A lightweight approach for predicting errors in chess matches
Abstract
Chess is becoming more popular and accessible by the day. For instance, online chess enables matches between players from different parts of the world, bringing new ways of learning the game and interacting with other Web users. With this growth in popularity, there is a possibility to empower amateur players with rich computer analysis and tools, which may assist them in their learning process. One of the ways to analyze chess matches is through the study of errors. In this context, we present a new approach for the task of error prediction in chess. Our motivation is that knowing when players are likely to make a mistake is knowing what types of situations lead to difficulties in making the right decision. To that end, we add an abstraction layer to the already studied error prediction task, providing graph-based features to the machine learning models. Our results show a increase in the accuracy of the tested models, improving the results obtained in recent studies.
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