shots 0-6 feet from the basket), here’s how to calculate RIMD for a given player:

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A Primer on Usage Rate

Usage rate describes the percentage of team plays used by a particular player while that player is on the court - basically, how likely a given player is to end a possession with a field goal attempt, free throws, or a turnover. This is a great indicator of how “ball dominant” a particular player is, and the degree to which a team’s central offensive player finished possessions can tell us a great deal about how that team plays on offense.

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Partly in an effort to learn Tableau a little better, partly in an effort to see what the Nat’s historical payroll looks like, and partly because I hadn’t seen a tool like this before, I put together a Tableau Public graphic detailing each MLB team’s payroll spending since the turn of the millenium. Check it out! Shoutout to Baseball Prospectus and Cot’s Contracts for supplying the underlying yearly data.

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A recent study by food scientists titled “Uncovering the Nutritional Landscape of Food” ranked the world’s healthiest foods, focusing on options which will help fulfill, but not exceed, your daily nutritional requirements. In this context, foods which are nutritionally well-rounded and adaptable to a variety of diets rate out highly, while more “one sided” foods slip down the rankings.

Luckily for us, the researchers also published the data they used for the study, allowing us to view and manipulate the information. I’ve built a tool using R and Shiny to get a quick-and-dirty look at the data, so that users can quickly and easily see the healthiest options available when looking for protein, fat, or carb-centric foods.

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I’ve been mucking around in R quite a bit lately and have grown tired of all the annoying configuration changes that are required to take your data/analysis from one machine to another. Even with my code in a Git repository, I was still dealing with package inconsistencies across R environments, differing file paths, and more - needless issues that took me away from the analysis at hand.

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This year’s All Star Game pitting Team LeBron vs Team Steph was an unexpected delight - seemingly the first time in years that any actual defense was played (thanks Joel Embiid!) and the players truly cared about the outcome. Accordingly, the total points scored dropped from last year’s all time high of 374 to a more “normal” 293.

That’s still a ton of points though - certainly a lot more than any old regular season basketball game… which got me thinking - was the All Star Game always like this? Take a peek at the Tableau visualization I put together to take a look at the question - it’s clear that there has been rampant score inflation over the last 5 or 6 years after a long period of relative stability. We’ll have to see if this year is any indicator of All Star Games to come, but at the very least it was a blast to see Team LeBron and Team Steph go down to the wire.

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Overview

Last time I posted about the Cavs, they were on the mend and in the midst of a 13-game winning streak. Since? Not great. The Cavs have been one of the worst teams in the league since, posting the NBA’s 5th worst Net Rating of -4.5, ahead of only the Magic, Nets, Kings, and Suns. Poor company for a team that fancied itself a perennial contender and Eastern Conference hegemon.

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Trading Blake makes the Clippers Better

There’s already a general consensus amongst NBA talking heads that the Blake trade was a smart move for the Clippers - getting off the nearly $140 million remaining on his contract will dramatically increase their flexibility in building for the future. What makes this deal especially interesting, though, is that I also think it makes this Clippers better, this season.

It’s common wisdom in the NBA that the team getting the best player usually wins the trade… not so here. With the Clippers battling injuries up and down the lineup (Gallinari and Beverly in particular), acquiring Tobias Harris and Avery Bradley gives them two new options who can both space the court and create for themselves as needed.

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Backstory

A little while ago, I impulse purchased one of Amazon’s $20 “AWS IoT Buttons”. These devices, which are really just souped-up and customizable versions of Amazon’s extremely popular Dash Buttons, allow developers to connect to and trigger actions within Amazon Web Services (AWS). As you might imagine - people much smarter than myself have found about a million things to do with these little guys: everything from ordering pizza and Sweetgreen to triggering IFTTT (If This Then That) actions.

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