These articles from 2013 were my debut as a chess and statistics writer. "Are the Chess World Champions Just Lucky?" Part 1 and Part 2 in ChessBase examined the results of World Championship matches. When Fischer crushed Spassky 12.5 - 7.5, there wasn't any doubt that he was the superior player. On the other hand, players such as Khalifman and Kasimdzhanov were never considered legitimate world champions. How can we tell if the winner of a match really is the better player? Fortunately, statistics provide a way to answer that question.
The second part of the article proposed a new format for the World Championship and gave forecasts for the 2013 Anand - Carlsen match. Of course, everyone knew that Carlsen was the favorite, but most people also acknowledged that Anand had reasonable chances of keeping his crown. The model (refined further in Part 3) went out on a limb by placing the probability of a Carlsen victory between 86% and 93%. In retrospect, this confidence in Carlsen looks entirely justified, but it was a bit provocative back then.
The reader feedback in Part 3 raised some fascinating questions. My favorite was the one about Fischer's proposed format for the 1975 World Championship match with Karpov. How unfair would it have been to Karpov? He would need 10 wins - 8 wins to prevail, while Fischer only needed 9 wins - 9 wins to retain his title. I used statistics to measure the unfairness and then compared it to other formats where the champion had draw odds.
Monday, March 31, 2014
Sunday, March 30, 2014
About e4stat
E4stat is a blog about chess and statistics. Here you will find forecasts for upcoming and ongoing chess tournaments as well as analysis of chess statistics.
E4stat is run by Matthew Wilson. I'm currently a PhD student in Economics at the University of Oregon. Economic data is often messy; we seldom have the luxury of using controlled laboratory experiments to obtain data. Due to this, all economists are trained quite heavily in statistics. As an amateur chess player, I'm delighted to apply this knowledge to our great game. Here are some projects I want to explore in the future:
-Is 1.e4 really more aggressive than 1.d4 ? More precisely, if all else is equal, will playing 1.d4 instead of 1.e4 significantly increase the probability of a draw?
-Improve the draw rate estimate in tournament forecasts
-Write a program that produces forecasts for Swiss System tournaments. Right now I'm only doing matches and round robins.
-What is the best defense to 1.e4? 1.d4? As explained in my essay "1.e4 - 'Best by Test'?", there is more to be done than simply comparing the percentage scores for each move.
-Move-by-move forecast updating: with enough data, I could use the computer evaluation of each position to update the win/draw/loss probabilities for the game and also the probabilities of each player winning the tournament. You could watch a player's chances plummet immediately after he makes a blunder. Currently I can revise forecasts after each game is finished, but this also could be refined (the estimates for the 2014 Candidates Tournament did not account for whether a player had white or black).
-What would be a reliable way to determine the best challenger to the world championship? Was the old method with the zonals, interzonals, and candidates matches a good one? The best format for the world championship was already discussed in my article "Are the Chess World Champions Just Lucky?" Part 2.
If you have an interesting chess question that can be studied statistically, feel free to let me know.
E4stat is run by Matthew Wilson. I'm currently a PhD student in Economics at the University of Oregon. Economic data is often messy; we seldom have the luxury of using controlled laboratory experiments to obtain data. Due to this, all economists are trained quite heavily in statistics. As an amateur chess player, I'm delighted to apply this knowledge to our great game. Here are some projects I want to explore in the future:
-Is 1.e4 really more aggressive than 1.d4 ? More precisely, if all else is equal, will playing 1.d4 instead of 1.e4 significantly increase the probability of a draw?
-Improve the draw rate estimate in tournament forecasts
-Write a program that produces forecasts for Swiss System tournaments. Right now I'm only doing matches and round robins.
-What is the best defense to 1.e4? 1.d4? As explained in my essay "1.e4 - 'Best by Test'?", there is more to be done than simply comparing the percentage scores for each move.
-Move-by-move forecast updating: with enough data, I could use the computer evaluation of each position to update the win/draw/loss probabilities for the game and also the probabilities of each player winning the tournament. You could watch a player's chances plummet immediately after he makes a blunder. Currently I can revise forecasts after each game is finished, but this also could be refined (the estimates for the 2014 Candidates Tournament did not account for whether a player had white or black).
-What would be a reliable way to determine the best challenger to the world championship? Was the old method with the zonals, interzonals, and candidates matches a good one? The best format for the world championship was already discussed in my article "Are the Chess World Champions Just Lucky?" Part 2.
If you have an interesting chess question that can be studied statistically, feel free to let me know.
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