Well now I know which marathon to play if I don't want to again spend 15 hours to win a trophy.
@Whitedancingrockstar said in #2:
> Well now I know which marathon to play if I don't want to again spend 15 hours to win a trophy.
The answer being "none of them"? :) In general the increment arenas seem a bit easier than the non-increment marathons. (Maybe because people like me try catching up by berserking, get flagged by lower-rated players and ragequit.)
> Well now I know which marathon to play if I don't want to again spend 15 hours to win a trophy.
The answer being "none of them"? :) In general the increment arenas seem a bit easier than the non-increment marathons. (Maybe because people like me try catching up by berserking, get flagged by lower-rated players and ragequit.)
No no, see, playing an increment marathon would decrease the 15-hour work-day to the fantastic 13 or 14 hours instead.
Or maybe I should just git gud.
Or maybe I should just git gud.
Hey, I'm the guy that lowered the rating "requirement" to get top 50 in bullet marathon! Kinda proud to "falsify" the statistics haha. I wish everyone (myself included) to stay healthy for the next one cuz we need it lul
I suppose I should inquire what source code produced these charts and graphs, in case people want to add machine learning-based predictions but don't want to rewrite Lichess API client code.
Good work, blessings!
Great job! Was really helpful!
@Toadofsky said in #6:
> I suppose I should inquire what source code produced these charts and graphs, in case people want to add machine learning-based predictions but don't want to rewrite Lichess API client code.
I've uploaded the code and the data to github.com/tmmlaarhoven/lichess/tree/main/marathons now. Some comments:
- The folder `/sheets` contains the score sheets of 10 players each, e.g., the string "022444444440023000210224400...". I fetched the top 500 score sheets to get the number of games played by each player, as this is not returned via `/api/tournament/{id}` or `/api/tournament/{id}/results`.
- The folder `/api` was used as the starting point for the .json and .ndjson files. With the score sheets downloaded, these .json and .ndjson files were updated/overwritten to the files in the folders `/1+0`, `/2+1`, etc., so the folder `/api` is the "backup" folder of the original data from the API.
- The folders `/1+0`, `/2+1` etc. contain the tournament .json and (modified) .ndjson files with the score sheets and some additional data.
- The folder `/plots` contains various figures, including the ones in the blog post.
- The file `logo.png` is used to add the Lichess logo in the background of the plots.
- The file `script.py` was used to update the .ndjson with these score sheets, to add more metadata, to calculate trophy rankings, and to generate plots.
- The file `README.md` is a copy of the blog post.
Let me know if you have issues reproducing the same plots and ranking.
> I suppose I should inquire what source code produced these charts and graphs, in case people want to add machine learning-based predictions but don't want to rewrite Lichess API client code.
I've uploaded the code and the data to github.com/tmmlaarhoven/lichess/tree/main/marathons now. Some comments:
- The folder `/sheets` contains the score sheets of 10 players each, e.g., the string "022444444440023000210224400...". I fetched the top 500 score sheets to get the number of games played by each player, as this is not returned via `/api/tournament/{id}` or `/api/tournament/{id}/results`.
- The folder `/api` was used as the starting point for the .json and .ndjson files. With the score sheets downloaded, these .json and .ndjson files were updated/overwritten to the files in the folders `/1+0`, `/2+1`, etc., so the folder `/api` is the "backup" folder of the original data from the API.
- The folders `/1+0`, `/2+1` etc. contain the tournament .json and (modified) .ndjson files with the score sheets and some additional data.
- The folder `/plots` contains various figures, including the ones in the blog post.
- The file `logo.png` is used to add the Lichess logo in the background of the plots.
- The file `script.py` was used to update the .ndjson with these score sheets, to add more metadata, to calculate trophy rankings, and to generate plots.
- The file `README.md` is a copy of the blog post.
Let me know if you have issues reproducing the same plots and ranking.
This is really interesting data! Thank you so much!