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DFS Multi-Source Player Props Scraper — Cross-Platform Lines

Crawloop DFS Player Props Suite — Real-time player props, over/under lines, and odds from US daily fantasy sports (DFS) pick'em platforms. Built for odds-comparison tools, sports analytics pipelines, and research dashboards.

Suite hub: github.com/PLCSPS-DEV/dfs-player-props

Product site: crawloop.com/sports-data

Disclaimer: These are unofficial integrations developed independently of PrizePicks, Betr, DraftKings, Underdog Fantasy, or any of their subsidiaries. They are not affiliated with, sponsored by, or endorsed by those companies.

Platform names are trademarks of their respective owners. Data is read from publicly accessible web sources only.

This Actor is provided for informational and research purposes only. It does not provide betting advice. You are solely responsible for ensuring your use complies with applicable laws, platform terms of use, and your organization's policies.

No warranty is given as to accuracy, completeness, or continued availability of third-party data. Use at your own risk.

One Apify run → PrizePicks, Betr Picks, DraftKings Pick6, and Underdog Fantasy in a single normalized dataset with shared schema, comparison_key for cross-platform joins, and an optional comparisons dataset with side-by-side line spreads.

Need only one platform? Use the cheaper single-source scrapers linked above.

When to use this Actor

Use the Multi-Source Board when you need cross-platform line shopping, gap analysis, or a unified props feed for a dashboard — not when monitoring a single book at minimum cost.

For PrizePicks-only demon/goblin tiers or Underdog-only odds metadata, use the dedicated scrapers. This Actor reuses the same proven collectors as the standalone actors with merge, enrichment, and comparison logic on top.

DFS Props Pipeline

Phase 1 — Single-platform boards Phase 2 — Cross-platform compare
──────────────────────────────── ─────────────────────────────────
 PrizePicks Scraper ───────────────────┐
 Betr Picks Scraper ───────────────────┤
 Pick6 Scraper ───────────────────┼──► Multi-Source Board ◄── you are here
 Underdog Scraper ───────────────────┘ │
 ▼
 comparisons dataset
 line_spread, source_count

Key Features

• Four platforms — prizepicks, betr-picks, draftkings-pick6, underdog.
• Unified schema — every row includes source, player_name, line, market, comparison_key.
• Comparison dataset — optional output with line_prizepicks, line_betr, line_pick6, line_underdog, line_spread.
• Matchup enrichment — Underdog props inherit matchup context from PrizePicks/Betr when the board omits teams.
• Partial success — if one source fails, successful sources still land in the dataset.
• Proven collectors — same Betr/Pick6/PrizePicks/Underdog logic as standalone actors.

Input Parameters

Input Example

{
"sources":["prizepicks","betr-picks","draftkings-pick6","underdog"],
"leagues":["MLB"],
"outputComparisons":true,
"minComparisonSources":2,
"maxProjectionsPerSource":10000,
"collectionMode":"parallel",
"proxyConfiguration":{
"useApifyProxy":true,
"apifyProxyGroups":["RESIDENTIAL"],
"apifyProxyCountry":"US"
}
}

Output Format — Props Dataset

{
"source":"betr-picks",
"player_name":"Juan Soto",
"market":"player_total_bases",
"market_display":"Total Bases",
"line":1.5,
"side":"over",
"league":"MLB",
"event_name":"NYM @ PHI",
"comparison_key":"mlb|juansoto|player_total_bases|nym@phi",
"collected_at":"2026-06-21T12:00:00+00:00"
}

Output Format — Comparisons Dataset

When outputComparisons is true, a second dataset named comparisons is populated:

{
"comparison_key":"mlb|juansoto|player_total_bases|nym@phi",
"player_name":"Juan Soto",
"market":"player_total_bases",
"market_display":"Total Bases",
"matchup":"nym@phi",
"source_count":3,
"sources":["betr-picks","prizepicks","underdog"],
"line_prizepicks":4.5,
"line_betr":1.5,
"line_underdog":1.5,
"line_pick6":null,
"line_min":1.5,
"line_max":4.5,
"line_spread":3.0,
"collected_at":"2026-06-21T12:00:00+00:00"
}

OUTPUT_SUMMARY

Each run stores a key-value summary with per-source status, timing, and cross-platform metrics:

Typical Workflow

1. Full slate snapshot

Run all four sources with leagues: ["MLB"] and outputComparisons: true.

2. Filter wide spreads

Export comparisons dataset; filter where line_spread >= 1.0 and source_count >= 3.

3. Schedule during game windows

Trigger via Apify scheduler or API every 5–15 minutes. Pull dataset via webhook or apify-client.

Actor Comparison

Limitations

• Slate coverage varies — Pick6 may feature a different slate than PrizePicks/Betr.
• Underdog is slowest — CloakBrowser adds 15–30s; use collectionMode: "sequential" to reduce peak memory.
• Matchup enrichment — inferred from player/league index when a source omits team metadata.
• Sleeper — not supported (account-gated).

API Integration

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("crawloop/dfs-multi-source-props-scraper").call(
 run_input={
"sources":["prizepicks","betr-picks","underdog"],
"leagues":["NBA"],
"outputComparisons":True,
}
)
props = client.dataset(run["defaultDatasetId"]).iterate_items()
comparisons = client.dataset(run["defaultKeyValueStoreId"])# use comparisons dataset ID from run meta

Learn more: Suite hub · GitHub docs

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