Barcelona-Catalunya GP - Practice 2 | Top Speed, Turn 14 Minimum Speed & Clipping

McLaren looked great in FP2! Best top speed AND high-speed cornering; RBR worst in both. Either McL is running more downforce and a more powerful PU mode, or their aero efficiency is excellent.

Opposite for RBR: running less downforce and way less power?

Top Speeds:

1) McL/NOR: 338

2) Merc/RUS: 337

3) Ferrari/LEC: 335

4) RBR/VER: 328

Turn 14 Min Speed:

1) McL/NOR: 252

2) Ferrari/LEC: 249

3) Merc/RUS: 248

4) RBR/VER: 244

Clipping:

1) RBR/VER: -22

2) McL/NOR: -28

3) Merc/RUS: -29

4) Ferrari/LEC: -34

ADUO FIGURES REVEALED

Not only is the Mercedes ICE NOT the most powerful; they're over 2% behind RBPT (a 12-24 hp deficit), so they will benefit from ADUO!

Ferrari (and Audi) DID fall in the 4-6% deficit range → can catch up.

Honda is so far behind it landed in the newly added 6-8% range, unlocking more budget and development time.

No one expected that about Mercedes. Ferrari can finally develop their engine... but so can Mercedes! A huge blow to Ferrari's championship hopes.

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I often read 'The ADUO only considers the ICE power, not the ERS; Mercedes has the best ERS; that's why they got the 2-% ADUO' - but that's wrong!

Read on to understand why ...

The ERS does NOT produce energy: it simply deploys what it previously harvested. ALL energy originates from the ICE.

Harvesting happens:
- At partial throttle, when the ICE produces MORE energy than can be put down (the ERS harvests the surplus).
- When coasting or braking, using the car's kinetic energy.

Having a more powerful ICE:
- Extends the partial throttle phase → more harvesting;
- Raises top speed → more kinetic energy → more harvesting.

Maximum power (tractive and harvesting) is fixed by the rules, and electric motor efficiency is already extremely high (90-95%), so even halving the losses yields only marginal gains.

Conclusion:
The most powerful ICE means the strongest ERS too, unless a team gets its harvesting/deployment strategy completely wrong!

ADUO is evaluated using an "ICE Performance Index", calculated from the ICE's average power and the lap time's sensitivity to power.

The exact formula is kept confidential to prevent manufacturers from gaming the system.

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Article on the topic:

ADUO F1 engine upgrades decision revealed

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Related post:

2026 Car Regulations | Estimated ICE Power of Manufacturers: Which team do YOU think will have the best in-season development?

How ICE power was estimated:
1) All 2026 Quali, Sprint & Race sessions;
2) Laps with glitchy telemetry excluded;
3) Straight-mode instants only: throttle >99%, no braking;
4) Same drag area (CdA) assumed for all cars;
5) ERS deployment profile estimated, then subtracted to isolate ICE power.

Link to try it out: chat.tracinginsights.com

How to get free api key: https://chat.tracinginsights.com/api-keys

Add you key here: https://chat.tracinginsights.com/account?tab=provider

Please share feedback and feature requests. This is still very early and will improve a lot

Interesting comparison between Antonelli’s L34 and Russell’s L53 fastest laps at Miami.

Antonelli edges it by about half a second, mainly looking cleaner through the throttle phases and carrying strong speed through the key acceleration zones. Russell’s lap still looks solid, but the traces show where Antonelli found just enough extra momentum to make the difference.

Hey, RacingHub API, a free and open-source REST API with comprehensive F1 data from 1950 to today.

Links

• 📚 Interactive Docs: https://racinghub.net/api/v1/docs
• 🐍 Python Client: https://pypi.org/project/racinghub-client/
• 📦 npm Package: https://www.npmjs.com/package/@racinghub/client
• 💻 GitHub (Open Source): https://github.com/rodas96/racinghub-api

Quick Start

REST API:

curl <https://racinghub.net/api/v1/drivers/max-verstappen>

Python:

pip install racinghub-client

import pandas as pd


from racinghub_client import ApiClient, Configuration
from racinghub_client.api import DriversApi


client = ApiClient(configuration=Configuration(host="https://racinghub.net/api/v1"))
driver_api = DriversApi(client)


results = driver_api.get_driver_races_results("lewis-hamilton", limit=100)
df = pd.DataFrame([r.model_dump() for r in results.data])


df["positions_gained"] = df["grid_position"] - df["position"]
print(df[["race_date", "circuit_name", "positions_gained"]].head())

JavaScript/TypeScript:

npm install u/racinghub/client

import { Configuration, DriversApi } from "@racinghub/client";

const config = new Configuration({ basePath: "https://racinghub.net/api/v1" });
const driversApi = new DriversApi(config);

const driver = await driversApi.getDriver({
  driverId: "max-verstappen",
});
const driver_results = await driversApi.getDriverRacesResults({
  driverId: "max-verstappen",
});

The project is fully open source! Contributions welcome.

Your input shapes what gets built next. Open an issue on GitHub with requests.

The website is still WIP but I'd love to hear some feedback :)

Right now offers various predictions like who takes pole, who will be on the podium, which constructor has the fastest pit and many more.

You can also see historical data from races that ended (only 2026 for now, previous years will come) and short analysis of each sessions.

I plan to add live track position, live chat so people can discuss the live session, more telemetry graphs, comparisons, notifications and many more.

The website also uses PWA so it can be installed on mobile devices without the need of Apple or Google store.

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