Cutting-edge research, made accessible.
Relevance-Based Prediction (RBP) is novel mathematical research developed by the founders of Cambridge Sports Analytics. RBP treats every prediction as an information problem — weighting historical observations by how much they actually inform the present.
Most prediction systems hand you a number. Our method tells the story behind every prediction.
By grounding forecasts in information theory, we surface which observations drove the result, how much each one mattered, and where the model is least confident.
No black boxes. Just transparent math, served at production speed.
Forecasts derived from relevance weighting grounded in formal information theory.
Every prediction comes with evidence behind it. Understand the story behind every prediction.
Designed for production from day one — licensed compiled binary, hosted API, or your own AI client.
The full Prediction Engine compiled to a single executable and unlocked with a license key. Run it on your own laptop or roll it out across your enterprise servers.
A straightforward REST API with a first-class Python client. Submit predictions, pull results and analyze your results all within your existing stack.
A Model Context Protocol (MCP) integration layer for the Prediction Engine. Research Relevance-Based Prediction scenarios, run prediction workflows, and analyze results directly through compatible AI clients.
The Python SDK is designed to allow users to focus on experiment design and analysis. Pass features, get back predictions with full attribution — which historical observations mattered, and by how much.
View full documentationfrom csa_prediction_engine import predict_grid, GridOptions, PredictionResults # Submit y, X, theta — predictions come back with full attribution yhat, output_details = predict_grid(y=y, X=X, theta=theta, options=GridOptions()) # Every insight packaged in one results object Results = PredictionResults(output_details)