Online Game Outcome Prediction Model Using Weighted-Based Feature Approach
Recently, the popularity of online games has risen drastically due to the latest technology that can connect players globally. League of Legends (LoL) holds the title of being the most extensively played Multiplayer Online Battle Arena (MOBA) game globally. This issue compels a substantial volume of...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
ASPG
2024
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/12345/1/J17761_9830e7fbd550a521b192ae0714b31c02.pdf http://eprints.uthm.edu.my/12345/ https://doi.org/10.54216/FPA.150212 |
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| Summary: | Recently, the popularity of online games has risen drastically due to the latest technology that can connect players globally. League of Legends (LoL) holds the title of being the most extensively played Multiplayer Online Battle Arena (MOBA) game globally. This issue compels a substantial volume of preceding research that still analyzes and predicts the game outcomes with traditional methods that can be inaccurate and imprecise. Furthermore, these methods are frequently associated with the high rates of both false positive and false negative results. Hence, this paper presents a weighted-based feature predictor model to enhance the prediction accuracy. The approach predicts
the game outcome of League of Legends matches in the Latin America North (LAN) and North America (NA) regions. We utilize player mastery and win rate for each summoner as the features. The data preparation process includes a weighted algorithm calculation and then evaluation using Naïve Bayes and Support Vector Machine algorithm. The outcomes illustrate that the weight-based feature approach can predict the outcome of LoL matches with an average accuracy of over 97 percent. This approach can be a valuable technique for players, teams, and coaches to analyze their performance and make strategic decisions. |
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