I Analyzed Over 1000 Games & Here's What I Learned

Kostya reflects on the most effective chess openings at all levels with 1000 chess games featured from the training program.
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[@laurentsaltoflife9267]

I'm happy to see you making use of the Training Program database. It would be nice if you pick some pieces of analysis from time to time (maybe not full games, just positions) and comment on the submitted analysis : good, bad, incomplete, how to improve, etc. It would certainly be useful for the students.

[@southernrun9048]

Interesting data and very impressed of such a high number of games submitted in the relative short time for program has been up and going.

[@chessconcepts3865]

As a young player around the expert range in USCF I can confirm me and a lot of my friends hate playing against 1.d4

[@mikem668]

This is an extremely hard data set to analyze and you were right to be cautious. The biggest issue is that it's observational data. In other words the openings weren't randomly assigned. Given that each player selected their own openings, and as white they dictated the choice while with black they were reactive, I wonder if there was an effect of player choice that's hidden. For example, perhaps bookish players choose the English, which prioritizes positional understanding, plus there's a familiarity effect. Also, if there are multiple games from the same players over a longer period of time, that's a time series. Meaning you would expect someone playing an opening the 10th time to do better than the 1st time, and probably be higher rated if they are under 1600. You've done a pilot study, which has generated some interesting hypotheses. Nice job.

[@gregfamilton865]

Somebody is studying Statistics at school :)

[@mikem668]

@@gregfamilton865 Not for a long time. I just love data. My hero, is a guy named John Tukey. (Other than R. A. Fisher.) He invented or at least emphasized Exploratory Data Analysis. Quite often, that's the best you can do. I've seen lots of bad data in my day and almost always those who collect it want to torture it so they can publish something. Typically, the problem is researchers want to know everything. So you have to keep asking them, what's the question again. There also tricks. For example, Kostya's binary division at 1600 probably won't work. However, five categories would capture most of the information. I told you, I love data. Especially about interesting problems I'm interested in. 😃

[@leninvelasquez9910]

A study where: you randomize openings players use ,would never work , for that to work all players would have to have the exact same knowledge and the exact same capacity to visualize play , those 2 vary so much at the same chess rating that an observational study is the best you can do

[@mikem668]

@@leninvelasquez9910 We agree it would be difficult. Depending on the question, it's still the only way to disentangle the factors. Educational research is a nightmare for the reasons you cite. Kostya's question is something like what openings give you the best chance of winning. He points out that that isn't the same as what openings are best for your chess development. I'm asking how chess talent or ability affects the first question. In effect, I'm trying to consider what's called a Latent Variable similar to math ability. In computerized testing it's easier. Say I ask you what is the square root of 9. If you say 3, I assume you know what a square root is and what 3 X 3 is. If you don't know the answer, I need to know if you can multiply. Chess involves so many elements, there are confounds everywhere.

[@dandimit8463]

This is great info. We appreciate your hard work.

[@madhatter1363]

This was an outstanding video. I am now subscribed solely on this content. This highlights that the game changes with level as evolution of players happens. Thanks

[@NickVisel]

Wow you put in a lot of work on this. Thanks Kostya!!

[@Graceclaw]

Would be interesting to do a follow-up now, with 6 more months of data

[@lastcraft]

A note on errors; standard deviations (assuming win/draw/loss have equal chances) are about 1.2% for n=1000, about 4% for n=100 and about 12% for n=10. So a hundred game sanple (say the Vienna) could just have a 3% (2SD) chance of being +8% or more. Given you present over 20 results, probably one or two are a bit wild.

[@Briston276]

Hey Kostya! Thanks for the analysis! Quick comment: I think that determining White Expected Score with White Average Rating vs Black average rating can be slightly misleading, as it should be the average of the expected scores for each game (say your pool consists of three White players, rated 2800, 1500 and 1500; and three Black players, all rated 1600. White average rating is 1933, Black average rating is 1600, so we would expect White to score 1.74 out of 3 points (around 58%), but looking only at the average ELO we would predict White to score 2.64 out of 3 points (around 88%)). Not sure how relevant the effect is for this pool of players, though. Thanks again for the insights!

[@ChessDojo]

That makes sense, thank you!

[@yotamchaimmoshe8013]

from my personal experiance playing many openings over my climb to ~1950 online, I think most of this data is reliable and should closely reflect what you should expect trying out different openings and studying them. I used to play e4 exclusively, and what terrified of the idea of touching the cursed d pawn on move 1. I was kinda "1.e4 best by test" kinda player, and would play pretty much anything slightly unusual and gambitlike in 1.e4 e5: Danish, Kings gambit, Vienna, Scotch gambit, Evans, Knight Attack and so on. At the time, I thought 1...e5 was just a joke that plays straight into my hands, And I just used it as a sandbox, choosing different hyper-aggressive offbeat lines to crush opponents early on. Later, I started experimenting with 1.d4, building a Catalan based repertoire where the bishop usually goes to g2 and you play a long game. In the beginning, I didn't really know how to treat the positions and would try to force through unsound exciting attacks. Overtime, I started realizing the real merits of playing 1.d4, Which for me is just to keep pieces on the board and keeping a long term structural advantage. I found that oftentimes with the kings pawn, and specifically 1.e4 e5, pieces were falling off the board, leading to early dry positions where White is equal or even flat out worse. But, with the Queens pawn, I could very easily keep my precious pieces and grind my opponent down, playing a long game that oftentimes even gave me my beloved attack at the very right moment. As for playing Black, I am surprised the French scores so well in low level, as I tried playing it myself with no more than decent success (I guess either the French isn't for me, or more likely I was studying it wrong). At the same time, I really hated facing the French with White so... And I truly believe the Caro in every level. I think most Caro players at low levels just don't quite play the right lines that give you this real dynamic Caro (specifically 3...c5 in the Advance). I would reccomend the Caro to antone struggeling against 1.e4 under my level. I would also like to adress the Sicilian statistics. In the video, it was said that the Sicilian basically slways scores somewhat poorly for Black at all levels, but I think that is a little misleading. Concidering the best try for White is the Open Sicilian, I would assume some Sicilians are just born better than others. What Sicilians are played? Dragon? Najdorf? Sveshnikov? Kalashnikov? Classical? Taimanov? This is the magic of the Sicilian, it is usually black who chooses the variations, unlike 1.e4 e5. And as for the Anti Sicilians, I am a very big believer of the Morra, And I think people usually just play it for tricks and traps, lowering the scores for those who know their stuff. The main problem is that it doesn't have much independent value... which sucks... I full heartly reccoment Marc Essermans book on the Morra, "Mayhem in the Morra". Amazing book. Lastly, I think first move sidelines like 1.f4 can be played with overwhelming succses using the right research, and again it is shadowed by those who just play it wrong. This is my general view. I think statistics are a really powerful tool that should be used togrther with engine analysis to build a killer repertoire for both colors. Usually, a sound idea that scores poorly can be made practical by just digging a little deeper. and of course, statistics on non commital moves like 1.e4, 1.Nf3 and 1...c5 shouldn't be taken seriously as the important decisions were not yet taken. Hope this was helpful for somebody reading this. Go to sleep.

[@OregonMikeH]

outstanding, .. thank you, .. m.

[@theunlearnedmind7374]

Scientific review I'm used to on KZbin. Bahaha!!! 🤣🤣🤣

[@mitchellfabian7694]

I haven't even submitted my games to skew the open Sicilian stats with my 86.4% winrate in it. Open Sicilian by far the best Sicilian

[@nomoreblitz]

It would be great to know the biggest mistakes in each game (in terms of centipawns) and analyze that data...

[@ChessDojo]

Tactics & K+P endgames would probably be the biggest offenders!

[@jaydub2971]

Oh, hey! The Pirc shows up (though no Benoni). Nice.

[@brasileirosim5961]

I would expect that the Alekhine scores better than all other defences against 1.e4, as it is rarely played and can be tricky for white.

[@easymoney1226]

in my experiemce the alekhine is one of the worst openings to play vs 1.e4 if White knows a little bit in the 4.Nf3 or Four Pawns Line

[@alfiealfie35]

@@easymoney1226 no, i play alekhine against e4, and 2.e5 ne5 3.nf3 is the only line where white is comfortable and a little better, but not that much and 4 pawns is incredibly tricky after c5

[@easymoney1226]

@@alfiealfie35 How is 5...c5 6.dxc5 tricky its just clearly worse endgame

[@alfiealfie35]

@@easymoney1226 sorry i mean dxe5 fxe5 c5, my bad

[@baldtrudger2704]

fascinating

[@ottosilver]

Right off the bat I would like to point how bad the science is on this. ;-) And what I learned is that I, as the 1000 rated player, should DEFINITELY switch to the English opening. :-P

[@ChessDojo]

Hahaha 😄

[@TheChessNeck]

And here I am too lazy to analyze more than 5 games lol

[@jonguy7944]

Interesting. Though I'm glad my games aren't poisoning the sampling! The summary @18:55 was worded the wrong way round. Or maybe that's a test to see if we're paying attention. 😉

[@ChessDojo]

Oops! You passed the test!

[@ChessWithMouselip]

"... the typical scientific rigor you see on youtube." :-)

[@GreenHope42]

Interesting 🤔

[@matthewheupel8681]

Can't wait to see the same analysis when you get to 5000 or 10000 games.

[@memeteam2016]

This video is kinda useless since you didn't adjust the ratings on each platform. The Lichess -> FIDE difference is so massive that you may as well not even do this. Saying that you "aren't trained" in mathematics doesn't excuse sloppy work.

[@ChessDojo]

Yes would be useful but very tough to adjust the ratings one by one as it was not always clear where the game was played. Seems like most found something of interest in this video but it's ok if you didn't! Video was mostly meant for fun as this is KZbin 😄 Hope you have a great day 💙

[@ChessJourneyman]

The difference is 250 points or more. No less. So yeah...

[@nicbentulan]

Is Kostya 'a professional chess player with a rating of about 2400 at the age of 29' ? XD

[@KeepChessSimple]

Everyone who makes decent money with chess (teaching, content, writing, playing) to make a living a chess professional. Rating has nothing to do with that. A chess professional is different than a professional player.

[@nicbentulan]

@@KeepChessSimple exactly. But kostya claimed to be a professional chess player as opposed to a chess professional. Sooo...?

[@joeldick6871]

Absolutely meaningless. Anyone who comes away from this video concluding that the English is better than e4 for 1500 players is completely misguided.

[@ChessDojo]

Good thing that was never recommended in the video! 😄