Judging quarterback performance is a tricky business. They are executing plays selected by someone else as part of an offense designed by others and relying on 10 other players to do what they are assigned. The long list of confounding factors renders just about every raw QB stat a team stat in actuality. One of the ways that we can approach teasing these things apart is to look at different measurements in terms of year to year stability. As things around the QB change how much does the metric we are looking at change? Does changing teams, hiring a new coach, or signing new players change the stat in question or is it “sticky” from QB to QB? Today we are going to look at this question from the point of view of sacks and sack rates.

Over the last few years I’ve used most of the major LLMs in some capacity or another for a variety of tasks. If I need to write a script to do something tedious my reflex is to first see if Claude can do it before writing it myself. If I want to compare different products I’m thinking about buying I generally use Perplexity to round up info sources and make a table showing the contrasts. Over at $DAY_JOB we have an internal version of a Cursor clone (which is unfortunately not very good) that I dabble with for brain-dead sorts of code changes. One thing I had never really done a deep dive on before was the state-of-the-art in coding assistance software. This involves two different dimensions: the models generating the code and the software that puts things in a nice package (or at least tries to). I had a pretty good understanding of the reputations of the different available models (which is no substitute for direct experience!) but I was less familiar with the different interfaces for interacting with them. One of the things I was especially interested in was the potential for integration with Emacs. I am one of those people (there are dozens of us! Dozens!) who more or less runs their digital life through Emacs. One of the benefits of Emacs is that it is highly extensible and has a robust ecosystem of packages so whenever some new cool thing comes around a package integrating it with Emacs quickly follows. After doing some further digging I stumbled upon a product called Aider. Aider had a few things going for it that I found appealing. First, the fact that it wasn’t bound to a particular model (and in fact has some interesting ways you can mix and match them) was a big plus. Given the speed at which new and better models drop the ability to easily swap them out and experiment is really nice. Second, the interaction model simply feels like the right approach. The ability to have granular control over the contents of the model’s context is great and the UX around adding, removing, and communicating what files are included is easy to grok.

Like many people out there, I only recently got in to the WNBA. As with most new things I encounter I wanted to see if I could get a quantitative understanding of what’s going on. With the new season coming up pretty soon and with a brand new team being created in my area (go Valkyries!) I wanted to see what sort of prior I could come up with for each team’s rating. As a starting point I wanted to look at the history of the league and understand how things change for teams from year to year. To do so I scraped the entire history of the league to get every regular and post-season game including scores and participants. One modification to the data that I made was to normalize the teams such that a “new” team that was simply a relocating team that decided to change their name was treated as the same team. (Side note: the Wikipedia rabbit holes this led me down were pretty interesting. The number of teams that were created and shuttered since the leagues founding in 1997 is a lot higher than I would have guessed!) The first thing that I looked at was the year to year correlation between win percentages for a given team. That turns out to be 0.396. This is actually higher than I would have expected given that we are treating each team as a black box and totally ignoring injuries, drafts, trades, coaching changes, etc.

During the 2023 NFL offseason a rule change was made that redefined how kickoffs worked. This new version was branded the “Dynamic Kickoff” and is in many ways a dramatic departure from the past. Per NFL commissioner Roger Goodell the motivation was twofold. First: the number of injuries that occurred during kickoffs was significantly higher than what was observed on other play types. Second: teams were overwhelmingly opting to kick the ball out of the endzone and take a touchback rather than incur the risk of a big return from the receiving team. As publicly available NFL injury data is for the most part not sufficiently granular to provide play level insights my focus in this post will be the second consideration. With the conclusion of the 2024 season we now have a full season of data to evaluate and we can look at what was changed, how teams behaved, and whether the stated goals were achieved or not.