Using Data to Improve Your Chess

Through methodical, data-driven analysis of your tournament results, you can quickly break through chess plateaus

When I was 16, I went on a hiatus from competitive chess. Although I would occasionally play in tournaments for fun and out of habit, I stopped actively training and sure enough plunged in the rankings. But by early 2012, I had grown frustrated with losing and was ready to come back. I was ready to return to rapid chess improvement. I spent all of January studying and playing training games, and I registered for a four round tournament in early February.

As is typical in Swiss paired tournaments, the first round was heavily mismatched, and I quickly dispatched a lower rated opponent. In the second round, I played black against a strong opponent. My opponent played an offbeat opening that gave me a nice, nearly winning advantage, which I promptly threw away. I had to settle for a hard fought draw.

The third round was a quick win with white against another fairly strong opponent. Although I wasn’t familiar with the opening, I was in control the entire game, leading to this flashy sacrificial win:

I was 2.5/3 moving into the final round, and winning this game would assure me first place in the tournament. I played as black, and the game started in a familiar opening. But by move ten I was feeling uncomfortable with my position. I struggled under the mounting psychological pressure, and collapsed on move 16, resigning a few moves later.

As I drove home from the chess center, no prize money nor rating gains in hand, I wondered about the results. Was it just a coincidence that I had struggled so much with black and dominated with white? Or was there something deeper going on? When I reached home, I looked through the results of all of the tournaments I had played in the last year. Was there a connection between my performance with white and black?

Here were my results:

Score: 7.5/13
Rating Performance: 2009

Score: 3.5/10
Rating Performance: 1661

The weighted average of my performance rating was 1858, identical to my rating after the tournament. Converting the rating difference to statistical predictions, my performance suggested that I was 7.3 times stronger as white than I was as black. What was causing this enormous disparity? The advantage white gets from having the first move is so slight that it shouldn’t have any impact below professional level. I analyzed my games, and noticed a pattern with the ones I played as black. In those games, I would often struggle in the opening, and make uncharacteristic positional mistakes or blunders. I often got far behind on time, overthinking positions. I didn’t have confidence in my play, and usually thought my position was much worse than it was objectively was. Eventually the psychological pressure would reach a breaking point and I would collapse.

This all had to do with the psychological effect of openings. With white, having the first move allowed me to steer the position into one that I was comfortable with, even if the opening was unknown. With black that comfort often isn’t there, and in many of my games the psychological pressure of being in an uncomfortable (though not necessarily bad) position and taken out of book1 caused me to make some terrible positional mistakes and outright tactical blunders. In the best case, I would hold onto a decent position but get far behind in time.

I spent the next week focusing on two things: improving my opening knowledge as black, and keeping my cool in psychologically uncomfortable positions. At the end of the week, I played in a large, multi-day tournament. There were some close calls, but in the end I won the tournament with a score of 4.5/5, 1.5 points ahead of the next player and with the widest margin of any section winner in the entire tournament. This was the first time in two years—since the time I was at my peak—that I had won a major tournament. My performance was as follows:


Score: 4.5/5

Rating Performance: 2075


Score: 2/2

Rating Performance: 2270


Score: 2.5/3

Rating Performance: 1920

There’s a viable argument that I just had a good tournament, since I outperformed with white as well as black, and the difference in performance rating was still about 350 points. But then, maybe it was a positive feedback loop from doing well with black. After all, there is a large psychological carryover from previous games in chess. I could have played much better as white by not being as psychologically or physically drained from games I played with black. It’s hard to tell from one tournament.

Nonetheless, I noticed a definite shift in the way I was playing and how I was psychologically reacting to unfamiliar positions. And regardless of the result after merely one week of training, I was able to pinpoint a weakness and target my training regimen appropriately. I seemed poised to make my comeback to chess. Unfortunately, the Atlanta Chess Center, where the vast majority of tournaments in Georgia are held, went bankrupt just a few weeks later and so my return was put on indefinite hold.

There is a lot of potential for this sort of approach. When chess players decide what to study, it’s typically off of gut instinct. It’s easy enough to see which specific areas of your strictly chess abilities are weak (there are books for that), but data driven analysis can give insights into the less obvious areas of chess performance. In addition to performance with each color, you could analyze performance at different time controls, different levels of tiredness (rounds early on in a tournament versus later rounds), and even individual opponents2.

It would be interesting to develop machine learning and data analysis algorithms to look at these areas. The main problem here is that data is not easily available. Although the United States Chess Federation (USCF) keeps a database of wins, losses, and rating changes, they have no API for access, and they only recently started (sporadically) tracking which games were played as white and black. Popular chess software, like Fritz and Chessbase, can automate this somewhat, because they allow you to filter games by ratings, openings, and dates. This would help narrow down the games to analyze, but most of the useful data analysis still has to be done by hand. Writing data mining software for chess would be a cool project, but it wouldn’t automate everything. Some components of chess performance (tiredness, psychology) are qualitative. Data mining can find patterns, but it’s up to you to figure out what those patterns mean. Using this software would also require the user to keep many detailed records that the USCF doesn’t: time control, color, notes on psychological and physical conditions, etc.

But in the end, the effort would be well worth it. Facebook and Google are successful because they mine data to offer targeted advertisements to their users. Amazon and Netflix use machine learning to predict what products and shows you’ll like. And using data, you can target your chess training for better results.


1 “Out of book” means out of the previously established opening theory.

2 Analyzing results against individuals can actually be a very useful exercise. Many times, a statistically poor score against an individual (say, scoring 2/7 against an equally rated opponent) can reveal weaknesses against particular openings, playing styles, or psychological conditions.

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