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!

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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:

• Treatment of Families
• Food/Cafeteria
• Nutritionist/Dietician
• Locker Room
• Training Room
• Training Staff
• Weight Room
• Strength Coaches
• Team Travel

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.

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Looking at Statcast’s leaderboard of the hardest-hitting players in baseball makes it all seem so easy: simply acquire the guys who smash the ball the hardest, and then profit!

It looks almost foolproof - only Kyle Schwarber and Giancarlo Stanton put up below-average WAR figures from this group of 15 hitters, and most of the obvious best hitters in the league (Judge, Acuña, Ohtani, Seager, Alvarez, Soto) are prominently featured:

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One of the many things I enjoy about living in Santa Monica is that it sits directly underneath the Western/Northern approach to LAX. Every 4 minutes, or so, a plane will fly directly overhead, having come across from Malibu, before continuing inland towards Downtown Los Angeles. There, it will execute a big sweeping turn that aligns it with the incoming traffic from the east, which it will zipper into for the final descent westward towards the sea.

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The past month of NBA basketball has been marked by a staggering amount of individual offensive brilliance, with Luka Doncic and Joel Embiid both going for 70 points, and Karl-Anthony Towns, Devin Booker, and Steph Curry all hitting the 60 point mark.

Prompted by this unique explosion of point scoring, I wanted to take a quick look at the history of high-scoring individual games, show how the recent events are really just the exclamation point on top of a long-term trend, and demonstrate how changes in the league’s offensive landscape have created a landscape more conducive to these outbursts.

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Earlier today, ESPN reported some bombshell news about Shohei Ohtani’s new contract: it’s heavily backloaded.

So heavily so that Ohtani will only take $2M of salary each of the next ten years, with the remaining $680M (over 97% of the value) paid out between 2034 and 2043.

Time Value of Money and Net Present Value (NPV)

The heavily deferred nature of contract piqued my curiosity for what the deal should realistically be valued at today. We all know that a bird in hand can be worth two in the bush - the longer the time horizon for a nominal sum to be paid back, the less valuable it is to hold that sum today.

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Litte Pictures Competition

Earlier this fall, I stumbled across a fun data visualization competition called the “Little Picture” competition, advertised with this delightful call to action:

help us transform decades of satellite climate data to compelling, impactful “Little Pictures” that spark awareness and drive action

As you might guess, I couldn’t resist putting a submission! Now - I am not sure I am technically even eligible to submit an entry to the competition, as the eligibility guidelines seems to target citizens of European Union (EU) member states and European Space Agency (ESA) contributors. But… I did some work on the graphic while I was in London, so I’m just gonna send it in and see what happens??

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Earlier this week, I came across an analysis with some interesting data and takeaways, but one of the worst data visualizations I’ve seen in a long, long time.

After some very careful study, I was able to make out that openness to purchasing an electric vehicle (EV) or hybrid surged in 2021, but has stagnated in 2022 and 2023. However, the chart is so unintuitive that it’s hard to quickly come to that conclusion - I can’t actually believe a big company like S&P Global Mobility would publish something like this!

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Last summer, I finally caved and bought an air conditioning (AC) unit for my apartment living room. With my job fully remote and a very hot summer season in LA across 2022, it was money well spent - no longer did I sweat at my desk in the afternoons.

Unfortunately, no good deed goes unpunished - I quickly realized that the AC unit consumed significantly more energy than the rest of my apartment put together! In turn, my electricity bills jumped up from the $40ish/month range to the low hundreds - quite a bit of money.

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Pythagorean record is one of those useful rules-of-thumb that every veteran baseball fan knows to consult when there’s an anomaly in the standings.

“Well, you see, the Mariners and the Rangers both have 74 wins - but the Rangers have a +173 run differential, and Seattle’s only at +102… I’d expect Texas to pull ahead.”

Regression to the mean is an incredibly powerful factor in sports, and Pythagorean record helps capture it much better than pure Win/Loss record, which is subject to the many idiosyncrasies of the sport and tends to be less predictive of future outcomes.

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