Overview
The validation layer is designed around three principles: model integrity diagnostics (heavy-tail spectral analysis of trained networks), signal calibration (statistical hypothesis testing against rolling baselines), and audit-trail reproducibility (deterministic logging of every validation decision).
Validation Philosophy
SPORTINDEX does not produce predictions. It validates the mathematical health of your models and signals. The output is a verdict — pass / fail / degraded — with a documented justification suitable for institutional governance review.
- Independence from the model owner's optimization process
- Reproducibility of every validation run with deterministic seeds and audit logs
- Statistical rigor: heavy-tail exponent thresholds derived from peer-reviewed literature (Martin & Mahoney, JMLR 2021)
- No reliance on observational forward-test data alone; spectral diagnostics precede any backtest
Model Governance
Each validation engagement produces a signed governance packet containing the model fingerprint, the validation timestamp, the rolling-window configuration, and the verdict. The packet is retained in a tamper-evident audit log.
- Versioned model fingerprints (SHA-256 of weight tensors)
- Timestamped validation runs with deterministic input replay
- Confidence bands disclosed at the configured significance level
- Failed validations explicitly reported; no silent acceptance
Limitations & Honest Disclosure
Methodology limitations are disclosed in writing prior to engagement:
- Spectral diagnostics measure training integrity, not future performance
- Spectral validation verdicts are conditional on the input model's training data. Ongoing data drift is tracked separately by the continuous monitoring layer (Layer B): input-distribution drift, prediction calibration, and edge decay
- Heavy-tail analysis assumes sufficient model depth; trivially small networks are flagged as out-of-scope
- The methodology does not claim to detect adversarial data poisoning beyond standard distribution-shift indicators
References
- Martin, C. H., & Mahoney, M. W. (2021). Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning. Journal of Machine Learning Research.
- WeightWatcher open-source project — spectral diagnostic framework for trained neural networks.