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This repository contains the (future) official rating and ranking system for online-go.com, as well as analysis code and data to develop that system and compare it to other reference systems.

License: MIT License

JavaScript 1.50% C++ 36.75% Python 49.91% C 0.39% TypeScript 10.12% Makefile 0.96% HTML 0.10% Stylus 0.27%

goratings's Introduction

This repository contains the (future) official rating and ranking system for online-go.com, as well as analysis code and data to develop that system and compare it to other reference systems.

Cloning

To successfully clone the project, you need to have the git large file storage extention installed. Otherwise the game database is missing.

Using and developing

There are four main directories to be aware of.

data houses raw game record databases. At a minimum we have 12M ranked games to work with that contains data from Online-Go.com, contributions of other large data sets are welcome.

analysis houses code we use to analyze the performance of different rating system configurations. This is where we try new things and house useful code during the our development efforts. Once particular system and configuration becomes stable, we can promote it into the goratings directory for publishing.

goratings and unit_tests house the (future) official rating and ranking code. In these directories at a minimum the official rating and ranking code and associated tests for online-go.com will be housed, other reference systems are welcome in here as well. All code in the goratings directory will need to be fully type annotated, lint and black clean, and have 100% test coverage. The code under this module will be packaged and published in an official ratings go ratings/ranking python module.

Goals

The ideal ranking system for Go will be able to quickly determine a player's strength and assign a ranking to that player such that the rank difference between two players can be used to compute an appropriate handicap in game.

In our quest to find the best rating and ranking system for the game of Go, we will be attempting to optimize the system to produce the best prediction results for handicap and non handicap games, and secondarily minimizing the number of games needed to establish a reasonable confidence of a player's strength.

Quick start

To do test runs you'll want to be editing and running files in the analysis directory. For example, this should work out of the box:

analysis/analyze_glicko2_one_game_at_a_time.py

Parameter tuning

The analysis scripts import some common utility code that includes some parameters to tune, such as the variables used in converting ratings to ranks, glicko2 variables, and that sort of thing. For a full up to date list, run an analysis script with --help

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