Analytics and monitoring decision
Predigoal
A single competent developer can reproduce a useful hosted prediction service using the cited open model and dataset; it's a multi-week build but prior-art components make it practical to self-host rather than pay.
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-off88 h to build
$100/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 Predigoal alternatives, with the arithmetic →
What a replacement has to do
- Ingest match/team/player data → run prediction model → store results → expose API/UI for queries → scheduled data updates
What it still won’t have
- Any proprietary training, curated datasets and model tuning the vendor may have
- Polished UI/UX, analytics dashboards and user features
- Hosted scalability, SLAs, and any paid data feeds or integrations
- Brand trust, user base, and any commercial partnerships
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Predigoal 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
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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 self-hosted Predigoal clone: use Python (FastAPI), PostgreSQL for storage, and Docker. Core features in scope: (1) ingestion scripts to import the FIFA dataset into Postgres and normalize fixtures/teams/players, (2) integrate and run the open-source Elo+Dixon-Coles+Monte-Carlo prediction model to compute match win/draw probabilities, (3) a REST API to serve predictions and a simple React single-page UI showing upcoming matches and probabilities, (4) a nightly scheduler (cron or cloud scheduler) to refresh data and recompute predictions, (5) basic auth for the API, logging, and Prometheus-compatible metrics. Out of scope: real-money betting integrations, multi-tenant billing, large-scale auto-scaling, and mobile apps. Include error handling, input validation, unit tests for data ingestion and model outputs, and a Docker Compose deployment for one small VM.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 3 cited sources+3
- Evidence score86
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 · 3
Every page the run actually retrieved.
- official productPredigoal — AI Football Predictions
- open sourceHicruben/world-cup-2026-prediction-model
- open sourcemominullptr/FIFA-World-Cup-2026-Dataset
Integrity checks
What held up, and what did not.
