Despite turning 30 last November, I ran my fastest times ever this year: 6.78 in the 60m, 10.55 in the 100m, and 21.26 in the 200m. Indoors, I missed qualifying for USATF nationals by just 0.03s. Outdoors, these were my first PRs since 2022, and my first time cracking 1000 points on the WA scoring tables.

The main change? I nearly doubled my sprint volume.

From 2022-2024, I averaged about 300 meters a week of maximal sprinting (accelerations and flies). This year, I pushed that to 556 meters, an almost 90% increase. At my age and with my injury history, this was a huge risk. But it paid off for me. Below, I’ll detail everything I did to make it work, as well as all the other changes I made this year.

Tracking a record’s last modified time is a common application requirement. In Postgres, a typical implementation might look like this, using a BEFORE UPDATE trigger on a last_modified_at column:

CREATE TABLE foo (
    id SERIAL PRIMARY KEY,
    name TEXT,
    last_modified_at TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE OR REPLACE FUNCTION update_last_modified_at()
RETURNS TRIGGER AS $$
BEGIN
    NEW.last_modified_at = now();
    RETURN NEW;
END;
$$ LANGUAGE 'plpgsql';

CREATE TRIGGER update_last_modified_at_trigger
BEFORE UPDATE ON foo
FOR EACH ROW
EXECUTE PROCEDURE update_last_modified_at();

This works in most cases, but breaks an assumption that’s likely made by the application: last_modified_at isn’t necessarily monotonically increasing, even if the system clock is. This can cause problems when the application uses last_modified_at as a watermark for processing changes.

Almost four years ago, I wrote a post about the relationship between temperature and speed, finding a linear relationship on 30m fly times with an R² of 0.21. I’ve kept tracking every rep since then—instead of 150 data points, I now have almost a thousand. I’ve also gotten more sophisticated with how I estimate fly and acceleration times from single reps that deserves its own blogpost. For now, what’s important is that across this four-year dataset, I have 642 30m acceleration reps and 451 10m fly reps, with temperatures ranging from 14° to 95° F.

I built a tool that predicts your 100m time given practice results—go check it out! This post details the development process, including data parsing and training and validating the model.

After a disappointing end to my season, I spent some time reflecting on what went wrong and what I can do better this year. The obvious acute answer is the back injury I suffered in March; the other obvious answer is the lack of racing between May and July to keep me sharp.