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Regression Incoming — 2026 NFL Fantasy Half-PPR Draft Board (Test)
x402
Regression Incoming — 2026 NFL Fantasy Half-PPR Draft Board (Test) is a paid API for AI agents from api.regressionincoming.fyi, paid per call via x402, $0.1/call, status unknown (last checked 2026-09-13).
Returns model-driven 2026 NFL fantasy half-PPR draft board rankings and player data, including confidence scores and injury-return signals, for a specific player by nflreadpy player ID.
Price / call$0.1
Success—
StatusUnknown
Output
Returns model-driven 2026 NFL fantasy draft board data for the requested player in half-PPR format, including ranking position, projected value, confidence score, and injury-return signals derived from 99 model features across 412 ranked players.
Example use cases
Build optimal fantasy draft strategy
“I'm doing my 2026 fantasy draft tomorrow and I want to compare three running backs I'm eyeing. Can you pull their half-PPR rankings, confidence scores, and any injury signals for me? The nflreadpy IDs are 00-0045123, 00-0045124, and 00-0045125. I need to know who the model trusts most.”
Validate injury recovery impact on ranking
“Christian McCaffrey was dealing with injuries last season — I want to see how that's factored into his 2026 draft position. Pull his model ranking and injury-return signals using nflreadpy ID 00-0038571. How much does the model think he'll bounce back?”
Scout late-round sleeper opportunities
“Help me find some underrated players in rounds 8-12 for my league. Can you check the confidence scores and projected values for these three depth guys? I have their nflreadpy IDs: 00-0047892, 00-0047893, and 00-0047894. I'm looking for high confidence scores on players the model likes but others are sleeping on.”
Technical details
Endpoint
GEThttps://api.regressionincoming.fyi/v1/draft-board/half-ppr/testWhen to prefer
Use this endpoint when you need model-driven, feature-rich 2026 NFL preseason fantasy rankings in half-PPR format for a specific player, especially when confidence scores and injury-return context are important. Prefer this over generic sports stats APIs when you need a curated fantasy model (99 features, 412 players) rather than raw stats.
Side effects
- Invalid or missing player_id returns an error or empty result
- Player not among the 412 modeled players returns no data
- Malformed nflreadpy player ID format causes a bad request
- Test endpoint may return incomplete or placeholder data compared to production
- Payment not sent or insufficient USDC results in 402 payment required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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