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Fundamentals

Understanding Prediction Market Data Normalization

Learn how prediction market data normalization works across Polymarket, Kalshi, and Gemini, and why a unified schema saves developers hundreds of hours of integration work.

Propheseer Team
8 min read

Introduction

If you've ever tried to integrate data from multiple prediction market platforms, you know the pain: Polymarket calls it outcomePrices, Kalshi uses yes_bid and yes_ask, and Gemini has its own format entirely. Field names differ, probability representations vary, and even basic concepts like market status use inconsistent terminology.

Prediction market data normalization solves this by transforming raw platform data into a single, consistent schema. Instead of writing separate parsers for each platform, you work with one clean format — regardless of whether the data originated from Polymarket, Kalshi, or Gemini.

In this article, we'll show exactly how each platform structures its data, why the differences matter, and how Propheseer's normalized schema simplifies everything.

Why Data Normalization Matters

Without normalization, building a cross-platform application means:

  1. Three separate API clients — each with different authentication, pagination, and error handling
  2. Three different data models — mapping fields manually for every platform
  3. Inconsistent probability formats — some use decimals (0.65), others use percentages (65), and some use price-based representations
  4. Category mismatches — Polymarket tags a market as "Politics" while Kalshi calls the same topic "Election"
  5. Status inconsistencies — active vs open vs trading all mean the same thing

For a simple dashboard displaying markets from all three platforms, you'd write roughly 3x the code just to handle data format differences. For anything more complex — like arbitrage detection or trend analysis — the overhead grows exponentially.

How Each Platform Structures Data

Let's look at what raw data looks like from each platform.

Polymarket Raw Response

{
  "condition_id": "0x1234abcd...",
  "question_id": "0x5678efgh...",
  "title": "Will Bitcoin reach $150,000 by end of 2026?",
  "description": "This market resolves YES if...",
  "outcomes": ["Yes", "No"],
  "outcomePrices": ["0.42", "0.58"],
  "volume": "2450000",
  "active": true,
  "closed": false,
  "marketMakerAddress": "0xabcdef...",
  "category": "Crypto",
  "endDate": "2026-12-31T23:59:59Z"
}

Key quirks:

  • Prices are strings, not numbers
  • Volume is also a string
  • Status is split across active and closed boolean fields
  • Outcomes and prices are separate parallel arrays

Kalshi Raw Response

{
  "ticker": "BTC-150K-2026",
  "event_ticker": "BTC-2026",
  "title": "Bitcoin above $150,000 on December 31?",
  "subtitle": "Resolves based on CoinDesk BPI",
  "yes_bid": 41,
  "yes_ask": 43,
  "no_bid": 57,
  "no_ask": 59,
  "volume": 15230,
  "status": "active",
  "category": "Financial",
  "close_time": "2026-12-31T20:00:00Z",
  "result": null
}

Key quirks:

  • Prices are integers representing cents (41 = $0.41 = 41% probability)
  • Bid/ask spread instead of single price
  • Volume is in number of contracts, not USD
  • Category taxonomy differs from Polymarket

Gemini Raw Response

{
  "pair": "BTCPRED150K",
  "market_type": "binary",
  "description": "BTC $150K by EOY 2026",
  "yes_price": "0.420",
  "no_price": "0.580",
  "total_volume": "185000.50",
  "state": "trading",
  "expiry": "2026-12-31",
  "tags": ["bitcoin", "crypto", "price"]
}

Key quirks:

  • Uses pair instead of a descriptive title
  • Prices are decimal strings
  • Status is state with value trading (not open or active)
  • Tags instead of a single category

The Propheseer Unified Schema

Propheseer normalizes all three formats into a single, consistent schema. Here's the same Bitcoin market after normalization:

{
  "id": "pm_12345abc",
  "question": "Will Bitcoin reach $150,000 by end of 2026?",
  "description": "This market resolves YES if...",
  "source": "polymarket",
  "category": "crypto",
  "status": "open",
  "outcomes": [
    { "name": "Yes", "probability": 0.42 },
    { "name": "No", "probability": 0.58 }
  ],
  "volume": 2450000,
  "endDate": "2026-12-31T23:59:59Z",
  "url": "https://polymarket.com/markets?_q=Bitcoin%20150000",
  "lastUpdated": "2026-02-25T14:30:00Z"
}

Every market from every platform follows this exact structure. The same Kalshi market would look almost identical:

{
  "id": "ks_BTC150K2026",
  "question": "Bitcoin above $150,000 on December 31?",
  "source": "kalshi",
  "category": "crypto",
  "status": "open",
  "outcomes": [
    { "name": "Yes", "probability": 0.42 },
    { "name": "No", "probability": 0.58 }
  ],
  "volume": 1523000,
  "endDate": "2026-12-31T20:00:00Z",
  "url": "https://kalshi.com/events?search=Bitcoin%20150000"
}

The only differences are id prefix, source, question wording, and url — the structural format is identical.

How Normalization Works

Probability Normalization

The most critical transformation is converting platform-specific price formats into consistent probabilities:

PlatformRaw FormatExampleNormalized
PolymarketDecimal string"0.42"0.42
KalshiInteger cents420.42
GeminiDecimal string"0.420"0.42

For Kalshi, which provides bid/ask spreads, Propheseer uses the midpoint: (yes_bid + yes_ask) / 2 / 100. This gives you the most representative probability without needing to handle order book complexity.

Status Mapping

Each platform uses different terminology for market states:

Propheseer StatusPolymarketKalshiGemini
openactive: true, closed: falsestatus: "active"state: "trading"
closedactive: false, closed: truestatus: "closed"state: "closed"
resolvedresolved: truestatus: "settled"state: "settled"

Category Classification

Propheseer maps platform-specific categories to a unified taxonomy:

Propheseer CategoryPolymarketKalshiGemini
politics"Politics", "Elections""Political", "Election""politics"
crypto"Crypto", "Bitcoin""Financial" (crypto subset)"bitcoin", "crypto"
economics"Economics""Economic", "Fed""economy"
sports"Sports""Sports""sports"
science"Science", "Climate""Climate", "Weather""science"

ID Prefixing

Every market gets a prefixed ID that indicates its source:

  • pm_ — Polymarket
  • ks_ — Kalshi
  • gm_ — Gemini

This makes it trivial to identify a market's origin without parsing the source field, and prevents ID collisions between platforms.

Code Examples

Fetching Normalized Data

With normalization, your code works identically regardless of source:

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.propheseer.com/v1"
headers = {"Authorization": f"Bearer {API_KEY}"}

# Fetch markets from all platforms — same format
response = requests.get(f"{BASE_URL}/markets", headers=headers, params={
    "q": "bitcoin",
    "status": "open",
    "limit": 20,
})

markets = response.json()["data"]

# This loop works for Polymarket, Kalshi, AND Gemini markets
for market in markets:
    yes_prob = market["outcomes"][0]["probability"]
    print(f"[{market['source']:>10}] {market['question']}")
    print(f"             Yes: {yes_prob:.0%} | Volume: ${market['volume']:,.0f}")

Cross-Platform Comparison

Normalization makes cross-platform analysis straightforward:

from collections import defaultdict

# Group markets by question similarity
markets_by_topic = defaultdict(list)
for market in markets:
    topic = market["question"].lower()
    markets_by_topic[topic].append(market)

# Find markets listed on multiple platforms
for topic, group in markets_by_topic.items():
    sources = set(m["source"] for m in group)
    if len(sources) > 1:
        print(f"\nCross-platform: {group[0]['question']}")
        for m in group:
            prob = m["outcomes"][0]["probability"]
            print(f"  {m['source']}: {prob:.0%}")

Without normalization, you'd need separate parsing logic for each platform's price format before you could even compare probabilities.

Benefits of Normalized Data

1. Faster Development

Instead of building and maintaining three API clients, you build one. A typical integration takes hours instead of weeks.

2. Reliable Cross-Platform Analysis

When probabilities are in the same format, you can directly compare markets across platforms. This is essential for arbitrage detection — you can't find price discrepancies if the prices are in different formats.

3. Future-Proof Architecture

When new platforms launch (and they will), Propheseer adds them to the normalized schema. Your code works with new data sources without any changes.

4. Simplified Caching and Storage

One schema means one database table, one cache strategy, and one set of indexes. No need for platform-specific data models or mapping layers.

Getting Started

Ready to work with normalized prediction market data?

  1. Create a free account — 100 requests per day, no credit card required
  2. Follow the quick start guide — make your first request in 5 minutes
  3. Read the full API docs — explore every endpoint and parameter

For a deeper dive into how the three platforms compare beyond just data formats, see our Polymarket vs Kalshi comparison.

Start building with normalized data today. Get your free API key and access Polymarket, Kalshi, and Gemini through a single, consistent interface.

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