Built by analysts and fans who believe competitive gaming deserves the same data infrastructure as traditional sports.
EsportIntel was founded with a single conviction: the esports ecosystem deserves the same rigorous data infrastructure that traditional sports have enjoyed for decades. From baseball's sabermetrics to basketball's advanced shot-tracking, statistical depth changes how fans, analysts, and organisations understand the game.
We aggregate data from official tournament APIs, partner organisations, and proprietary tracking pipelines — then apply machine-learning models to surface insights that are neither obvious nor superficial. All data and predictions are provided free of charge, strictly for informational and educational purposes.
We backtest every model, audit every data source, and publish methodology notes so our users understand confidence intervals and limitations.
Professional-grade analytics should not be paywalled. All core features on EsportIntel are and will remain completely free.
We build in response to what analysts, fans, and commentators actually need — not what looks impressive in a pitch deck.
We collect only what is necessary to operate the platform, never sell personal data, and comply fully with GDPR.
Our models are retrained monthly, our data pipelines updated with each major patch, and our UI refined with every user-research cycle.
We clearly distinguish between factual match data and model-generated predictions. No prediction is presented as a guarantee.
Former sports statistician with 8 years in esports data engineering.
PhD in computational statistics; specialises in ensemble prediction models.
Architected the real-time data ingestion pipeline covering 12 titles.
Ex-professional CS2 player turned analyst with 4 years covering major tournaments.
EsportIntel goes live with CS2 and VALORANT coverage, processing 500 matches in the first month.
First-generation win-probability model deployed; expanded to League of Legends and Dota 2.
Platform scales to 12 disciplines; introduces deep-dive team profiling with form curve analysis.
Ensemble model reaches 91% backtested accuracy; real-time in-match probability updates introduced with sub-10ms latency.
Esport