DeepAI AI Chat
Log In Sign Up

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

by   Yifan Gao, et al.
Northeastern University

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 state-of-the-art approaches (64 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.


page 1

page 5


The ProfessionAl Go annotation datasEt (PAGE)

The game of Go has been highly under-researched due to the lack of game ...

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators...

VREN: Volleyball Rally Dataset with Expression Notation Language

This research is intended to accomplish two goals: The first goal is to ...

Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models

The research creates a professional certification survey to test large l...

DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

We propose DefogGAN, a generative approach to the problem of inferring s...

Beyond the Meta: Leveraging Game Design Parameters for Patch-Agnostic Esport Analytics

Esport games comprise a sizeable fraction of the global games market, an...

Ludometrics: Luck, and How to Measure It

Game theory is the study of tractable games which may be used to model m...