{"id":2483,"date":"2026-03-10T16:12:44","date_gmt":"2026-03-10T16:12:44","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=2483"},"modified":"2026-03-10T16:12:44","modified_gmt":"2026-03-10T16:12:44","slug":"prediction-market-dashboard-python-streamlit","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/prediction-market-dashboard-python-streamlit\/","title":{"rendered":"Prediction Market Dashboard: Build a Live Tracker with Python &#038; Streamlit"},"content":{"rendered":"<p>Polymarket shows one price. DraftKings shows another. Pinnacle \u2014 the sharpest book in the world \u2014 shows a third. Which one is right?<\/p>\n<p>If you&#8217;re trading prediction markets or sharp betting, you already know the answer: <strong>all of them matter<\/strong>. The edge lives in the gap between what a prediction market thinks and what the sharp sportsbooks think. The problem is seeing that gap in real time without tabbing between five different sites.<\/p>\n<p>In this tutorial, you&#8217;ll build a <strong>live prediction market dashboard<\/strong> using Python and Streamlit that pulls odds from Polymarket, Kalshi, Pinnacle, Bet365, and DraftKings \u2014 all from a single API call.<\/p>\n<h2>Why a Unified Dashboard Matters<\/h2>\n<p>Prediction markets like Polymarket are crowd-driven. Sportsbooks like Pinnacle are set by professional traders with skin in the game. When these two sources disagree on the probability of an outcome, <strong>one of them is wrong<\/strong> \u2014 and that&#8217;s where the opportunity lives.<\/p>\n<p>But right now, getting a unified view requires:<\/p>\n<ul>\n<li>Polymarket: REST API + blockchain wallet + CLOB client<\/li>\n<li>Kalshi: Separate REST API, separate auth, US-only<\/li>\n<li>Pinnacle: Closed API, enterprise-only access<\/li>\n<li>Bet365 \/ DraftKings: No public API at all<\/li>\n<\/ul>\n<p>That&#8217;s 4 different integrations with 4 different auth methods. Or you can use <strong>one API key from OddsPapi<\/strong> and get all of them in a single JSON response.<\/p>\n<h2>Prediction Market Data: Old Way vs OddsPapi<\/h2>\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Feature<\/th>\n<th>DIY (3+ APIs)<\/th>\n<th>OddsPapi (1 API)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Polymarket odds<\/td>\n<td>py-clob-client + wallet setup<\/td>\n<td>\u2705 Included (slug: polymarket)<\/td>\n<\/tr>\n<tr>\n<td>Kalshi odds<\/td>\n<td>Separate API, US KYC required<\/td>\n<td>\u2705 Included (slug: kalshi)<\/td>\n<\/tr>\n<tr>\n<td>Pinnacle odds<\/td>\n<td>\u274c Closed to public<\/td>\n<td>\u2705 Included (slug: pinnacle)<\/td>\n<\/tr>\n<tr>\n<td>Bet365 \/ DraftKings<\/td>\n<td>\u274c No public API<\/td>\n<td>\u2705 Included (350+ books)<\/td>\n<\/tr>\n<tr>\n<td>Authentication<\/td>\n<td>4 different methods<\/td>\n<td>1 API key, query parameter<\/td>\n<\/tr>\n<tr>\n<td>Historical data<\/td>\n<td>Polymarket on-chain (slow), others paid<\/td>\n<td>\u2705 Free on all tiers<\/td>\n<\/tr>\n<tr>\n<td>Format<\/td>\n<td>Different JSON schemas per source<\/td>\n<td>Unified JSON, consistent structure<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<h2>What We&#8217;re Building<\/h2>\n<p>A Streamlit dashboard that:<\/p>\n<ul>\n<li>Fetches upcoming fixtures (soccer, basketball) from OddsPapi<\/li>\n<li>Pulls odds from Polymarket, Pinnacle, Bet365, and DraftKings in one call<\/li>\n<li>Converts decimal odds to implied probabilities<\/li>\n<li>Displays grouped bar charts comparing all sources<\/li>\n<li>Flags edges where Polymarket diverges from Pinnacle&#8217;s sharp line<\/li>\n<li>Auto-refreshes every 60 seconds<\/li>\n<\/ul>\n<h2>Step 1: Install Dependencies<\/h2>\n<pre class=\"wp-block-code\"><code>pip install streamlit plotly requests<\/code><\/pre>\n<p>That&#8217;s it. No blockchain wallet, no CLOB client, no OAuth flows.<\/p>\n<h2>Step 2: Fetch Prediction Market Fixtures<\/h2>\n<p>OddsPapi doesn&#8217;t separate prediction markets into their own sport \u2014 Polymarket and Kalshi are integrated as <strong>bookmakers<\/strong> alongside traditional sportsbooks. This means you fetch fixtures the same way you would for any sport, and the odds response includes prediction market prices right next to Pinnacle and Bet365.<\/p>\n<pre class=\"wp-block-code\"><code>import requests\r\n\r\nAPI_KEY = \"YOUR_API_KEY\"\r\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\r\n\r\n# Fetch upcoming Champions League fixtures\r\nparams = {\r\n    \"apiKey\": API_KEY,\r\n    \"sportId\": 10,          # Soccer\r\n    \"tournamentId\": 7,      # Champions League\r\n    \"limit\": 20\r\n}\r\nfixtures = requests.get(f\"{BASE}\/fixtures\", params=params).json()\r\n\r\nfor fx in fixtures[:5]:\r\n    print(f\"{fx['participant1Name']} vs {fx['participant2Name']} \u2014 {fx['startTime'][:10]}\")<\/code><\/pre>\n<p>Output:<\/p>\n<pre class=\"wp-block-code\"><code>Galatasaray vs Liverpool \u2014 2025-03-11\r\nAtletico Madrid vs Tottenham \u2014 2025-03-11\r\nNewcastle vs Barcelona \u2014 2025-03-12\r\nAtalanta vs Bayern Munich \u2014 2025-03-12\r\nReal Madrid vs Manchester City \u2014 2025-03-12<\/code><\/pre>\n<h2>Step 3: Pull Odds from All Sources<\/h2>\n<p>One API call returns odds from every bookmaker that covers the fixture \u2014 including prediction markets.<\/p>\n<pre class=\"wp-block-code\"><code># Get odds for a fixture\r\nfixture_id = fixtures[0][\"fixtureId\"]\r\nodds = requests.get(f\"{BASE}\/odds\", params={\"apiKey\": API_KEY, \"fixtureId\": fixture_id}).json()\r\n\r\nbookmaker_odds = odds[\"bookmakerOdds\"]\r\nprint(f\"Bookmakers covering this fixture: {len(bookmaker_odds)}\")\r\n\r\n# Check which prediction markets are present\r\nfor slug in [\"polymarket\", \"kalshi\", \"pinnacle\", \"bet365\", \"draftkings\"]:\r\n    if slug in bookmaker_odds:\r\n        print(f\"  \u2705 {slug}\")\r\n    else:\r\n        print(f\"  \u274c {slug}\")<\/code><\/pre>\n<p>A typical Champions League fixture returns <strong>100+ bookmakers<\/strong> including both prediction markets and traditional sportsbooks.<\/p>\n<h2>Step 4: Extract and Compare Implied Probabilities<\/h2>\n<p>The key insight: decimal odds convert to implied probabilities with <code>1 \/ price * 100<\/code>. When Polymarket says 2.00 on an outcome, that&#8217;s a 50% implied probability. When Pinnacle says 1.80, that&#8217;s 55.6%. The 5.6 percentage point gap is your edge signal.<\/p>\n<pre class=\"wp-block-code\"><code># Market 101 = Full Time Result (1X2) for soccer\r\n# Outcomes: 101 = Home, 102 = Draw, 103 = Away\r\nMARKET_ID = \"101\"\r\nOUTCOMES = {\"101\": \"Home\", \"102\": \"Draw\", \"103\": \"Away\"}\r\n\r\ndef get_implied_probs(bookmaker_odds, slug, market_id, outcomes):\r\n    \"\"\"Extract implied probabilities for a bookmaker.\"\"\"\r\n    if slug not in bookmaker_odds:\r\n        return {}\r\n    market = bookmaker_odds[slug].get(\"markets\", {}).get(market_id, {})\r\n    probs = {}\r\n    for oid, label in outcomes.items():\r\n        price = (market.get(\"outcomes\", {}).get(oid, {})\r\n                 .get(\"players\", {}).get(\"0\", {}).get(\"price\"))\r\n        if price and price &gt; 0:\r\n            probs[label] = round(1 \/ price * 100, 1)\r\n    return probs\r\n\r\n# Compare across sources\r\nfor slug in [\"polymarket\", \"pinnacle\", \"draftkings\"]:\r\n    probs = get_implied_probs(bookmaker_odds, slug, MARKET_ID, OUTCOMES)\r\n    print(f\"\\n{slug.title()}:\")\r\n    for label, prob in probs.items():\r\n        print(f\"  {label}: {prob}%\")<\/code><\/pre>\n<p>Example output for Galatasaray vs Liverpool:<\/p>\n<pre class=\"wp-block-code\"><code>Polymarket:\r\n  Home: 31.5%\r\n  Draw: 19.5%\r\n  Away: 56.0%\r\n\r\nPinnacle:\r\n  Home: 21.7%\r\n  Draw: 24.0%\r\n  Away: 57.6%\r\n\r\nDraftkings:\r\n  Home: 22.2%\r\n  Draw: 24.4%\r\n  Away: 58.8%<\/code><\/pre>\n<p>Notice Polymarket has Galatasaray at 31.5% while Pinnacle&#8217;s sharp line says 21.7%. That&#8217;s a <strong>9.8 percentage point divergence<\/strong> \u2014 exactly the kind of signal a trader wants to see.<\/p>\n<h2>Step 5: Build the Streamlit Dashboard<\/h2>\n<p>Here&#8217;s the complete dashboard. Save this as <code>dashboard.py<\/code>:<\/p>\n<pre class=\"wp-block-code\"><code>import time, requests, streamlit as st, plotly.graph_objects as go\r\n\r\nAPI_KEY = \"YOUR_API_KEY\"\r\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\r\nBOOKS = [\"polymarket\", \"pinnacle\", \"bet365\", \"draftkings\"]\r\nCOLORS = {\"polymarket\": \"#6366f1\", \"pinnacle\": \"#f59e0b\",\r\n          \"bet365\": \"#10b981\", \"draftkings\": \"#ef4444\"}\r\nEDGE_THRESHOLD = 5.0  # percentage-point divergence to flag\r\n\r\nSPORT_CFG = {\r\n    \"Soccer \u2014 Champions League\": {\r\n        \"sportId\": 10, \"tournamentId\": 7,\r\n        \"marketId\": \"101\",\r\n        \"outcomes\": {\"101\": \"Home\", \"102\": \"Draw\", \"103\": \"Away\"}\r\n    },\r\n    \"Basketball \u2014 NBA\": {\r\n        \"sportId\": 11, \"tournamentId\": 132,\r\n        \"marketId\": \"111\",\r\n        \"outcomes\": {\"111\": \"Home\", \"112\": \"Away\"}\r\n    },\r\n}\r\n\r\n\r\n@st.cache_data(ttl=120)\r\ndef fetch_fixtures(sport_id, tournament_id):\r\n    params = {\"apiKey\": API_KEY, \"sportId\": sport_id,\r\n              \"tournamentId\": tournament_id, \"limit\": 300}\r\n    r = requests.get(f\"{BASE}\/fixtures\", params=params, timeout=15)\r\n    r.raise_for_status()\r\n    now = time.strftime(\"%Y-%m-%dT%H:%M:%S\")\r\n    return [f for f in r.json() if f.get(\"startTime\", \"\") &gt;= now][:20]\r\n\r\n\r\n@st.cache_data(ttl=60)\r\ndef fetch_odds(fixture_id):\r\n    r = requests.get(f\"{BASE}\/odds\",\r\n                     params={\"apiKey\": API_KEY, \"fixtureId\": fixture_id},\r\n                     timeout=15)\r\n    r.raise_for_status()\r\n    return r.json().get(\"bookmakerOdds\", {})\r\n\r\n\r\ndef implied(price):\r\n    return round(1 \/ price * 100, 1) if price and price &gt; 0 else None\r\n\r\n\r\ndef extract_probs(bo, market_id, outcomes):\r\n    rows = {}\r\n    for slug in BOOKS:\r\n        if slug not in bo:\r\n            continue\r\n        market = bo[slug].get(\"markets\", {}).get(market_id, {})\r\n        outs = market.get(\"outcomes\", {})\r\n        probs = {}\r\n        for oid, label in outcomes.items():\r\n            price = (outs.get(oid, {}).get(\"players\", {})\r\n                     .get(\"0\", {}).get(\"price\"))\r\n            p = implied(price)\r\n            if p:\r\n                probs[label] = p\r\n        if probs:\r\n            rows[slug] = probs\r\n    return rows\r\n\r\n\r\ndef detect_edges(probs):\r\n    poly = probs.get(\"polymarket\", {})\r\n    pin = probs.get(\"pinnacle\", {})\r\n    return {lbl: poly[lbl] - pin[lbl]\r\n            for lbl in poly if lbl in pin\r\n            and abs(poly[lbl] - pin[lbl]) &gt;= EDGE_THRESHOLD}\r\n\r\n\r\n# \u2500\u2500 UI \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\nst.set_page_config(page_title=\"Prediction Market Dashboard\", layout=\"wide\")\r\nst.title(\"Prediction Market vs Sportsbook Odds\")\r\nst.caption(\"Powered by OddsPapi API\")\r\n\r\nsport = st.selectbox(\"Sport\", list(SPORT_CFG.keys()))\r\ncfg = SPORT_CFG[sport]\r\nauto = st.toggle(\"Auto-refresh (60s)\", value=False)\r\n\r\nfixtures = fetch_fixtures(cfg[\"sportId\"], cfg[\"tournamentId\"])\r\nif not fixtures:\r\n    st.warning(\"No upcoming fixtures found.\")\r\n    st.stop()\r\n\r\nfor fx in fixtures[:8]:\r\n    bo = fetch_odds(fx[\"fixtureId\"])\r\n    if \"polymarket\" not in bo:\r\n        continue\r\n    probs = extract_probs(bo, cfg[\"marketId\"], cfg[\"outcomes\"])\r\n    if len(probs) &lt; 2:\r\n        continue\r\n    edges = detect_edges(probs)\r\n    labels = list(cfg[\"outcomes\"].values())\r\n    name = f\"{fx['participant1Name']} vs {fx['participant2Name']}\"\r\n\r\n    fig = go.Figure()\r\n    for slug in BOOKS:\r\n        if slug not in probs:\r\n            continue\r\n        vals = [probs[slug].get(l, 0) for l in labels]\r\n        fig.add_trace(go.Bar(\r\n            name=slug.title(), x=labels, y=vals,\r\n            marker_color=COLORS[slug],\r\n            text=[f\"{v}%\" for v in vals], textposition=\"outside\"))\r\n    for lbl, diff in edges.items():\r\n        tag = \"POLY HIGHER\" if diff &gt; 0 else \"POLY LOWER\"\r\n        fig.add_annotation(\r\n            x=lbl,\r\n            y=max(probs[\"polymarket\"].get(lbl, 0),\r\n                  probs[\"pinnacle\"].get(lbl, 0)) + 6,\r\n            text=f\"{abs(diff):.1f}pp {tag}\", showarrow=False,\r\n            font=dict(color=\"#ef4444\", size=12))\r\n    fig.update_layout(\r\n        barmode=\"group\", title=f\"{name}  ({fx['startTime'][:16]})\",\r\n        yaxis_title=\"Implied Probability (%)\",\r\n        template=\"plotly_dark\", height=420)\r\n    st.plotly_chart(fig, use_container_width=True)\r\n    for lbl, diff in edges.items():\r\n        side = \"overestimates\" if diff &gt; 0 else \"underestimates\"\r\n        st.info(f\"Edge: Polymarket {side} {lbl} by \"\r\n                f\"{abs(diff):.1f}pp vs Pinnacle sharp line.\")\r\n\r\nif auto:\r\n    time.sleep(60)\r\n    st.rerun()<\/code><\/pre>\n<h2>Step 6: Run the Dashboard<\/h2>\n<pre class=\"wp-block-code\"><code>streamlit run dashboard.py<\/code><\/pre>\n<p>Your browser opens to a live dashboard comparing Polymarket against Pinnacle, Bet365, and DraftKings. Select Soccer or Basketball, and the charts update with implied probabilities from every source. Red annotations flag any outcome where Polymarket diverges from Pinnacle&#8217;s sharp line by more than 5 percentage points.<\/p>\n<h2>How to Read the Edge Signals<\/h2>\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Signal<\/th>\n<th>What It Means<\/th>\n<th>Possible Action<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>POLY HIGHER (+5pp+)<\/td>\n<td>Polymarket overprices this outcome vs sharp books<\/td>\n<td>Fade on Polymarket, or back the opposite on sportsbook<\/td>\n<\/tr>\n<tr>\n<td>POLY LOWER (-5pp+)<\/td>\n<td>Polymarket underprices this outcome vs sharp books<\/td>\n<td>Buy on Polymarket, or bet the same side on sportsbook<\/td>\n<\/tr>\n<tr>\n<td>No flag<\/td>\n<td>Consensus \u2014 sources agree within 5pp<\/td>\n<td>No clear edge, wait for movement<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>The threshold is configurable. Set <code>EDGE_THRESHOLD = 3.0<\/code> for more signals, or <code>10.0<\/code> for only the most extreme divergences.<\/p>\n<h2>Why Prediction Markets Diverge from Sportsbooks<\/h2>\n<p>You&#8217;ll notice Polymarket prices often differ from Pinnacle by 5-15 percentage points. This isn&#8217;t noise \u2014 prediction markets and sportsbooks have fundamentally different pricing mechanisms:<\/p>\n<ul>\n<li><strong>Sportsbooks (Pinnacle):<\/strong> Prices set by professional traders managing risk. The vig is baked in. Lines move on sharp action.<\/li>\n<li><strong>Prediction markets (Polymarket):<\/strong> Prices set by crowd order flow. Thinner liquidity means bigger swings. Retail sentiment drives prices more than fundamentals.<\/li>\n<\/ul>\n<p>This structural difference is <em>why<\/em> the dashboard works. It&#8217;s not comparing the same thing twice \u2014 it&#8217;s comparing professional pricing against crowd pricing. When they disagree, someone has better information.<\/p>\n<h2>Extending the Dashboard<\/h2>\n<p>This is a starting point. Some ideas for upgrades:<\/p>\n<ul>\n<li><strong>Add Kalshi:<\/strong> Already in the API response (slug: <code>kalshi<\/code>). Just add it to the <code>BOOKS<\/code> list.<\/li>\n<li><strong>More markets:<\/strong> Switch from Full Time Result (market 101) to Over\/Under 2.5 (market 1010) or Both Teams to Score (market 104). Polymarket covers 8+ markets on soccer.<\/li>\n<li><strong>Historical tracking:<\/strong> OddsPapi includes free historical data on all tiers. Fetch closing prices to backtest whether Polymarket or Pinnacle was more accurate (see our <a href=\"https:\/\/oddspapi.io\/blog\/polymarket-api-kalshi-api-vs-sportsbooks-the-developers-guide\/\">Polymarket API guide<\/a> for more).<\/li>\n<li><strong>WebSocket upgrade:<\/strong> Replace polling with <a href=\"https:\/\/oddspapi.io\/blog\/websocket-odds-api-real-time-betting-data\/\">OddsPapi&#8217;s WebSocket API<\/a> for sub-second updates.<\/li>\n<\/ul>\n<h2>Why OddsPapi for Prediction Market Data<\/h2>\n<p>Building this dashboard without OddsPapi means integrating Polymarket&#8217;s CLOB, Kalshi&#8217;s REST API, and somehow getting Pinnacle data (their API is closed to the public). That&#8217;s three different auth systems, three different JSON schemas, and zero access to the sharpest bookmaker on earth.<\/p>\n<p>With OddsPapi, it&#8217;s one API key and one JSON response containing <strong>350+ bookmakers<\/strong> \u2014 including every prediction market exchange and every sharp book. The free tier includes historical data for backtesting, and you can upgrade to <a href=\"https:\/\/oddspapi.io\/blog\/websocket-odds-api-real-time-betting-data\/\">WebSockets<\/a> when you need real-time feeds.<\/p>\n<h2>Stop Tabbing Between Polymarket and DraftKings<\/h2>\n<p>You just built a prediction market tracker in under 80 lines of Python. Every divergence between Polymarket and Pinnacle is now visible on one screen \u2014 no blockchain wallet, no enterprise API access, no scraping.<\/p>\n<p><strong><a href=\"https:\/\/oddspapi.io\/en\/register\">Get your free API key<\/a><\/strong> and run this dashboard yourself. The code works out of the box \u2014 just replace <code>YOUR_API_KEY<\/code> and run <code>streamlit run dashboard.py<\/code>.<\/p>\n<p><!-- Focus Keyphrase: prediction market dashboard SEO Title: Prediction Market Dashboard: Build a Live Tracker with Python & Streamlit Meta Description: Build a Polymarket vs sportsbook dashboard with Python and Streamlit. Compare prediction market odds against Pinnacle, Bet365 and 350+ bookmakers in real time. Slug: prediction-market-dashboard-python-streamlit --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Build a Polymarket vs sportsbook dashboard with Python and Streamlit. Compare prediction market odds against Pinnacle, Bet365 and 350+ bookmakers in real time.<\/p>\n","protected":false},"author":2,"featured_media":2485,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[8,9,14,11],"class_list":["post-2483","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-how-to-guides","tag-free-api","tag-odds-api","tag-polymarket","tag-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Prediction Market Dashboard: Build a Live Tracker with Python &amp; Streamlit | Odds API Development Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/oddspapi.io\/blog\/prediction-market-dashboard-python-streamlit\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prediction Market Dashboard: Build a Live Tracker with Python &amp; Streamlit | Odds API Development Blog\" \/>\n<meta property=\"og:description\" content=\"Build a Polymarket vs sportsbook dashboard with Python and Streamlit. 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