# Evaluating Go Game Records for Prediction of Player Attributes

arXiv: https://arxiv.org/abs/1512.08969 · HTML: https://mlsanity.com/paper/1512.08969/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2015.

## Built on by (most cited first, 1 of 1)

- [Policies of Multiple Skill Levels for Better Strength Estimation in Games](https://mlsanity.com/paper/2505.00279/) (2025)

## Abstract (arXiv)

We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or statistic of sente/gote sequences. Using machine learning algorithms, the evaluations can be utilized to predict different relevant target variables. We apply this methodology to predict the strength and playing style of the player (e.g. territoriality or aggressivity) with good accuracy. We propose a number of possible applications including aiding in Go study, seeding real-work ranks of internet players or tuning of Go-playing programs.