ScoutAI – Automated Opponent Scouting Using Match Data

ScoutAI – Automated Opponent Scouting Using Match Data Hackathon / College Project Report

1. Inspiration Professional esports teams spend significant time manually reviewing opponent matches to prepare for upcoming games. This process is often time-consuming, subjective, and difficult to scale, particularly during tournaments where preparation time is limited. ScoutAI was inspired by Moneyball-style analytics, applying data-driven decision-making to esports to uncover strategic patterns and tendencies that are not immediately visible through manual review.
2. What the System Does ScoutAI automatically generates a concise scouting report for an upcoming opponent using recent match data. The system analyzes team strategies, map and site preferences, and player-specific tendencies, then summarizes these insights into a clear, coach-friendly report supported by visual analytics.
3. System Design and Implementation The system was developed using Python with Pandas and NumPy for data processing and statistical analysis. Rule-based logic was applied to identify recurring behaviors and trends. An interactive dashboard was created to visualize agent or champion usage, map preferences, and player tendencies, prioritizing clarity, speed, and usability.
4. Challenges Encountered Key challenges included cleaning and standardizing raw match data across multiple formats and ensuring that statistical outputs translated into meaningful insights for coaches. Maintaining simplicity while delivering actionable analysis without relying on complex machine learning models was also a significant challenge.
5. Results and Accomplishments ScoutAI successfully delivers a fully automated scouting report system that transforms raw match data into actionable insights. The final product is demo-ready, easy to interpret, and suitable for real-world esports analysis within a limited development timeframe.
6. Learning Outcomes The project demonstrated that effective esports analytics does not require complex AI models. Clear summaries, interpretable metrics, and strong problem framing proved more valuable than raw numerical outputs.
7. Future Scope Future enhancements include live match scouting, video clip integration, draft and ban recommendations, and deeper player matchup analysis. ScoutAI has the potential to evolve into a comprehensive analytics assistant for professional esports teams.
8. Descriptive Statistics ScoutAI uses basic descriptive statistics to summarize match data and player performance. These include counts, averages, and percentages. Win Rate = Rounds Won / Rounds Played
9. Ratio and Proportion Analysis Ratio-based calculations are used to identify strategic preferences such as map usage, site executions, and agent or champion selection patterns. Map Preference = Matches Played on Map / Total Matches
10. Probability-Based Metrics Probability-style metrics are used to measure tendencies like early aggression and engagement behavior. Aggression Rate = First Bloods / Total Rounds
11. Threshold-Based Decision Rules ScoutAI converts numerical metrics into actionable insights using predefined thresholds. If Metric > Threshold → Insight Identified
12. Comparative Analysis Comparative metrics are used to identify outliers by comparing individual player statistics against team averages. Player Difference = Player Metric − Team Average
13. Trend and Recent Performance Analysis Recent match performance is prioritized using simple weighted aggregation to emphasize recent form. Recent Form = Σ(Performance × Weight) / Σ Weight
14. Mathematical Scope ScoutAI intentionally avoids complex mathematical models such as deep learning, optimization algorithms, and neural networks. The focus is on interpretability, transparency, and practical usefulness.

Built With

• javascript
• numpy
• pandas
• python