Analytics and monitoring decision
Elofoot
A competent developer can build a useful, smaller replacement (data ingestion + simple model + UI) in a few weeks, but reproducing Elofoot's validated track record, tuned proprietary data/model and polished product/affiliate ecosystem is unlikely without the vendor's data and operational history.
Visit website↗Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off86 h to build
$50/mo6 h/mo upkeep
No published price to break even against.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All Elofoot alternatives, with the arithmetic →
What a replacement has to do
- Fetch match schedules and event data, preprocess xG/form/lineup features, run a scoring model to produce 1X2 and exact-score probabilities, store predictions and serve them via a web API, display predictions in a simple web UI with live updates.
What it still won’t have
- Vendor-tuned model weights and any proprietary feature engineering
- Public, audited historical track record and product credibility
- Polished UX, affiliate program management and manual moderation workflows
- Operational scalability and real-time ingestion at audience scale
What remains hard
- Proprietary data
Un modèle IA nourri aux données réelles
- Brand trust
Utilisé par le top 1% des pronostiqueurs et des influenceurs
- Proprietary data
un historique vérifiable, affiché publiquement
First-year cost
No published price
Elofoot does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal Elofoot-like service: use Python (FastAPI) + Postgres + React. Core features in scope: scheduled ingestion jobs to fetch match schedules and static data (use Transfermarkt datasets or public APIs), normalization and storage in Postgres, implement a deterministic scoring model (Poisson or Monte Carlo using xG and recent form) that outputs 1X2 probabilities, most-likely exact score, and top scorers; backend endpoints to request predictions and a small React UI to display predictions and live updates via websockets/SSE. Out of scope: training large proprietary ML models, multi-region scaling, paid subscription billing, and an affiliate dashboard. Require error handling, retries for external feeds, basic unit tests for ingestion and model code, and a Dockerfile + simple deploy script (DigitalOcean or similar).
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 2 cited sources+1
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score59
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 2
Every page the run actually retrieved.
- official productElofoot — Pronostic & prédiction foot IA
- open sourcedcaribou/transfermarkt-datasets
Integrity checks
What held up, and what did not.
