Project Summary
The purpose of this project is to develop a predictive model that accurately forecasts the outcome of professional Dota 2 matches. It also offers unexpected insights into the critical aspects of gameplay that players seeking improvement should focus on. By leveraging machine learning techniques and statistical modeling, the project aims to provide valuable insights into the factors that influence match outcomes in Dota 2.
Problem Statement
What are the best predictors for team success?
Dota 2 matches are known for their unpredictable nature, which poses challenges for players and fans seeking to understand the key factors contributing to winning or losing. By developing a predictive model, we can gain insights into the determinants of match outcomes and enhance our understanding of the game dynamics.
Techniques Used
In this project, a combination of data preprocessing, exploratory data analysis, feature engineering, and machine learning modeling techniques were employed. The data preprocessing phase involved collecting fresh data and developing a custom Elo algorithm for team ranking. The raw dataset was cleaned and transformed for further analysis. Exploratory data analysis and domain knowledge helped uncover patterns and relationships within the data. Feature engineering was performed to create meaningful predictors for the model. Machine learning algorithms, specifically random forests and logistic regression, were utilized to build predictive models for match outcomes. Finally, the models were tested against the performance of 18 teams from around the world at the highly competitive Bali Major tournament with a $500,000 prize pool.
Key Outcomes and Insights
Through this project, a predictive model with high accuracy in forecasting Dota 2 match outcomes was successfully developed. The model helped discover various features that can teach players what leads to a win in a given match. Analyzing the model's coefficients provided insights into the relative importance of different factors in determining match outcomes. Additionally, key patterns and trends in the data were identified, shedding light on successful gameplay strategies and dynamics.
Overall, this project not only showcases proficiency in data science techniques but also demonstrates the ability to apply these skills in the context of an engaging and complex domain like Dota 2. It reflects a passion for both data analysis and gaming, and I'm excited to share the project and its insights! Feel free to explore the highlights of the project below!
