Quantifying the complexity and similarity of chess openings using online chess community data

TL;DR

This paper leverages online chess community data to analyze the complexity and similarity of chess openings, creating a relatedness network, predicting future opening choices, and assessing opening difficulty and player skill levels.

Contribution

It introduces a novel network-based approach to quantify opening relatedness, predict future openings, and evaluate opening difficulty and player expertise using crowd-sourced data.

Findings

Identified communities of common openings in the relatedness network.

Achieved accurate predictions of future opening choices.

Quantified opening difficulty and player skill levels.

Abstract

Hundreds of years after its creation, the game of chess is still widely played worldwide. Opening Theory is one of the pillars of chess and requires years of study to be mastered. Here we exploit the "wisdom of the crowd" in an online chess platform to answer questions that, traditionally, only chess experts could tackle. We first define the relatedness network of chess openings that quantifies how similar two openings are to play. In this network, we spot communities of nodes corresponding to the most common opening choices and their mutual relationships, information which is hard to obtain from the existing classification of openings. Moreover, we use the relatedness network to forecast the future openings players will start to play and we back-test these predictions, obtaining performances considerably higher than those of a random predictor. Finally, we use the Economic Fitness and Complexity algorithm to measure how difficult to play openings are and how skilled in openings players are. This study not only gives a new perspective on chess analysis but also opens the possibility of suggesting personalized opening recommendations using complex network theory.