Chess databases as a research vehicle in psychology: Modeling large data

Abstract

The game of chess has often been used for psychological investigations, particularly in cognitive science. The clear-cut rules and well-defined environment of chess provide a model for investigations of basic cognitive processes, such as perception, memory, and problem solving, while the precise rating system for the measurement of skill has enabled investigations of individual differences and expertise-related effects. In the present study, we focus on another appealing feature of chess—namely, the large archive databases associated with the game. The German national chess database presented in this study represents a fruitful ground for the investigation of multiple longitudinal research questions, since it collects the data of over 130,000 players and spans over 25 years. The German chess database collects the data of all players, including hobby players, and all tournaments played. This results in a rich and complete collection of the skill, age, and activity of the whole population of chess players in Germany. The database therefore complements the commonly used expertise approach in cognitive science by opening up new possibilities for the investigation of multiple factors that underlie expertise and skill acquisition. Since large datasets are not common in psychology, their introduction also raises the question of optimal and efficient statistical analysis. We offer the database for download and illustrate how it can be used by providing concrete examples and a step-by-step tutorial using different statistical analyses on a range of topics, including skill development over the lifetime, birth cohort effects, effects of activity and inactivity on skill, and gender differences.

The Impact of Domain-Specific Experience on Chess Skill: Reanalysis of a Key Study

“…We could not perform these analyses because we did not have the necessary data (i.e., changes in rating over time), and the players were deidentified, which prevented us from searching for this information using chess databases. Vaci and Bilalić (2017) recently outlined methods for longitudinal data analysis using chess databases, demonstrating how one can assess, for example, age-related declines in skill, birth cohort effects, and differences between male and female chess players using openly available data. This type of study is an important goal for future research (for examples, see the recent special issue of Topics in Cognitive Science [Gray, 2017] and also Stafford & Dewar, 2014).…”

Female Chess Players Outperform Expectations When Playing Men

“…Recently, chess has been a focus for large scale analytics (Howard, 2006;Chassy & Gobet, 2015;Leone, Slezak, Cecchi, & Sigman, 2014;Vaci & Bilalić, 2016), and I see this study as part of that trend. Future work with these data has great potential for investigating differences in change in expertise, as well as performance.…”

A Computational Aesthetics Assessment of Chess Playing Quality Between the Genders

“…player Elo rating at the time of play) was not taken into account. The difference in aesthetic quality of play could have been due simply to playing strength because men, on average, tend to have higher ratings than women despite some controversy in this area of research as well [11][12][13][14][15][16][17]. Our objectives in the present research were therefore; 1) to perform a more thorough examination of the aesthetic quality of chess sequences taken from games between men and women using artificial intelligence (AI) as in the previous work; 2) to determine if there were any significant differences and; 3) to use the results in order to derive recommendations for training and promotion methods to females in order to improve their participation in the sport.…”