Welcome to our GitHub repository for the Chess Data Analysis Project! Here, we delve into the fascinating world of chess, aiming to uncover patterns hidden within vast amounts of chess game data and rating database using web-scraping.
This is our project for the data analysis track.
Contributors:
1.Rakshith Srinivasan
2.Sathish Adithiyaa SV
Exploring Chess Rating Patterns Across Major Chess Nations
Chess, a game of strategy and intellect, transcends borders and cultures, captivating minds worldwide. In this project, we aim to delve into the intricate world of chess ratings, focusing on major chess-playing nations such as India, Russia, USA, and China. Our objective is to analyze and visualize the patterns inherent in the chess ratings of these countries, uncovering insights that illuminate the dynamics of their chess scenes.
Analyzing some simple patterns like win rate and visualization of reults from the chess.com games of some prominent players.
Data Collection: Gather comprehensive data sets encompassing chess ratings from major chess federations in India, Russia, USA, and China.
Data Analysis: Employ advanced analytical techniques to uncover patterns, trends, and anomalies within the chess ratings data.
Country-wise Comparison: Conduct comparative analyses to identify similarities and differences in rating distributions, growth trajectories, and performance trends among the major chess nations.
Visualization: Develop visually compelling representations of the data, including interactive charts and graphs to elucidate key findings and facilitate intuitive understanding.
This project will be able to provide some useful stats from chess.com games and ratings list from fide.com.
1.Python
2.Jupyter Notebook
3.VS code
4.Git
For RatingAnalysis.ipynb code a simple run would give all the analysis.
For Chessgamesstats.ipynb after clicking on run all
the following inputs have to be given:
1.player_name : chess.com username of the player.(eg. MagnusCarlsen, Hikaru)
2.host : the host of the computer on whihc it is running.(eg. if host=raksh then file path=C:\raksh\Downloads...)
3.year: year in which stats are needed (eg. 2024)
4.month: month in which stats are needed( eg.03)
By addressing all the objectives, we aim to provide a comprehensive understanding of the chess rating landscape across major nations, offering valuable insights for players, coaches, federations, and enthusiasts alike. Join us on this journey as we unravel the intricate tapestry of chess ratings and illuminate the strategic nuances of the game on a global scale.
Analyzing chess.com statistics gives some helpful information about where one is lagging and what are his/her strengths.
1.Using machine learning algorithms to predict the future of chess in various countries.
2.Analyzing openings and coming up with a more detailed statistics from the chess.com games.
3.We could have built UI