The Eleven is a full-stack prediction market platform for live football events, deployed on X Layer (chain 196) using Uniswap v4 hooks. The system is powered by 11 autonomous AI agent personas (e.g., IlRegista, IlBomber) that monitor live match data from API-Football or Football-Data.org, evaluate binary propositions (e.g., clean sheet, corners over, next goal), and create markets via a commit-reveal mechanism. Markets are implemented as PropMarketHook contracts deployed through a factory using CREATE2. Users can stake on outcomes gaslessly via EIP-3009 signatures relayed by the x402-facilitator service, and markets are resolved deterministically after match events. The monorepo (pnpm workspaces) contains five packages: `contracts` (Foundry-based Solidity), `agent` (TypeScript runtime with Prisma persistence), `web` (Next.js 15 dApp with RainbowKit, Three.js 3D pitch, and Farcaster Frame v2 integration), `x402-facilitator` (standalone npm package for gasless USDT0 transfers), and `mcp-server` (MCP bridge exposing market tools to AI agents). The architecture is event-driven: the agent's TickLoop polls matches, emits deltas, and triggers market creation/reveal/resolution skills. The web app provides a full dApp experience including a markets list, agent detail views, a status page, and documentation. Key reusable patterns include a generic proposition template system with ABI encoding/decoding, a CREATE2 salt miner for hook deployment, a commit-reveal skill with cryptographic safety, and a gasless staking flow that can be extracted for any EIP-3009-compatible token. The codebase is well-tested (172 web tests, 229 agent tests, 85 contract tests with 100% coverage on the hook) and actively maintained with CI/CD via GitHub Actions and Railway deployment.
Repository of structured JSON datasets containing football match fixtures and results across major European leagues (English Premier League, Bundesliga, Serie A, La Liga, UEFA Champions League) spanning 2011-2027. Files are auto-generated from upstream Football.TXT text sources via a GitHub Actions workflow that refreshes data daily. Each league/season file follows a consistent schema: a top-level object with league metadata and a `matches` array containing per-match objects with round, date, teams, and score objects (half-time, full-time, optionally extra time and penalties). The repository is purely a data source - no application code, scripts, tests, or configuration beyond the JSON files. Raw GitHub links serve as a free public API for web applications, mobile apps, prediction models, or analytics pipelines. The data is clean and machine-readable but lacks schema validation, versioning, and formal documentation. Known issues include schema drift across seasons (missing half-time scores, awarded matches with `status: 'awarded'`, team name variations) that downstream consumers must handle defensively. The primary value is the breadth and consistency of match data across 15+ leagues and 15+ seasons, making it suitable for football statistics applications, prediction models, or league visualization tools. The auto-update pipeline keeps current seasons fresh, but upstream Football.TXT sources may not be maintained for older seasons, and the lack of schema enforcement risks silent data drift. This is a data_research repository - a data source rather than an application - with no executable code or processing pipeline present.
This repository implements an end-to-end predictive modeling pipeline for Premier League football match outcomes (home win, draw, away win). It ingests raw match statistics from the api-football.com REST API, processes JSON data into structured DataFrames, engineers features using rolling averages (5- and 10-game windows) and team-vs-opponent differentials, trains multiple classifiers (Random Forest, SVM, KNN, MLP) with hyperparameter tuning, and serves live predictions via a Flask web application. The dominant character is data research: the primary deliverable is a set of trained models and an analytical pipeline, with the web server being a secondary deployment artifact. The best-performing model is a Random Forest achieving ~50-51% test accuracy, demonstrating that football outcomes are modestly predictable from historical statistics. The codebase includes modular helper modules for feature engineering, data scaling, and evaluation plotting that are reusable for other sports or time-series classification tasks. However, the project suffers from hardcoded absolute paths, outdated dependencies (Python 3.7, sklearn 0.23.1), plain-text API keys in the repository, and a static dataset that will drift over time.