College basketball analytics with a built-in reason to say no.
Swishr is a documented rules-based decision architecture built around the Quad Filter. It takes multiple analytical inputs, applies sequential filters and a dedicated veto layer, and produces a per-game recommendation that can end in an explicit NO BET.
The public story is the decision architecture — not a claim that the current private workbook, automation pipeline, or subscriber product has been independently verified.
A detailed rules architecture whose current executable implementation still needs re-verification.
The current project contains a detailed specification for the Quad Filter and supporting portfolio logic, but no workbook, executable code, dashboard, results log, or automation workflow was available in the audited project folder to independently run. O2S therefore presents Swishr as an advanced prototype / active-development architecture rather than a verified production product.
Current evidence level
Inputs → signal → filters → veto → human decision.
The useful part to show publicly is the structure of the decision, not the proprietary formulas underneath it.
The framework is designed to challenge its own first impression.
Swishr's documented architecture is valuable because the first signal is not treated as the final answer.
Start with a candidate
The system begins with model-derived game information rather than treating every matchup as equal.
Test the setup
Rules examine whether the apparent opportunity survives known risk conditions.
Override when needed
A dedicated veto layer can convert an attractive raw signal into a pass.
Control combination risk
The private specification includes exposure-management guidance for multi-leg decisions, but exact caps remain private and are not presented as finalized here.
See the difference between a surface signal and a disciplined final decision.
This demo uses entirely fictional teams, fictional probabilities, and fictional market information. It does not use or reproduce data from any real analytics provider or sportsbook.
Formalizing expert judgment into an auditable decision system.
Explicit no-bet logic
The framework can return a pass instead of manufacturing a recommendation for every game.
Rules-based transparency
The documented engine is deterministic spreadsheet logic, not a black-box trained prediction model.
Human-in-the-loop execution
The system provides a recommendation and risk structure; the operator remains responsible for the final action.
What the current evidence does — and does not — support.
What this page deliberately does not claim or expose.
Swishr can demonstrate disciplined systems design without relying on performance marketing or disclosing reproducible strategy logic.
Decision support does not make wagering predictable.
Swishr is presented as an example of rules-based analytical design. It does not guarantee outcomes, profit, or accuracy.
21+ where applicable
Wagering involves risk of loss. Only wager where lawful, within personal limits, and with money you can afford to lose.
No performance figures are published for Swishr because the current project does not contain a reproducible, publication-ready results record.
Need a rules-based decision system that challenges the first answer?
O2S builds custom decision-support systems with filters, vetoes, exception logic, and explicit human review.