#Fastf1 Python Library F1 Telemetry

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#Fastf1 Python Library F1 Telemetry Reel by @tom.developer (verified account) - What do you rate my commentary skills? 😂 My latest micro project "F1 Race Replay" allows you to watch the race from above using telemetry from the F
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@tom.developer
What do you rate my commentary skills? 😂 My latest micro project “F1 Race Replay” allows you to watch the race from above using telemetry from the FastF1 python package! 🏎️ I think it would be cool to see what other features could be added to this project. Driver telemetry analysis? Gap to car ahead? A viewer for qualifying or practice? 🤔 Follow for more coding videos! ❤️
#Fastf1 Python Library F1 Telemetry Reel by @tiffintech (verified account) - Let's build a machine learning mode to make predictions about F1s upcoming Japan GP 🏎️ These are super popular projects right now (thanks ChatGPT fo
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@tiffintech
Let’s build a machine learning mode to make predictions about F1s upcoming Japan GP 🏎️ These are super popular projects right now (thanks ChatGPT for the suggestion) and I wanted to put my learnings to the test and see what I could do! GitHub for the project: GitHub.com/tiffintech/f1_predictions 💡save this so you can reference it later! Here is what is going on… 1. Data Collection - Uses FastF1 API to fetch qualifying session data - Collects data from recent 2025 races (rounds 1-4) - Includes 2024 Japanese GP data as reference 2. Data Processing - Converts lap times from timedelta to seconds - Handles missing values using SimpleImputer - Cleans and structures data for analysis 3. Model Development - Uses Linear Regression to establish baseline predictions - Features: Q1 and Q2 times - Target: Q3 times - Includes train-test split for validation 4. Performance Factors - Implements team-specific performance coefficients - Adds driver-specific performance adjustments - Base lap time calibrated to ~89.5 seconds - Includes small random variation for realism 5. Prediction System - Combines model predictions with performance factors - Accounts for 2025 driver-team combinations - Sorts and displays predicted qualifying order 6. Validation - Calculates Mean Absolute Error (MAE) - Provides R² score for model accuracy - Visualizes qualifying time distributions #tech #technology #coding #stem #developer
#Fastf1 Python Library F1 Telemetry Reel by @tom.developer (verified account) - What insight would you build? 📊 The F1 Race Replay project is becoming its own pit wall for F1 data analysis! 🏎️ I've added this new Driver Teleme
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@tom.developer
What insight would you build? 📊 The F1 Race Replay project is becoming its own pit wall for F1 data analysis! 🏎️ I’ve added this new Driver Telemetry window which allows you to view the Speed, Gears, Throttle and Braking traces coming off of the car. 📈 I wonder what awesome features we can add to this project before the start of the new season! 🏆
#Fastf1 Python Library F1 Telemetry Reel by @jagger_sa - Comment F1 for the link BUILD YOUR OWN F1 AI PROJECTS THIS WEEKEND 🏎️ Want to get into AI and machine learning? Here are 3 beginner-friendly F1 pro
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@jagger_sa
Comment F1 for the link BUILD YOUR OWN F1 AI PROJECTS THIS WEEKEND 🏎️ Want to get into AI and machine learning? Here are 3 beginner-friendly F1 projects you can actually build: 1️⃣ RACE FINISHING POSITION PREDICTOR Use the Fast F1 API to pull telemetry data and train a model to predict where all 20 drivers will finish based on grid position and past performance. Perfect first ML project. 📂 GitHub: JaideepGuntupalli/f1-predictor 2️⃣ MINI-SECTOR COMPARISON TOOL Compare how different drivers perform in tiny micro-segments of a lap during quali or the race. Visualize where Hamilton is faster than Verstappen, corner by corner. Super cool charts! 📂 Uses Fast F1 API 3️⃣ PIT STOP TIMING PREDICTOR This one’s more advanced but still doable. Use historical telemetry to predict the optimal lap to pit. Recent research achieved 81% accuracy using Bi-LSTM models! 📂 GitHub: Casper-Guo/Armchair-Strategist Stuck? The F1 technical subreddit (r/F1Technical) is full of people who can help. Now go build something cool 🔥 #Formula1 #F1 #MachineLearning #AI #Python
#Fastf1 Python Library F1 Telemetry Reel by @mar_antaya (verified account) - Which one are you ready to do first? Alsooooo I cannot wait for 2026 and all of the car unveilings that are coming in the next few weeks! I'm sooo ex
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@mar_antaya
Which one are you ready to do first? Alsooooo I cannot wait for 2026 and all of the car unveilings that are coming in the next few weeks! I’m sooo excited to see the @cadillacf1 car 🏎️🥰🥹🥹 #f1 #codingprojects
#Fastf1 Python Library F1 Telemetry Reel by @tom.developer (verified account) - Have you checked out F1 Race Replay recently? 🏎️ A whole bunch of new features have been added, including this one right here! 💻 The CLI menu make
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@tom.developer
Have you checked out F1 Race Replay recently? 🏎️ A whole bunch of new features have been added, including this one right here! 💻 The CLI menu makes it much easier for users to select the session that they want to watch! 👀 Have you contributed to F1 Race Replay? There have been over 100 pull requests so far, and yours could be the next one! 🚀
#Fastf1 Python Library F1 Telemetry Reel by @tom.developer (verified account) - This is huge for the F1 Python Project! 🏎️ Being able to stream data between windows enables us to build so many more insights and features to make
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@tom.developer
This is huge for the F1 Python Project! 🏎️ Being able to stream data between windows enables us to build so many more insights and features to make the project feel like a real pit wall!! 💻 I reckon this is going to get a lot of traction (pun intended) when the 2026 season starts!! 🗓️
#Fastf1 Python Library F1 Telemetry Reel by @irieti - Comment Link to get the code and try it yourself
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@irieti
Comment Link to get the code and try it yourself
#Fastf1 Python Library F1 Telemetry Reel by @originoftheuniverse.daily - In C++, an iterator is a small, lightweight object that behaves very much like a pointer, but with the additional advantage of working uniformly acros
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@originoftheuniverse.daily
In C++, an iterator is a small, lightweight object that behaves very much like a pointer, but with the additional advantage of working uniformly across all standard library containers. Rather than exposing how a container stores its elements internally, the iterator offers a controlled pathway through them, moving step by step from one element to the next. Because of this abstraction, the same algorithms—such as those that search, sort, or transform data—can operate on a vector, a list, or even a map without knowing anything about how these structures are built. An iterator typically begins its journey at a container’s begin() and advances toward its end(), a symbolic boundary marking one position past the final element. As it moves, it grants access through dereferencing: the familiar *it syntax. Whether the underlying container stores its elements contiguously like an array or in scattered nodes linked together, the programmer interacts with them in the same fluid way. This consistency is one of the central design principles of the C++ STL. Different containers provide iterators with varying abilities. Some can only move forward, others can move backwards, and the most versatile—random-access iterators—can jump arbitrarily through a sequence with pointer-like arithmetic. These differences reflect the containers themselves: a linked list cannot jump around freely, while a vector can. Yet the iterator interface hides these details unless the programmer explicitly needs them. Ultimately, iterators make the C++ standard library feel cohesive. They allow code to be generic, expressive, and efficient, turning the act of traversing a data structure into a simple, almost narrative progression: beginning, advancing, accessing, and concluding—regardless of where or how the data lives beneath the surface.
#Fastf1 Python Library F1 Telemetry Reel by @coding_fundamentals (verified account) - Stop brute-forcing Fibonacci. Start optimizing it. #leetcode509 #fibonacci #dynamicprogramming #dpproblems #codinginterview #techinterviewprep #codin
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@coding_fundamentals
Stop brute-forcing Fibonacci. Start optimizing it. #leetcode509 #fibonacci #dynamicprogramming #dpproblems #codinginterview #techinterviewprep #codingsimplified #learncoding #programmingtips #recursionvsdp #dsaproblems #pythoncoding #cplusplustutorial #javacoding #softwareengineering #codereels #codingcontent
#Fastf1 Python Library F1 Telemetry Reel by @autoshorts.app - 👩‍💻 Programmer alert! Peter Steinberger is the creator of OpenClaw breaks down the core workflow difference between GPT Codex and Claude Opus. Codex
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@autoshorts.app
👩‍💻 Programmer alert! Peter Steinberger is the creator of OpenClaw breaks down the core workflow difference between GPT Codex and Claude Opus. Codex reads a lot of code by default, while Opus needs a 'plan mode' push! Which is better for your projects? 🚀 Follow for daily AI coding hacks! #ProgrammingLife #AIforDevs #GPTCodex #ClaudeOpus #CodeGeneration #DeveloperTools #LexClips #TechTips

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