ChessMonitor – Analytics for Lichess and Chess.com

How many combinations are there for Chess960?

I'm wondering whether people could also prepare for it if it became popular.

I'm aware of Chess960, but this will probably never be supported on ChessMonitor for three reasons: First, Chess960 basically tries to prevent players from preparing (by having a random starting position) which does not align well with providing statistics. Second, it would require major UI and storage changes on ChessMonitor (like adding an option to select the starting position to compare games). And third, Chess960 is simply not as popular amongst non-professional chess players.

I'm currently not doing Stockfish evaluations (everything is based on simple win/loss-rate) but I hope I'm able to integrate this in the future. Then I can show much more in-depth statistics like the one you mention.

There are many similar features. Overall the UI on ChessMonitor is much more focused on statistics.

Another key feature Lichess does not have, is the Openings page. It is very powerful when used correctly. You can look at your openings and immediately see for which openings you might need to consume more theory (or which you should not play at all).

Here is an example: https://www.chessmonitor.com/u/cdPiOkUArRJ7shVNJcT3/openings...

Thanks, I just like it when all state is in the URL and you can just bookmark it. Also using the browser back/forward button should work well thanks to that. I'll check out your library!

My code for that is rather simple. I'm using Next.js and the code looks something like this (a little simplified):

  const router = useRouter(); // get the router/location object
  const information = useFetch({ position: router.query.position }); // fetch information from the server
  
  function changeChessPosition(newPosition) { // called when position changes
      router.replace({ query: { postition: newPosition } }); // replaces the state in the URL
  }

In addition, I have some caching in place so that each position is only downloaded once and the change function looks a little more complicated in my case as there are multiple values that can be change for each page.

I’d suggest fitting an isotonic regression instead. I think it’s a reasonable expectation that online and FIDE ratings are monotonically related but it seems unlikely to me that they are linearly related even though it may be an ok approximation for players in a particular rating range.

I suspect that the online population — especially at lower ratings — is significantly different to the over the board population. I also expect — especially in the FIDE case — that ratings stratify the players into hobbyists, serious amateurs, professionals, etc and so different FIDE rating ranges are likely to scale to online ratings differently.

All of the above should be implicitly accounted for in an isotonic regression so long as monotonicity holds globally. You can easily do it with sklearn and I suspect it may give you better results.