A Deadlock demo / replay parser
Free Super Smash Bros. Ultimate analytics — GSP & Elite Smash tracker, start.gg + parry.gg sync, matchup analytics, AI scouting reports. Use it live:
Local-first CS2 demo analysis platform and reusable analysis packages built on cs2-demo-format, with TypeScript/Python tooling and a desktop Studio.
Projeto de análise exploratória de dados de partidas competitivas de League of Legends (2015-2017). Inclui modelagem de banco de dados MySQL, desenvolvimento de queries e visualização de gráficos sobre campeões, rotas, estruturas e estatísticas de abates.
Self-hosted, coach-grade CS2 demo analysis — 2D replay, opponent playbooks, grenade pattern finder, and locally-trained prediction models. No external AI, all on your own machine.
Local, read-only CS2 economy decision assistant with evidence-backed buy policies validated on competitive demo data.
CS2 demo analysis and coaching tool with local benchmarks, ML-based round impact, evidence-backed feedback, and VOD review priorities.
My first data analysis project revolving around analysing my own ranked match data to see what skills translate into winning a match.
this tool parses your slpi replays and displays stats on your gameplay with tons of views. It also displays some data viz pulled from Liquipedia on how the ssbm scene has developed over the years.
Enable automatic topic-based memory for Claude Code to maintain context across sessions and data compactions without losing information.
LSTM neural network for predicting player actions in CS:GO matches with 87.9% accuracy on professional gameplay data
A data analysis and visualization project exploring professional Valorant esports matches, teams, players, agents, and maps.
Competitive Esports SaaS platform for scrim tracking and team analytics. Built with Next.js 15 (App Router), TypeScript, Prisma ORM, Supabase (PostgreSQL + SSR Auth), and Server Actions.
This project has the objective of training valorant teams stats throughout the championships and predict the VALORANT Champions winners @ Setember 2026
Machine Learning project to predict Counter-Strike 2 (CS2) match outcomes using data scraped from HLTV.org.
Polyglot esports scouting platform with Next.js UI, Spring Boot API, and Python analytics engine that convert natural-language prompts into contract-versioned tactical reports.
Unsupervised discovery of Counter-Strike 2 player roles from raw .dem replays: behaviour-only features, per-side Self-Organizing Maps, and a leakage-free test of whether role consistency predicts round outcomes across 76 pro maps. Bachelor's thesis.
Twitch category live stream data extractor
Applying Extreme Value Theory (EVT) and Machine Learning to model heavy-tailed risks, volatility, and tail events in esports analytics.
Advanced esports analytics pipeline that uses PostgreSQL to process a massive dataset of professional League of Legends matches.
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