Building a Multi-Model AI Sports Prediction Platform
SharpLines is a product I built and run. I am including it here because it shows how I approach data-heavy systems: model pipelines, scheduled retraining, and turning statistical output into an interface a non-technical person can actually use.
The problem
Most sports prediction tools run a single algorithm and present its output as fact. That hides the most useful information a user could have: how confident the system actually is, and whether different approaches agree with each other. A single model also fails quietly. When it drifts, nothing in the interface tells you.
I wanted to build the opposite: a system that runs several independent models, shows where they disagree, and keeps score of its own accuracy over time.
What I built
SharpLines analyses games across NBA, NFL, MLB, NHL, college basketball, college football, and soccer, covering competitions from the Premier League through to the World Cup. Each game is evaluated against a large set of features rather than a handful of surface statistics.
Instead of one algorithm producing one number, an ensemble of models each produce an independent probability. The interface exposes that breakdown directly, so a user can see how each model voted, where the spread of opinion is wide, and which inputs are moving the line. Disagreement between models is treated as signal worth showing, not noise worth hiding.
Keeping the models honest
Every completed game feeds back into the system. Ratings update, priors shift, and model weights are retrained on the new results. This matters because sports data is non-stationary: rosters change, teams regress, and a model trained on last season degrades steadily if nothing corrects it.
Automating that feedback loop was the difference between a demo and a product. The retraining runs on a schedule without manual intervention, and historical accuracy is tracked so performance claims are measurable rather than asserted.
Beyond predictions
A second layer of the platform handles daily fantasy sports. It produces player projections with floor and ceiling ranges rather than single-point estimates, a lineup optimiser that solves against salary-cap constraints, a prop analyser that compares model projections against posted lines, and correlation stacking for tournament play.
The optimiser was the most interesting engineering problem in this half of the product. Building lineups under salary constraints while accounting for correlation between players is a constrained optimisation problem, not a sorting problem, and naive approaches produce lineups that look reasonable and perform poorly.
Product decisions worth noting
The platform is explicitly an analysis tool. It does not accept, place, or facilitate wagers, and the interface states plainly that past model performance does not guarantee future results. That constraint shaped the product: the goal is to expose reasoning and uncertainty rather than to present predictions as certainties.
Access is tiered, with a free tier offering a limited number of picks per week and paid plans unlocking the model breakdown, DFS tooling, and deeper analytics. Subscription billing, trials, and plan changes are handled through a payment provider rather than rolled by hand.
Stack
- Machine learning ensemble with scheduled retraining
- Elo-style rating system and Bayesian updating
- Constrained optimisation for lineup building
- Automated daily data ingestion across seven sports
- Subscription billing with tiered access and free trials
- Web application front end with model transparency views
Outcome
SharpLines runs in production across seven sports, ingesting and analysing games daily, retraining on completed results, and serving both prediction and fantasy tooling to free and paid users. The parts that transfer to client work are the ones that usually matter most: scheduled data pipelines, model output turned into a usable interface, and subscription billing that runs itself.