PageRankAlgorithmFormula1 🏎️

This project conducts analysis on Formula 1 race results, utilizing the PageRank algorithm to evaluate both drivers and teams based on their head-to-head performance throughout the season.

Overview

• The project is implemented in Python and relies on Pandas, NumPy, and network analysis techniques.🔄
• It includes functions for data retrieval, adjacency matrix creation, normalization, and PageRank calculation for both drivers and teams.🔄

Dependencies

• Python 3
• Pandas
• NumPy

Project Structure

• Data Retrieval:

  • URLs for 2021 Formula 1 race results are provided.
  • The read_html function extracts relevant columns from race result tables.

• Driver Analysis:

  • The adj_matrix function creates an adjacency matrix reflecting driver interactions and assigns points based on performance.
  • Cumulative matrices are updated for each race and normalized to create stochastic matrices.

• PageRank Algorithm:

  • Stochastic matrices serve as the basis for the Google matrix in the google_matrix function.
  • The power_method function iteratively calculates PageRank scores until convergence.

• Results and Rankings:

  • Final PageRank scores for drivers are obtained and presented in a sorted DataFrame.
  • Similar procedures are applied to teams, resulting in comprehensive rankings.