| name | nfl-fanduel-slate-prep |
| description | FanDuel NFL DFS slate prep for gambling-wiki W8 — read projection CSV exports (Stokastic/FantasyLabs), apply wiki GPP playbook, run pydfs lineup gen, write briefs/ slate card. Use when user says slate prep, FanDuel NFL, DFS lineups, Stokastic export, or Sunday NFL DFS. |
NFL FanDuel slate prep (W8)
Operator stack: FanDuel GPP (not Underdog BBM7, not Hard Rock book). Read wiki playbook before building.
Wiki pages (read first)
- @wiki/concepts/nfl-weekly-slate-hub-workflow.md — if hub brief exists, read
briefs/{season}-w{NN}-slate-hub.md first; do not re-research schedule/weather/injury
- @wiki/entities/platforms/fanduel.md — half-PPR, 4-player stack cap
- @wiki/sources/web-dfs-hero-nfl-gpp-strategy-2026-06-20.md — game stacks, MME pools
- @wiki/entities/tools/stokastic-dfs.md — CSV export workflow (primary paid tool)
- @wiki/entities/tools/fantasylabs-dfs.md — alternate paid + CSV export
- @wiki/entities/tools/pydfs-lineup-optimizer.md — FOSS lineup engine
Inputs
- Projection CSV in
research to be indexed/ from Stokastic spreadsheet export or FantasyLabs model export
- Optional: slate date, contest name, entry count (default 150 MME), core locks/excludes from user
Workflow
1. Normalize CSV (if from paid export)
python3 scripts/normalize_dfs_projection_csv.py \
--in "research to be indexed/<export>.csv" \
--out "research to be indexed/fanduel-nfl-pydfs-YYYY-MM-DD.csv" \
--source stokastic
If normalize fails, inspect headers — ensure Team column exists in paid export. Output must match FanDuel pydfs columns (see @entities/tools/pydfs-lineup-optimizer.md).
2. Generate lineups
pip install pydfs-lineup-optimizer
python3 scripts/fanduel_slate_optimize.py \
--csv "research to be indexed/fanduel-nfl-pydfs-YYYY-MM-DD.csv" \
--count 150 \
--max-exposure 0.35 \
--stack qb:2 \
--out "briefs/fanduel-lineups-YYYY-MM-DD.csv"
Tune --count and --stack per slate size. FanDuel upload expects player names matching site IDs — verify a sample lineup in FD upload UI before mass entry.
3. Write slate brief (briefs/YYYY-MM-DD_fanduel-nfl-slate.md)
Include:
| Section | Content |
|---|
| Slate environment | Top 3 game stacks (Vegas totals, both QBs) |
| Chalk RBs | High ownership backs to accept |
| Leverage WR/TE | Low-owned pass-catchers with stack fit |
| Core locks | User/core plays in all or most lineups |
| Exposure notes | Max exposure settings used |
| Upload file | Path to briefs/fanduel-lineups-*.csv |
Apply K124 rules: 3 game environments × 2 QBs for MME; RB in flex on half-PPR; avoid TE flex; leave ≤$500 salary unused.
4. Do not
- Scrape Stokastic/FantasyLabs (ToS)
- Commit paid projection CSVs to public git (briefs/ and research folders are gitignored)
- Conflate with BBM7 Underdog draft advice
Paid tool without CSV
If user has Stokastic/Labs UI only: produce slate brief + stack plan from wiki + injury news; skip pydfs step until export available.
Integration limits
No MCP server exists for any major DFS optimizer. Integration = CSV drop + scripts above.
