Game Stat Tracker

League of Legends Champion Analytics Platform (In Development)

This project is an in-development analytics platform designed to aggregate, process, and rank League of Legends champion performance using live data from the Riot Games API. The system ingests raw match and store data, runs scheduled calculations, and produces derived metrics that power leaderboards and tier-based champion rankings.

The focus of the project is backend data processing, metric design, and building a scalable pipeline capable of turning high-volume game data into meaningful, comparable insights.

Problem

While the Riot Games API exposes extensive raw match data, it does not provide higher-level analytics such as relative champion strength, normalized rankings, or aggregated performance metrics across large sample sizes. Raw stats alone are noisy, difficult to compare, and often misleading without additional processing.

Solving this problem requires:

• Reliable data ingestion at scale

• Scheduled processing and recalculation

• Carefully defined metrics

• Normalization across champions, roles, and patches

This project was built to explore those challenges end-to-end.

Data Ingestion & Scheduling

The platform integrates directly with the Riot Games API to ingest match data and related champion information. Because this data changes continuously, ingestion is handled through scheduled background jobs rather than real-time requests.

Key elements of the ingestion pipeline include:

• Cron-based jobs to fetch new data on a defined schedule

• Rate-limit–aware API access

• Separation of raw data storage from processed metrics

• Incremental updates to avoid redundant data processing

This approach ensures data freshness while maintaining stability and API compliance.

Metric Calculation & Processing Pipeline

Raw game data is processed into structured, comparable champion metrics through a calculation pipeline that runs independently of the frontend.

The processing layer:

• Aggregates champion performance data across matches

• Normalizes results to reduce sample-size bias

• Applies weighted calculations to smooth statistical outliers

• Supports recalculation as new data or patches are introduced

These calculations produce a consistent metric foundation that can be reused across multiple views.

Leaderboards & Tier Lists

The computed metrics power two primary views:

Champion Leaderboards

Champions are ranked based on calculated performance scores, allowing relative comparison across the entire roster.

Tier-Based Rankings

Champions are grouped into tiers based on metric thresholds, making it easier to interpret relative strength without relying solely on raw rankings.

Both views are generated from the same underlying data pipeline, ensuring consistency and repeatability.

Technical Focus

This project emphasizes backend architecture and data workflows over UI polish.

Key technical areas include:

• External API integration

• Scheduled background processing

• Data modeling and persistence

• Metric definition and normalization

• Separation of ingestion, computation, and presentation layers

The system is intentionally designed to support future expansion without requiring changes to the core data pipeline.

Status

This project is currently in active development. Core ingestion, scheduling, and metric calculation systems are implemented, with ongoing work focused on refining calculations, improving data coverage, and expanding frontend visualization.