eSports Analytics Dashboard LATAM

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Project Overview

Challenge	Solution	Impact
eSports LATAM performance data is fragmented across teams, players, competitions, and match results.	A MySQL -> Python ETL -> JSON bridge -> web dashboard pipeline consolidates the data into a single analytical product.	Teams, players, competitions, squad usage, experience, age-performance, and ML projections are explored in one consistent dashboard.
Frontend modules can easily break when dataset shape changes.	Data contracts, validators, expected-value helpers, and automated tests protect the JSON bridge.	Safer iterations on UX and analytics without destabilizing the app.
Predictive views are often disconnected from operational analytics.	A Random Forest layer projects annual player performance for 2026 and feeds a premium ML module.	Historical analysis and forward-looking projections live in the same product experience.

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Key Metrics (Latest Snapshot)

Metric	Value
Teams analyzed	15
Active players	33
Countries represented	8
Competitions tracked	5
Total prize pool	$325,000
ML projections generated	33
Automated tests in suite	126

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What the Dashboard Covers

• KPI overview for the whole LATAM ecosystem
• Team comparison and contextual ranking panels
• Competition catalog and prize distribution
• Squad usage split: starters vs substitutes
• Team experience and veteran role analysis
• Age vs performance contextual module
• ML premium module: annual player projection for 2026

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Pipeline Architecture

MySQL 8.0
  -> SQL extraction queries
  -> Python / pandas transformations
  -> validators + data contracts
  -> Random Forest predictor (ML projection 2026)
  -> JSON bridge (src/frontend/assets/data/datos-dashboard.json)
  -> Vanilla JS + Chart.js dashboard
  -> GitHub Pages deployment

Delivery Flow

Stage	Responsibility
Extract	Pull operational data from the relational MySQL model
Validate	Enforce business rules, ranges, required fields, and contract compliance
Transform	Build frontend-ready analytical modules and filter-ready datasets
Predict	Generate player-level 2026 annual projections and ML aggregates
Publish	Ship the static dashboard to GitHub Pages

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Tech Stack

Layer	Technologies
Data Source	MySQL 8.0
ETL	Python, pandas, python-dotenv
Data Quality	Pandera, JSON Schema validators
Machine Learning	scikit-learn (RandomForestRegressor)
Frontend	HTML, CSS, Vanilla JavaScript, Chart.js
Testing	Python unittest, frontend regression tests
Delivery	GitHub Actions, GitHub Pages

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Quick Start

# clone
git clone https://github.com/Sam-24-dev/eSports-Analytics-Dashboard.git
cd eSports-Analytics-Dashboard

# install ETL requirements
pip install -r src/etl/requirements.txt

# generate the dashboard snapshot
python src/etl/pipeline.py

# run tests
python -m unittest discover tests

# serve the frontend locally
python -m http.server 8000 --directory src/frontend

Open http://127.0.0.1:8000/index.html in your browser.

Optional shortcuts

make etl
make test
make check-js
make serve

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Automation

GitHub Actions

• deploy.yml
  • boots MySQL 8.0 in CI
  • loads database/schema.sql
  • runs src/etl/pipeline.py
  • uploads the generated static frontend
  • deploys the dashboard to GitHub Pages

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Project Structure

eSports-Analytics-Dashboard/
|- .github/workflows/            # CI + GitHub Pages deploy
|- database/                     # MySQL schema and seed source
|- docs/                         # Architecture, validation, and module rules
|- src/
|  |- etl/                       # Pipeline, validators, expected values, contracts
|  |- frontend/                  # Static dashboard (HTML/CSS/JS + generated JSON)
|  \\- ml/                       # Predictor logic for annual projections
|- tests/                        # Python + frontend regression coverage
\\- README.md

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Author

Samir Caizapasto
Junior Data Engineer & Analyst
ESPOL - Guayaquil, Ecuador

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