League of Legends RIFT REWIND ADT

🎯 Inspiration

Inspired by Spotify Wrapped, providing fun and shareable yearly summaries.
Drew from Op.gg’s match analysis and competitive LCS-style stats highlights.
Goal: combine entertainment with deep, data-driven insights for League of Legends players.

🧩 What It Does

1. Wrapped Up:
Generates a personalized yearly summary for each player based from his most funny stats and choose by Claude (our LLM friend) including:
- Total kills, deaths, assists, pentakills ...
- Most played champions, role, winrate ...
- KDA, global win rate, and highlight stats ...
- Ends with a shareable year summary card
2. Coaching-Oriented Game Analysis:
- Breaks each match into early, mid, and late game phases.
- Focuses on mechanical skills early, macro decisions mid/late.
- Uses an LLM-powered coaching agent to detect strengths and weaknesses.
- Compares player performance to benchmarks from Diamond & Master-ranked players and other players in the match.
3. Yearly Evolution Tracking:
- Tracks player progress over time through key metrics (KDA, damage, deaths).
- Focused on top 10 most-played champions.
- Displays progressive KPI evolution to show improvement month by month.

📌 Note

For this project, we chose not to integrate a conversational LLM to answer questions, as we found its usage too imprecise for game analysis and fun stats.
Instead, we pre-defined the requests to ensure the LLM pulls out relevant stats, adds commentary, and provides a full analysis based on our key criteria.

🏗️ How We Built It

Infrastructure:
- AWS Lambda for data ingestion, processing, and analysis.
- AWS S3 for structured data storage and fast retrieval.
- AWS Bedrock for contextualizing and interpreting data efficiently.
- IAM roles for fine-grained access control.
Languages & Tools:
- Python (data processing, API interaction, analytics)
- CSS & JS (frontend)
- Riot Games API (player and match data collection)
- boto3 for AWS interactions
- Pandas & NumPy for data manipulation
- Matplotlib / Seaborn for visualization (internal tests)
Architecture Philosophy:
- Provide micro-level analysis for each match (actionable insights).
- Maintain macro-level tracking of yearly progression.
- Designed for scalability, low-cost operations, and automation via event-driven design.

⚙️ Challenges We Faced

Limited API quotas and LLM request budgets caused slower processing.
Complex data cleaning and feature engineering.
Budget constraints prevented us from using Aurora or SageMaker.
Time limitations restricted deeper analysis (e.g., map control, vision data, jungle pathing).

🏆 Accomplishments

Built a robust, structured data pipeline from scratch.
Created clear, reliable KPIs for real coaching insights.
Designed a clean, intuitive user experience for data visualization.
Enabled context-aware analysis, not just number-based evaluation.
Built a nice Sumonner card easy to share

🧠 What We Learned

Learned to contextualize LLMs, making them analyze patterns and player behaviors, not just raw stats.
Understood the value of clean, structured data for consistent AI output.
Gained strong experience with AWS service integration and IAM management.
Reinforced our ability to build scalable, event-driven analytics systems.

🔍 Methodology

Uses Riot API data, cleaned and structured into minimal datasets.
Stored in S3 buckets for efficient LLM access.
Compares player data with Diamond/Master reference metrics to evaluate relative performance.
Analyzes performance by game phase (early/mid/late) to produce:
- Global rating
- Champion-specific rating
- Phase-based actionable advice
Provides concrete coaching suggestions that can be applied in the next games.
Each month, generates a detailed summary of KPI progression and behavioral improvements.

🚀 What’s Next

Deepen analysis with behavioral event detection (bad duels, poor map control, etc.).
Use a service account + EC2 instance to record key gameplay moments tied to detected mistakes.
Improve contextual coaching to go beyond stats, helping players understand why errors happen.
Enhance champion-specific metrics to adapt recommendations per champion, role, and playstyle.
Add gamification (score comparisons, ranking among friends).