| dc.description.abstract | The Mobile Legends: Bang Bang M6 tournament showcases a high level of competition
with complex gameplay dynamics and professional player performances. This study aims
to analyze match data from the M6 tournament to identify key factors influencing team
victories and evaluate player performance based on statistics such as kill, death, assist
(KDA), hero selection, and MVP achievements. Two classification algorithms, Gaussian
Process Classifier (GPC) and XGBoost, were applied to predict match outcomes using
these features. The dataset, collected in CSV format from M6 match results, was
processed using Python. The preprocessing stage included data cleaning, categorical
feature encoding, and numerical normalization. Model performance was evaluated using
accuracy, precision, recall, and F1 Score metrics, with results showing that XGBoost
achieved superior performance (F1 Score 0.75) compared to GPC (F1 Score 0.74).
Additionally, a visualization system was developed using Streamlit to present insights
such as team win rates, hero pick and ban frequencies based on unique match IDs, and
top players by stage and role. The analysis revealed that players with consistently high
KDA and frequent MVP recognition significantly contributed to team success. This study
concludes that XGBoost is more effective for predicting match outcomes in highcomplexity datasets, while GPC is more suitable for balanced data and probabilistic
interpretation. These findings are expected to assist esports analysts and professional
team coaches in making strategic decisions for competitive Mobile Legends tournaments. | en_US |
