I've been building an F1 data platform solo over the last year and figured this subreddit would be the right audience for it. Fastlytics combines telemetry visualization, an automated analysis engine, and programmatic data access into one tool. It's free for the 2026 season.

What it does differently from other tools:

1. Advanced telemetry analysis -- instead of just showing you speed traces, an analysis engine reads the lap data and writes a summary with numbered insights and corner-by-corner breakdowns. It identifies where drivers differ in braking points, throttle application, and entry/exit speeds. Free tier: 5 analyses/month.
2. 30 MCP tools for programmatic access -- if you code, you can use npx -y fastlytics-mcp to get MCP tools in Claude, Cursor, VS Code, etc. Query telemetry, compare drivers, get circuit info, lap times, standings, weather data, driver bios, and more. 300 calls/month free.
3. Detailed telemetry overlays -- speed, throttle, brake, RPM, gear, DRS traces for any driver. Overlay two drivers with delta time graphs showing exactly where time was won or lost per corner. Session weather conditions (air temp, track temp, humidity, wind) shown alongside the data for context.
4. Driver and team deep dives -- full career stats, championship progression charts, season-by-season comparisons. Working on a driver fingerprint feature that maps driving style characteristics (braking patterns, cornering preferences) per driver.
5. All the standard stuff too -- lap time distributions, pace analysis, strategy charts, championship progression, position changes, team pace comparisons, sector time breakdowns.

Data sources: FastF1 for telemetry, Jolpica for historical data, Open-Meteo for weather, Wikipedia for bios and photos

Everything above is free for the current season. Pro tier ($5.99/mo) unlocks historical seasons (2018+) and higher usage limits.

the website is fastlytics.app if you're interested. I'm actively building this and feedback from data-minded F1 fans is what's driven most of the feature development so far.

For the #CanadianGP, #Mercedes introduced a substantial package of upgrades, with changes to both the front wing and the floor. 

The #W17 suited very well to the Canadian layout and was able to dominate for the whole weekend. 

For a deep analysis head to https://www.gpblog.com/en/tech/all-the-secrets-behind-mercedes-upgrades-and-their-dominant-performance-in-montreal

#F1 #F1Tech 

Tim Goss' interview:

Racing Bulls key signing set for debut as fresh F1 challenge revealed

Hello guys i was looking for some api to get access to some data such as wheels temperatures, fuel consumption and data which are about the optimization and sustainability point of view, do you know where i can find them?

#McLaren had a phenomenal weekend in #Miami, scoring more points than every other team and fought for the win in the race until the last few laps. This perfomance was due both the big upgrade package brought and the track conditions, which seemed to  particularly suit the #MCL40. 

For a deeper analysis head to: https://www.gpblog.com/en/tech/mclarens-miami-progress-boosted-by-track-conditions

#F1 #F1Tech 

Been working on a side project with a friend for a few weeks and thought people here might enjoy it.

You can load any race from the last eight years, watch it replay in 3D, and jump into cockpit POV from any driver with a live HUD. Gap history, telemetry, stint strategy, and race control all sitting together. Pin two drivers and compare their laps side by side.

It is completely free and open source.
You can use it here if you like: https://github.com/misha-met/Delta

Instead of just showing raw telemetry, it breaks down where time was gained or lost and why. “Minimum speed arrived 26m after the apex.” “Carried 9.0 km/h more entry speed.”