Freeze the source set
Record the lineup, source URLs, access time, festival edition, and every known stage or host.
Profile the stages first, score every viable artist-stage pairing, preserve the runner-up, then lock the forecast before official assignments are known.
The same sequence applies to every festival, while each edition gets its own evidence and stage profiles.
Record the lineup, source URLs, access time, festival edition, and every known stage or host.
Define each stage by sound, scale, curators, historical booking patterns, capacity, and special roles.
Capture same-festival history, style, billing, production scale, special billing, trajectory, and related-event placements.
Apply the 100-point rubric to each artist-stage candidate. Missing evidence is neutral; contradictory evidence must reduce the score.
Apply hard constraints and balance the full day. Keep the winning stage, runner-up, score gap, rationale, and evidence trail.
Use evidence strength, candidate margin, and unresolved conflicts. Confidence is not the same as the raw fit score.
Timestamp the immutable forecast and methodology version. Official information is stored later without overwriting the call.
Award one point for an exact stage match, zero for a miss, exclude cancellations, then publish accuracy and calibration.
The fit score totals 100 points. It ranks candidates; it does not claim to be the probability of a correct prediction.
Recent exact-stage appearances at the same festival carry the strongest signal.
Genre, set style, stage identity, and the way the festival programs that room.
Artist billing, audience demand, show scale, and likely placement in the day.
Announced stage hosts, labels, collectives, or curated programming restrictions.
Sunset, live, B2B, takeover, debut, or other explicitly billed formats.
Placements at related events are supporting evidence, never a replacement for local history.
Capacity, day balance, competing headliners, local stages, and schedule feasibility.
A top individual score can still be impossible in context. Before locking, the model applies these edition-wide constraints:
Recent same-festival exact-stage evidence with a decisive score gap and no unresolved conflict.
Several independent signals point to the same stage.
The best stage fit is clear, but direct local precedent is limited.
The leading stage narrowly beats a credible adjacent-stage option.
Local, emerging, sponsored-stage, or otherwise thin-evidence placement.
The prediction and the answer live in separate records, so hindsight cannot improve the forecast.
The 2026 Orlando forecast predates both this formal rubric and the published exact-match scoring contract. Its 108 calls remain unchanged and are labeled as the founding legacy record. The scoring rule was locked before official results, but not before the forecast; every future edition must lock both methodology and scoring prospectively.
View the 2026 Orlando archive →Observed accuracy must be shown with its scored sample size and uncertainty; a small bin is evidence in progress, not a stable performance claim.
No eligible result is available.
Too few eligible calls for a stable calibration conclusion.
Useful directional evidence; uncertainty remains material.
Large enough for routine comparison while retaining the uncertainty interval.
Observed accuracy uses a 95% Wilson score interval. Calibration error and Brier score remain model-version specific; bins are never merged across incompatible prediction standards.
Every released festival lineup receives the same reconciled, private-first playlist workflow. Collection may be automated; public presentation requires an exact owner-approved Spotify record.
Confirm the official lineup through registered authoritative sources and approve the material monitoring change.
Save the approved lineup snapshot, source URL, checked time, source order, and unique billing identities.
The released-lineup handoff automatically creates a hash-bound, non-public playlist draft.
Match every billing to the correct Spotify artist and record ambiguity or missing-catalog exceptions.
Apply the declared coverage and track-selection rules consistently across the complete lineup.
Use the authorized Spotify integration to create the playlist privately in the connected account.
Check coverage, duplicates, identity matches, title, description, artwork, ordering, and exceptions.
Record exact owner approval, then change the approved playlist to public in Spotify.
Store the exact public Spotify and embed links; the shared festival component renders the companion.
Treat later lineup or catalog changes as revisions with evidence, review, and approval before publication.
Coverage standard: one representative official-catalog track per unique billed act when available. Identity ambiguities and missing catalogs are recorded as exceptions, never silently substituted. Spotify creation is private-first; public embedding requires exact owner approval.
Each published prediction, scoring, calibration-reporting, playlist-publication, and sanitized-workbook definition has a canonical SHA-256 fingerprint. A definition cannot change under the same version: changes must be published as a new version.
sha256:f64a6f67604d1bfd133c03cb819e0e661777915326cd561f889d1f0a4ebc5ac9
sha256:768addbb8ee6c5361aae0bb3475b8613f74309f5f8adee3586a591351fac4eb5
sha256:f8f34a7b82b20cad40ca29ec1c0f3ef7259a064c0c3ad8c54559f94c7a5c1682
sha256:56e54f524db0e5c2651a46f9c4ad561d8290c3c7b09d86b0e4375ae5c33f294e
sha256:ae2b91f15d1ea4a562b900feb0169dce899a4fe80d5ad5c7fd45bc5cd12048e1
Download the machine-readable methodology →