rivalr-engine

Local FPL engine. No web app — everything runs from the CLI and outputs to terminal + JSON. The differentiator is mini-league intelligence: it optimises against your rivals, not just for raw points.

Modules

Module	What it does
src/rivalr/fetch.py	FPL API client, disk cache (6h bootstrap, 1h standings), 429 backoff, cache-miss logging
src/rivalr/rivals.py	rival squads, chips, banks, transfer history; mini-league effective ownership; SHIELD/SWORD/NEUTRAL classification; per-rival overlap and differentials → rivals_report.json
src/rivalr/model.py	OpenFPL trained-model inference (no retraining), 228 rolling-window features from FPL + Understat
src/rivalr/understat.py	Understat JSON API client (league + player endpoints), cross-season history, loud failure logging
src/rivalr/minutes.py	v0 expected-minutes: rolling starts, minutes trend, availability flag, congestion; news-based v1 hook
src/rivalr/optimise.py	wraps the FPL-Optimization-Tools HiGHS MILP with our projections; three objectives: points / chase / defend
src/rivalr/report.py	one-command gameweek brief (Telegram-friendly plain text)
src/rivalr/ledger.py + score.py	append-only prediction ledger, post-GW RMSE/MAE by outcome bucket + transfer counterfactual

Setup

Requires uv and git.

uv sync
uv run python scripts/setup_vendors.py   # clones OpenFPL (~750MB models) + FPL-Optimization-Tools into vendor/

Run

The gameweek brief (the main command):

uv run python -m rivalr.report --team <your_entry_id> --league <classic_league_id> --mode chase --target <rival_entry_id>

• --mode points — plain xPts maximisation (no target needed)
• --mode chase --target <id> — attack a rival above you: differentials + variance
• --mode defend --target <id> — protect a lead: cover the chaser's squad, prefer shields

All three plans are always solved and shown side by side with honest (unweighted) expected points and a next-GW swing-vs-target estimate.

Score a finished gameweek against the ledger:

uv run python -m rivalr.score --gw 5

Smoke tests (no team/league needed):

uv run python scripts/smoke.py            # fetch + model + minutes
uv run python scripts/smoke_optimise.py   # full MILP solve

Tests:

uv run pytest

Upstream integrations (input formats)

OpenFPL (vendor/OpenFPL, daniegr/OpenFPL): 250 joblib models (xgboost 3.0.2 / sklearn 1.5.2-era pickles; deps pinned in our pyproject to their plug.txt). Input is a 228-feature vector per (player, fixture): rolling means of FPL + Understat stats over the previous {1,3,5,10,38} gameweeks, min-max scaled by models/xscaler.save, sliced per position via models/features.save, predicted by 50 models per position (5 CV folds x 10 candidates), inverse-scaled (points = y*33 - 7), median-ensembled. Predicts one GW at a time; we re-run per future fixture with the opponent block swapped.

FPL-Optimization-Tools (vendor/FPL-Optimization-Tools, sertalpbilal): HiGHS MILP via highspy. We bypass its CLI and call dev.solver.prep_data + solve_multi_period_fpl directly, writing our projections into vendor/.../data/rivalr_<mode>.csv in its fplreview format: ID,Name,Pos,Value,Team,{gw}_Pts,{gw}_xMins,... (ID = FPL element id; Pos in G/D/M/F; the solver re-derives prices/teams from bootstrap-static). Options come from its comprehensive_settings.json plus our overrides (see SOLVER_OVERRIDES in optimise.py).

Web API

uv sync --extra api
uv run uvicorn rivalr.api:app --port 8000

• GET /health — status, gameweek, deadline, last snapshot time
• GET /brief?team_id&league_id&mode&target — the brief as JSON (squad with base/defcon/flags, captain, transfers with per-rival swings, rivals block, warnings). Slow solves return {job_id} (202); poll GET /brief/status?job_id=.
• GET /league?league_id — standings + entry ids for a target picker.

Caching: Postgres via DATABASE_URL (in-memory fallback without it), keyed (team, league, mode, target, gw), TTL 1h, always bypassed inside the 4h pre-deadline window. Rate limit 30 req/min/IP. CORS allows localhost dev ports plus RIVALR_CORS_ORIGINS (comma-separated). Run a SINGLE uvicorn worker process — jobs and cache-bypass state are in-process.

A rivals failure degrades to rivals: null plus a warning; squad and transfers still return.

Railway

The Dockerfile serves both processes: web (default CMD, uvicorn) and worker (python -m rivalr.worker — the same hourly snapshot code path as the local Task Scheduler entry). Model artifacts (~750MB of OpenFPL joblibs) are NOT in the image: the entrypoint clones both vendor repos into RIVALR_VENDOR_DIR on first boot — mount a Railway volume at /data per service so it happens once. Env per service: RIVALR_TEAM_ID, RIVALR_LEAGUE_ID, RIVALR_TELEGRAM_BOT_TOKEN, RIVALR_TELEGRAM_CHAT_ID, RIVALR_CORS_ORIGINS (web), DATABASE_URL (web; from the Railway Postgres plugin). Once the Railway worker is verified, disable the local Task Scheduler entry (schtasks /Change /TN rivalr-snapshot /DISABLE) so one snapshot source is canonical.

以上为 README 的开头部分。其余内容请阅读原文。 查看完整 README ↗