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 , 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. Chess.com 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. Connect, Follow or Endorse me on LinkedIn if you found this read useful. To learn more about me visit: Here I am also nominated for the HackerNoon 2022 Noonies Award, Vote for me: | HackerNoon Contributor of the Year — Data Data Science Demon Also Published here

This story contains new, firsthand information uncovered by the writer.

Harmony

These Google Maps Insights Uncovered by a Data Scientist are Unbelievable

The Design of Model Productionization Architecture in Data Science

Master Data Science Skills

Nominated for 2022 - Best Data Science Newsletter

Nominated for 2022 - HackerNoon Contributor of the Year - Data

Nominated for 2022 - Data Science Demon

Nominated for 2022 - HackerNoon Contributor of the Year - Data Science

Nominated for 2022 - HackerNoon Contributor of the Year - India

Too Long; Didn't Read

Design a Real-time Player Matching Algorithm For Chess Games

Design a Real-time Player Matching Algorithm For Chess Games

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Beyond A/B Testing — Switchbacks and Synthetic Control Group

10 Best AI Content Generation Tools for All Your Content Needs in 2022

12 Use Cases of AI and Machine Learning In Finance

139 Stories To Learn About Ai Trends

3 Futuristic AI Trends in Finance Sector: 2020 Edition

186.9 Million Visits to Unbounce Landing Pages Converted at 3.57%

Beyond A/B Testing — Switchbacks and Synthetic Control Group

10 Best AI Content Generation Tools for All Your Content Needs in 2022

12 Use Cases of AI and Machine Learning In Finance

139 Stories To Learn About Ai Trends

3 Futuristic AI Trends in Finance Sector: 2020 Edition

186.9 Million Visits to Unbounce Landing Pages Converted at 3.57%

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps