Odds Channel (/ws/odds)

The /ws/odds channel is the raw firehose. It emits every price_change from every book we track, plus everything the /ws/live channel emits (score_change, status_change, game_final, new_game). This is the channel for arbitrage, alerting, and real-time market analysis.

wss://books.polynode.dev/ws/odds?key=pn_live_YOUR_KEY

Event types on this channel

Event `type`	When it fires
`welcome`	Once, on connect. First frame.
`snapshot`	Once, right after welcome. Game-state baseline only — no market snapshots.
`price_change`	Any outcome’s price moves on any book
`score_change`	Same as `/ws/live`
`status_change`	Same as `/ws/live`
`game_final`	Same as `/ws/live`
`new_game`	Same as `/ws/live`

The snapshot frame on /ws/odds contains the same game-state baseline as /ws/live — the currently-live games and their scores/clocks. It does not contain market snapshots. For initial market state, call the REST endpoint GET /v1/games/{id}/markets for each game you care about, then stream price_change deltas from there.

Real captured sample

This is the actual wire content from wss://books.polynode.dev/ws/odds during a live match where Estudiantes went up 2-1 (Copa Libertadores, 2H 64’):

{"type":"welcome","channel":"odds","conn_id":2,"message":"connected to books-relayer odds channel"}
{"type":"score_change","game_id":"10035-20332-2026-04-14","pn_slug":null,"pn_league_code":"lib","score_home":1,"score_away":0,"period":"2H","clock":"64","ts":1776208974186}
{"type":"score_change","game_id":"40664-16839-2026-04-14","pn_slug":"lib-gar-est-2026-04-14","pn_league_code":"lib","score_home":2,"score_away":1,"period":"2H","clock":"64","ts":1776208974186}
{"type":"price_change","game_id":"40664-16839-2026-04-14","pn_slug":"lib-gar-est-2026-04-14","pn_league_code":"lib","pn_market_type":"correct_score","book":"888sport","outcome":"Club Estudiantes de La Plata 2:1","old_price":275.0,"new_price":230.0,"points":null,"ts":1776208974186}
{"type":"price_change","game_id":"40664-16839-2026-04-14","pn_slug":"lib-gar-est-2026-04-14","pn_league_code":"lib","pn_market_type":"correct_score","book":"BetRivers","outcome":"Club Estudiantes de La Plata 2:1","old_price":275.0,"new_price":240.0,"points":null,"ts":1776208974186}
{"type":"price_change","game_id":"40664-16839-2026-04-14","pn_slug":"lib-gar-est-2026-04-14","pn_league_code":"lib","pn_market_type":"correct_score","book":"Betano","outcome":"Club Estudiantes de La Plata 2:1","old_price":260.0,"new_price":235.0,"points":null,"ts":1776208974186}

Notice the pattern: Estudiantes scores, their 2-1 correct-score outcome is now more likely, and we see 888sport, BetRivers, and Betano all shortening the price simultaneously (from +275/+260 to +230-240). That’s the sort of signal this channel exists to surface.

Volume expectations

During a mid-afternoon window with 5-10 games live, sustained rate is typically 100-500 price_change events per second, with bursts over 1,000/s immediately after goals or scoring plays. Plan your client-side processing accordingly.

Memory: each event is ~400 bytes JSON, so 500/s = ~200 KB/s ≈ 1.6 Mbit/s per subscriber.
CPU: JSON parsing + any per-event logic. On a modern laptop, ~5,000 events/sec is easy; beyond that you want to batch or pre-filter.
Storage: 500 events/sec × 86,400 seconds/day × 400 bytes ≈ 17 GB/day if you log everything. Filter aggressively or sample.

Filtering

Server-side filtering is not yet implemented in v1. Filter client-side on whichever of these fields you care about:

pn_league_code (e.g., only NBA → keep nba)
pn_market_type (e.g., only moneyline → keep moneyline)
book (e.g., only Polymarket vs DraftKings → keep ["Polymarket", "DraftKings"])
pn_slug or game_id (e.g., only a single game)

A proper subscription-filter layer is on the roadmap for v1.1.

Example: arbitrage detector

import asyncio, json, websockets
from collections import defaultdict

KEY = "pn_live_YOUR_KEY"
WATCH_BOOKS = {"Polymarket", "DraftKings", "FanDuel", "BetMGM", "bet365"}

# (game_id, market_type, outcome_name) -> {book: price}
book_prices = defaultdict(dict)

def american_to_decimal(odds):
    return 1 + (odds / 100 if odds > 0 else 100 / -odds)

async def main():
    uri = f"wss://books.polynode.dev/ws/odds?key={KEY}"
    async with websockets.connect(uri) as ws:
        async for raw in ws:
            e = json.loads(raw)
            if e.get("type") != "price_change":
                continue
            if e["book"] not in WATCH_BOOKS:
                continue
            key = (e["game_id"], e["pn_market_type"], e["outcome"])
            book_prices[key][e["book"]] = e["new_price"]

            # Simple arb check: best decimal odds across books
            prices = book_prices[key]
            if len(prices) >= 2:
                best = max(prices.items(), key=lambda x: american_to_decimal(x[1]))
                worst = min(prices.items(), key=lambda x: american_to_decimal(x[1]))
                spread = american_to_decimal(best[1]) - american_to_decimal(worst[1])
                if spread > 0.10:
                    print(f"SPREAD {e['pn_slug'] or e['game_id']} {e['pn_market_type']} {e['outcome']}: "
                          f"{best[0]}@{best[1]} vs {worst[0]}@{worst[1]} (Δ={spread:.3f})")

asyncio.run(main())

Ordering guarantees

Within a single poll cycle, score_change events are emitted before price_change events for the same game. Clients can rely on seeing the new score before seeing the price movements that respond to it.
Across poll cycles, events are delivered in emission order per connection.
If your consumer drops (channel full, 256 events queued), events are silently dropped, not buffered indefinitely. Detect gaps by tracking the ts field.

Real-time Streaming

Event Reference

Perps Streaming

More

Odds Channel (/ws/odds)

Event types on this channel

Real captured sample

Volume expectations

Filtering

Example: arbitrage detector

Ordering guarantees

See also

​Event types on this channel

​Real captured sample

​Volume expectations

​Filtering

​Example: arbitrage detector

​Ordering guarantees

​See also

Event types on this channel

Real captured sample

Volume expectations

Filtering

Example: arbitrage detector

Ordering guarantees

See also