During the COVID-19 pandemic, traditional (offline) chess tournaments were prohibited and instead held online.
We exploit this unique setting to assess the impact of remote work policies on the cognitive performance of individuals. Using the artificial intelligence embodied in a powerful chess engine [Stockfish 11] to assess the quality of chess moves and associated errors:
we find a statistically-significant & economically-important decrease in performance when an individual competes remotely versus offline in a face-to-face setting.
The effect size decreases over time, suggesting an adaptation to the new remote setting.
…During the COVID-19 pandemic, the current chess world champion, Magnus Carlsen, initiated an online tournament series, the Magnus Carlsen Chess Tour. We analyse the performance of players who have participated in these online tournaments and the performance of players participating in recent events of the World Rapid Chess Championship as organized by the World Chess Federation in a traditional offline format. In particular, our main comparison is based on 20 elite chess players who competed both in the online and offline tournaments. We selected these tournaments because they were organized under comparable conditions, in particular, giving players the same amount of thinking time per game, offering comparable prize funds, and implementing strict anti-cheating measures.
We base our performance benchmark on evaluating the moves played by the participants using a currently leading chess engine that substantially outperforms the best human players in terms of playing strength. We use the engine’s evaluation to construct a measure of individual performance that offers a high degree of objectivity and accuracy. Overall, we analyse 214,810 individual moves including 59,273 moves of those 20 players who participated in both the remote online and the traditional offline tournaments. Using a regression model with player fixed effects that allows us to estimate changes in within-player performance, we find the quality of play is substantially worse (at a statistical-significance level of 5%) when the same player competed online versus offline. The adverse effect is particularly pronounced for the first 2 online tournaments, suggesting a partial adaptation to the remote setting in later tournaments.
…We find that playing online leads to a reduction in the quality of moves. The error variable as defined in equation (3) is, on average, 1.7 units larger when playing online than when playing identical moves in an offline setting. This corresponds to a 1.7% increase of the measure (RawError+ 1) or an ~7.5% increase in the RawError…To better assess the size of the effect, we provide a back-of-the-envelope calculation for the change in playing strength when playing online, as expressed in terms of the Elo rating. In our sample, the coefficient on the Elo rating of the player (−0.0001308) is based on a regression without individual fixed effects,18 indicating that if a player’s Elo rating increases by one point, the error variable as defined in equation (3) is reduced by 0.013 units on average. Playing online increases the error variable, on average, by 1.7 units, which corresponds to a loss of 130 points of Elo rating. The factual drop in playing strength, however, is likely to be lower, because our analysis excludes the opening stage of the game, which is less likely to be affected by the online setting. Moreover, our linear regression model might not account for smaller average error margins at the top of the Elo distribution.
