Most fantasy football leagues have a notion of “start percentage” for players - as ESPN says, “start percentage shows the number of fantasy leagues a player is started in divided by the number of leagues he is eligible in. This helps indicate how the public views a player.”

Watching football last weekend, I was struck by the number of touchdowns being scored by users who I knew weren’t being started! Particularly in the Broncos vs Chargers matchup, it seemed like the players who might have been “broadly started” in fantasy (like Courtland Sutton and Ladd McConkey) did very little, while the following fantasy-unknowns all grabbed receiving scores: DeVaughn Vele, Michael Burton, Hassan Haskins, and Derius Davis.

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Well, at least the wait is over. In the latest bit of intra-NL East star player migration, yet another former National has signed a megadeal with a hated rival.

Throughout the Boras-led bidding process, I had expected the Yankees to retain Juan Soto, but it seems that Steve Cohen decided he would simply not be outbid, and the Mets flexed their financial muscles to lock Soto down with a fifteen year deal valued at $765 million.

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Earlier this fall, I entered into a machine learning contest to predict plane take-off weights, organized by the Opensky Network, an aviation group. A week or two ago, I finished a write-up of the final model I built for the competition, overviewing the approach I used to engineer features and train and test my model.

Since finishing that post, I’ve been excited to point my attention to a planned follow-up post: understanding what the winning submissions did to outperform everyone else!

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The past few months I’ve used my spare blog energy to work on a fun project that I’d like to loop everyone into!

On August 1st I received an email from the OpenSky listserv (I became a member while building) my Planes Overhead app for the Tidbyt) announcing a data science challenge, focused on predicting plane take off weights. This challenge felt precision targeted for me, as a great lover of planes and machine learning dilettante!

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I’ve got a long-running project in the works which involves temperature sensors that I’ve deployed in a number of locations in Santa Monica. Earlier today, it came time to finally go recover those temperature sensors - I wanted to share a little tool I made to make the job quick and easy!

The Task

When I put out the sensors, snapping a quick photo was all I did to track their location. I knew that iPhones record the locations where photos are taken, by default, and assumed I would probably just flip through and manually transcribe the list of coordinates when it came time to retrieve the sensors.

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It’s been a tough year to be a Washington Nationals fan. It’s been a tough five seasons honestly… since that amazing 2019 World Series win over the Astros, the franchise has been mired in a teardown and rebuild, which is finally showing some signs of progress but has yet to deliver consistent winning baseball.

One thing that’s particularly evident when watching the team is the degree to which the Nats get outhomered by their opponents. While the pitching has been surprisingly respectable this year, the offense has been much too reliant on small ball and baserunning to generate runs, which makes it very difficult to score in batches and compete in a brutally competitive NL East, full of clubs that have no problem putting the ball out of play.

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With the NFL season right around the corner, I thought it would be fun to preview the potential impact of one of the biggest rule changes the NFL has made.

Anyone who has watched football in the past decade knows that kickoffs have been a “dead” play - kickers are so good at generating touchbacks that we rarely see returns, formerly one of most fun plays in the game. Furthermore, when returns do happen, injury rates have been concerningly high, with tacklers and blockers colliding at high speeds after long run-ups.

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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.

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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”.

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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.”

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