Learning From Wins and Losses: An Analysis of Improvement in Online Speed Chess

Background Learning from feedback is an important part of skill improvement. A common assumption is that failure offers superior learning opportunities when compared to success. However, this assumption remains underexplored in natural time-constrained settings. Objective To examine the impact of self-analysis on performance improvement in online speed chess, with a focus on whether analyzing wins versus losses differentially predicts gains in player ratings. Methods Using observational data from roughly 2 million online speed chess games, we studied the association between analysis and performance using regression. Results Analyzing game outcomes was positively associated with rating improvement, with stronger effects observed when players analyzed wins rather than losses. The total number of games played showed only a weak association with improvement, indicating that repetition of an activity alone is insufficient without reflective practice. Conclusions The findings challenge the notion that failure is inherently more instructive than success, suggesting instead that success-based analysis may also offer opportunities for learning improvement. While based on observational data, this study contributes to understanding how feedback mechanisms support learning and performance, particularly in domains requiring rapid, repeated decisions under time constraints.