How to Scrape Polymarket Live Odds & Events in 2026

Polymarket has become the de-facto real-time barometer for elections, politics, sports and crypto outcomes — a market where the price is the probability. That makes its live odds enormously valuable to dashboards, forecasting tools and trading bots, but it also means the data moves fast and lives across several feeds: market prices and volumes, live sports game state, AI-generated betting takes, and the Twitter/X chatter that moves markets. This guide covers what a Polymarket events and odds pipeline should capture, how the data is structured, and how to pull a continuously fresh, multi-feed snapshot. (If you’re after who the winning traders are rather than what the markets say, see the companion guide on the wallet leaderboard scraper — that’s a different job.)

What’s worth extracting

This scraper produces four pluggable feeds, each emitting structured rows, all oriented around market telemetry and context — not trader identities:

• Events & markets — every active event with its markets, outcomes and current prices (= implied probabilities), market and series metadata, and event rankings.
• Time-windowed volume — volume aggregated over multiple lookback windows (5m / 1h / 6h / 24h), so you can see momentum, not just a single total.
• Liquidity & depth — liquidity figures and bid/ask spreads per market, the difference between a thin market and a confident one.
• Live sports — for sports markets, real-time game state: teams, live score, period/clock and game status, paired with the odds.
• AI betting analysis — an AI-generated recommendation and rationale per event, with contextual evidence and a sentiment read.
• Twitter/X signals — a curated feed of tweets tied to markets, each with a significance score and the list of impacted events plus their implied probabilities — a tweet → market-impact map.

The point isn’t one number per market; it’s the price plus the context around it — momentum, depth, the AI take and the social catalyst — all in one run.

Prediction-market data, not on-chain scraping

Polymarket settles on-chain, but you don’t need to index the blockchain to get tradable market data. This actor ingests structured APIs (from future.news) that already aggregate the market state, sports feeds, AI analyses and curated tweets, and reshapes them into clean rows. That has real consequences for how it behaves:

• API-based, not browser-based. It calls POST/GET endpoints and parses JSON. No headless Chrome, no DOM scraping of the Polymarket front-end.
• Four feeds, one run. Events, sports/live-odds, curated tweets, and batch AI analyses are independent feeds you can enable together. Event identifiers propagate automatically into the batch AI analysis so the takes line up with the right markets.
• Built for refresh cadence. Paginated Twitter cursoring, proxy and concurrency controls, and retry/backoff for transient errors mean it’s designed to run every 1–5 minutes as a live feed, not as a one-off dump.
• Row expansion is configurable. You can emit per-market or per-outcome rows depending on whether you want one row per event or one row per tradable outcome.

How the feeds fit together

A typical run looks like this: pull the active events and their markets with current prices and time-windowed volumes; pull the live sports feed and join game state to the relevant markets; run batch AI analysis over the events to attach a recommendation and rationale to each; and page through the curated Twitter feed, scoring each tweet’s significance and mapping it to the events it moves. The output is a coherent snapshot where a single election or game can be seen from four angles at once — price, momentum, AI take and social signal.

▶ Run the Polymarket Events, Odds & AI Scraper — every active event with live odds, 5m/1h/24h volume, liquidity, spreads, live sports scores, AI analysis and a tweet → market impact map. Schedule it for a 1–5 minute live feed.

Schema design for downstream use

When the data lands in your dashboard or model, you want it shaped for time-series and joins. A clean per-market row:

{
  "feed": "events",
  "event_id": "us-presidential-election-2028",
  "market_id": "will-candidate-x-win",
  "title": "Will Candidate X win the 2028 election?",
  "outcome": "Yes",
  "price": 0.41,
  "spread": 0.01,
  "liquidity_usd": 1842000,
  "volume_5m": 12400,
  "volume_1h": 98200,
  "volume_6h": 540300,
  "volume_24h": 2110500,
  "series": "elections",
  "scraped_at": "2026-05-26T12:00:00Z"
}

And a Twitter-signal row, linking social activity to markets:

{
  "feed": "twitter",
  "tweet_id": "1924...",
  "author": "@somehandle",
  "significance": 0.86,
  "category": "politics",
  "impacted_events": [
    { "event_id": "us-presidential-election-2028", "implied_prob": 0.41 }
  ],
  "scraped_at": "2026-05-26T12:00:00Z"
}

A few schema choices worth making early:

• Treat price as a probability. A 0.41 price is a 41% implied chance. Store it as a float in [0,1] and don’t confuse it with a dollar amount.
• Keep all the volume windows. The whole point of 5m/1h/6h/24h is to read momentum. Collapsing them to a single 24h total throws away the signal.
• Tag every row with its feed. Events, sports, twitter and AI rows have different shapes; the feed tag keeps your loaders honest.
• Persist scraped_at on every row. Odds move minute to minute; a quote without a timestamp is meaningless for backtesting or charting.

Typical use cases

What customers actually do with this multi-feed data:

• Live odds dashboards — refresh every 1–5 minutes to show real-time prediction-market probabilities.
• Election forecasting — monitor political events and watch the implied probabilities move with the news cycle.
• Sports betting analytics — combine live odds with live scores and period/clock to model in-game value.
• AI trading bots — feed structured AI recommendations plus current prices into an automated workflow.
• Market-impact analysis — correlate the scored Twitter signals with subsequent price moves to find which accounts actually move markets.
• Auto-generated previews and newsletters — turn events plus AI analysis into nightly digests or pre-game previews.
• Research datasets — build labeled corpora linking events, tweets and AI takes for ML.

The common thread is freshness plus context. A stale odds snapshot is useless; a live feed that also carries momentum, the AI take and the social catalyst is sellable infrastructure.

Cost math for the managed approach

This actor charges a small per-result fee on top of the per-run start cost, which fits the live-feed pattern: a live odds dashboard refreshing every five minutes is roughly 288 runs a day, each emitting a focused set of event/market rows rather than a giant dump. You enable only the feeds you need — events-only runs are cheaper than runs that also pull sports, AI and Twitter. Because it’s API-based with no browser, compute per run is light and there’s no residential-proxy bandwidth bill for page rendering.

What you avoid by using a managed actor rather than building your own:

• Integrating and maintaining four separate upstream feeds
• Joining event IDs across the AI-analysis and Twitter-signal feeds
• Twitter cursor pagination and significance scoring
• Retry/backoff tuning for a feed you intend to poll every few minutes

Common pitfalls

A few things to know before wiring Polymarket odds into production, whether you build or buy:

• Prices are probabilities, and they’re noisy. Thin markets can show jumpy prices on tiny volume. Always read liquidity and spread alongside price before trusting a move.
• Volume windows must be read together. A spike in 5m volume against flat 24h volume is a fresh catalyst; the opposite is a fading story. One window in isolation misleads.
• AI analysis is a signal, not an oracle. The AI rationale is useful context and a feature for your own models — not a trade instruction. Treat it as one input.
• Twitter significance is a heuristic. A high significance score flags a candidate catalyst; confirm the actual price move before acting on it.
• Resolved vs. active events. Make sure you’re filtering to active markets when you want tradable odds; resolved events still appear in historical pulls.

Wrapping up

Polymarket’s value is in its live, multi-faceted view of real-world outcomes — and capturing that means more than one price feed. If you only need an occasional snapshot, you can stitch the upstream APIs yourself. If you need a continuously fresh feed that joins odds, momentum, live scores, AI takes and social signals into coherent rows, a managed actor delivers it on a 1–5 minute cadence without you maintaining four integrations. And if your question is about traders rather than markets, reach for the wallet-leaderboard scraper instead.

▶ Open the Polymarket Events & Odds Scraper on Apify — live odds, time-windowed volume, liquidity, sports scores, AI analysis and tweet impact mapping. API-based, schedulable, pay per result. Start with Apify’s free monthly credit.