734 reads

Design a Real-time Player Matching Algorithm For Chess Games

by Shaurya UppalSeptember 16th, 2022

Too Long; Didn't Read

Chess is a free game that can be played by anyone, regardless of language, age, gender, physical ability, or social status. We take the following factors into account: Match the closest online player based on rating. Matching rude or abusive players with kind ones to maintain harmony on the platform and to improve retention. Avoid matches with players who have quit without resigning (aka Bad Sportsmanship): Analyse player win/loss streaks: Boost morale of players with a lower rating in matches with whom there is a high probability of winning.

People Mentioned

Coin Mentioned

featured image - Design a Real-time Player Matching Algorithm For Chess Games

I’ve recently resumed playing chess after a very long break. My time spent on wasteful and unproductive entertainment has decreased as a result.

Chess has many benefits:

Enhances one’s capacity for problem-solving.
Raises reading compression and IQ

There are numerous other advantages that are crucial to the engineering and data science professions.

My data science side was ignited after spending some time playing at Chess.com, wondering how their player-matching algorithm works. In this newsletter, we will discuss in detail which features are useful and how we can build a player-matching algorithm.

If you have not subscribed to this newsletter, visit this link and hit the subscribe button.

Problem Statement

Build a real-time player-matching algorithm for a Chess game.

Business Goals

To maximize retention on the platform.
To increase the number of games played per user per session, which would eventually lead to users buying a pro or diamond membership to learn chess.
Maximize session duration on the platform.

Approach

Features

For real-time player matching, we take the following factors into account:

Match the closest online player based on rating: A beginner should not be matched with Viswanathan Anand, a Grand Master level player.
It avoids matching rude or abusive players with kind ones: To maintain harmony on the platform and to improve retention, this is very important.
Check internet connection, avoids matches of solid internet connectivity players to poor connectivity ones.

Avoid matches with players who have quit without resigning (aka Bad Sportsmanship): Analyse the past behavior of players on the platform, annoying players should get matched with similar kinds of players.

Many players believe the matching algorithm is based on opening styles and win/loss streaks. I completely agree with this. After playing multiple games, this really happens, and that makes the matching algorithm even more interesting. It doesn’t make you feel like you know it all. Chess is a mind game, and rote learning a few opening styles doesn’t make you a pro player!

Predicting a win or loss prior to the match when matching players is a masterstroke. It is very true that playing against a difficult opponent with a lower rating makes you play more. I have experienced this myself. Analyze player win/loss streaks: Boost the morale of players with a lower rating in matches with whom there is a high probability of winning (vice-versa in a tougher match if there is a long win streak).

Some of you may be wondering why the user profile was not considered. I purposefully did not include the user profile demographics or geographics because chess is a free game activity that can be played by anyone, regardless of language, age, gender, physical ability, or social status.

Data Science Algorithm

This is the perfect problem for Learning To Rank-Pairwise Ranking.

We pairwise compare the players who would like to play in a particular format with all the other players in the queue and match those who have the best match.

{Pi, Pj} -> 1 or 0; where 1 means Pi and Pj are relevant matches & 0 means Pi and Pj are not a relevant match, use the prediction probability of the classifier to find which two pairs are the best match or more relevant.

Bookish Definition — Pairwise ranking is analogous to classification. Each data point is associated with another data point, and the goal is to learn a classifier that will predict which of the two is “more” relevant to a given query.

Refer to my Blog: Machine Learning-Powered, Pairwise Ranking of Reviews at 1mg (Part One)

Conclusion.

There is a lot more to this problem, and it can be optimized further. Matching algorithms are always fun to design and think about. I would love to hear your thoughts on this problem and share your views in the comments.