PGD: A Large-scale Professional Go Dataset for Data-driven Analytics

• URL: http://arxiv.org/abs/2205.00254v1
• Date: Sat, 30 Apr 2022 12:53:04 GMT
• Title: PGD: A Large-scale Professional Go Dataset for Data-driven Analytics
• Authors: Yifan Gao
• Abstract summary: This paper creates the Professional Go dataset, containing 98,043 games played by 2,148 professional players from 1950 to 2021. The dataset includes analysis results for each move in the match evaluated by advanced AlphaZero-based AI. With the help of complete meta-information and constructed in-game features, our results prediction system achieves an accuracy of 75.30%.
• Score: 3.747666374070152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lee Sedol is on a winning streak--does this legend rise again after the competition with AlphaGo? Ke Jie is invincible in the world championship--can he still win the title this time? Go is one of the most popular board games in East Asia, with a stable professional sports system that has lasted for decades in China, Japan, and Korea. There are mature data-driven analysis technologies for many sports, such as soccer, basketball, and esports. However, developing such technology for Go remains nontrivial and challenging due to the lack of datasets, meta-information, and in-game statistics. This paper creates the Professional Go Dataset (PGD), containing 98,043 games played by 2,148 professional players from 1950 to 2021. After manual cleaning and labeling, we provide detailed meta-information for each player, game, and tournament. Moreover, the dataset includes analysis results for each move in the match evaluated by advanced AlphaZero-based AI. To establish a benchmark for PGD, we further analyze the data and extract meaningful in-game features based on prior knowledge related to Go that can indicate the game status. With the help of complete meta-information and constructed in-game features, our results prediction system achieves an accuracy of 75.30%, much higher than several state-of-the-art approaches (64%-65%). As far as we know, PGD is the first dataset for data-driven analytics in Go and even in board games. Beyond this promising result, we provide more examples of tasks that benefit from our dataset. The ultimate goal of this paper is to bridge this ancient game and the modern data science community. It will advance research on Go-related analytics to enhance the fan experience, help players improve their ability, and facilitate other promising aspects. The dataset will be made publicly available.

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