University of Twente Student Theses


Improving the Chess Elo System With Process Mining

Bos, N.D (2021) Improving the Chess Elo System With Process Mining.

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Abstract:Over the last decade, the amount of data generated by software applications e.g. information systems, websites, mobile applications etc. has increased tremendously. Process mining, a subdiscipline of data science, uses this data to analyse and improve processes. In this research, the possibilities of process mining on chess event logs are explored, to ultimately improve the chess Elo system. The chess Elo system is a widely used and well accepted rating system. The Elo system is, however, flawed in multiple ways. Two major flaws of the Elo system, are its incapability to review a player’s strength and the excessive time needed to gain the appropriate Elo rating. This research explores the potential of process mining to identify chess expertise. To be more specific, multiple process mining techniques are applied on chess event logs, and the generated process models are analysed to identify chess expertise. This research presents a method to analyse the differences between high and low rated players. This is achieved by comparing process models generated from high and low rated chess games. The results show that by comparing the process models differences between high and low rated players can be observed. Process mining is therefore a promising approach to improve the Elo system and might be applicable to other software too. However, only the first twelve moves of a game were used. To gain more insight into the differences between high and low rated players, the mid and end games should be included in the event logs as well. Future research should be conducted with more chess games added to the event logs to increase the validity.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science, 57 mining engineering, 58 process technology
Programme:Creative Technology BSc (50447)
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