Technology Shocks, Relative Performance Measures, and Outcomes: Evidence from Classical Chess

Abstract

In the fall of 2020, neural-network methods produced a large improvement in chess engines that became freely and widely available. By the end of 2021, the monthly draw rate in classical chess had risen by about four percentage points, but the distribution of player ratings, which are commonly read as measures of playing strength, had changed little. Ratings, however, are a relative measure, built from results against other rated players rather than from an absolute scale of play quality, so an improvement shared broadly across players need not change their ratings. Using 3.9 million rated classical games from March 2015 to November 2023, we document that the increased draw rate remains after conditioning on both players' ratings, holds within repeated same-color matchups, is not a continuation of a pre-existing trend, and persists through the end of the sample. A linear transformation that maps post-Covid ratings to higher pre-Covid equivalents, with a larger gap at lower ratings, accounts for more than 90 percent of the post-minus-pre shift in the fitted draw, White-win, and Black-win probabilities. Players' ratings and ranks, by contrast, show no additional rank reshuffling and no general widening of within-group dispersion relative to the pre-Covid benchmark. We interpret these findings as consistent with adoption across rating levels, with larger rating-equivalent gains for lower-rated players.