Polymarket Sports Bots: NFL, NBA, Soccer Microstructure

Polymarket Bot Tutorial · Sura ya 15 kati ya 32

Sports microstructure bots kwenye Polymarket: in-game edge, scoreline-driven mispricing, NBA tag (745) na Tennis tag (864), live data sources, na execution patterns kwa high-frequency sports markets.

Na Harley Young, mwandishi mkuu wa Polymarkets.co.il. Marekebisho ya mwisho: Mei 2026.

Sura hii inafunika nini

Sports markets ni most consistently active non-political segment kwenye Polymarket. Bots zinazofanya kazi zinaaanguka katika buckets safi mbili: pre-game line-catchers wanaotrade mara line inapowekwa, na in-game microstructure bots wanaoitikia kwenye order-book movement wakati wa play. Sura hii inafunika zote mbili na specific tag IDs, data sources, na latency budgets zinazoapply kwa kila moja.

• Kwa nini sports markets ni tradeable
• Pre-game vs in-game (bots tofauti)
• Verified tag IDs (745 NBA, 864 Tennis)
• Data sources: ESPN, official APIs, on-screen
• Latency budget kwa in-game
• 0.99 / 0.01 trap
• Code: jisajili kwa games book na uitikie

Kwa nini sports markets ni tradeable

Sports markets zinaclear katika defined timeframes (masaa hadi siku), zina public live data, na zinavutia continuous order flow wakati wa games. Zote tatu ni necessary kwa tradeable market - political markets zinakosa "defined timeframe," weather markets zinakosa "continuous flow," obscure tournaments zinakosa "public live data."

Trader population kwenye sports markets pia ni diverse zaidi kuliko, mfano, election markets. Casual sports bettors wanaprice emotionally; informed traders wanacorrect kuelekea fair value wakati wa game. Gap kati ya wawili ni bot edge.

Volume distribution ni uneven: NFL Sunday itarotate mamilioni ya dollars kwenye Polymarket sports markets; Tuesday-night Saudi Pro League fixture inaweza kufanya chini ya $50k. Size strategy yako mahali action ipo kweli.

Pre-game vs in-game (bots tofauti)

Bot designs mbili fundamentally tofauti.

Pre-game line-catcher: scan markets zilizofungua, identify mis-priced lines dhidi ya model yako au dhidi ya sharper venue's number, weka FOK buy. Shikilia hadi in-play, wakati mwingine hadi resolution. Speed: dakika-sio-sekunde. Edge: model + line-shopping.

In-game microstructure: jisajili kwa live game's order book WebSocket, itikia kwa imbalance signals + score events ndani ya sekunde. Speed: sekunde-sio-dakika. Edge: latency + kusoma order flow.

Wawili wanashare karibu hakuna code. Wana risk profiles tofauti, data sources tofauti, exit strategies tofauti. Bot inayojaribu kufanya zote mbili inamalizia kufanya hakuna vizuri; chagua moja.

Verified tag IDs (745 NBA, 864 Tennis)

Production tag IDs verified Mei 2026 kwa major sports categories. Tumia hizi kuchuja /events calls efficiently.

Tag IDs ni stable kwa miaka. Tags mpya zinaongezwa (Saudi Pro League, IPL) lakini old tags hazirenumberediwi.

Data sources: ESPN, official APIs, on-screen

Kwa traditional sports free ESPN scoreboard API inafunika kila kitu unachohitaji: scores, period/clock, win-probability, wakati mwingine shot location. Hakuna key inayohitajika; rate-limited tu kwa IP level. Endpoint pattern: https://site.api.espn.com/apis/site/v2/sports/<sport>/<league>/scoreboard.

Kwa esports, ESPN haina coverage. Options: PandaScore ($30-60/mo, industry standard), HLTV (CS2-only, scrapeable, hakuna API), Liquipedia (community-maintained, scrapeable, slower update cadence).

On-screen feeds (kulipa kwa TV stream na OCR-reading scorebug) zinafanya kazi lakini ni operationally heavy. Imependekezwa tu ikiwa una strategy inayohitaji sub-3-second updates kwenye sport ambayo hakuna API inafunika kwa real time.

Latency budget kwa in-game

End-to-end latency budget kwa in-game reactive bot.

• Score event inatokea: t=0
• Source feed inareflect: t+3-15s (ESPN: ~10s; PandaScore: ~3s)
• Bot yako inasoma feed: t+10-16s
• Bot inaamua action: +50ms
• FOK order imewekwa: +200-500ms
• Matched kwenye CLOB: +300-1000ms (network + matching)

Jumla: sekunde 11-17. Fastest professional firms wanaachieve sekunde 3-5 end-to-end na paid premium feeds na co-located VPS. Retail bots zinazoendesha kwenye standard hosts na free ESPN ziko katika slower end.

Strategies zinazohitaji sub-5s sio viable kwa retail. Strategies zinazofanya kazi katika 10-17s window ni: line-catching baada ya score, fading overreactions, late-game certainty plays.

0.99 / 0.01 trap

In-play sports bot failure ya kawaida zaidi: kununua heavy favorite kwa 0.99 na dakika moja iliyobaki, ukitarajia easy +1¢. Sababu tatu kwa nini inashindwa.

Kwanza, 1% implied probability ya underdog sio sifuri - late comebacks zinatokea kwa non-trivial frequency. 99.5% certain win, iliyochezwa mara 200, inazalisha loss moja kwa full position size.

Pili, spread kwenye 0.99/0.01 inamaanisha unalipa 99c per share, kushinda 1c kwenye success, kupoteza 99c kwenye rare reversal. Risk-reward ni brutal.

Tatu, bot inayotumia GTC sell kwa 0.999 itafill rarely - hakuna buyers kwa price hiyo. Position inaridi hadi resolution. Ikiwa inashinda, ulipata 1c. Ikiwa reversal inatokea, unapoteza 99c.

Trap ni real money iliyopotezwa na builders ambao hawakurun math. Kaa nje ya markets zilizopriced 0.95+ isipokuwa strategy yako imejengwa specifically kwa redemption-arbitrage profile.

Code: jisajili kwa games book na uitikie

Reference: jisajili kwa specific NBA game WebSocket, log book updates, fire FOK kwenye imbalance signal.

import websocket, json
THRESHOLD = 0.5  # imbalance level to trigger

def on_message(ws, message):
    msg = json.loads(message)
    if msg.get("event_type") != "book": return
    bids = msg.get("bids", [])
    asks = msg.get("asks", [])
    bid_depth = sum(float(b["price"]) * float(b["size"]) for b in bids[:5])
    ask_depth = sum(float(a["price"]) * float(a["size"]) for a in asks[:5])
    total = bid_depth + ask_depth
    if total < 100: return  # too illiquid
    imb = (bid_depth - ask_depth) / total
    if abs(imb) > THRESHOLD:
        print(f"signal imb={imb:.2f} bid={bid_depth:.0f} ask={ask_depth:.0f}")
        # fire FOK here

ws = websocket.WebSocketApp(
    "wss://ws-subscriptions-clob.polymarket.com/ws/market",
    on_open=lambda ws: ws.send(json.dumps({"type":"Market","markets":["<CONDITION_ID>"]})),
    on_message=on_message
)
ws.run_forever()

Production additions: cooldown kati ya fires, per-token inventory cap, kill kwenye stale book (hakuna message katika sekunde 30).

Maswali yanayoulizwa mara kwa mara