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sports roster-impact simulation core for injury, trade, and draft analysis.","maintainers":[{"name":"jayscambler","email":"jay@greyhaven.ai"}],"readme":"# sports-impact-core\n\nReusable TypeScript library and CLI for sports roster-impact simulations.\n\nMVP support:\n\n- normalized NBA/WNBA basketball baseline ingestion from CSV/JSON rows\n- NBA injury-impact scenarios\n- NBA trade-impact scenarios with outgoing/incoming player deltas\n- shared player availability constraints for simulations and lineup matchup ranking\n- WNBA roster-impact scenarios from approved roster/minutes exports or BALLDONTLIE WNBA API pulls using `league: \"WNBA\"`, `sport: \"wnba\"`, 40-minute regulation assumptions, header templates, and `import-baseline-dataset --validate-only`\n- deterministic NBA-like trade-feasibility guardrails for salary matching, luxury-tax/apron thresholds, hard caps, salary aggregation, roster-count checks, and optional required salary/payroll/apron source-license provenance\n- deterministic minutes redistribution and retained-rotation compression\n- player stat/fantasy deltas\n- transparent team rating heuristic\n- markdown report rendering\n- historical absence fixture generation\n- fixture-backed injury and trade backtest metrics\n- multi-suite injury and trade calibration reports\n- deterministic trade model parameter tuning and profile comparison\n- trade fixture quality validation, case-level provenance, historical fixture-pack manifests, input-hash evidence, reproducibility and deterministic rerun verification with drift grouping, configurable regression gates, maturity-aware benchmarks, benchmark comparisons, train/validation/test splitting, readiness gates, holdout scoring, unified audits, and model diagnostics\n- deterministic analysis of precomputed NBA game-footage tracking/detection outputs (player/ball tracks, quality audit warnings, possession segments/estimates, and court-zone summaries) without raw video inference\n- deterministic external handoff validation reports for schema/version, metadata, pixel-scale, raw-video boundary, coordinate completeness, detection completeness, and confidence checks\n- deterministic external handoff normalization into `sports-impact-core/game-footage-analysis-input/v1`, including direct `court_feet` coordinates and explicit-scale pixel/bbox-center conversion\n- downstream external handoff fixture-gate CI adoption plans, adoption verification, and adoption registries that generate/verify deterministic GitHub Actions YAML for both valid fixture-pack gates and invalid-fixture gates across projects\n- deterministic SVG/Markdown court visualizations for selected precomputed frames and player/ball track paths\n- dependency-free normalization for precomputed Roboflow/YOLO-style JSON, CSV detection rows, ByteTrack/Supervision-style tracking JSON, and validated external handoff JSON into the game-footage input schema\n- game-footage fixture-pack manifests with adapter source files including external handoffs, input hashes, validation evidence, deterministic visualization artifact hashes, reusable baseline lifecycle policy scaffolds/conformance reports/run audits/CI adoption generators/adoption verifiers/trend reports/CI-hardened trend gates/trend registries/dashboard aggregation/dashboard adoption verification/dashboard adoption registries, materialized/verifiable/comparable/gated/baseline-checked/promotable/promotion-verifiable/suite-checkable/ledger-verifiable/ledger-registry-verifiable/ledger-registry-gated/ledger-registry-comparable/ledger-registry-comparison-gated SVG/Markdown artifact bundles, golden expectation checks, and deterministic rerun verification\n- file-backed run bundle summaries with `run-manifest.json` and `EXECUTIVE_SUMMARY.md`\n- manifest-driven scenario bundles with dry-run validation, execution-manifest gates, trade/draft/lineup orchestration, and operational comparison reports across bundles\n- coaches lineup matchup ranking from precomputed game-footage reports, candidate lineups, and availability constraints\n\nTrade feasibility can require financial provenance via embedded `financialProvenance.sources` or CLI/API ingestion of salary ledger, team payroll, and apron-threshold CSV/JSON fixtures with source, license, retrieval timestamp, and rule-profile version/effective-season metadata.\n\nThe repository includes an optional BALLDONTLIE-primary internal demo importer, `npm run generate:balldontlie-nba-demo-pack -- --out-dir /tmp/sports-impact-balldontlie-nba-demo-pack --team-abbreviations LAL,BKN --contract-season 2025 --stats-season 2024 --effective-season 2025-26 --include-player-averages` (or `--all-current-teams` for all 30 current NBA teams). It reads `BALLDONTLIE_API_KEY` from the environment, never writes the key to artifacts, and emits salary-ledger, contract-detail, derived known-payroll, NBA PR/NBPA apron-threshold, optional player-baseline, provenance, source-fit, summary, and hash files. BALLDONTLIE is third-party aggregated/best-effort data and not official league data; generated payroll is not official NBA cap payroll.\n\nFor WNBA roster-impact baselines, use `BALLDONTLIE_API_KEY=... sports-impact import-balldontlie-wnba-baseline --season 2026 --team-abbreviations NY,CON --out-dir runs/wnba-balldontlie`. It fetches WNBA teams, active players, season averages, and advanced stats; writes `teams.csv`, `players.csv`, `baseline-dataset.json`, and `summary.json`; and never writes the API key. BALLDONTLIE WNBA stats endpoints require the appropriate WNBA/ALL-ACCESS tier. Generated WNBA data is third-party aggregated best-effort data, not official league data or roster advice.\n\nUse `npm run summarize:run -- --run-dir runs/<trade-draft-or-lineup-run>` or exported `summarizeRun` to index run artifacts and write `run-manifest.json` plus `EXECUTIVE_SUMMARY.md`. Use `npm run validate:scenario-bundle -- --manifest path/to/scenario-bundle.json --report runs/scenario-bundle-validation.md` or exported `validateScenarioBundleManifestFromFile` / `renderScenarioBundleValidationMarkdown` to dry-run required fields and input paths before executing a bundle. Use `npm run run:scenario-bundle -- --manifest path/to/scenario-bundle.json --validate-before-run --validation-report runs/scenario-bundle-validation.md --execution-manifest runs/scenario-bundle-execution.json` or exported `runScenarioBundleFromFile({ validateBeforeRun: true })` when one manifest should be checked before supported trade/draft/lineup steps run. Successful runs write `scenario-bundle-execution.json` with step statuses and output/report paths. Use `npm run gate:scenario-bundle-execution -- --execution-manifest runs/scenario-bundle-execution.json --expect-step lineup=pass --expect-step live-wnba=skipped --require-validation-report --report runs/scenario-bundle-execution-gate.md` or exported `gateScenarioBundleExecutionManifestFromFile` / `renderScenarioBundleExecutionGateMarkdown` to turn that execution manifest into CI-ready ran/skipped/failed step and artifact-path evidence. Use `npm run compare:scenario-bundles -- --manifest path/to/scenario-bundle-comparison.json` or exported `runScenarioBundleComparisonFromFile` to rank multiple bundle runs operationally: clean runs first, then more indexed artifacts. Copyable templates live under `examples/scenario-bundles/`. Summaries are deterministic decision-support evidence for trade, draft, and coaches lineup artifacts, not recommendations.\n\nUse `rankCoachesLineupMatchupsFromFiles` with a precomputed game-footage analysis report, modeled players, opponent lineup, candidate lineups, and optional availability constraints to write coaches lineup matchup JSON/Markdown artifacts. This is coaching decision support only, not an authoritative coaching decision.\n\nUse `npm run generate:balldontlie-demo-trade -- --pack-dir /tmp/sports-impact-balldontlie-nba-demo-pack --out-dir /tmp/sports-impact-balldontlie-demo-trade --target-team LAL --partner-team BKN --season 2025-26` to turn an existing BALLDONTLIE demo pack into ready-to-run `trade-feasibility.json` and `trade-simulation-input.json` files with embedded financial provenance and internal-demo caveats. Add `--outgoing-player <playerId-or-exact-name>` and `--incoming-player <playerId-or-exact-name>` to override deterministic picks. The generated trade is a deterministic demo scenario, not an official transaction recommendation or CBA ruling.\n\nUse `npm run optimize:balldontlie-demo-trades -- --pack-dir /tmp/sports-impact-balldontlie-nba-demo-pack --out-dir /tmp/sports-impact-balldontlie-trade-search --target-team LAL --partner-teams BKN,ATL,DAL --max-outgoing 8 --max-incoming-per-team 8 --trade-families 1-for-1,1-for-2,2-for-1,2-for-2 --max-candidate-count 500 --top 10 --objective-preset production_preserving --salary-relief-weight 1 --fantasy-delta-weight 1 --net-rating-weight 10 --protect-top-n-minutes 2 --max-outgoing-salary 20000000 --min-fantasy-delta -12 --exclude-player \"LeBron James\"` to run multistep deterministic scenario search over many candidate single- or multi-player trades. Presets include `salary_relief`, `production_preserving`, `apron_escape`, `bench_upgrade`, and `star_protected`; explicit weights/constraints override preset defaults. If `--trade-families` is omitted, the optimizer preserves the original `1-for-1` search behavior. The optimizer enumerates candidates, applies constraints and aggregate outgoing-salary caps, runs feasibility and impact simulation for each remaining candidate, ranks by explicit objective weights, explains top-candidate scores, and emits JSON/Markdown plus top-candidate runnable artifacts. Use `npm run evaluate:balldontlie-multi-team-demo-trade -- --pack-dir /tmp/sports-impact-balldontlie-nba-demo-pack --out-dir /tmp/sports-impact-balldontlie-multi-team-trade --trade-legs-path /tmp/sports-impact-balldontlie-multi-team-legs.json` to evaluate an explicit multi-team transaction from every participating team's perspective; the legs file is a JSON array of `{ fromTeam, toTeam, player }` objects. The shared implementations are exported as `optimizeBalldontlieDemoTrades` and `evaluateBalldontlieMultiTeamDemoTrade` for package and Pi/autocontext integrations. These workflows are internal demo/source-fit optimization/evaluation, not official recommendations.\n\nUse `npm run optimize:balldontlie-three-team-demo-trades -- --pack-dir /tmp/sports-impact-balldontlie-nba-demo-pack --out-dir /tmp/sports-impact-balldontlie-three-team-trade-search --target-team LAL --partner-teams BKN,ATL,DAL --max-players-per-team 4 --max-candidate-count 250 --top 10 --objective-preset balanced_multi_team --three-team-trade-families 1-for-1-for-1,2-for-1-for-1 --protect-top-n-minutes 3 --max-target-outgoing-salary 30000000 --min-target-fantasy-delta -15 --min-all-team-fantasy-delta 0 --min-team-fantasy-delta -8 --max-team-net-salary-increase 5000000 --max-team-incoming-salary 30000000 --require-no-team-fantasy-loss-below -12 --require-all-teams-feasible true --dedupe-by-target-swap true --max-candidates-per-target-outgoing-player 3 --max-candidates-per-target-incoming-player 3 --exclude-player \"LeBron James\" --exclude-player \"Luka Doncic\" --generate-candidate-comparison --candidate-comparison-retained-limit 5 --candidate-comparison-alternatives 3` to generate deterministic bounded three-team cycles (`target → partner A → partner B → target`), including optional multi-player leg families (`1-for-1-for-1`, `2-for-1-for-1`, `1-for-2-for-1`, `1-for-1-for-2`), protect top-minute players from every participating team's outgoing pool, evaluate each participating team, enforce target/all-team and partner-team fantasy/salary guardrails, de-duplicate near-identical target swaps, and rank candidates with transparent target/all-team objective weights. The run also writes `three-team-guardrail-audit.json` / `.md` with protected players by team, excluded-player matches, filter reason counts, fairness filter details, and a pass/fail verification of returned candidates; `sourceFit` confidence summaries; and per-top-candidate `candidate-explanation.json` / `.md` artifacts explaining survival, guardrail/diversity impact, source-fit warnings, and nearby filtered alternatives grouped by failure reason with closest guardrail-boundary details; and `filtered-candidates/` near-miss artifacts (`index.json`, `filtered-candidates.md`, and `by-reason/*.json`) with failed guardrails, actual/limit/gap-to-pass boundaries, affected teams, phases, and trade legs. Presets include `salary_relief`, `production_preserving`, `apron_escape`, `star_protected`, and `balanced_multi_team`; explicit weights/constraints/diversity controls override preset defaults. `balanced_multi_team` defaults to `minAllTeamFantasyDelta: 0`, `apron_escape` defaults to `minTargetSalaryReliefUsd: 5000000`, and `star_protected` defaults to top-minute protection plus target-swap diversity caps; override the new floors with `--min-all-team-fantasy-delta` and `--min-target-salary-relief`. The shared implementation is exported as `optimizeBalldontlieThreeTeamDemoTrades` and exposed through Pi as `optimize_balldontlie_three_team_demo_trades`; guardrail and source-fit/explanation gates are exposed through Pi as `gate_balldontlie_three_team_guardrail_audit` and `gate_balldontlie_three_team_source_fit_explanations`, and reusable source-fit/explanation gates are exported as `gateBalldontlieThreeTeamSourceFitExplanations`. Use `npm run inspect:balldontlie-three-team-filtered-candidates -- --summary /tmp/sports-impact-balldontlie-three-team-trade-search/summary.json --out /tmp/sports-impact-balldontlie-three-team-trade-search/filtered-candidates/filtered-candidate-inspection.json --report /tmp/sports-impact-balldontlie-three-team-trade-search/filtered-candidates/filtered-candidate-inspection.md --reason maxTeamNetSalaryIncrease --team BKN --limit 10` or Pi action `inspect_balldontlie_three_team_filtered_candidates` to summarize top failure reasons, closest near misses, and by-team guardrail gaps without hand-reading JSON. Add `--generate-candidate-comparison` to the optimizer, or set Pi `generateCandidateComparison: true`, to write retained-vs-filtered `candidate-comparison.json` / `.md` artifacts during search without changing ranking. Use `npm run compare:balldontlie-three-team-candidates -- --summary /tmp/sports-impact-balldontlie-three-team-trade-search/summary.json --out /tmp/sports-impact-balldontlie-three-team-trade-search/candidate-comparison.json --report /tmp/sports-impact-balldontlie-three-team-trade-search/candidate-comparison.md --reason maxTeamNetSalaryIncrease --alternatives 3 --retained-limit 5` or Pi action `compare_balldontlie_three_team_candidates` to pair retained top candidates with nearest filtered alternatives by target-swap similarity and guardrail gap. Use `npm run gate:balldontlie-three-team-candidate-comparison -- --comparison /tmp/sports-impact-balldontlie-three-team-trade-search/candidate-comparison.json --comparison-report /tmp/sports-impact-balldontlie-three-team-trade-search/candidate-comparison.md --out /tmp/sports-impact-balldontlie-three-team-candidate-comparison-gate.json --report /tmp/sports-impact-balldontlie-three-team-candidate-comparison-gate.md` or Pi action `gate_balldontlie_three_team_candidate_comparison` to fail on comparison schema/content drift. Output remains source-fit scenario search, not a recommendation or CBA ruling. Use `npm run gate:balldontlie-three-team-guardrail-audit -- --audit /tmp/sports-impact-balldontlie-three-team-trade-search/three-team-guardrail-audit.json --out /tmp/sports-impact-balldontlie-three-team-guardrail-gate.json --report /tmp/sports-impact-balldontlie-three-team-guardrail-gate.md` in CI to fail on non-pass returned-candidate verification, positive violations, or missing required constraint/filter-count audit fields. Use `npm run gate:balldontlie-three-team-source-fit-explanations -- --summary /tmp/sports-impact-balldontlie-three-team-trade-search/summary.json --out /tmp/sports-impact-balldontlie-three-team-source-fit-explanation-gate.json --report /tmp/sports-impact-balldontlie-three-team-source-fit-explanation-gate.md --minimum-confidence 0` to fail when retained top candidates lose `sourceFit` fields, candidate-explanation JSON/Markdown artifacts drift/disappear, or filtered-candidate near-miss artifacts disappear/schema-drift. CI/release preflight also runs `npm run gate:balldontlie-three-team-guardrail-audit:smoke`, which now emits one combined guardrail + source-fit + candidate-comparison gate smoke summary/report with candidate counts, explanation/comparison and filtered-near-miss artifact counts, source-fit confidence, and paths/hashes for gate and near-miss artifacts.\n\nUse `npm run evaluate:draft-candidate-fit -- --prospects /tmp/sports-impact-draft/prospects.json --team-needs /tmp/sports-impact-draft/team-needs.json --target-team LAL --top 10 --out /tmp/sports-impact-draft/draft-candidate-fit.json --report /tmp/sports-impact-draft/draft-candidate-fit.md` to rank fixture-backed draft prospects against a target team's modeled needs. The shared implementation is exported as `evaluateDraftCandidateFitFromFiles` and exposed through Pi as `evaluate_draft_candidate_fit`. Inputs are source-agnostic JSON/CSV fixtures for prospects and team needs; outputs include roster-need alignment, skill fit, minutes pathway, risk/timeline fit, source-fit warnings, and deterministic score breakdowns. Use `npm run analyze:draft-board-sensitivity -- --fit-report /tmp/sports-impact-draft/draft-candidate-fit.json --top 10 --out /tmp/sports-impact-draft/draft-board-sensitivity.json --report /tmp/sports-impact-draft/draft-board-sensitivity.md` to test robustness across preset boards (`default`, `win_now_readiness`, `upside_swing`, `low_risk`, `minutes_pathway`, `skill_need_heavy`, and `source_confidence_penalty`) and report consensus, volatile, upside, win-now, risk-sensitive, source-sensitive, and tier-stability evidence. The shared implementation is exported as `analyzeDraftBoardSensitivityFromFile` and exposed through Pi as `analyze_draft_board_sensitivity`. Use `npm run reconcile:draft-board -- --fit-report /tmp/sports-impact-draft/draft-candidate-fit.json --sensitivity /tmp/sports-impact-draft/draft-board-sensitivity.json --scout-board /tmp/sports-impact-draft/scout-board.json --out /tmp/sports-impact-draft/draft-board-reconciliation.json --report /tmp/sports-impact-draft/draft-board-reconciliation.md` to compare model rankings with scout rankings/notes and emit consensus, model-high/scouts-low, scouts-high/model-low, high-upside disagreement, source/data-gap disagreement, film-review, provenance-blocker, and meeting-agenda evidence. The shared implementation is exported as `reconcileDraftBoardFromFiles` and exposed through Pi as `reconcile_draft_board`. Use `npm run plan:draft-availability -- --fit-report /tmp/sports-impact-draft/draft-candidate-fit.json --sensitivity /tmp/sports-impact-draft/draft-board-sensitivity.json --reconciliation /tmp/sports-impact-draft/draft-board-reconciliation.json --market-board /tmp/sports-impact-draft/market-board.json --pick-slots 17,47 --out /tmp/sports-impact-draft/draft-availability-plan.json --report /tmp/sports-impact-draft/draft-availability-plan.md` to turn fit, sensitivity/reconciliation, market/mock-board, and team pick slots into likely/maybe/unlikely availability bands, reach/value flags, contingency paths, and trade-up/down evidence. The shared implementation is exported as `planDraftAvailabilityFromFiles` and exposed through Pi as `plan_draft_availability`. Use `npm run update:draft-war-room-state -- --availability /tmp/sports-impact-draft/draft-availability-plan.json --selected-picks /tmp/sports-impact-draft/selected-picks.json --current-pick 14 --pick-slots 17,47 --out /tmp/sports-impact-draft/draft-war-room-state.json --report /tmp/sports-impact-draft/draft-war-room-state.md` during live draft-room updates to remove selected prospects, refresh remaining board and target stacks, activate/archive trade-up/down evidence, compute decision pressure, and render a clock-ready briefing. The shared implementation is exported as `updateDraftWarRoomStateFromFiles` and exposed through Pi as `update_draft_war_room_state`. Use `npm run run:draft-workflow -- --manifest /tmp/sports-impact-draft/draft-workflow-manifest.json --out /tmp/sports-impact-draft/draft-workflow-run.json --report /tmp/sports-impact-draft/draft-workflow-run.md --gate` for a manifest-driven actual/demo workflow that runs fit → sensitivity → reconciliation → availability → war-room → brief from supplied fixture paths and optionally gates the produced artifact bundle. The manifest uses `sports-impact-core/draft-workflow-manifest/v1` with `inputs.prospects`, `inputs.teamNeeds`, `inputs.scoutBoard`, `inputs.marketBoard`, `inputs.selectedPicks`, `targetTeam`, `currentPick`, and `pickSlots`. The shared implementation is exported as `runDraftWorkflow` and exposed through Pi as `run_draft_workflow`. Use `npm run smoke:draft-workflow -- --work-dir /tmp/sports-impact-draft-workflow-ci-smoke --current-pick 14 --pick-slots 17,47` for a synthetic CI smoke that runs fit → sensitivity → reconciliation → availability → war-room → brief and writes a summary artifact bundle with output paths and hashes. The shared implementation is exported as `runDraftWorkflowCiSmoke` and exposed through Pi as `run_draft_workflow_smoke`. Use `npm run gate:draft-workflow -- --fit-report /tmp/sports-impact-draft-workflow-ci-smoke/draft/draft-candidate-fit.json --sensitivity /tmp/sports-impact-draft-workflow-ci-smoke/draft/draft-board-sensitivity.json --reconciliation /tmp/sports-impact-draft-workflow-ci-smoke/draft/draft-board-reconciliation.json --availability /tmp/sports-impact-draft-workflow-ci-smoke/draft/draft-availability-plan.json --war-room /tmp/sports-impact-draft-workflow-ci-smoke/draft/draft-war-room-state.json --brief /tmp/sports-impact-draft-workflow-ci-smoke/draft/front-office-draft-decision-brief.json --out /tmp/sports-impact-draft-workflow-ci-smoke/draft-workflow-gate.json --report /tmp/sports-impact-draft-workflow-ci-smoke/draft-workflow-gate.md` to gate actual/demo workflow artifacts for schema drift, linked source path/hash drift, candidate/team consistency, Markdown caveats, and not-a-draft-recommendation language. The shared implementation is exported as `gateDraftWorkflowArtifacts` and exposed through Pi as `gate_draft_workflow`. Use `npm run build:draft-decision-brief -- --fit-report /tmp/sports-impact-draft/draft-candidate-fit.json --top 10 --out /tmp/sports-impact-draft/front-office-draft-decision-brief.json --report /tmp/sports-impact-draft/front-office-draft-decision-brief.md` to convert the fit report into a GM/data-analyst brief with draft board tiers, archetype picks (best fit, safest fit, highest upside, best value), risk register, opportunity costs, analyst appendix, and next-review questions. The shared implementation is exported as `buildDraftDecisionBriefFromFile` and exposed through Pi as `build_draft_decision_brief`. Output is internal demo/source-fit evidence only, not a draft recommendation.\n\nUse `npm run compare:nba-contract-source-packs -- --primary-salary-ledger /tmp/sports-impact-balldontlie-nba-demo-pack/salary-ledger.json --espn-salary-ledger /tmp/sports-impact-espn-contract-prototype/salary-ledger.json --sportradar-salary-ledger /tmp/sports-impact-sportradar-financial-pack/salary-ledger.json --out-dir /tmp/sports-impact-nba-contract-source-comparison` to compare BALLDONTLIE primary salary-ledger rows against optional ESPN/Sportradar ledgers by normalized player name + team alias. The report is deterministic source-fit evidence only, not an official NBA/CBA determination or front-office cap-sheet ruling.\n\nThe repository also includes an optional internal-evaluation importer, `npm run generate:sportradar-financial-pack -- --out-dir /tmp/sports-impact-sportradar-financial-pack`, for account-permitted Sportradar NBA v8 Team Profile salary data. It reads `SPORTRADAR_API_KEY` from the environment, sends it only as an `x-api-key` header, never writes the key to artifacts, and emits uncommitted salary-ledger, derived known-salary payroll, apron-threshold, provenance, summary, and hash files. Treat generated outputs as internal evaluation unless your Sportradar account/order-form terms permit broader use; derived payroll is not an official cap-payroll determination.\n\nFor ESPN source-fit exploration, `npm run generate:espn-contract-prototype -- --players /tmp/espn-nba-contract-players.json --out-dir /tmp/sports-impact-espn-contract-prototype --espn-season 2026 --effective-season 2025-26` reads a JSON player list containing `espnAthleteId` values and fetches ESPN Sports Core contract rows. It emits salary-ledger, contract-detail, derived known-salary payroll, provenance, source-fit report, summary, and hash files. The ESPN endpoint is undocumented web infrastructure, not a licensed public developer feed discovered for this project; use generated outputs for internal evaluation/source-fit comparison only unless ESPN grants permission. Derived payroll covers only requested players and is not official NBA cap payroll.\n\n## Boundaries and external handoff\n\nSee the package-local boundary document [`BOUNDARIES.md`](BOUNDARIES.md), the machine-readable handoff schema [`schemas/game-footage-external-handoff.schema.json`](schemas/game-footage-external-handoff.schema.json), and the canonical repository boundary contract at `docs/BOUNDARIES.md`. Game-footage APIs start from precomputed court-coordinate tracking/detection outputs. Use `validateGameFootageExternalHandoffFromFile` or the `validate-game-footage-external-handoff` CLI command to produce deterministic JSON/Markdown contract reports for schema/runtime provenance, bbox format, pixel-scale, and raw-video boundary alignment, then `normalizeExternalHandoffGameFootageInputFromFile` or `normalize-external-handoff-game-footage` to bridge valid handoffs into the existing analysis input schema before `analyzeGameFootageFromFile`. Fixture-pack manifests can also use `normalization.adapter: \"external_handoff\"` plus `files.externalHandoffInput` to audit validation, normalization, input hashes, normalized-input comparison, and rerun drift in CI; `gateGameFootageExternalHandoffFixturePackFromManifest` / `gate-game-footage-external-handoff-fixture-pack` turns positive fixture-pack evidence into a hard pass/fail gate, `gateGameFootageExternalHandoffInvalidFixturesFromManifest` / `gate-game-footage-external-handoff-invalid-fixtures` gate reusable invalid-fixture manifests so invalid checked-in handoffs fail with stable validation check IDs and are rejected before normalization, `generateGameFootageExternalHandoffFixtureGateCiPlanFromManifest` / `generate-game-footage-external-handoff-gate-ci` emits downstream GitHub Actions YAML that runs both gates together, `verifyGameFootageExternalHandoffFixtureGateCiAdoptionFromFiles` / `verify-game-footage-external-handoff-gate-ci` verifies adopted YAML still matches the deterministic generator, `verifyGameFootageExternalHandoffFixtureGateCiAdoptionRegistryFromManifest` / `verify-game-footage-external-handoff-gate-ci-registry` verifies a manifest-backed registry of downstream adopted workflows across projects, and `gateGameFootageExternalHandoffFixtureGateCiAdoptionRegistryFromFiles` / `gate-game-footage-external-handoff-gate-ci-registry` enforces required-project, package-version, YAML-match, command-coverage, valid+invalid fixture-gate, boundary, and optional previous/current drift policy over registry reports. `generateGameFootageExternalHandoffFixtureGateCiAdoptionRegistryGateCiPlanFromManifest` / `generate-game-footage-external-handoff-gate-ci-registry-gate` emit downstream registry-gate CI YAML, `verifyGameFootageExternalHandoffFixtureGateCiAdoptionRegistryGateCiAdoptionFromFiles` / `verify-game-footage-external-handoff-gate-ci-registry-gate` verify adopted registry-gate CI YAML still matches the deterministic generator, and `verifyGameFootageExternalHandoffFixtureGateCiAdoptionRegistryGateCiAdoptionRegistryFromManifest` / `verify-game-footage-external-handoff-gate-ci-registry-gate-registry` verify registries of downstream registry-gate CI adoptions, and `gateGameFootageExternalHandoffFixtureGateCiAdoptionRegistryGateCiAdoptionRegistryFromFiles` / `gate-game-footage-external-handoff-gate-ci-registry-gate-registry` enforce required-project, minimum-package-version, command-coverage, YAML-match, previous-registry, and optional previous/current drift policy over those registry reports. `generateGameFootageExternalHandoffFixtureGateCiAdoptionRegistryGateCiAdoptionRegistryGateCiPlanFromManifest` / `generate-game-footage-external-handoff-gate-ci-registry-gate-registry-gate` emit downstream CI YAML for that verifier+gate pair, `verifyGameFootageExternalHandoffFixtureGateCiAdoptionRegistryGateCiAdoptionRegistryGateCiAdoptionFromFiles` / `verify-game-footage-external-handoff-gate-ci-registry-gate-registry-gate` verify adopted YAML still matches the deterministic generator, and `verifyGameFootageExternalHandoffFixtureGateCiAdoptionRegistryGateCiAdoptionRegistryGateCiAdoptionRegistryFromManifest` / `verify-game-footage-external-handoff-gate-ci-registry-gate-registry-gate-registry` verify registries of downstream adoption for that generated workflow. The package does not perform raw video inference, object detection, segmentation, homography estimation, or jersey OCR, and it does not provide betting/gambling recommendations, medical advice, or guaranteed-performance claims.\n\n```ts\nimport {\n  analyzeGameFootageFromFile,\n  validateGameFootageExternalHandoffFromFile,\n  normalizeExternalHandoffGameFootageInputFromFile,\n  renderGameFootageExternalHandoffValidationMarkdownReport,\n  appendGameFootageVisualizationArtifactBundleBaselinePromotionLedgerEntry,\n  auditGameFootageVisualizationBaselineLifecycleRun,\n  auditGameFootageFixturePackFromManifest,\n  normalizeCsvGameFootageInput,\n  checkGameFootageVisualizationArtifactBundleBaselineFromFiles,\n  checkGameFootageVisualizationArtifactBundleBaselineSuite,\n  compareGameFootageVisualizationArtifactBundlesFromFiles,\n  promoteGameFootageVisualizationArtifactBundleBaselineFromFiles,\n  verifyGameFootageVisualizationArtifactBundleBaselinePromotion,\n  verifyGameFootageVisualizationArtifactBundleBaselinePromotionLedger,\n  verifyGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistry,\n  gateGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistry,\n  compareGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistries,\n  createGameFootageVisualizationBaselineLifecycleDashboard,\n  verifyGameFootageVisualizationBaselineLifecycleDashboardAdoption,\n  createGameFootageVisualizationBaselineLifecycleTemplate,\n  generateGameFootageVisualizationBaselineLifecycleCiPlan,\n  verifyGameFootageVisualizationBaselineLifecycleCiAdoption,\n  compareGameFootageVisualizationBaselineLifecycleRuns,\n  gateGameFootageVisualizationBaselineLifecycleTrend,\n  verifyGameFootageVisualizationBaselineLifecycleTemplateConformance,\n  gateGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistryComparison,\n  gateGameFootageVisualizationArtifactBundleComparisonFromFile,\n  materializeGameFootageFixturePackVisualizationsFromManifest,\n  normalizeRoboflowGameFootageInput,\n  verifyGameFootageVisualizationArtifactBundleFromManifest,\n  renderGameFootageCourtVisualizationFromFile,\n  normalizeTrackingGameFootageInput,\n  auditTradeFixturePackFromManifest,\n  auditTradeFixtures,\n  benchmarkTradeFixturePackFromManifest,\n  buildTradeSimulationInput,\n  compareTradeFixturePackBenchmarks,\n  calibrateInjuryBacktests,\n  calibrateTradeBacktests,\n  generateInjuryBacktestSuiteFromFixtures,\n  calibrateTradeModel,\n  diagnoseTradeModel,\n  evaluateTradeModelHoldout,\n  evaluateTradeModelReadiness,\n  generateTradeBacktestSuiteFromFixtures,\n  importNbaBaselineDataset,\n  splitTradeBacktestSuite,\n  simulateImpact,\n  validateTradeBacktestFixtures,\n  verifyGameFootageExternalHandoffFixtureGateCiAdoptionRegistryFromManifest,\n  verifyGameFootageFixturePackRerun,\n  verifyTradeFixturePackBenchmarkReproducibility,\n  verifyNbaTradeFeasibility,\n  renderNbaTradeFeasibilityMarkdownReport,\n  verifyTradeFixturePackBenchmarkRerun,\n} from \"sports-impact-core\";\n\nconst dataset = importNbaBaselineDataset({ season, teams, players });\nconst tradeInput = buildTradeSimulationInput(dataset, {\n  teamId: \"LAL\",\n  outgoingPlayerIds: [\"star_forward\"],\n  incomingPlayerIds: [\"incoming_shooter\"],\n});\nconst salaryByPlayerId: Record<string, number> = {\n  star_forward: 20_000_000,\n  incoming_shooter: 25_000_000,\n};\nconst feasibility = verifyNbaTradeFeasibility({\n  schema: \"sports-impact-core/nba-trade-feasibility-input/v1\",\n  schemaVersion: 1,\n  team: tradeInput.team,\n  players: tradeInput.players.map((player) => ({ ...player, salaryUsd: salaryByPlayerId[player.playerId] })),\n  scenario: { ...tradeInput.scenario, assumeLegal: false },\n  teamFinancialContext: {\n    teamId: \"LAL\",\n    salaryBeforeTradeUsd: 149_000_000,\n    rosterCountBeforeTrade: 15,\n    salaryCapUsd: 141_000_000,\n    luxuryTaxLineUsd: 171_315_000,\n    firstApronUsd: 178_132_000,\n    secondApronUsd: 188_931_000,\n  },\n  financialProvenance: {\n    sources: [\n      {\n        sourceId: \"salary-ledger\",\n        label: \"Salary/payroll source label\",\n        dataKind: \"synthetic\",\n        retrievedAt: \"2026-06-15T00:00:00.000Z\",\n        license: { name: \"Synthetic fixture license\" },\n        covers: [\"player_salaries\", \"team_payroll\"],\n      },\n      {\n        sourceId: \"apron-thresholds\",\n        label: \"Apron threshold source label\",\n        dataKind: \"synthetic\",\n        version: \"synthetic-2025-26-v1\",\n        effectiveSeason: \"2025-26\",\n        retrievedAt: \"2026-06-15T00:00:00.000Z\",\n        license: { name: \"Synthetic fixture license\" },\n        covers: [\"salary_cap\", \"luxury_tax_line\", \"first_apron\", \"second_apron\", \"rule_profile\"],\n      },\n    ],\n  },\n  rules: { requireFinancialProvenance: true },\n});\nconst feasibilityMarkdown = renderNbaTradeFeasibilityMarkdownReport(feasibility);\nconst tradeResult = simulateImpact(tradeInput);\nconst suite = generateInjuryBacktestSuiteFromFixtures({ dataset, absences, teamOutcomes, playerOutcomes });\nconst calibration = calibrateInjuryBacktests({ inputs: [{ suiteId: \"injury-history\", value: suite }] });\nconst tradeSuite = generateTradeBacktestSuiteFromFixtures({ dataset, trades, teamOutcomes, playerOutcomes });\nconst tradeCalibration = calibrateTradeBacktests({ inputs: [{ suiteId: \"trade-history\", value: tradeSuite }] });\nconst quality = validateTradeBacktestFixtures(tradeSuite);\nconst split = splitTradeBacktestSuite(tradeSuite, { trainFraction: 0.7, validationFraction: 0.15, testFraction: 0.15 });\nconst tradeModelCalibration = calibrateTradeModel({\n  suite: tradeSuite,\n  parameterGrid: { incomingMinutesCap: [28, 30, 36] },\n  trainCaseIds: split.splits.train.caseIds,\n  validationCaseIds: split.splits.validation.caseIds,\n});\nconst readiness = evaluateTradeModelReadiness({ split, calibration: tradeModelCalibration });\nconst holdout = evaluateTradeModelHoldout({ split, calibration: tradeModelCalibration });\nconst audit = auditTradeFixtures({ suite: tradeSuite, split, calibration: tradeModelCalibration });\nconst diagnostics = diagnoseTradeModel({ suite: tradeSuite, calibration: tradeModelCalibration });\nconst fixturePackAudit = await auditTradeFixturePackFromManifest({ manifestPath: \"examples/nba-trade-historical-template/trade-fixture-pack.json\" });\nconst fixturePackBenchmark = await benchmarkTradeFixturePackFromManifest({ manifestPath: \"examples/nba-trade-historical-template/trade-fixture-pack.json\" });\nconst fixturePackReproducibility = await verifyTradeFixturePackBenchmarkReproducibility({ report: fixturePackBenchmark });\nconst fixturePackRerunVerification = await verifyTradeFixturePackBenchmarkRerun({ report: fixturePackBenchmark });\nconst fixturePackBenchmarkComparison = compareTradeFixturePackBenchmarks({ reports: [fixturePackBenchmark] });\nconst roboflowInput = normalizeRoboflowGameFootageInput(roboflowDetections, {\n  gameId: \"demo-game\",\n  sourceLabel: \"Precomputed Roboflow/YOLO detections\",\n  fps: 5,\n  pixelsPerFootX: 10,\n  pixelsPerFootY: 10,\n});\nconst csvInput = normalizeCsvGameFootageInput(csvDetectionRows, {\n  gameId: \"demo-game-csv\",\n  sourceLabel: \"CSV precomputed detections\",\n  pixelsPerFootX: 10,\n  pixelsPerFootY: 10,\n});\nconst trackingInput = normalizeTrackingGameFootageInput(trackingJson, {\n  gameId: \"demo-game-tracking\",\n  sourceLabel: \"ByteTrack/Supervision precomputed tracks\",\n  pixelsPerFootX: 10,\n  pixelsPerFootY: 10,\n  bboxFormat: \"xyxy\",\n  sourceKind: \"bytetrack\",\n});\nconst gameFootageAnalysis = await analyzeGameFootageFromFile({ inputPath: \"examples/nba-game-footage/footage-analysis-input.json\" });\nconst courtVisualization = await renderGameFootageCourtVisualizationFromFile({ inputPath: \"examples/nba-game-footage/footage-analysis-input.json\", frameIndex: 1 });\nconst gameFootageFixturePackAudit = await auditGameFootageFixturePackFromManifest({ manifestPath: \"examples/nba-game-footage/game-footage-fixture-pack.json\" });\nconst gameFootageVisualizationArtifacts = await materializeGameFootageFixturePackVisualizationsFromManifest({\n  manifestPath: \"examples/nba-game-footage/game-footage-fixture-pack.json\",\n  outputDir: \"runs/demo-game-footage/visualizations\",\n});\nconst gameFootageVisualizationArtifactVerification = await verifyGameFootageVisualizationArtifactBundleFromManifest({\n  manifestPath: gameFootageVisualizationArtifacts.artifactManifestPath,\n  artifactDir: gameFootageVisualizationArtifacts.outputDir,\n});\nconst gameFootageVisualizationArtifactComparison = await compareGameFootageVisualizationArtifactBundlesFromFiles({\n  inputPaths: [gameFootageVisualizationArtifacts.artifactManifestPath, gameFootageVisualizationArtifacts.artifactManifestPath],\n});\nconst gameFootageVisualizationDriftGate = await gateGameFootageVisualizationArtifactBundleComparisonFromFile({\n  comparisonPath: \"runs/demo-game-footage/game_footage_visualization_artifacts_comparison.json\",\n  policyPath: \"examples/nba-game-footage/visualization-drift-gate-policy.json\",\n});\nconst gameFootageVisualizationBaselineCheck = await checkGameFootageVisualizationArtifactBundleBaselineFromFiles({\n  baselinePath: gameFootageVisualizationArtifacts.artifactManifestPath,\n  currentPath: gameFootageVisualizationArtifacts.artifactManifestPath,\n  policyPath: \"examples/nba-game-footage/visualization-drift-gate-policy.json\",\n});\nconst gameFootageVisualizationBaselinePromotion = await promoteGameFootageVisualizationArtifactBundleBaselineFromFiles({\n  baselinePath: gameFootageVisualizationArtifacts.artifactManifestPath,\n  currentPath: gameFootageVisualizationArtifacts.artifactManifestPath,\n  policyPath: \"examples/nba-game-footage/visualization-drift-gate-policy.json\",\n  reason: \"Reviewed deterministic SVG/Markdown diffs.\",\n  reviewer: \"qa-reviewer\",\n  changeId: \"VIS-29\",\n});\nconst gameFootageVisualizationBaselinePromotionVerification = verifyGameFootageVisualizationArtifactBundleBaselinePromotion({\n  promotion: gameFootageVisualizationBaselinePromotion,\n  baselineBundle: gameFootageVisualizationArtifacts,\n  currentBundle: gameFootageVisualizationArtifacts,\n  policy: { schema: \"sports-impact-core/game-footage-visualization-artifact-bundle-drift-gate-policy/v1\", schemaVersion: 1 },\n  requireReviewer: true,\n  requireChangeId: true,\n});\nconst gameFootageVisualizationBaselineSuite = await checkGameFootageVisualizationArtifactBundleBaselineSuite({\n  manifest: {\n    schema: \"sports-impact-core/game-footage-visualization-artifact-bundle-baseline-suite-manifest/v1\",\n    schemaVersion: 1,\n    suiteId: \"demo-game-footage-visualization-baselines\",\n    entries: [\n      {\n        entryId: \"demo-baseline\",\n        baselinePath: gameFootageVisualizationArtifacts.artifactManifestPath,\n        currentPath: gameFootageVisualizationArtifacts.artifactManifestPath,\n        policyPath: \"examples/nba-game-footage/visualization-drift-gate-policy.json\",\n      },\n    ],\n  },\n});\nconst gameFootageVisualizationBaselinePromotionLedger = appendGameFootageVisualizationArtifactBundleBaselinePromotionLedgerEntry({\n  ledgerId: \"demo-game-footage-visualization-baseline-promotions\",\n  promotionVerification: gameFootageVisualizationBaselinePromotionVerification,\n  entryId: \"VIS-30\",\n  sourceCommit: \"example-source-commit\",\n  ciRunId: 1,\n});\nconst gameFootageVisualizationBaselinePromotionLedgerVerification = verifyGameFootageVisualizationArtifactBundleBaselinePromotionLedger({\n  ledger: gameFootageVisualizationBaselinePromotionLedger,\n  requireReviewer: true,\n  requireChangeId: true,\n  requireSourceCommit: true,\n  requireCiRunId: true,\n});\nconst gameFootageVisualizationBaselinePromotionLedgerRegistry = await verifyGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistry({\n  manifest: {\n    schema: \"sports-impact-core/game-footage-visualization-artifact-bundle-baseline-promotion-ledger-registry-manifest/v1\",\n    schemaVersion: 1,\n    registryId: \"demo-game-footage-visualization-baseline-promotion-ledger-registry\",\n    entries: [\n      {\n        entryId: \"demo-ledger\",\n        ledgerPath: \"runs/demo-game-footage/game_footage_visualization_baseline_promotion_ledger.json\",\n        requireReviewer: true,\n        requireChangeId: true,\n        requireSourceCommit: true,\n        requireCiRunId: true,\n      },\n    ],\n  },\n});\nconst gameFootageVisualizationBaselinePromotionLedgerRegistryGate = gateGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistry({\n  registry: gameFootageVisualizationBaselinePromotionLedgerRegistry,\n  policy: {\n    schema: \"sports-impact-core/game-footage-visualization-artifact-bundle-baseline-promotion-ledger-registry-gate-policy/v1\",\n    schemaVersion: 1,\n    requiredLedgerIds: [\"demo-game-footage-visualization-baseline-promotions\"],\n    minVerifiedLedgerEntryCount: 1,\n    maxWarnings: 0,\n  },\n});\nconst gameFootageVisualizationBaselinePromotionLedgerRegistryComparison = compareGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistries({\n  previous: gameFootageVisualizationBaselinePromotionLedgerRegistry,\n  current: gameFootageVisualizationBaselinePromotionLedgerRegistry,\n  previousGate: gameFootageVisualizationBaselinePromotionLedgerRegistryGate,\n  currentGate: gameFootageVisualizationBaselinePromotionLedgerRegistryGate,\n});\nconst gameFootageVisualizationBaselinePromotionLedgerRegistryComparisonGate = gateGameFootageVisualizationArtifactBundleBaselinePromotionLedgerRegistryComparison({\n  comparison: gameFootageVisualizationBaselinePromotionLedgerRegistryComparison,\n  policy: {\n    schema: \"sports-impact-core/game-footage-visualization-artifact-bundle-baseline-promotion-ledger-registry-comparison-gate-policy/v1\",\n    schemaVersion: 1,\n    maxAddedLedgerCount: 0,\n    maxRemovedLedgerCount: 0,\n    maxChangedLedgerCount: 0,\n    maxLatestEntryHashChangedCount: 0,\n    maxFailedCheckCountDelta: 0,\n    maxWarningCountDelta: 0,\n    minVerifiedEntryCountDelta: 0,\n  },\n});\nconst lifecycleTemplate = createGameFootageVisualizationBaselineLifecycleTemplate({\n  preset: \"strict-ci\",\n  packId: \"demo-game-footage-pack\",\n  baselineId: \"demo-baseline\",\n  registryId: \"demo-registry\",\n});\nconst lifecycleConformance = verifyGameFootageVisualizationBaselineLifecycleTemplateConformance({\n  template: lifecycleTemplate,\n  preset: \"strict-ci\",\n});\nconst lifecycleCiPlan = generateGameFootageVisualizationBaselineLifecycleCiPlan({\n  template: lifecycleTemplate,\n  rootDir: \"runs/demo-game-footage/lifecycle\",\n  format: \"github-actions-workflow\",\n});\nconst lifecycleCiAdoption = verifyGameFootageVisualizationBaselineLifecycleCiAdoption({\n  template: lifecycleTemplate,\n  adoptedYaml: lifecycleCiPlan.generatedYaml,\n  rootDir: \"runs/demo-game-footage/lifecycle\",\n  format: \"github-actions-workflow\",\n});\nconst lifecycleRunAudit = await auditGameFootageVisualizationBaselineLifecycleRun({\n  template: lifecycleTemplate,\n  rootDir: \"runs/demo-game-footage/lifecycle\",\n  preset: \"strict-ci\",\n});\nconst lifecycleTrend = compareGameFootageVisualizationBaselineLifecycleRuns({\n  audits: [lifecycleRunAudit, lifecycleRunAudit],\n  ciAdoptions: [lifecycleCiAdoption, lifecycleCiAdoption],\n  labels: [\"previous\", \"current\"],\n});\nconst lifecycleTrendGate = gateGameFootageVisualizationBaselineLifecycleTrend({ trend: lifecycleTrend });\nconst gameFootageFixturePackRerun = await verifyGameFootageFixturePackRerun({ report: gameFootageFixturePackAudit });\nconst result = simulateImpact(input);\n```\n\nThe library intentionally keeps projection math deterministic and inspectable. Use LLM/autocontext layers only for context gathering and narrative explanation.\n","readmeFilename":"README.md"}