Analyzing Amateur Esports: Descriptive Statistics and EDA for Ld2L.GG
Python · pandas · data visualization · View the code
The question
Professional esports get endless analysis. Amateur leagues get almost none. I wanted to find trends, patterns, and possible performance indicators in an amateur Dota 2 league, Ld2L.GG.
The approach
- Collected match data from the league
- Ran descriptive statistics and exploratory data analysis to find key statistics, outliers, and trends
- Looked at player performance, team composition, win rates, and related metrics
- Charted team KPIs over the season as rolling averages, and plotted the distribution of players by ranked tier to show the spread of skill
- Structured the final CSV so it can load straight into Power BI or Tableau, or feed machine learning models across seasons
Who it's for
League organizers structuring fairer competitions, players looking to improve, and anyone curious about what the data says about amateur esports.