Box Box Box 🏁

F1 Pit Strategy Optimization Challenge

"Box, Box, Box" - F1 radio call for pitting

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🎯 The Challenge

Reverse-engineer the race simulation algorithm from 30,000 historical F1 races. Your goal is to predict race finishing positions based on pit stop strategies, tire choices, and track conditions.

Your Mission: Analyze the data, discover the hidden patterns, and build a race simulator that accurately predicts outcomes.

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📊 What's Included

• 30,000 Historical Races - Complete race data with strategies and results
• 100 Test Cases - Validate your solution against known results
• Comprehensive Documentation - F1 rules, data formats, and regulations
• Language Agnostic - Solve in any programming language you prefer
• Starter Templates - Example templates in Python, JavaScript, and Java

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🚀 Quick Start

# 1. Fork this repository on GitHub
# 2. Clone your fork
git clone https://github.com/yourusername/box-box-box.git
cd box-box-box

# 3. Read the problem statement
cat PROBLEM_STATEMENT.md

# 4. Explore the historical data
ls data/historical_races/

# 5. Implement your solution in the solution/ directory
# See solution_templates/ for starter templates

# 6. Configure your run command
echo "python solution/race_simulator.py" > solution/run_command.txt

# 7. Test your solution
./test_runner.sh

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🏆 Scoring

Your solution is tested against 100 test cases with varying complexity.

Your Score = (Correct Predictions / 100) × 100%

A prediction is correct only if the entire finishing order (all 20 positions) matches exactly.

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🧠 What You'll Learn

• Data Analysis: Extract patterns from large datasets
• Reverse Engineering: Discover hidden mechanics from examples
• Algorithm Design: Build accurate simulation models
• Optimization: Balance computational efficiency with accuracy
• Problem Solving: Work through ambiguity to find solutions

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💡 Recommended Approach

1. Explore the Data: Start by examining a few historical races manually
2. Identify Patterns: Look for relationships between tire compounds, lap times, and conditions
3. Build Incrementally: Start with a simple model, then add complexity
4. Test Frequently: Validate against historical results before running test cases
5. Refine: Use test results to identify and fix inaccuracies

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📖 Documentation

• PROBLEM_STATEMENT.md - Formal problem description with requirements
• SUBMISSION_GUIDE.md - Step-by-step submission instructions
• docs/regulations.md - F1 rules and racing constraints
• docs/data_format.md - JSON structure specification
• docs/faq.md - Frequently asked questions

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🎯 Success Criteria

Your solution should:

• ✅ Read race configuration and strategies from stdin (JSON format)
• ✅ Simulate the race lap-by-lap for all 20 drivers
• ✅ Calculate lap times based on tire compound, degradation, and temperature
• ✅ Handle pit stops and apply time penalties correctly
• ✅ Output finishing positions (1st to 20th) to stdout (JSON format)
• ✅ Achieve high accuracy on test cases (aim for 80%+)

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🔧 Testing Your Solution

# Run all 100 test cases
./test_runner.sh

# The test runner will:
# - Read your command from solution/run_command.txt
# - Run your solution against all test cases
# - Display pass/fail results
# - Show your final score

Note: Your solution must read from stdin and write to stdout. See SUBMISSION_GUIDE.md for details.

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🏁 Ready to Race?

# Read the problem
cat PROBLEM_STATEMENT.md

# Choose your language and copy a template
cp solution_templates/python/solution_template.py solution/race_simulator.py

# Update run command
echo "python solution/race_simulator.py" > solution/run_command.txt

# Start coding!
code solution/race_simulator.py

# Test your solution
./test_runner.sh

Good luck, and happy racing! 🏎️💨

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📧 Contact

For questions or issues, reach out to: azeem@sansatech.com

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A coding challenge for algorithm enthusiasts and F1 fans Prepared by an F1 fan