Raw data and RDS files and analysis scripts can be found and downloaded here: github.com/TheDataDigest/Chess/tree/main/input Have fun analyzing and exploring all 2023 Titled Tuesday Tournaments.
I wasn’t expecting much when I clicked. But man you kept delivering more and more. Well done. Subscribed and will definitely watch everything you produce 👏🏼👏🏼
Thank you so much for your comment. I am glad you enjoyed it. It was actually performing really poorly in the first days with regards to click-through rate and views, but now it seems that the algorithm is slowly starting to find potential viewers that might enjoy it. So again, thanks for engaging by leaving a comment and subscribing.
I think you are capable! If you are curious about a topic, or like to learn some form of data analysis there is always a simple step to be taken to get a little bit better. Start with some easy text books that have loads of examples. Or listen to david robinsons tidy tuesday screencasts on youtube.
Well, thank you :) But in all honesty i don't think i could. My intelligence is almost decent, but the work you put in, the research and hours. it's also beyond me.@@TheDataDigest
No problem, glad you liked it and left a comment. On what kind of data or projects are you working on these days? If you need help with any R code please let me know.
Great programming skills! I think you could make an extra video just showing the graph/ statistics, so that it would be more appealing for the brighter chess community :)
Hi there. Very good suggestion. I was thinking about several Shorts that just go through some of the results. Maybe with some interactive charts. But you are right, maybe a short ~5 min video would be best for that.
Women are not limited to WGM, WIM, etc. These are additional titles only available to women. So any calculations would only express the share holding that subset of titles, and wouldn't be a proxy for gender. For example, there are female IMs and GMs that participate in TTs. I loved this video, and will definitely check out your channel!
Hi Tristan, first of all, you are correct. Second of all, thanks for leaving such a nice comment. I am glad you enjoyed the video. I did some more analysis and only found two cases of players both playing und men and women titles. 1) Meri-Arabdize (from GEO) participated 26x as WGM and 8x as IM. 2) Jiner Zhu (from CHN) participated 10x as WGM and 5x as GM. The best placed women gets $100 in each tournament, so I assume that most women will play under the women title to qualify for that. Unless chess.com has another way of know or they don't care that much about the prize money.
Unless I missed it, did you not calculate what was the probability, or % win rate I guess you could say (out of the ones who have won TT in 2023) of the players winning the TT? I guess its something as simple as dividing the number of TT wins they have by the number of total times they entered? Thats what I was expecting to see when i clicked, knowing Magnus probably participated in less TT than Hikaru. So of course while number of TT wins is interesting, unless it is relative to number of tournaments played it doesnt tell me all that much? And how often someone gets top 3 etc would also be interesting.
@40:42 I briefly show the winning percentage but I forgot to make a chart. Is it okay when I list the top 10 highest winning percentage with (wins/participations) in parenthesis? Below I will answer the same with the chance to place in the top 3. 1) Liem Le, VNM: 30% (3/10) 2) Hikaru Nakamura, USA: 24.3% (18/74) 3) Magnus Carlsen, NOR: 23.1% (9/39) 4) Maxime Vachier-Lagrave, FRA: 20% (5/25) 5) Platon Galperin, UKR: 16.7% (1/6) 6) Eduardo Iturrizaga, ESP: 14.3% (1/7) 7) Shakhriyar Mamedyarov, AZE: 12.5% (1/8) 8) Daniil Dubov, RUS: 12.2% (5/41) 9) Alexander Grischuk, RUS: 11.1% (3/27) 10) Nihal Sarin, IND: 1.11% (5/45)
Cool to see that Caruana and Artemiev make it in the highest % to place as top3. Thanks for the extra request/question. 1) Susanto Megaranto, IDN: 66.7% (2/3) 2) Magnus Carlsen, NOR: 46.2% (18/39) 3) Hikaru Nakamura, USA: 43.2% (32/74) 4) Eduardo Iturrizaga, ESP: 42.9% (3/7) 5) Liem Le, VNM: 40% (4/10) 6) Khumoyun Begmuratov, UZB: 33.3% (1/3) 7) Maxime Vachier-Lagrave, FRA: 32% (8/25) 8) Fabiano Caruana, USA: 26.8% (11/41) 9) Vladislav Artemiev, RUS: 25% (1/4) 10) Aram Hakobyan, ARM: 24% (12/50) I should probably also do a top 5 which stands for % winning prize money.
You said you calculated the "average score needed to win" but what you actually calculated could more accurately be described as "average score of the winner". There are a number of tournaments where winners did not actually need to get the score they got in order to win the tournament, as discussed later in the video: Hikaru and Magnus did not need 11 points to win the tournaments, 9 points and the right tiebreak would have been enough. If you would really try to calculate the "average score needed to win" then it should be calculated based on how many points the 2nd player got because all that was needed to win was the same number of points plus a better tiebreak.
Great catch. Very precise use of language, but you are correct. So in order to show the average of what I meant I would have to look at the score of number 2 and then add half a point. Or when there is a tight live with the fact that the Sonnenborn-Berger Score will resolve this.
will love to watch extremly data like, wich FM player are playing like GM and how many games wins vs them. In lucky day a average player (FM, IM, GM) how many points get vs a extremly player.. how many sigma are the extreme players above of the average with sam title... how often a player with less elo win vs high elo ith a diference of 300 points elo.. what theory (elo) said abouta that
Oh wow, these are really good suggestions. I like the rating comparisons by different brackets of difference. And whether it makes a difference who has the white pieces. I think this would be great for some models. I will look into it!
This information was not included in the tournament csv files that I analyzed for this video. There are however files for each tournament that contain the game information. I am also interested in such questions like "flagging %" and decisive games depending on titles and rating differences. And the bongcloud opening or the cow :) But to analyze these huge text files I have to write some specific functions first (or let ChatGPT do it) :D
@@TheDataDigest Thanks for the swift answer! I am not able to sift through that data myself, so I was hoping that someone had done the job for me ;-) I am curious because of the "clash of claims" match - a comparison between 3+1 and 3+2 flagging rate would be interesting. I suspect that Kramnik wants 3+2 because he's afraid of flagging, and that makes the "clash" a bit weird since it's not titled tuesday format. But without data I am disinclined to propose that theory.
@@kristianfagerstrom7011 Titled Tuesday is 3+1, right? Would you have examples of 3+2 tournaments where I could download some games to check your hypothesis? I want to look into these game analyses very soon.
Awesome video! Could you share the name of the player who has a rating of approximately 3125 ELO and really high average points, as depicted on the thumbnail graph?
I will soon share the data and scripts on GitHub, then you could answer these kind of questions yourself, if you download R/R-Studio, which is free. Only if you are interested in data analysis of course :)
I would love that. I am actually planning to analyze all games to find out what the most common opening was and the rarest one and how often a player wins on time etc. Edit: I first did not understand the reference and the matching emoji. But another comment made me aware of certain accusations 😅
You are of course right. Judit Polgar comes to mind. I checked the data again and I could only find two women that are listed with women and men titles. Maybe there are some that use the men title only but I wouldn't be sure from the name alone. I found a list of 41 women with GM title and did some spot check of the most recent ones but could not find their name in the data set. The 2 I found are: Meri-Arabidze (GEO) with WGM and IM and a best rating of 2745 Jiner Zhu (CHN) with WGM and GM and a best rating of 2692.
I missed that controversy but found some interesting articles discussing the matter. Thanks for bringing it up. Now I better understand another comment that mentioned Kramnik and Hikaru before. But the winning streak analysis did not show any big surprises. Six players with 12 games, Hikaru with 15 and Magnus with 17. Seems fine given that Magnus is the GOAT and Hikaru has an incredible Blitz rating and participates so many times in TT.
Raw data and RDS files and analysis scripts can be found and downloaded here:
github.com/TheDataDigest/Chess/tree/main/input
Have fun analyzing and exploring all 2023 Titled Tuesday Tournaments.
I wasn’t expecting much when I clicked. But man you kept delivering more and more. Well done. Subscribed and will definitely watch everything you produce 👏🏼👏🏼
Thank you so much for your comment. I am glad you enjoyed it. It was actually performing really poorly in the first days with regards to click-through rate and views, but now it seems that the algorithm is slowly starting to find potential viewers that might enjoy it. So again, thanks for engaging by leaving a comment and subscribing.
This is seriously impressive! Well done!
Thank you so much for leaving a comment. I am glad you liked it. It was definitely fun to do the analysis.
Amazing! great analysis, and even greater explanation of how each result was determined!
Glad you liked it Patrick and thank you for leaving a comment.
Very cool analysis! I am looking forward to the second part where you go beyond the descriptive statistics!
amazing! statistics are fun. and its great to have someone doing the work i wish i was capable of! TY
I think you are capable! If you are curious about a topic, or like to learn some form of data analysis there is always a simple step to be taken to get a little bit better. Start with some easy text books that have loads of examples. Or listen to david robinsons tidy tuesday screencasts on youtube.
Well, thank you :)
But in all honesty i don't think i could.
My intelligence is almost decent, but the work you put in, the research and hours. it's also beyond me.@@TheDataDigest
Thank you for the guidance. Even step by step to understand better for the coding, it is very helpful!
No problem, glad you liked it and left a comment. On what kind of data or projects are you working on these days? If you need help with any R code please let me know.
Great programming skills! I think you could make an extra video just showing the graph/ statistics, so that it would be more appealing for the brighter chess community :)
Hi there. Very good suggestion. I was thinking about several Shorts that just go through some of the results. Maybe with some interactive charts. But you are right, maybe a short ~5 min video would be best for that.
@@TheDataDigesthell yeah 🦅
Women are not limited to WGM, WIM, etc. These are additional titles only available to women. So any calculations would only express the share holding that subset of titles, and wouldn't be a proxy for gender. For example, there are female IMs and GMs that participate in TTs. I loved this video, and will definitely check out your channel!
Hi Tristan, first of all, you are correct. Second of all, thanks for leaving such a nice comment. I am glad you enjoyed the video. I did some more analysis and only found two cases of players both playing und men and women titles.
1) Meri-Arabdize (from GEO) participated 26x as WGM and 8x as IM.
2) Jiner Zhu (from CHN) participated 10x as WGM and 5x as GM.
The best placed women gets $100 in each tournament, so I assume that most women will play under the women title to qualify for that. Unless chess.com has another way of know or they don't care that much about the prize money.
Unless I missed it, did you not calculate what was the probability, or % win rate I guess you could say (out of the ones who have won TT in 2023) of the players winning the TT?
I guess its something as simple as dividing the number of TT wins they have by the number of total times they entered?
Thats what I was expecting to see when i clicked, knowing Magnus probably participated in less TT than Hikaru.
So of course while number of TT wins is interesting, unless it is relative to number of tournaments played it doesnt tell me all that much?
And how often someone gets top 3 etc would also be interesting.
@40:42 I briefly show the winning percentage but I forgot to make a chart. Is it okay when I list the top 10 highest winning percentage with (wins/participations) in parenthesis? Below I will answer the same with the chance to place in the top 3.
1) Liem Le, VNM: 30% (3/10)
2) Hikaru Nakamura, USA: 24.3% (18/74)
3) Magnus Carlsen, NOR: 23.1% (9/39)
4) Maxime Vachier-Lagrave, FRA: 20% (5/25)
5) Platon Galperin, UKR: 16.7% (1/6)
6) Eduardo Iturrizaga, ESP: 14.3% (1/7)
7) Shakhriyar Mamedyarov, AZE: 12.5% (1/8)
8) Daniil Dubov, RUS: 12.2% (5/41)
9) Alexander Grischuk, RUS: 11.1% (3/27)
10) Nihal Sarin, IND: 1.11% (5/45)
Cool to see that Caruana and Artemiev make it in the highest % to place as top3. Thanks for the extra request/question.
1) Susanto Megaranto, IDN: 66.7% (2/3)
2) Magnus Carlsen, NOR: 46.2% (18/39)
3) Hikaru Nakamura, USA: 43.2% (32/74)
4) Eduardo Iturrizaga, ESP: 42.9% (3/7)
5) Liem Le, VNM: 40% (4/10)
6) Khumoyun Begmuratov, UZB: 33.3% (1/3)
7) Maxime Vachier-Lagrave, FRA: 32% (8/25)
8) Fabiano Caruana, USA: 26.8% (11/41)
9) Vladislav Artemiev, RUS: 25% (1/4)
10) Aram Hakobyan, ARM: 24% (12/50)
I should probably also do a top 5 which stands for % winning prize money.
You said you calculated the "average score needed to win" but what you actually calculated could more accurately be described as "average score of the winner". There are a number of tournaments where winners did not actually need to get the score they got in order to win the tournament, as discussed later in the video: Hikaru and Magnus did not need 11 points to win the tournaments, 9 points and the right tiebreak would have been enough. If you would really try to calculate the "average score needed to win" then it should be calculated based on how many points the 2nd player got because all that was needed to win was the same number of points plus a better tiebreak.
Great catch. Very precise use of language, but you are correct. So in order to show the average of what I meant I would have to look at the score of number 2 and then add half a point. Or when there is a tight live with the fact that the Sonnenborn-Berger Score will resolve this.
will love to watch extremly data like, wich FM player are playing like GM and how many games wins vs them. In lucky day a average player (FM, IM, GM) how many points get vs a extremly player.. how many sigma are the extreme players above of the average with sam title... how often a player with less elo win vs high elo ith a diference of 300 points elo.. what theory (elo) said abouta that
Oh wow, these are really good suggestions. I like the rating comparisons by different brackets of difference. And whether it makes a difference who has the white pieces. I think this would be great for some models. I will look into it!
Can you please share the dataset with the community? I would like to work on it as well
Yes of course. @Kivallara also asked for it. I am currently moving it into my GitHub and then pin the link to it in the top comment.
Dataset can be found here: github.com/TheDataDigest/EDA/tree/main/Chess
Have fun analyzing.
@@TheDataDigest thanks a lot and keep doing the amazing things
I'm looking for % of TTuesday games decided on time. Is that data in there? I can't see it in the timelines titles.
This information was not included in the tournament csv files that I analyzed for this video. There are however files for each tournament that contain the game information. I am also interested in such questions like "flagging %" and decisive games depending on titles and rating differences. And the bongcloud opening or the cow :)
But to analyze these huge text files I have to write some specific functions first (or let ChatGPT do it) :D
@@TheDataDigest Thanks for the swift answer!
I am not able to sift through that data myself, so I was hoping that someone had done the job for me ;-)
I am curious because of the "clash of claims" match - a comparison between 3+1 and 3+2 flagging rate would be interesting.
I suspect that Kramnik wants 3+2 because he's afraid of flagging, and that makes the "clash" a bit weird since it's not titled tuesday format.
But without data I am disinclined to propose that theory.
@@kristianfagerstrom7011 Titled Tuesday is 3+1, right? Would you have examples of 3+2 tournaments where I could download some games to check your hypothesis? I want to look into these game analyses very soon.
@@TheDataDigest Yes, TT is 3+1, and no, sorry I don't have any tournament data, but maybe chess.com would be willing to share?
Awesome video! Could you share the name of the player who has a rating of approximately 3125 ELO and really high average points, as depicted on the thumbnail graph?
Yes I can, here are the top 10 players, that participated at least 5 times in 2023, sorted by average rating with their average blitz rating and N-participations:
1. Hikaru Nakamura: 8.64 | 3289 | 74
2. Wesley So: 8.59 | 3102 | 22
3. Magnus Carlsen: 8.51 | 3268 | 39
4. Dmitry Andreikin: 8.35 | 3055 | 84
5. Denis Lazavik: 8.25 | 3049 | 40
6. Yu Yangyi: 8.14 | 3051 | 7
7. Bogdan Daniel Deac: 8.11 | 3033 | 59
8. David Navara: 8.08 | 2943 | 6
9. Aleksei Sarana: 8.06 | 3066 | 74
10. Jan-Krzysztof Duda: 8.04 | 3030 | 60
I will soon share the data and scripts on GitHub, then you could answer these kind of questions yourself, if you download R/R-Studio, which is free. Only if you are interested in data analysis of course :)
Thanks, that one dot really caught my interest, turns out it was Wesley So.
Once again, great video keep it up 😁
@@TheDataDigest Well done video and looking forward to try to analyze the data myself. What's your github?
@@Kivallara github.com/TheDataDigest/EDA/tree/main/Chess
But I also pinned it as top comment.
Just waiting for Kramnik or Nakamura to use this video for their Interesting analysis...😅
I would love that. I am actually planning to analyze all games to find out what the most common opening was and the rarest one and how often a player wins on time etc.
Edit: I first did not understand the reference and the matching emoji. But another comment made me aware of certain accusations 😅
Cheating Tuesdays FTW!
great video. fyi there are women with "male" titles i.e. the top women achieve a rating of 2400 plus norms
You are of course right. Judit Polgar comes to mind. I checked the data again and I could only find two women that are listed with women and men titles. Maybe there are some that use the men title only but I wouldn't be sure from the name alone. I found a list of 41 women with GM title and did some spot check of the most recent ones but could not find their name in the data set.
The 2 I found are:
Meri-Arabidze (GEO) with WGM and IM and a best rating of 2745
Jiner Zhu (CHN) with WGM and GM and a best rating of 2692.
You should have counted how many players marked by GM Kramnik as cheaters participated in each tournament and how many events they won.
I missed that controversy but found some interesting articles discussing the matter. Thanks for bringing it up. Now I better understand another comment that mentioned Kramnik and Hikaru before. But the winning streak analysis did not show any big surprises. Six players with 12 games, Hikaru with 15 and Magnus with 17. Seems fine given that Magnus is the GOAT and Hikaru has an incredible Blitz rating and participates so many times in TT.
Erster. Good job after a long pause!
6 month or 183days exactly :)