Projects→Dota2 Betting Analytics Pipeline
STATUS
● Production
CLIENT
Internal Data Operations
YEAR
2025–2026
ROLE
Builder & Architect
STACK
PythonSupabasePostgreSQLOpenDota APIStratz APIPinnacle/BettingIsCool APIPandas
EVIDENCE LINKS
Note: Where client systems are private, screenshots are anonymized and architecture is shown without protected data.

Dota2 Betting Analytics Pipeline

A high-throughput data ingestion pipeline—engineered to scrape, normalize, and analyze massive datasets across 50,000+ records.

Dota2 Betting Analytics Pipeline

Problem

Conducting deep statistical analysis required combining raw telemetry with historical API data. The challenge: manual correlation across massive datasets was infeasible, and rate limits across disparate third-party APIs caused constant ingestion failures. A robust, scalable data engineering solution was needed.

Architecture & Solution

// System Pipeline

Built in Python, the pipeline connects external telemetry APIs with historical data sources. A resilient local tracking layer manages distributed scraping states. Data processing utilizes fuzzy timestamp matching to correlate disparate records, before executing batched upserts into a centralized PostgreSQL data warehouse.

Implementation Decisions

  1. Resumable state management — Utilizing local databases for progress tracking ensures massive 50k+ ingestion runs can pause and resume seamlessly without data loss.
  2. Fuzzy timestamp mapping — Implementing a fuzzy matching algorithm reliably correlates external API fixtures with internal telemetry records.
  3. Tiered backoff algorithms — A dynamic backoff strategy safely navigates free-tier rate limits, ensuring continuous ingestion without dropping records.
  4. Idempotent data writes — Leveraging robust upsert logic in PostgreSQL guarantees that ingestion reruns produce zero database duplicates, maintaining data integrity.