synoros/ml-labplugin · ships on every Synoros Foundry ServerMachine Learning Lab
A testing ground for our data pipelines and ML models. We use real-world competitive gaming datasets as a sandbox for building and validating production-grade analytics infrastructure.
Each Paragon project targets a different data domain with unique challenges -- different data shapes, update frequencies, and prediction targets. The goal is to stress-test our tooling across varied real-time datasets before applying it to broader use cases.
4
Pipeline Stages
4
Game Datasets
1
Live Experiment
Pipeline Architecture
Paragon Royale
Our first ML testing ground. Real-time match analytics and predictive modeling built on top of live competitive data from Clash Royale.
- Real-time data ingestion pipeline
- Predictive win-rate modeling
- Pattern recognition across match histories
- Live dashboard with streaming updates
Paragon Rivals
Expanding our pipeline to a new data domain. Hero-level statistical modeling, team composition analysis, and meta prediction.
- Multi-variable classification models
- Team composition clustering
- Meta shift detection algorithms
- Cross-match trend analysis
Paragon Overwatch
Next target dataset for our analytics pipeline. High-dimensional performance tracking across heroes, maps, and skill tiers.
- High-dimensional feature extraction
- Skill rating trend prediction
- Composition synergy scoring
- Session-level performance modeling
Paragon Strike
Applying our pipeline to FPS data. Round-by-round economy modeling, spatial aim analysis, and map-specific performance metrics.
- Economy state prediction
- Spatial heatmap generation
- Round outcome classification
- Player performance fingerprinting