DraftEm.gg: a football management simulation built on real player data

01The idea

We created DraftEm.gg to explore how real-world football data could power a management simulation with genuine tactical depth. Waffle Samurai designed and built the complete product, including the web application, statistical rating pipeline, drafting and match simulation engines, and league-management systems.

02What we built

A player rating engine built from real data. Every player's attributes are derived from real per-match statistics pulled from the current Premier League season (goals, assists, shots, passes, tackles, duels, and more), with a reliability correction that shrinks a player's rating toward their prior-season baseline until they've played enough minutes for the current season's numbers to be trusted. Attributes are then percentile-ranked within each position group and remapped onto a realistic 40 to 99 scale, so ratings actually spread out instead of clustering in the middle.

Squad building and formations. Players assemble a starting XI, subs, and reserves on a drag-and-drop pitch, with position-group validation so a goalkeeper can't be dropped into a striker slot unnoticed. Six formations are built as a genuine rock-paper-scissors cycle, each with exactly one hard counter and one weakness, rather than being cosmetic flavor text.

A statistically grounded match engine. Goals are drawn from a Poisson distribution seeded by each side's attack and defense ratings, home advantage, and the formation matchup. Match events are then generated afterward, weighted by each player's individual stats, so a squad's best striker is genuinely more likely to be the one who scores, not a random pick from the squad list.

03The stack

1. Frontend

   React 19 on TanStack Start, a full SSR meta-framework built on TanStack Router, with TypeScript and Vite. Zustand handles squad-builder and in-progress season state (persisted to local storage), TanStack Query handles server-state fetching against the API. The squad-builder pitch is powered by dnd-kit for drag-and-drop with position validation.

2. Backend

   Laravel 13 on PHP 8.3+, a lean JSON REST API. Auth runs on Sanctum, with guest play built directly into the auth layer: a full season can be played anonymously via a guest token, then claimed into a real account afterward. Unclaimed guest seasons expire automatically after 7 days via a scheduled cleanup job.

3. Data pipeline

   Real Premier League player and match data is pulled from API-Sports via first-party Artisan commands, one to sync league, team, and per-player-per-season stats, and one to run the rating pipeline on top of them. The prior season is synced too and used purely as a statistical baseline for the reliability correction.

4. Simulation engine

   A season is a full 20-team, 38-matchday double round-robin, generated with a circle-method scheduling algorithm so fixture pairings vary season to season. Match results come from a Poisson-distributed goal model with a formation-matchup modifier, and a five-tier difficulty system scales AI opponents' ratings only when they're facing the human player, leaving AI-versus-AI fixtures untouched.

5. Data

   SQLite in development with Postgres and MySQL both supported as production options, via Eloquent. The schema has real relational integrity behind it: leagues, seasons, and teams feed player stats and computed ratings, while a separate game-state cluster tracks each user's squad, fixtures, and results independently.

6. Infrastructure

   A multi-stage Docker build compiles frontend assets in a Node stage before handing off to a PHP runtime that runs migrations and serves the API. Still early-stage: no CI pipeline or orchestration layer yet, which is the honest next step rather than something we're claiming is already there.

04What this demonstrates

DraftEm demonstrates our ability to turn complex real-world data into an accessible, interactive product, combining data processing, statistical modelling and a user-friendly web application.

• Player ratings built from real match data, not hand-tuned numbers
• Small-sample reliability correction so a hot streak can't spike a rating
• Formations built as a genuine rock-paper-scissors system, not decoration
• Poisson-distributed match simulation weighted by individual player stats
• Guest-first play: a full season with zero signup, claimable into an account later
• Past match results are immutable once played, so a mid-season squad change can never retroactively alter them