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Prediction Market data is currently in beta. It is actively being worked on and improved, but may be unreliable. Polymarket data is live, and Kalshi data will be added soon.At least for the time being, this endpoint requires a Growth or Enterprise plan. Learn more.

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Returns

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Query example

Get all categories with subcategories 
{
  predictionCategories {
    name
    slug
    subcategories {
      name
      slug
      subcategories {
        name
        slug
      }
    }
  }
}
Example response (truncated)
{
  "data": {
    "predictionCategories": [
      {
        "name": "Politics",
        "slug": "politics",
        "subcategories": [
          {
            "name": "Trump",
            "slug": "trump",
            "subcategories": []
          },
          {
            "name": "Biden",
            "slug": "biden",
            "subcategories": []
          },
          {
            "name": "Congress",
            "slug": "congress",
            "subcategories": []
          }
          // ... more political subcategories
        ]
      },
      {
        "name": "Sports",
        "slug": "sports",
        "subcategories": [
          {
            "name": "Basketball",
            "slug": "basketball",
            "subcategories": [
              {
                "name": "NBA",
                "slug": "nba"
              },
              {
                "name": "EuroLeague",
                "slug": "euroleague"
              },
              {
                "name": "NCAA Basketball",
                "slug": "ncaa-basketball"
              }
            ]
          },
          {
            "name": "American Football",
            "slug": "american-football",
            "subcategories": [
              {
                "name": "NFL",
                "slug": "nfl"
              },
              {
                "name": "NCAA Football",
                "slug": "ncaa-football"
              }
            ]
          },
          {
            "name": "Soccer",
            "slug": "soccer",
            "subcategories": [
              {
                "name": "Premier League",
                "slug": "premier-league"
              },
              {
                "name": "La Liga",
                "slug": "la-liga"
              }
            ]
          }
          // ... more sports subcategories
        ]
      },
      {
        "name": "Crypto",
        "slug": "crypto",
        "subcategories": [
          {
            "name": "Bitcoin",
            "slug": "bitcoin",
            "subcategories": []
          },
          {
            "name": "Ethereum",
            "slug": "ethereum",
            "subcategories": []
          },
          {
            "name": "Solana",
            "slug": "solana",
            "subcategories": []
          }
        ]
      }
      // ... 10 more top-level categories
    ]
  }
}

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Usage Guidelines

  • Categories form a 3-level hierarchy: top-level categories contain subcategories, which may contain sub-subcategories
  • Each category has a name (display name) and slug (URL-friendly identifier)
  • Use the slug values to filter markets in filterPredictionMarkets (e.g., filters: { categories: ["sports", "nba"] })
  • Category matching in filters is case-insensitive
  • Categories are dynamically generated based on active markets across all protocols

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Understanding the Category Hierarchy

The category structure has up to 3 levels of nesting:
  1. Top-level categories (e.g., “Politics”, “Sports”, “Crypto”) - Broad topic areas
  2. Subcategories (e.g., “Basketball”, “White House”) - More specific topics within the top-level category
  3. Sub-subcategories (e.g., “NBA”, “Donald Trump”) - Highly specific topics
Use the slug field when filtering markets by category. The name field is the human-readable display name.

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Common Categories

Top-level categories include:
  • Politics - Elections, White House, Congress, Approval Ratings
  • Sports - Basketball (NBA), Football (NFL), Soccer, Baseball, MMA, Tennis
  • Crypto - Bitcoin, Ethereum, DeFi, NFTs
  • Finance - Stocks, Earnings, Markets
  • Economy - Fed decisions, Inflation, GDP
  • Tech - AI, Companies, Products
  • Culture - Entertainment, Media, Awards
  • World - Geopolitics, International events
  • Climate & Science - Weather, Research, Environment

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Troubleshooting Tips

Use the categories array in filterPredictionMarkets. You can specify one or more categories at any level. For example, categories: ["sports", "nba"] returns markets tagged with either Sports or NBA. Category matching is case-insensitive and hierarchical.
Categories are sometimes included in the hierarchy for organizational purposes even if they currently have no active markets. Additionally, all markets in that subcategory may have closed or been resolved. To check if a category has active markets, use filterPredictionMarkets with that category slug and check the count.
Yes, use the phrase parameter in filterPredictionMarkets along with the categories filter. The phrase searches market questions and event labels, while categories filter by assigned tags. Combining both gives precise results.
Categories are updated dynamically as new markets are created and tagged. When markets close or resolve, the market counts decrease accordingly. The category structure reflects the current state of all active prediction markets.
Categories are broad topic tags (e.g., “Politics”, “NBA”) used for filtering and organization. Event labels are specific market groupings (e.g., “Fed decision in March?”) that represent a single real-world event. An event can have multiple category tags.
Yes, markets can be tagged with multiple categories at different hierarchy levels. For example, a market about “Lakers vs Celtics” might be tagged with “Sports”, “Basketball”, and “NBA”. Use filterPredictionMarkets with multiple category filters to find markets at the intersection.