Yesterday the NFL Players Association (NFLPA) released their latest set of Team Report cards, following the 2024 release and 2023’s inaugural data. These report cards include grades by 1,695 players, scoring their own teams on a variety of player experience dimensions from A+ to F-:

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

As I’ve done the last two seasons, I spent a bit of time this evening sprucing the data up into a few easy-to-read visuals!

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A few days ago, CNBC released their latest valuations of NBA franchises. The data shows the Golden State Warriors with a nearly $2B lead on the Knicks as the most valuable franchise, and biggest revenue earner in the 2023-2024 season.

The Warriors’ move to the Chase Center and San Francisco has clearly earned huge financial dividends, though I can’t help but feel that their departure from Oakland has cost them something spiritually - trading away that blue-collar soul which made Oracle Arena so electric in exchange for more luxury boxes…

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On December 18th, the Senate Budget Committee dropped a doozy of a blog post, titled “New Data Reveal Climate Change-Driven Insurance Crisis is Spreading”.

They didn’t bury the lede at all, getting right to their conclusion that “climate change is increasing insurance non-renewals around the country” and that “the failure to deal with climate change is also affecting whether families can even get homeowners insurance”.

How I Found This Data

I wish I could say that I stumbled upon this post and the accompanying data as part of a research project. Unfortunately, that is not the case - I was turned onto this data in the aftermath of the enormous Palisades fire, which burned about 3 miles north of my house.

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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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