Yesterday I wrote a quick blog post discussing how the distribution of field goal attempts by distance has shifted quite a bit over the last twenty five years: teams are attempting fewer short field goals and more long field goals.

In this post, I’ll look to round out my analysis, focusing not on attempts, but the actual make rate of kicks by distance, and how that has evolved over time.

Visualizations

What we find when we slice the data by time is that kickers have NFL kickers have become more accurate at every level of accuracy since 1999. We find that kickers in 2024 are:

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A major storyline this NFL season has been the unprecedented success of kickers on extra-long field goals. In this post, we’ll look at how the kicks being attempted have skewed longer and longer over time - I plan to fast-follow this post with another focusing on how field goal make rate has also evolved through the years!

What’s Going On

It should stand as no surprise to NFL fans that two big trends have emerged in the kicking game:

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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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