tarai+
Statistical rankings that earn their keep on race day.
Four modern models score every runner. Race history fuels evaluation. A final report puts prediction against the result.
Ranking engines
Four independent views of the same field.
Each model ranks runners with a different statistical lens — then you compare them side by side.
Weighted scoring
Composite form factors blended into a transparent weighted score and rank.
Logistic regression
Probabilistic win hypothesis from calibrated LogReg estimates.
LightGBM
Gradient-boosted trees capturing non-linear patterns in race history.
RRF fusion
Reciprocal Rank Fusion merges model signals into a consensus order.
From card to verdict
History in. Rankings out. Results measured.
The workflow is built for continuous evaluation — not a one-off tip sheet.
Collect race history
Ingest meetings, entries, and past runs so every model sees the same evidence.
Produce four rankings
Weighted, LogReg, LightGBM, and RRF each publish an ordered win hypothesis.
Record the result
Official finishing order is captured alongside the pre-race rankings.
Evaluate & report
Hit rates and model agreement surface in the race ranking report.
Racing result report
Actual finish versus every ranking.
Export a final report that lines up the race result with Weighted, LogReg, LightGBM, and RRF — so you can see which hypothesis held.
Ready to rank the next meeting?
Open race cards, run the four models, and export the comparison when the result is in.