VS Predicts

Prediction proof

CFB h2h San Jose State Spartans

Cryptographically anchored record. CertifiedData proves a prediction record existed at the timestamp shown. The public proof discloses the settled output and result without exposing exact closing quotes, row-level CLV, provider identity, or proprietary model mechanics.

Settlement

Status: won

Settled at:

Units P/L: +1.30u

Identity method: team_pair

Final score: eastern michigan eagles 21 · san jose state spartans 27

Exact closing quote, provider-specific benchmark identity, row-level CLV, and correction values are withheld. Aggregate CLV performance remains available on results summaries.

Record

Pick ID
1d0ca096-7c3b-43b1-979a-3fc6443449c7
Sport
cfb
Market
h2h
Selection
San Jose State Spartans
Posted odds
+130
Predicted at
Event start
CertifiedData signed at
Model version
EV-8.2-SHARP
Market snapshot hash
c869da8c1fb1e28be4d5ad3b2aeb92775a452af299b001827cb543363bb1152d

CertifiedData verification

This prediction is cryptographically certified through CertifiedData.io— an independent trust framework whose signature anchors the record's existence and content at the signing timestamp shown above. The signature (2026-09-04 21:20:47Z) predates the scheduled event start (2026-09-04 22:30:00Z), so this record is verifiable evidence that the forecast was recorded pregame.

https://certifieddata.io/verify/54e5d79a-3dee-4d1e-937b-db5de4337e99

What this proves: a prediction record with this identity, model version, and market snapshot existed at the timestamp above and has not been silently altered. What it does NOT prove: prediction accuracy, profitability, fairness, or legal compliance — and, in the current scheme, it does not cryptographically bind the selection shown above. Pregame receipts deliberately withhold the selection so the public digest cannot be used to recover it before the event; selection-level commit/reveal verification is designed but not yet implemented.

Powered by the CertifiedData Trust Framework. VS Predicts publishes historical outcomes transparently while keeping proprietary model construction and benchmark precision private.