# #008

#### *October 24, 202&#x35;**:-** Matchmaking Refinement, Anti-Bot Groundwork & Referral Backend Stub*

### Purpose

To strengthen core competitive infrastructure through purpose-built matchmaking and anti-fraud systems, laying groundwork for future reputation layers and user acquisition flows while maintaining rigorous data ethics.

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### Key Highlights

🔍 Foundational Anti-Bot Heuristics (Phase 1, In-House Engine)

* Launched Tapzi's native behavior monitoring layer—combines IP throttling, entropy scoring, and interaction timing analysis.
* Data visualized through internal ELK + Grafana stack, fully anonymized (no PII or user fingerprinting stored).
* Engine built from scratch to ensure full control over detection policy and tuning—no reliance on third-party providers.
* Tuning in collaboration with Trust & Safety reviewers for socially aware detection thresholds.\
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🎯 Adaptive Matchmaking Progression

* Core matchmaking engine (under internal development) enhanced with ELO drift support, allowing temporary bracket flexibility for players with inconsistent streaks.
* Added detailed rejection telemetry (abandonment, connection mismatches, invalid rating gap) to help refine pairing logic.\
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📱 Real-Device Testing Matrix Expanded

* Internal QA process validated matchmaking and anti-abuse behavior across 9 diverse mobile environments, including budget Androids and stylus inputs, ensuring fairness across device classes.

🧩 Referral Logic (Backend Laid, UX TBD)

* Signed invite code and referral timestamping backend system implemented.
* UI entry points and attribution visualization planned for Q4 once backend is stabilized.

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### Why It Matters

* Trust-First Architecture: Every layer is built internally for full control, transparency, and long-term independence.
* Infrastructure-Driven Integrity: Fraud resistance, fair matchmaking, and audit trails are part of Tapzi’s core—not tacked on.
* Scalable Foundations: System design anticipates millions of matches and diverse players, not short-term shortcuts.
* Ethical Data Practices: We observe gameplay behavior—not identity—ensuring Tapzi is future-compliant by design.
* Growth Readiness: Referral logic allows low-friction user growth with attribution—ready when UI drops.

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### Open Issues

* Edge Case False Positives from unique user hardware patterns (stylus, low-refresh Android tablets).
* Thread Cleanup Gaps under rare match timeout conditions still causing lingering matchmaking locks.
* Referral Interaction Missing—only backend support; users can’t yet send or redeem invites.

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### Fixed

* Granular Rejection Logs—rejection types and timestamps now indexed for analytics and debug.
* Match Queue Visualization—wait time percentile buckets now live in telemetry dashboards.

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### Next Steps

* Iterate detection heuristics with QA + Trust reviewers to tune accuracy.
* Build and test frontend referral interface, including invite generation and sign-up flow.
* Introduce ELO decay policy for inactive users to maintain leaderboard competitiveness.
* Expand anti-abuse monitoring: real-time dashboard for bot flag volumes, disputes, and manual overrides.\\

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