Tomorrow the first #F1 race weekend of 2023 starts!
You'll probably see a lot of data 📈 flying around your timeline.
Why not analyze the data yourself?! Let me show you how with #Python 👀
Start with firing up a Jupyter notebook and installing Fastf1:
I've been developing this data-driven story platform - you can read the full article here on Qualifying.
Take a look and leave me your feedback!
https://t.co/XPMACFFIaD
Austin quali: Ferrari were quickest through the slow and medium corners, then lost 0.702 s on the straights by being the team that clipped the most.
Mercedes had the more even car and took pole.
Red Bull over Ferrari is closer than the grid says.
Leclerc's Q2 lap, 1:34.198, was quicker than Verstappen's 1:34.214 in Q3.
Leclerc was slower in Q3 and starts fourth.
Who's interested in the Austin pre-weekend review?
It's a fast & flowing track, which has typically been dominated by Mercedes
Is the Red Bull momentum real, or will be be back to how things were?
See the full article here: https://t.co/ck7eKaAZau
@Dexter_Corley @EngineBraking @TheRedFlags_Pod Today's #BahrainGP? 👀
For the rest, great analysis! You might also want to check out and compare the out laps of Verstappen and Perez. Verstappen was 2 seconds quicker during his outlap on the mediums, so it's very interesting telemetry!
@barot_dvij That’s a challenging one because a lot of events in a race are… hard to predict.
I would start with predicting strategies based on tyre wear if i were you!
Applying #processmining on #Formula1 data?
Are you curious what process mining is, and what we're exactly seeing here? Join the webinar!
⏰ June 1st, 11:00 - 12:00 CEST
🔗 https://t.co/6eghizrmdK
Kiedy widzę połączenie Formuły 1 i analizy danych, nie mogę ukrywać podekscytowania. @f1dataanalytics prowadzi znakomity wykład, dzielenie się wiedzą w sporcie to potęga. #F1pl#ViaF1
Do you recognize a #MonacoGP lap through this process flow?
Learn all about process mining and sign up for the webinar "Unleashing the power of process mining in Formula 1 data" this Thursday!
⏰ June 1st, 11:00 - 12:00 CEST
🔗 https://t.co/pX25dDe45B
@F1Mohamed0 Depends on what you consider data science! It’s for example not necessarily machine learning or AI related if that’s what you’re looking for
@MitchellMay33 Agreed - it's not about the visualisation per sé, but it's about what you do with it.
Process mining unlocks a very different angle on data (e.g. times between activities: brake to full throttle), which I will be highlighting during the webinar!