I love the Home Run Derby. Though initially disappointed by the move to timed scoring rather than the traditional “10 outs” framework, I’ve had an amazing time watching sluggers like Aaron Judge go up against the clock in today’s bracket-style tournament. With no “monster” sluggers like Judge or Stanton in tonight’s Derby, things are a bit more wide open - I think you could make a case for a number of different players tonight.
Baseball is a sport of inches - one half inch here and a fly out turns into a home run, one half inch there and a liner turns into a double play. Timing is also critical - striking with runners on and in scoring position is so much more valuable than a parade of solo home runs. In 2013, the St. Louis Cardinals were able to capitalize on this more than any other team in baseball history, riding a 0.330 batting average with runners in scoring position (RISP) to a 97-win season and World Series appearance.
Chris Paul. James Harden. LeBron James. Rajon Rondo.
It’s tough deciding on who the best passers in the NBA are - everyone’s preferences are different, and there’s basically an option for everything. Love full court outlet passes? Kevin Love’s your man. Laser-like kickouts for three? John Wall is happy to show you what he’s got. With so many different aspects of the basketball becoming more science than art, it’s refreshing to watch the NBA’s best passers work their magic in their own particular ways.
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.
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.
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.
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:
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.
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.
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.