Major League Baseball (MLB) has a long history of using the minors as a sandbox for rules reforms - with the recent testing and rollout of the pitch clock as a prime example.
This season, the “robo ump”, formally known as the automatic ball-strike (ABS) system, is being trialed across all of Triple-A. This system implements the strike zone as a rectangle set in the middle of the plate, measuring 17 inches wide and spanning between 27% and 53.5% of the batter’s height.
Despite a surprisingly-solid record of 36-37 out of the gate for the Washington Nationals (thank you pitching staff!), one recurring theme has bugged me: a lack of patience by our hitters.
Too often, I’ve felt, have they swung at sub-optimal pitches early in at-bats, leading to mediocre contact and letting opposing pitchers skate through the game with low pitch counts. “If only they could be patient like the Yankees!”, I’ve murmured to myself, too many times to count - “then they’d be able to get the right pitches to drive”.
Earlier this week, I saw a post on one of my favorite internet forums (Airliners.net) which shared some cool data: the number of international passengers at the 30 largest US airports, split by foreign and domestic passport holders.
I’ll let the original poster LAXdude1023 point out what is meaningful about this data:
“While the below is NOT O&D data, it does give a significant window into each market. A higher concentration of foreign passport holders implies that the international demand for the market is more skewed towards foreign point of sale. A high point of US sale would imply that market demand comes from the US.”
For the 2024 season, Major League Baseball (MLB) upgraded the Statcast cameras in each stadium so that path, extension, and velocity data can be captured for every swing. This data is now available in the Statcast portal, and it’s so cool that I couldn’t help but mess around with it!
To be honest, I haven’t had the time to put together a really thoughtful analysis of the data and how it interacts with batted ball metrics, pitch locations, game situations, etc. - but I have started visualizing the bat tracking data at an individual and team level, and found that it’s still pretty cool as a standalone dataset!
A few months ago, I spent a week in Paris with my mom and sister, visiting museums and exploring the city. One of the more exciting things we stumbled upon was the 2024 Summer Olympics countdown timer in the Place de la Concorde, which signaled 7 months until opening ceremonies - pretty cool to see!
As we read about the government’s plans to host events throughout the city, we discovered that they intend to use the Seine to host swimming events - a proposal our group found to be completely laughable. If you’ve seen the river in person, you’ll know exactly what I mean - it’s wide, full of current, and notorious for being the final landing place for the city’s sewer runoff - so it’s no surprise that swimming has been illegal since 1923.
Last February, I began writing an app for my Tidbyt smart display. This April, at long last, I finally published it!
If you recall my initial post on the topic, I had planned to create a flight arrivals/departures board for my home airport, LAX. However, difficulties with metered API access (most data sources had low daily API caps of 50-200 uses) and schedule caching pushed me towards a “simpler” question to solve - can I identify planes flying overhead?
Last year, the Washington Nationals finished 12th in baseball in stolen bases. This year, they’re 1st. What’s changed? In a word: Davey.
If we look at all managers coaching the same teams in 2023 and 2024, we find that Martinez has changed his approach the most, with his club more than twice as likely to attempt steals of second base. Changes in personnel are surely a factor, but it’s nevertheless remarkable to see an MLB manager change his approach so significantly on a year-over-year basis. I certainly thought it was a noticeable (and notable!) trend to open the season - wanted to write a quick post here to share!
While not the primary focus of this website, the US housing market is nevertheless a notable interest of mine and something that I will post updates on from time to time.
Last summer, I wrote a piece hypothesizing that the large share of current mortgages being held with now-unattainably low rates would interact with the still-expensive price of homes to limit liquidity in the housing market by 1) “trapping” current homeowners in their current mortgages (“golden handcuffs”), reducing supply and 2) suppressing demand from buyers who cannot afford to buy homes which remain expensive, but have much higher borrowing costs.
A few weeks ago, I posted some work-in-progress code that I’ve been putting together to identify planes flying overhead. One thing that I’ve really wanted to include in that data output, but which is not natively returned by the OpenSky States API, is the model of the plane flying overhead.
This post shows the unorthodox way that I’ve managed to make model information programmatically available, so that we can quickly query for any given plane callsign and retrieve its typecode. Let’s talk it through!
This morning, the NFL Players Association (NFLPA) released their latest set of Team Report cards, following last year’s inaugural data. These report cards include grades by 1,706 players, scoring their own teams on a variety of player experience dimensions from A+ to F-.
This year, we saw Head Coach and Owner grades added on top of the eight areas from last year:
When this data came out last year, I found the presentation a bit lacking - the NFLPA just dumped the data into an ugly, unwieldy table! As such, I found it an easy and valuable exercise to build two sets of artifacts to better communicate the report cards: first, a league-wide heatmap of grades, and second, division-by-division circumplex charts. Feel free to take a peek at that post to see the data and how I put those charts together.