Making Sense of DC’s Public Salary Data

In the interest of transparency, most government institutions post the salary data of their employees, so that the public can have insight and visibility into where their tax dollars are going. FederalPay.org in particular is a great resource to look up the salaries of federal employees. However, this same set of tools does not typically exist for local and state governments.

In the case of the District of Columbia, the DC Department of Human Resources posts a quarterly update of public employee salary information in PDF form. While useful, this information is quite unwieldy - there are over 36,000 rows and there aren’t any easy ways to manipulate or aggregate the data beyond simple search.

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Soto’s Debut

Nationals fans eagerly anticipated Juan Soto’s arrival to the major leagues, and boy, did he deliver. In his second MLB at-bat, and his first start, Soto laced a 422 foot drive off Robbie Erlin, driving in 3 and showing why he made such meteoric rise through the National’s minor league system.

Soto’s promotion was the result of a particularly ill-timed injury to do-everything superstar Howie Kendrick, who was pressed into an everyday role at second base and the outfield following injuries to Daniel Murphy and Adam Eaton. With Kendrick now out for the season following Achilles surgery and Eaton recovering from ankle surgery, Soto looks to get plenty of run for the next few months as the Nationals assess exactly how special he is.

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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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