  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.
