One of my favorite aspects of baseball has always been its grounding in statistical reality - while every player has their own style, everybody is shooting for the same things: Hits, Runs, and RBIs. What I find particularly interesting is that there can be an enormous difference in what kinds of players achieve the same statistical milestones. Sure, Giancarlo Stanton hit 39 home runs last season - he’s a beast.
Despite losing Bryce Harper to the Phillies in Free Agency, expectations were high for the Washington Nationals opening the 2019 season. With two twenty-something phenoms starting in the outfield (Soto, Robles), one of the best players in baseball at the hot corner (Rendon), an offensively dynamic shortstop (Turner), a revamped catching corps (Gomes, Suzuki), a third ace (Corbin), and the reigning best pitcher in baseball (Scherzer) still taking the mound on Opening Day, the Nats projected to be the most talented team in perhaps the toughest division in baseball.
Earlier this month, the City of Chicago became the first major American city to make rideshare data public. Following a number of recent controversies over user anonymity and privacy in publicly-released location data, the City performed an extensive amount of data de-identification before making the datasets public. With datasets for trips, drivers, and vehicles all available, I thought it would be fun to play around and see what we might be able to find in the data!
The Special Counsel’s Investigation into Russian interference in the 2016 United States election, better known as the Mueller Report, has been one of the more fascinating stories of our time. Responsible for a number of indictments and convictions of political operatives related to the President and the Trump campaign, the Report ultimately did not conclude that the President committed obstruction of justice beyond a reasonable doubt. It did, however, indicate that it could not clear the President of those allegations either - leaving the truth-seeking portion of the US public in a tough spot.
Following their rival Lyft’s recent public offering, Uber’s much awaited IPO seems to be finally coming to pass. On Thursday, Uber filed their S-1 with the Securities and Exchange Commission (SEC), which outlines the details of their current operations, financial health, and planned use of offering funds. While a variety of other outlets have reported extensively on some of the details in the filing (such as the fun fact that a quarter of Uber’s revenue comes from only five cities), I thought it might be handy to build a few charts to more easily visualize some of the financial metrics being reported.
With the NBA regular season concluded and the playoffs now on deck, I thought it would be fun to peek back at the players and teams that defined this season. There’s a million things one could investigate, but I wanted to call out just a few of my findings that really resonated in combination with what I observed watching games this season! MVP Candidates It’s commonly accepted that there is an inverse relationship between efficiency and utilization - as a player bears more offensive responsibility, their scoring typically becomes less effective on a marginal basis.
I was recently reading Hacker News and noticed a post linking to the USCIS H1-B data repository, which contains information on the H1-B Visa applications process and statistics about visa submission, approval, and rejection by Citizen and Immigration Services. As a bit of background, the H1-B visa program was created to allow US employers to employ foreign nationals in jobs requiring specialized knowledge and a bachelor’s or master’s degree. H1-B visas must be sponsored by an employer, and are typically valid for 3 - 6 years.
Given Major League Baseball’s status as the only major American sports league without a salary cap, I’m always interested in seeing how teams choose to allocate their resources every year. There’s a lot that plays into how much any given team will spend on their roster, depending on everything from how badly a team is looking to compete, the proportion of their production coming from young players vs older players, how their local cable TV deals are looking, and how much of a dip each team takes in that season’s free agent market.
With March Madness right around the corner, I thought it would be fun to visualize a bit of data about each region and team, with the end goal of being a bit better informed for filling out my bracket! The data below is mostly drawn from Ken Pomeroy’s college basketball rankings, and is intended to give a brief overview of the teams and regions in this year’s tournament. Comparing Regions Right off the bat, one of the things that jumps out is how tough the South region is from top to bottom.
After writing my recent article about Bryce Harper’s signing with the Phillies, I started getting really excited about the return of baseball! With Spring Training in full swing and most free agents signed and with their teams, we can start to look at how each team projects to perform in the 2019 season. As a big Washington Nationals fan, my attention naturally gravitated to the NL East. Take a look below to see how each team in the division projects to stack up!