ballparkpal.com APIwww.ballparkpal.com ↗
Access MLB simulation-based predictions from Ballpark Pal. Get probability scores, betting lines, and outcomes across 22 categories for batters, pitchers, teams, and games.
curl -X GET 'https://api.parse.bot/scraper/2947b4d9-1c07-4bc2-b79b-0b14a6508e9b/get_most_likely?tab=batters&limit=5&category=HR' \ -H 'X-API-Key: $PARSE_API_KEY'
Get most likely MLB outcomes for a given date, optionally filtered by tab (batters/pitchers/teams/games) and/or specific category. Returns player/team names, probabilities, betting prices, matchup details, and more.
| Param | Type | Description |
|---|---|---|
| tab | string | Filter by tab: 'batters', 'pitchers', 'teams', or 'games'. |
| date | string | Date in YYYY-MM-DD format. Defaults to today's date if omitted. |
| limit | integer | Maximum number of results to return. |
| category | string | Filter by category ID (1-22) or partial name match (e.g. 'HR', 'Quality Start'). Use get_categories endpoint for the full list of IDs and names. |
{
"type": "object",
"fields": {
"date": "string - Display date (e.g. 'May 14, 2026')",
"items": "array of outcome objects with category_id, category_name, team, time, game_id, name, entity_id, park, venue_id, score, opponent, vs_rating, pitcher, pitcher_id, probability, book_price",
"total": "integer - Number of items returned",
"last_updated": "string - Last update time (e.g. '5:03 AM')"
},
"sample": {
"data": {
"date": "May 14, 2026",
"items": [
{
"name": "Nick Kurtz",
"park": "ATH",
"team": "ATH",
"time": null,
"score": null,
"game_id": "825008",
"pitcher": "McGreevy",
"opponent": "STL",
"venue_id": "2529",
"entity_id": "701762",
"vs_rating": "10",
"book_price": "+269",
"pitcher_id": "700241",
"category_id": "1",
"probability": "27.1%",
"category_name": "Hit a HR"
}
],
"total": 5,
"last_updated": "5:03 AM"
},
"status": "success"
}
}About the ballparkpal.com API
The Ballpark Pal API exposes MLB simulation-based prediction data across 22 statistical categories through 3 endpoints. The core get_most_likely endpoint returns player and team outcome objects including probability scores, betting prices, matchup details, park, and opponent — filterable by tab (batters, pitchers, teams, games), date, and category. Two supporting endpoints let you enumerate available categories and navigate valid dates programmatically.
What the API covers
Ballpark Pal publishes simulation-driven MLB predictions for each game day. The get_most_likely endpoint is the primary data surface: it returns an array of outcome objects for a given date, each carrying fields like category_name, name (player or team), score (probability), team, opponent, park, venue_id, game_id, entity_id, and time. Results span four tabs — batters, pitchers, teams, and games — and cover 22 distinct statistical categories including home runs, quality starts, and similar performance metrics.
Filtering and navigation
You can narrow get_most_likely results with four optional parameters: tab restricts results to one of the four groupings, category accepts either a numeric ID (1–22) or a partial name string such as 'HR' or 'Quality Start', date takes a YYYY-MM-DD string and defaults to today when omitted, and limit caps the result count. The get_categories endpoint returns the authoritative list of all 22 categories with their id, name, and tab assignment — useful for building dynamic filters without hardcoding IDs. The get_available_dates endpoint returns current_date, previous_date, and next_date alongside a last_updated timestamp, giving you a reliable way to page through available prediction windows.
Data freshness and scope
Each response from get_most_likely includes a last_updated field (e.g., '5:03 AM') indicating when the simulation output was last refreshed for that date. The total field tells you exactly how many items matched your filter, and date returns a human-readable display string alongside whatever query date you supplied. Coverage is MLB only; no minor-league or international leagues are included in the current endpoint set.
- Build a daily prop-bet tracker that surfaces the highest-probability outcomes using the
scorefield across all 22 categories - Filter by
tab=battersandcategory=HRto pull home run probability rankings for a given slate of games - Automate date-range pulls using
get_available_datesto backfill simulation predictions across multiple game days - Compare
scorevalues acrosstab=pitchersoutcomes to rank starting pitcher performance expectations - Display venue-specific prediction tendencies by grouping results on
parkorvenue_idfromget_most_likelyresponses - Cross-reference
game_idandopponentfields to join prediction data with live box score feeds - Enumerate all 22 categories via
get_categoriesto dynamically populate a category picker in a fantasy baseball dashboard
| Tier | Price | Credits/month | Rate limit |
|---|---|---|---|
| Free | $0/mo | 100 | 5 req/min |
| Hobby | $30/mo | 1,000 | 20 req/min |
| Developer | $100/mo | 5,000 | 250 req/min |
One credit = one API call regardless of which marketplace API you call. Exceeding the rate limit returns a 429 response. Authenticate with the X-API-Key header.
Does Ballpark Pal have an official developer API?+
What does the `score` field in a `get_most_likely` response represent?+
score field is a probability value associated with the predicted outcome for that player or team in the given category. Higher scores indicate the simulation rates that outcome as more likely for the specified date and matchup.Does the API cover historical prediction data beyond just today and adjacent dates?+
get_available_dates endpoint surfaces the previous and next navigable dates relative to a given date, and get_most_likely accepts any YYYY-MM-DD date string. How far back the underlying data extends depends on what Ballpark Pal retains on their site; there is no guaranteed multi-season archive exposed through these endpoints. You can fork the API on Parse and revise it to add pagination or extended date-range logic if you need to systematically walk a longer history.Are betting odds or lines returned alongside probabilities?+
get_most_likely includes betting price fields in addition to the score probability, so you can compare the simulation's implied probability against available market lines directly from the same response.