This work focuses on the analysis of Chess 960, also known as Fischer Random
Chess, a variant of traditional chess where the starting positions of the
pieces are randomized. The study aims to predict the game outcome using machine
learning techniques and develop an opening theme for each starting position.
The first part of the analysis utilizes machine learning models to predict the
game result based on certain moves in each position. The methodology involves
segregating raw data from .pgn files into usable formats and creating datasets
comprising approximately 500 games for each starting position. Three machine
learning algorithms -- KNN Clustering, Random Forest, and Gradient Boosted
Trees -- have been used to predict the game outcome. To establish an opening
theme, the board is divided into five regions: center, white kingside, white
queenside, black kingside, and black queenside. The data from games played by
top engines in all 960 positions is used to track the movement of pieces in the
opening. By analysing the change in the number of pieces in each region at
specific moves, the report predicts the region towards which the game is
developing. These models provide valuable insights into predicting game
outcomes and understanding the opening theme in Chess 960.

这项研究集中于分析象棋 960（又称费舍尔随机象棋），这是传统象棋的一种变种，棋子的初始位置是随机的。研究旨在利用机器学习技术预测比赛结果，并为每个初始位置开发一个开局主题。本分析的第一部分利用机器学习模型根据每个位置的某些着法预测比赛结果。通过将.pgn 文件中的原始数据分离成可用格式，创建了大约 500 局游戏的数据集，每个数据集对应一个初始位置。预测比赛结果采用了三种机器学习算法，即 KNN 聚类、随机森林和梯度提升树。为了确定开局主题，棋盘被分为五个区域：中心、白方王翼、白方后翼、黑方王翼和黑方后翼。使用所有 960 个位置上顶级引擎对弈的数据来跟踪开局中棋子的移动。通过分析每个区域在特定着法的棋子数量的变化，该研究预测了比赛向哪个区域发展。这些模型为预测比赛结果和理解象棋 960 的开局主题提供了有价值的见解。