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Conference proceeding
Using Machine Learning to Predict Game Outcomes Based on Player-Champion Experience in League of Legends
Proceedings of the 16th International Conference on the Foundations of Digital Games, pp 1-5
03 Aug 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
League of Legends (LoL) is the most widely played multiplayer online battle arena (MOBA) game in the world. An important aspect of LoL is competitive ranked play, which utilizes a skill-based matchmaking system to form fair teams. However, players’ skill levels vary widely depending on which champion, or hero, that they choose to play as. In this paper, we propose a method for predicting game outcomes in ranked LoL games based on players’ experience with their selected champion. Using a deep neural network, we found that game outcomes can be predicted with 75.1% accuracy after all players have selected champions, which occurs before gameplay begins. Our results have important implications for playing LoL and matchmaking. Firstly, individual champion skill plays a significant role in the outcome of a match, regardless of team composition. Secondly, even after the skill-based matchmaking, there is still a wide variance in team skill before gameplay begins. Finally, players should only play champions that they have mastered, if they want to win games.
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Details
- Title
- Using Machine Learning to Predict Game Outcomes Based on Player-Champion Experience in League of Legends
- Creators
- Tiffany D. Do - University of Central Florida, United StatesSeong Ioi Wang - The University of Texas at DallasDylan S. Yu - The University of Texas at DallasMatthew G. McMillian - The University of Texas at DallasRyan P. McMahan - University of Central Florida
- Contributors
- Allan Fowler (Editor)Johanna Pirker (Editor)Alesandro Alessandro Canossa (Editor)Ali Ali Arya (Editor)Casper Harteveld (Editor)
- Publication Details
- Proceedings of the 16th International Conference on the Foundations of Digital Games, pp 1-5
- Conference
- FDG'21: The 16th International Conference on the Foundations of Digital Games 2021
- Series
- ACM Other Conferences
- Publisher
- ACM
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:001124866500040
- Scopus ID
- 2-s2.0-85118270665
- Other Identifier
- 991021916517804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications