A project for learning some processes in data science and building data visualisations to provide insight! The lichess dataset was selected because I like chess.

Notes on some themes that can/probably cannot be explored:

Endgame insights - seems hard

Maybe:

• Classify games into different types of endgames - use chess rules to determine the pieces remaining on the board. For instance, if only kings and pawns are left, classify it as 'King and Pawn' endgame.
• Separate games that ended in a win from those that were draws or losses.

Dataset cannot be used for mistake analysis - need data from a chess engine

Piece Activity

• Defining Critical Squares: Determine what constitutes a critical square. For pawn promotion, this would be the 8th rank for White and the 1st rank for Black.
• Then analyse when and how often pieces reach these critical squares.
  • For pawns:
    • Count how many times pawns are promoted in the dataset.
    • Examine the context of these promotions - for instance, in which types of positions or game phases (opening, middle game, endgame) they occur most frequently.
    • Look for any patterns in pawn promotion, such as specific opening moves leading to higher promotion rates.
• Heatmaps or bar charts - illustrate the frequency and distribution of piece movements to critical squares.
• Compare these frequencies across different levels of play or different player demographics?
• Correlation with winning:
  • Creating a Correlation Variable: Create a binary variable indicating the occurrence of the event (e.g., pawn promotion) in each game.
  • Logistic Regression: If analyzing a binary outcome (win/loss - what about draw?), to quantify the relationship between promotion and winning.
  • The correlation varies by the strength of the players (e.g., different correlations in amateur vs. professional games)?
  • What graph or chart to use?