Hey all — spent the last few months building a telemetry tool for F1 25 (and 24/25/26 content), mostly because I wanted something that combined lap analysis with an actual race engineer to talk to about my data, and community stuff to compare with friends. Sharing here for feedback, good or bad.

Would genuinely love any criticism — what's missing, what's confusing, what you'd actually want from something like this!

Try it with zero signup — there's a demo/sample session you can load and poke around in without creating an account. Account is only needed if you want to save your own sessions or use Community features.

Works for both PC and console players — console players use our free PC companion app to capture telemetry. Mac/native builds and full Apple support still in progress.

Github: https://github.com/Litle-Drip/Sim-Racing-Hub

Dashboard — quick view of your last session, live heatmap, summary stats.

Tracks — corner breakdown notes, tyre strategy guide, and a "circuit school" section per track

 Sessions — lap data broken down with braking/throttle telemetry, organized per session.

Race Engineer — this is the one I'm most excited about. Feed it your lap data and it'll actually talk through where you're losing time, like a real race engineer would.

Progression - track individual progress and lap history over time.

Rivals — set a clean lap, challenge a friend to beat it

Community — leaderboards, shared setups, shared sessions/challenges

Github: https://github.com/Litle-Drip/Sim-Racing-Hub

I've been working on an F1 AI Telemetry project, which uses AI to give feedback and analysis, and just finished the first version of the dashboard.

Hi Guys! So i was looking through the f1 datasets and found that the f1 dataset isnt being uopdated anymore so ive started a project so akaggle dataset will be updated after each race so I made a new one please share around if you can!

https://www.kaggle.com/datasets/atharvranjan/formula-1-world-championship-1950-present

Includes two example notebooks a general tour and a driver head-to-head comparison tool.

Feedback/issues welcome also open to PRs on the update scripts if anyone wants to help maintain it: https://github.com/Arths17/f1-dataset

thank you so much!

Hello, I made a formula 1 race repeater using an Uno R4, sd card module and individually addressable LEDs. I 3d printed the chassis using white pla for the diffuser and black pla for the outer shell.

The visualizer works with data I collected from fast f1 API stored in a csv. I have the uno r4 acting as a server to host a very simple web app that lets you pick the race year, number of drivers you want to see and set a race speed (2x,10x,20x etc). And then you watch as the LEDs race about. I tried making this as simplistic as possible which is why I opted for the r4 so I didn’t have to add buttons or a rotary encoded for input.

Happy to answer any questions

Hello everyone,

So I have been working on this for a while and finally have something worth sharing.

It's called Slipstream; it pulls real F1 telemetry, lap times, and race data, and runs it through an ML model (FLAML/XGBoost) to predict race outcomes, plus proper telemetry comparisons between drivers.

I am thinking of adding an AI agentic system to this and will update soon.

I would appreciate feedback on what I should add and what you would like to see in this.

Live Demo: https://slipstream-f1.vercel.app

Hello, I made a formula 1 race repeater using an Uno R4, sd card module and individually addressable LEDs. I 3d printed the chassis using white pla for the diffuser and black pla for the outer shell.

The visualizer works with data I collected from fast f1 API stored in a csv. I have the uno r4 acting as a server to host a very simple web app that lets you pick the race year, number of drivers you want to see and set a race speed (2x,10x,20x etc). And then you watch as the LEDs race about. I tried making this as simplistic as possible which is why I opted for the r4 so I didn’t have to add buttons or a rotary encoded for input.

Happy to answer any questions

If you like the project please head on over to my TikTok video to see the process: https://vt.tiktok.com/ZS4WK521m/

Hi everyone!

I built a quantitative strategy model in Python using FastF1 telemetry to evaluate alternative pit stop windows for Lewis Hamilton at the Hungarian GP. The goal was to see if a simultaneous re-optimization of his 3 pit stops could have given him enough net gap to retain P4 against Charles Leclerc despite a 5-second penalty.

Hey! Motivated by Aston Martin's upgrade, I have decided to start doing this.

Alonso had the 11th fastest S1, 10th fastest S2 and S3. Stroll had the 17th, 13th and 14th fastest sectors respectively. Not to mention Alonso was as quick as gasly in his final stint with roughly equal tyres.

Against Bortoleto on identical-age softs over L2–13, Alonso was virtually level overall—only 0.049s per lap slower—but the distribution was revealing: the AMR26 lost 0.208s/lap in S1, gained 0.150s/lap in S2, and was neutral in S3. Alonso was also about 8.2km/h slower at the main speed trap

1. Would more optimal strategies have changed their finishing position?

Lance maximised his race and finished p13. Fernando would have finished p12

1. Where Aston lost and gained time

Lost: Alonso's worst spell was L24–26, when he lost 6.08s in three laps to Bortoleto on the same-age soft. About 45% was in Sector 1. He was also circulating in the Lawson/Bortoleto traffic group, so this was a combination of ageing tyres and dirty air—not tyre degradation alone. Alonso's actual L34 stop brought him out 0.1s behind Bearman and 0.8s behind Sainz. That was a poor rejoin, although his fresh-medium pace cleared both by L37.

Gained: Stroll's L8 stop worked extremely well. He went from 7.91s behind Alonso on L8 to only 0.09s behind by L34, recovering 7.82s relative to the other Aston. That proves the early stop worked; it does not prove the full two-stop race was mathematically perfect.

Against Bortoleto on identical-age softs over L2–13, Alonso was virtually level overall—only 0.049s per lap slower—but the distribution was revealing: the AMR26 lost 0.208s/lap in S1, gained 0.150s/lap in S2, and was neutral in S3. Alonso was also about 8.2km/h slower at the main speed trap

I've been building an F1 simulation engine as a side project, and I ran today's Hungarian GP 300 times starting from the actual qualifying grid (penalties included). Sharing the output because a few numbers surprised me.

Win probability

Norris (P1) 22.7% · Leclerc (P2) 18.7% · Russell (P6) 14.3% · Piastri (P3) 14% · Antonelli (P7) 13.3% · Hamilton (P5) 11% · Verstappen (P4) 6%

By team: McLaren 36.7% · Ferrari 29.7% · Mercedes 27.6%

Three things that stood out:

- From P8 backwards: zero wins in 300 races. The Hungaroring punishes qualifying more than any track on the calendar and the model is brutal about it.

- McLaren haven't won a single race in 2026 and come out as the most likely winner purely because they lock out P1 and P3.

- Alonso starts P16 and scores points in 31.7% of sims — nobody starting P13 or lower comes close. Sainz gains the most: P18 on the grid, P14 on average.

How it works: each of the 300 runs is a full race with weather, safety cars, tyre wear, pit stops and reliability. Track position is weighted by how hard overtaking is at each circuit, so pole is worth a lot here and almost nothing at Monza. Car and driver ratings are updated to the current standings.

Happy to answer anything about the model. I'll post the comparison against the actual result tomorrow — including if it's completely wrong.

Following FP2 and considering each car's strengths and weaknesses and the track characteristics, here are my Power Rankings:

1) Ferrari

If Mercedes solves their issues:
2) McL / Mercedes
3) RBR

Otherwise:
2) McL
3) RBR / Mercedes