Blog GilaChess

Will come out straight and say this is in no way competing with what MCF is doing in introducing their MCF Rating System. MCF system is an opt-in, paid-for (organizers RM 100 per tournament, players RM 50 for two years) , serious, and official rating system calculated monthly. It's an "Opt-in" system because say for example, you may have a MCF rating but a friend may not because he/she has not opted in.

The difference to be stressed: MCF Rating is official. This "Gila Rating System" is not even official or unofficial! It's just a list published by some random blogger based on some calculation that holds no weight in anything, especially for things like initial seeding, etc.

The following "Gila Rating System" is for fun. Sort of like the live rating ELO system but should be taken in with a grain of salt. The advantage is all Malaysians if they have participated in a chess event would have their ratings calculated. However, if "ideas" posted in the past in this blog are any indication, will never get to see the light of day because this blogger is plain lazy :)

In the past, the National Rating System for Malaysia was maintained by the herculean efforts of Mr Lim Tze Pin - Secretary of the Chess Association of Selangor. It was a labour of love and purely that - a really intensive labour of effort. I would never want to even attempt to do what he has done. Efforts which has gone thankless by the Malaysian Chess community in my humble opinion!

However with the advent of AI systems like ChatGPT, Google Bard, Bing AI, ChatSonic, CoPilot, YouChat, Perplexity, and Jasper just to name a few, the heavy lifting work can be automated. As a programmer, I just have to write the base code get how ELO does its calculation, and let the AI systems handle the rest.

Here is a high-level overview of how such a system could work:

1. Scrape the chess-results.com website for the latest game results. This can be done using a variety of web scraping tools and libraries.
2. Parse the game results into a structured format. This will involve extracting the player names, FIDE ratings, and game outcomes.
3. Use the AI systems to calculate the new national ratings for each player. This can be done by implementing the national rating algorithm in code, or by using an existing AI library that provides this functionality.
4. Aggregate the new national ratings for all players in Malaysia to calculate the country's average ELO rating.

Why am I writing this post if it will never see the light of day? Well, it was a crazy inspiration that popped into my mind for no reason and I had to document it somewhere! :)

But wouldn't it be nice to have a rating system for all Malaysians for free and track their classical, rapid, and blitz ratings for all their games played and recorded on chess-results.com? I for one would welcome it! Perhaps someone will take up the challenge and make this a reality. It's certainly a possibility with today's newest AI systems.