{"id":2555,"date":"2026-04-02T10:00:00","date_gmt":"2026-04-02T10:00:00","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=2555"},"modified":"2026-03-25T14:03:28","modified_gmt":"2026-03-25T14:03:28","slug":"value-betting-scanner-python","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/value-betting-scanner-python\/","title":{"rendered":"Build a Value Betting Scanner with Python (Free Odds API)"},"content":{"rendered":"
What If You Could Scan 350+ Bookmakers for Value Bets in Seconds?<\/h2>\n
Value betting is the simplest profitable strategy in sports betting: find odds that are higher than the “true” probability of an outcome. The problem? Manually comparing odds across bookmakers is slow and error-prone. By the time you find value, the line has moved.<\/p>\n
This tutorial builds a Python value betting scanner<\/strong> that uses Pinnacle’s closing line as the “true price” benchmark and scans soft bookmakers for mispriced odds \u2014 automatically. Every line of code is tested against the OddsPapi API with real data.<\/p>\n
What Is Value Betting? (The 60-Second Version)<\/h2>\n
A bet has positive expected value (+EV)<\/strong> when the odds offered are higher than the true probability of the outcome. The question is: how do you know the “true” probability?<\/p>\n
Professional bettors use Pinnacle’s closing line<\/strong> as the benchmark. Pinnacle accepts the highest limits from sharps, so their closing odds reflect the most efficient price in the market. If a soft bookmaker (Bet365, DraftKings, 1xBet) offers higher odds than Pinnacle on the same outcome, that’s value.<\/p>\n
\n
\n\n
\n
Concept<\/th>\n
Example<\/th>\n<\/tr>\n<\/thead>\n
\n
\n
Pinnacle price<\/strong><\/td>\n
Arsenal to win: 1.85 (implied prob: 54.1%)<\/td>\n<\/tr>\n
\n
Soft book price<\/strong><\/td>\n
1xBet Arsenal to win: 1.90 (implied prob: 52.6%)<\/td>\n<\/tr>\n
+EV bet \u2014 the soft book is offering more than the true price<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
A 2-3% edge per bet doesn’t sound like much. But over 1,000 bets, it compounds. That’s how sharps make money \u2014 volume + edge, not lucky parlays.<\/p>\n
Why Pinnacle? The Sharp Benchmark<\/h2>\n
Not all bookmakers are created equal. Pinnacle is the sharpest book in the world because:<\/p>\n
\n
Lowest margins:<\/strong> 2-3% vig vs 5-8% at soft books<\/li>\n
Highest limits:<\/strong> They don’t limit or ban winning players<\/li>\n
Market-driven pricing:<\/strong> Their odds reflect the collective wisdom of the sharpest bettors<\/li>\n<\/ul>\n
Academic research consistently shows that Pinnacle’s closing line is the single best predictor of match outcomes. If you’re not benchmarking against Pinnacle, you’re not value betting \u2014 you’re guessing.<\/p>\n
The catch? Pinnacle doesn’t have a public API. OddsPapi aggregates Pinnacle odds alongside 350+ other bookmakers through a single API \u2014 including sharps like Singbet and SBOBet that are even harder to access directly.<\/p>\n
import requests\nimport time\nfrom datetime import datetime\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\n# Configuration\nSHARP_BOOK = \"pinnacle\" # Benchmark bookmaker\nSOFT_BOOKS = [\"bet365\", \"1xbet\", \"draftkings\", \"fanduel\", \"betmgm\"]\nMIN_EDGE = 2.0 # Minimum edge % to flag as value\nMIN_ODDS = 1.30 # Ignore heavy favourites\nMAX_ODDS = 10.0 # Ignore extreme longshots\nSPORT_ID = 10 # Soccer\nMARKET_ID = 101 # Full Time Result (1X2)\n\ndef api_get(endpoint, params=None):\n \"\"\"Make an authenticated API call.\"\"\"\n if params is None:\n params = {}\n params[\"apiKey\"] = API_KEY\n response = requests.get(f\"{BASE_URL}\/{endpoint}\", params=params)\n response.raise_for_status()\n return response.json()<\/code><\/pre>\n
Step 2: Fetch Upcoming Fixtures<\/h3>\n
def get_fixtures(sport_id, tournament_id=None):\n \"\"\"Fetch upcoming fixtures with odds available.\"\"\"\n params = {\"sportId\": sport_id, \"hasOdds\": True, \"per_page\": 50}\n if tournament_id:\n params[\"tournamentId\"] = tournament_id\n else:\n # When using sportId alone, date range is required (max 10 days)\n from datetime import timedelta\n today = datetime.utcnow().strftime(\"%Y-%m-%d\")\n end = (datetime.utcnow() + timedelta(days=7)).strftime(\"%Y-%m-%d\")\n params[\"from\"] = today\n params[\"to\"] = end\n\n fixtures = api_get(\"fixtures\", params)\n\n # Filter to upcoming only\n now = datetime.utcnow()\n upcoming = [\n f for f in fixtures\n if datetime.fromisoformat(f[\"startTime\"].replace(\"Z\", \"\")) > now\n ]\n\n return upcoming\n\n# Example: Premier League fixtures\nfixtures = get_fixtures(sport_id=10, tournament_id=17)\nprint(f\"Found {len(fixtures)} upcoming fixtures with odds\")\nfor f in fixtures[:5]:\n print(f\" {f['participant1Name']} vs {f['participant2Name']} \u2014 {f['startTime'][:10]}\")<\/code><\/pre>\n
Step 3: Compare Sharp vs Soft Odds<\/h3>\n
This is the core engine. For each fixture, we fetch odds from Pinnacle and compare against each soft bookmaker. The API allows up to 3 bookmakers per request, so we batch them.<\/p>\n
OUTCOME_MAP = {\"101\": \"Home\", \"102\": \"Draw\", \"103\": \"Away\"}\n\ndef get_odds_comparison(fixture_id, sharp=SHARP_BOOK, softs=SOFT_BOOKS):\n \"\"\"\n Fetch odds from sharp + soft bookmakers and calculate edge.\n Returns list of value opportunities.\n \"\"\"\n # Batch soft books (max 3 bookmakers per request)\n all_odds = {}\n\n # First request: sharp book + first 2 soft books\n batch1 = [sharp] + softs[:2]\n data = api_get(\"odds\", {\n \"fixtureId\": fixture_id,\n \"bookmakers\": \",\".join(batch1),\n \"marketId\": MARKET_ID\n })\n bookmaker_odds = data.get(\"bookmakerOdds\", {})\n all_odds.update(bookmaker_odds)\n\n # Additional requests for remaining soft books (3 at a time)\n for i in range(2, len(softs), 3):\n batch = softs[i:i+3]\n time.sleep(1)\n data = api_get(\"odds\", {\n \"fixtureId\": fixture_id,\n \"bookmakers\": \",\".join(batch),\n \"marketId\": MARKET_ID\n })\n all_odds.update(data.get(\"bookmakerOdds\", {}))\n\n # Get Pinnacle prices as benchmark\n if sharp not in all_odds:\n return []\n\n sharp_prices = {}\n sharp_markets = all_odds[sharp].get(\"markets\", {}).get(str(MARKET_ID), {})\n for outcome_id, label in OUTCOME_MAP.items():\n player = sharp_markets.get(\"outcomes\", {}).get(outcome_id, {}).get(\"players\", {}).get(\"0\", {})\n if isinstance(player, dict) and \"price\" in player:\n sharp_prices[outcome_id] = player[\"price\"]\n\n if not sharp_prices:\n return []\n\n # Compare each soft book\n value_bets = []\n for soft_slug in softs:\n if soft_slug not in all_odds:\n continue\n\n soft_markets = all_odds[soft_slug].get(\"markets\", {}).get(str(MARKET_ID), {})\n for outcome_id, label in OUTCOME_MAP.items():\n if outcome_id not in sharp_prices:\n continue\n\n player = soft_markets.get(\"outcomes\", {}).get(outcome_id, {}).get(\"players\", {}).get(\"0\", {})\n if not isinstance(player, dict) or \"price\" not in player:\n continue\n\n soft_price = player[\"price\"]\n sharp_price = sharp_prices[outcome_id]\n\n # Calculate edge\n edge = ((soft_price \/ sharp_price) - 1) * 100\n\n # Filter\n if edge >= MIN_EDGE and MIN_ODDS <= soft_price <= MAX_ODDS:\n value_bets.append({\n \"outcome\": label,\n \"bookmaker\": soft_slug,\n \"soft_odds\": soft_price,\n \"pinnacle_odds\": sharp_price,\n \"edge\": round(edge, 2),\n \"implied_prob\": round(1 \/ sharp_price * 100, 1),\n })\n\n return value_bets<\/code><\/pre>\n
Step 4: Scan All Fixtures and Rank by Edge<\/h3>\n
def scan_for_value(fixtures, sharp=SHARP_BOOK, softs=SOFT_BOOKS):\n \"\"\"Scan all fixtures and return sorted value bets.\"\"\"\n all_value = []\n\n for i, f in enumerate(fixtures):\n fixture_id = f[\"fixtureId\"]\n match_name = f\"{f['participant1Name']} vs {f['participant2Name']}\"\n kickoff = f[\"startTime\"][:16].replace(\"T\", \" \")\n\n print(f\"Scanning [{i+1}\/{len(fixtures)}]: {match_name}...\")\n\n value_bets = get_odds_comparison(fixture_id, sharp, softs)\n\n for vb in value_bets:\n vb[\"match\"] = match_name\n vb[\"kickoff\"] = kickoff\n all_value.append(vb)\n\n time.sleep(2) # Respect rate limits\n\n # Sort by edge (highest first)\n all_value.sort(key=lambda x: x[\"edge\"], reverse=True)\n return all_value\n\n# Run the scanner\nprint(\"=\" * 60)\nprint(\" VALUE BETTING SCANNER\")\nprint(f\" Benchmark: {SHARP_BOOK} | Min edge: {MIN_EDGE}%\")\nprint(f\" Scanning {len(fixtures)} fixtures...\")\nprint(\"=\" * 60)\n\nvalue_bets = scan_for_value(fixtures)\n\nif value_bets:\n print(f\"\\n{'='*60}\")\n print(f\" FOUND {len(value_bets)} VALUE BETS\")\n print(f\"{'='*60}\\n\")\n\n for vb in value_bets:\n print(f\" {vb['match']}\")\n print(f\" \u251c\u2500\u2500 Outcome: {vb['outcome']}\")\n print(f\" \u251c\u2500\u2500 Book: {vb['bookmaker']} @ {vb['soft_odds']}\")\n print(f\" \u251c\u2500\u2500 Pinnacle: {vb['pinnacle_odds']}\")\n print(f\" \u251c\u2500\u2500 Edge: +{vb['edge']}%\")\n print(f\" \u2514\u2500\u2500 Kickoff: {vb['kickoff']}\")\n print()\nelse:\n print(\"\\nNo value bets found. Markets may be efficient right now.\")\n print(\"Try scanning closer to kickoff \u2014 lines diverge more pre-match.\")<\/code><\/pre>\n
Step 5: Kelly Criterion \u2014 How Much to Stake<\/h3>\n
Finding value is step one. Sizing your bets correctly is step two. The Kelly Criterion<\/strong> tells you the optimal stake based on your edge and the odds.<\/p>\n
def kelly_stake(odds, true_prob, bankroll, fraction=0.25):\n \"\"\"\n Calculate Kelly Criterion stake.\n\n Args:\n odds: Decimal odds offered by soft book\n true_prob: True probability (from Pinnacle implied prob)\n bankroll: Current bankroll in units\n fraction: Kelly fraction (0.25 = quarter Kelly, recommended)\n\n Returns:\n Recommended stake in units\n \"\"\"\n # Kelly formula: f = (bp - q) \/ b\n # b = odds - 1 (net odds)\n # p = true probability\n # q = 1 - p\n b = odds - 1\n p = true_prob\n q = 1 - p\n\n kelly = (b * p - q) \/ b\n\n if kelly <= 0:\n return 0 # No edge \u2014 don't bet\n\n # Apply fractional Kelly (reduces variance)\n stake = bankroll * kelly * fraction\n return round(stake, 2)\n\n# Example: Apply Kelly to our value bets\nBANKROLL = 1000 # Starting bankroll in units\n\nprint(f\"\\n{'='*60}\")\nprint(f\" STAKING PLAN (Quarter Kelly, Bankroll: {BANKROLL} units)\")\nprint(f\"{'='*60}\\n\")\n\nfor vb in value_bets[:10]:\n true_prob = 1 \/ vb[\"pinnacle_odds\"]\n stake = kelly_stake(vb[\"soft_odds\"], true_prob, BANKROLL, fraction=0.25)\n\n if stake > 0:\n potential_profit = stake * (vb[\"soft_odds\"] - 1)\n print(f\" {vb['match']} \u2014 {vb['outcome']}\")\n print(f\" \u251c\u2500\u2500 Stake: {stake} units @ {vb['soft_odds']} ({vb['bookmaker']})\")\n print(f\" \u251c\u2500\u2500 Edge: +{vb['edge']}%\")\n print(f\" \u2514\u2500\u2500 Profit: {potential_profit:.2f} units (if won)\")\n print()<\/code><\/pre>\n
Closing Line Value: The Ultimate Validation<\/h2>\n
How do you know your scanner is actually finding real value and not just noise? Closing Line Value (CLV)<\/strong> is the answer.<\/p>\n
CLV measures whether the odds you bet at were better than the closing line. If you consistently get odds above the closing price, you have an edge \u2014 even if individual bets lose. Here's how to track it:<\/p>\n
def track_clv(bet_log, fixture_id, outcome_id, bet_odds):\n \"\"\"\n After a match starts, compare your bet odds to Pinnacle's closing line.\n Positive CLV = you're beating the market.\n \"\"\"\n try:\n data = api_get(\"historical-odds\", {\n \"fixtureId\": fixture_id,\n \"bookmakers\": \"pinnacle\",\n \"marketId\": MARKET_ID\n })\n except Exception:\n return None\n\n markets = data[\"bookmakers\"][\"pinnacle\"][\"markets\"][str(MARKET_ID)]\n snapshots = markets[\"outcomes\"][outcome_id][\"players\"][\"0\"]\n\n # Closing line = first snapshot (newest, pre-match)\n closing_price = snapshots[0][\"price\"]\n clv = ((bet_odds \/ closing_price) - 1) * 100\n\n return {\n \"bet_odds\": bet_odds,\n \"closing_odds\": closing_price,\n \"clv\": round(clv, 2),\n \"beating_close\": clv > 0\n }\n\n# Example usage after matches are played:\n# clv_result = track_clv(bet_log, \"id123\", \"101\", 1.95)\n# print(f\"CLV: {clv_result['clv']:+.2f}%\")\n# If CLV is consistently positive across 100+ bets, your scanner works.<\/code><\/pre>\n
Advanced: Multi-Sport Scanning<\/h2>\n
The scanner works for any sport \u2014 just change the sportId<\/code>, tournamentId<\/code>, and marketId<\/code>. Here are the most popular configurations:<\/p>\n
# Scan NBA instead of soccer:\nnba_fixtures = get_fixtures(sport_id=11, tournament_id=132)\nnba_scanner = scan_for_value(nba_fixtures)\n\n# Scan MLB:\nmlb_fixtures = get_fixtures(sport_id=13, tournament_id=109)\nmlb_scanner = scan_for_value(mlb_fixtures)<\/code><\/pre>\n
What Makes OddsPapi the Best Tool for Value Betting<\/h2>\n
\n
\n\n
\n
Feature<\/th>\n
Why It Matters for Value Betting<\/th>\n<\/tr>\n<\/thead>\n
\n
\n
350+ bookmakers<\/strong><\/td>\n
More books = more mispriced odds to find<\/td>\n<\/tr>\n
\n
Pinnacle + Singbet + SBOBet<\/strong><\/td>\n
Three sharp benchmarks, not just one. Cross-validate your \"true price.\"<\/td>\n<\/tr>\n
\n
Real-time odds<\/strong><\/td>\n
Value windows close fast. Sub-second latency with WebSocket.<\/td>\n<\/tr>\n
\n
Free historical data<\/strong><\/td>\n
Backtest your value strategy against past odds \u2014 included on the free tier<\/td>\n<\/tr>\n
\n
Simple auth<\/strong><\/td>\n
One API key as query parameter. No OAuth, no tokens, no headers.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
Common Value Betting Mistakes<\/h2>\n
\n
\n\n
\n
Mistake<\/th>\n
What Happens<\/th>\n
Fix<\/th>\n<\/tr>\n<\/thead>\n
\n
\n
Chasing huge edges<\/strong><\/td>\n
15%+ edge usually means a palp (pricing error) \u2014 your bet gets voided<\/td>\n
Cap max edge at 10%. Real value is 2-5%.<\/td>\n<\/tr>\n
\n
Ignoring limits<\/strong><\/td>\n
Soft books limit winners fast<\/td>\n
Spread bets across multiple books. Use the scanner to find which book has the best price.<\/td>\n<\/tr>\n
\n
No CLV tracking<\/strong><\/td>\n
You don't know if your edge is real<\/td>\n
Track CLV on every bet. 100+ bets of positive CLV = confirmed edge.<\/td>\n<\/tr>\n
\n
Flat staking on all bets<\/strong><\/td>\n
Equal stake on 1.5 odds and 5.0 odds wastes bankroll<\/td>\n
Use Kelly Criterion (quarter Kelly recommended).<\/td>\n<\/tr>\n
\n
One sport only<\/strong><\/td>\n
Limited volume, slower compounding<\/td>\n
Scan soccer, NBA, MLB, tennis. Value is sport-agnostic.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
Frequently Asked Questions<\/h2>\n
How much edge do I need for profitable value betting?<\/h3>\n
A consistent 2-3% edge over Pinnacle's closing line is enough to be profitable long-term. Over 1,000 bets at 2% edge, your expected profit is approximately 20 units (at 1 unit per bet). The key word is \"consistent\" \u2014 you need volume, not occasional big edges.<\/p>\n
Won't soft bookmakers limit my account?<\/h3>\n
Eventually, yes. Most soft books limit accounts that consistently beat the closing line. That's why scanning 350+ bookmakers matters \u2014 when Bet365 limits you, move to Betway, 888sport, or regional books. OddsPapi covers bookmakers most scanners miss.<\/p>\n
Can I use Singbet or SBOBet instead of Pinnacle as the benchmark?<\/h3>\n
Yes. Singbet and SBOBet are equally sharp, especially for Asian Handicap markets. Change SHARP_BOOK = \"singbet\"<\/code> in the scanner configuration. Using multiple sharp benchmarks (comparing all three) gives you higher confidence in the true price.<\/p>\n
What's the difference between value betting and arbitrage?<\/h3>\n
Arbitrage guarantees profit on every event by betting all outcomes across different bookmakers. Value betting only bets one side \u2014 you'll lose individual bets, but win over time with sufficient volume. Value betting has higher expected returns but higher variance.<\/p>\n
How many bets per day can I find?<\/h3>\n
Depends on the sports and bookmakers you scan. A typical scan across Premier League, La Liga, NBA, and MLB on a busy day yields 5-20 value bets above 2% edge. More bookmakers and more leagues = more opportunities.<\/p>\n