UEFA does not sell a public Champions League odds API. There is no developer portal, no self-serve key, and no endpoint that returns what 17 bookmakers think about tonight’s qualifier. What exists instead is a scattering of scraped feeds, enterprise contracts that start with a sales call, and generic football APIs that refresh their odds once a day.<\/p>\n
You can get the data anyway. This guide pulls live Champions League, Europa League and Conference League prices from 383 bookmakers through one endpoint, in Python, on a free key. Every number below came off the live API on 29 July 2026 while the qualifying round was being priced.<\/p>\n
UEFA runs three club competitions, and OddsPapi carries all three as separate tournaments under soccer ( Two details matter before you write a single parser.<\/p>\n First, the qualifying bracket is published before anyone knows who is in it. Of the 119 European fixtures in that window, 33 carried participant names like Second, book depth is a function of how close kick-off is. The eight ties kicking off within hours carried 15 to 17 bookmakers each. The ties five days out carried one. Books post European qualifiers roughly two days ahead, so a census run on a quiet Tuesday will understate coverage badly.<\/p>\n The key rides as a query parameter on every call. It is never a header.<\/p>\n Resolve the IDs at runtime rather than pasting them into a config file. Tournament IDs are stable across a season, but new competitions and women’s variants share the same name prefixes, and hardcoding is how you end up parsing the Women’s Champions League by accident.<\/p>\n Team names live on Soccer carries 32,815 market IDs once you count every handicap and total line as its own market. Nobody should hardcode that. Pull the catalog once and resolve markets by name, handicap and period.<\/p>\n The 1X2 outcomes come back labelled The worked example is Gornik Zabrze against Fenerbahce Istanbul ( Filter on Thirteen slugs quoted this tie. Eleven opinions came back. BetMGM and Borgata posted byte-identical prices, and so did BetParx and FourWinds, yet Pinnacle priced every UCL qualifier in the sample. Strip its margin and you have a benchmark to grade the other ten books against.<\/p>\n That 6.42% is wide for Pinnacle. On a major-league fixture it runs closer to 2%. Qualifying ties between clubs the market barely knows carry more margin because the book is less sure, and the limit data in step 7 confirms it.<\/p>\n Kalshi’s 7.143 on Gornik returns 30% more than SBOBet’s 5.5 for the same stake on the same outcome. That gap is the entire argument for reading more than one book.<\/p>\n A price 11.5% above the sharp fair line looks like free money. Two API fields say otherwise, and both are worth checking before you stake anything.<\/p>\n Kalshi is an exchange, so the outcome carries an You can get $914 down at 7.143. After that the price drops to 6.667, then 6.25. The edge is real and it is small, which is the normal shape of a genuine one.<\/p>\n Pinnacle’s own Then check whether the line has been moving. Free historical odds settle it:<\/p>\n Pinnacle opened Gornik at 5.67 and drifted to 6.02 across five days and 78 snapshots. Money has been leaving Gornik the whole time, and Kalshi sits further down that same path. A price that agrees with the direction of sharp movement is a much better bet than one that fights it. Grade every outlier this way before you trust it, because the ones that fight the drift are usually a book that has not updated.<\/p>\n Note that US sportsbooks build deep player-prop menus. European books build corner ladders, and the Champions League feed is where that shows. This single tie carried 64 distinct corner markets: full-time totals from 4.5 up to 13.5, first-half and second-half splits, per-team counts, odd\/even, a corners handicap, and a corners 1X2.<\/p>\n The main line at 9.5 drew nine books including both sharps:<\/p>\nsportId=10<\/code>). Here is what the API returned for the next nine days:<\/p>\n\n\n
\n \nCompetition<\/th>\n tournamentId<\/th>\n Fixtures<\/th>\n With odds<\/th>\n<\/tr>\n<\/thead>\n \n UEFA Champions League<\/td>\n 7<\/td>\n 18<\/td>\n 12<\/td>\n<\/tr>\n \n UEFA Europa League<\/td>\n 679<\/td>\n 24<\/td>\n 10<\/td>\n<\/tr>\n \n UEFA Conference League<\/td>\n 34480<\/td>\n 77<\/td>\n 45<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Winner Match 11<\/code> against Winner Match 6<\/code>. They are real fixture objects with real IDs and real kick-off times, and they all return hasOdds: false<\/code>. Filter them out or your fixture list fills with ties that have no teams.<\/p>\nOld way vs OddsPapi<\/h3>\n
\n\n
\n \nJob<\/th>\n Scraping or a generic football API<\/th>\n OddsPapi<\/th>\n<\/tr>\n<\/thead>\n \n Books per tie<\/td>\n One at a time, or a handful<\/td>\n 15 to 17 in one call<\/td>\n<\/tr>\n \n Sharp pricing<\/td>\n Rarely included<\/td>\n Pinnacle and SBOBet on every priced tie<\/td>\n<\/tr>\n \n Refresh<\/td>\n Often once a day<\/td>\n Live, with WebSocket push<\/td>\n<\/tr>\n \n Corners, correct score<\/td>\n Usually dropped<\/td>\n 64 corner markets, correct score to 57 scorelines<\/td>\n<\/tr>\n \n Price history<\/td>\n Paid add-on<\/td>\n Free tier<\/td>\n<\/tr>\n \n Access<\/td>\n Sales call<\/td>\n Self-serve key<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Step 1: Authenticate and find the competitions<\/h2>\n
import time\nimport requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\n\ndef get(path, **params):\n params[\"apiKey\"] = API_KEY\n r = requests.get(f\"{BASE_URL}{path}\", params=params)\n r.raise_for_status()\n time.sleep(1.0) # the free tier rate-limits per endpoint\n return r.json()\n\n\ndef european_tournaments():\n wanted = {\"UEFA Champions League\", \"UEFA Europa League\", \"UEFA Conference League\"}\n return {\n t[\"tournamentName\"]: t[\"tournamentId\"]\n for t in get(\"\/tournaments\", sportId=10)\n if t[\"tournamentName\"] in wanted\n }\n\n\nfor name, tid in european_tournaments().items():\n print(f\"{tid:>6} {name}\")<\/code><\/pre>\n 7 UEFA Champions League\n 679 UEFA Europa League\n34480 UEFA Conference League<\/code><\/pre>\nStep 2: Pull fixtures and drop the placeholders<\/h2>\n
\/fixtures<\/code> takes a date range of up to ten days. Filter on hasOdds<\/code> and on the bracket placeholder pattern in the same pass.<\/p>\nfrom datetime import datetime, timedelta, timezone\n\n\ndef priced_fixtures(tournament_name, days=9):\n today = datetime.now(timezone.utc)\n fixtures = get(\"\/fixtures\", sportId=10, **{\n \"from\": today.strftime(\"%Y-%m-%d\"),\n \"to\": (today + timedelta(days=days)).strftime(\"%Y-%m-%d\"),\n })\n return [\n f for f in fixtures\n if f[\"tournamentName\"] == tournament_name\n and f[\"hasOdds\"]\n and \"Winner Match\" not in f[\"participant1Name\"]\n ]\n\n\nfor f in priced_fixtures(\"UEFA Champions League\"):\n print(f[\"fixtureId\"], f[\"participant1Name\"], \"v\", f[\"participant2Name\"])<\/code><\/pre>\nid1000000772176812 FC Kairat Almaty v AC Omonia Nicosia\nid1000000772176794 FK Kauno Zalgiris v KI Klaksvik\nid1000000772176798 KKS Lech Poznan v AGF Aarhus\nid1000000772176808 CS Universitatea Craiova v PFC Levski Sofia\nid1000000772176838 Hapoel Be`er Sheva FC v Vikingur Reykjavik\nid1000000772176722 FK Crvena Zvezda Belgrade v Larne FC\nid1000000772177116 Gornik Zabrze v Fenerbahce Istanbul\nid1000000772176844 SK Slovan Bratislava v FC Iberia 1999<\/code><\/pre>\nparticipant1Name<\/code> and participant2Name<\/code>. The nested participants<\/code> list on the fixture object comes back empty, so do not reach for it.<\/p>\nStep 3: Build the market lookup<\/h2>\n
CATALOG = get(\"\/markets\", sportId=10)\n\nOUTCOME_NAME = {\n (m[\"marketId\"], o[\"outcomeId\"]): o[\"outcomeName\"]\n for m in CATALOG for o in m.get(\"outcomes\", [])\n}\n\n\ndef find_market(name, handicap=0, period=\"fulltime\"):\n for m in CATALOG:\n if (m[\"marketName\"] == name\n and m.get(\"handicap\") == handicap\n and m.get(\"period\") == period):\n return m[\"marketId\"]\n return None\n\n\nFT_RESULT = find_market(\"Full Time Result\")\nprint(FT_RESULT) # 101<\/code><\/pre>\n1<\/code>, X<\/code> and 2<\/code>, matching the European convention rather than home\/draw\/away.<\/p>\nStep 4: Read every book on one tie<\/h2>\n
id1000000772177116<\/code>), a second qualifying round tie that drew 15 bookmakers.<\/p>\ndef read_market(fixture_id, market_id):\n payload = get(\"\/odds\", fixtureId=fixture_id)\n table = {}\n for slug, data in payload.get(\"bookmakerOdds\", {}).items():\n market = data[\"markets\"].get(str(market_id))\n if not market:\n continue\n prices = {}\n for outcome_id, outcome in market[\"outcomes\"].items():\n quote = outcome[\"players\"].get(\"0\")\n if not quote or quote.get(\"active\") is False:\n continue\n label = OUTCOME_NAME.get((market_id, int(outcome_id)), outcome_id)\n prices[label] = quote[\"price\"]\n if prices:\n table[slug] = prices\n return table\n\n\nboard = read_market(\"id1000000772177116\", FT_RESULT)\nfor slug, prices in sorted(board.items()):\n print(f\"{slug:20s} {prices}\")<\/code><\/pre>\nbet365 {'1': 5.75, 'X': 4.33, '2': 1.48}\nbetmgm {'1': 6.25, 'X': 4.5, '2': 1.49}\nbetparx {'1': 6.5, 'X': 4.3, '2': 1.47}\nborgata {'1': 6.25, 'X': 4.5, '2': 1.49}\ndraftkings {'1': 6.0, 'X': 4.3, '2': 1.51}\nfanduel {'1': 6.0, 'X': 4.2, '2': 1.48}\nfourwinds {'1': 6.5, 'X': 4.3, '2': 1.47}\nhardrockbet {'1': 6.5, 'X': 4.25, '2': 1.476}\nkalshi {'1': 7.143, 'X': 4.545, '2': 1.515}\npinnacle {'1': 6.02, 'X': 4.24, '2': 1.51}\npointsbet.com.au {'1': 6.0, 'X': 4.5, '2': 1.48}\npolymarket {'1': 6.667, 'X': 4.545, '2': 1.562}\nsbobet {'1': 5.5, 'X': 3.93, '2': 1.45}<\/code><\/pre>\nactive is False<\/code> rather than on a truthy active<\/code>. The live feed ships active: null<\/code> next to perfectly valid prices on pre-match fixtures, and a truthy test silently throws those away.<\/p>\nDedupe before you average anything<\/h3>\n
\/v4\/bookmakers<\/code> reports cloneOf: null<\/code> for all four. Average the raw list and you triple-count one trading desk.<\/p>\ndef dedupe(board):\n seen, unique = {}, {}\n for slug, prices in board.items():\n key = tuple(sorted(prices.items()))\n if key in seen:\n seen[key].append(slug)\n continue\n seen[key] = [slug]\n unique[slug] = prices\n clones = {v[0]: v[1:] for v in seen.values() if len(v) > 1}\n return unique, clones\n\n\nunique, clones = dedupe(board)\nprint(f\"{len(board)} slugs -> {len(unique)} independent quotes\")\nfor keeper, dupes in clones.items():\n print(f\" {keeper} == {', '.join(dupes)}\")<\/code><\/pre>\n13 slugs -> 11 independent quotes\n betmgm == borgata\n betparx == fourwinds<\/code><\/pre>\nStep 5: De-vig Pinnacle for a fair price<\/h2>\n
def devig(prices):\n overround = sum(1 \/ p for p in prices.values())\n return {k: (1 \/ p) \/ overround for k, p in prices.items()}, overround - 1\n\n\nfair, vig = devig(board[\"pinnacle\"])\nprint(f\"vig {vig * 100:.2f}%\")\nfor label, prob in fair.items():\n print(f\" {label}: {prob * 100:.1f}% fair price {1 \/ prob:.3f}\")<\/code><\/pre>\nvig 6.42%\n 1: 15.6% fair price 6.407\n X: 22.2% fair price 4.512\n 2: 62.2% fair price 1.607<\/code><\/pre>\nStep 6: Find the best available price<\/h2>\n
def best_price(board):\n best = {}\n for slug, prices in board.items():\n for label, price in prices.items():\n if label not in best or price > best[label][1]:\n best[label] = (slug, price)\n return best\n\n\nfor label, (slug, price) in best_price(unique).items():\n delta = (price \/ (1 \/ fair[label]) - 1) * 100\n print(f\"{label}: {price:6.3f} @ {slug:12s} vs fair {1 \/ fair[label]:6.3f} ({delta:+.1f}%)\")<\/code><\/pre>\n1: 7.143 @ kalshi vs fair 6.407 (+11.5%)\nX: 4.545 @ kalshi vs fair 4.512 (+0.7%)\n2: 1.562 @ polymarket vs fair 1.607 (-2.8%)<\/code><\/pre>\nStep 7: The honest read on that 11.5%<\/h2>\n
exchangeMeta<\/code> ladder instead of a single number. The top rung is thin:<\/p>\npayload = get(\"\/odds\", fixtureId=\"id1000000772177116\")\nquote = payload[\"bookmakerOdds\"][\"kalshi\"][\"markets\"][\"101\"][\"outcomes\"][\"101\"][\"players\"][\"0\"]\n\nfor level in quote[\"exchangeMeta\"][\"back\"]:\n print(f\" price {level['price']:.3f} stake capacity ${level['limit']:,.0f}\")<\/code><\/pre>\n price 7.143 stake capacity $914\n price 6.667 stake capacity $384\n price 6.250 stake capacity $3,283<\/code><\/pre>\nlimit<\/code> field tells the same story from the other side. It quoted a $450 max win on Gornik and $882 on Fenerbahce. On an MLB moneyline the equivalent figure runs to $7,500. Pinnacle is holding this tie at a sixteenth of its baseball confidence, which explains the fat 6.42% margin.<\/p>\nhistory = get(\"\/historical-odds\", fixtureId=\"id1000000772177116\", bookmakers=\"pinnacle\")\nsnaps = history[\"bookmakers\"][\"pinnacle\"][\"markets\"][\"101\"][\"outcomes\"][\"101\"][\"players\"][\"0\"]\n\nprint(f\"{len(snaps)} snapshots\")\nprint(\"open \", snaps[0][\"createdAt\"], snaps[0][\"price\"])\nprint(\"last \", snaps[-1][\"createdAt\"], snaps[-1][\"price\"])<\/code><\/pre>\n78 snapshots\nopen 2026-07-24T09:16:11.479Z 5.67\nlast 2026-07-29T13:09:09.238Z 6.02<\/code><\/pre>\n\/historical-odds<\/code> takes a maximum of three bookmakers per call and returns large payloads. Loop it with a longer pause than you use on \/odds<\/code>.<\/p>\nStep 8: Corners, the market European books actually compete on<\/h2>\n
corner_lines = [m for m in CATALOG\n if m[\"marketName\"] == \"Corners - Over Under Full Time\"]\n\npayload = get(\"\/odds\", fixtureId=\"id1000000772177116\")\nbooks = payload[\"bookmakerOdds\"]\n\nfor m in sorted(corner_lines, key=lambda m: m[\"handicap\"]):\n quotes = {}\n for slug, data in books.items():\n market = data[\"markets\"].get(str(m[\"marketId\"]))\n if not market:\n continue\n sides = {}\n for outcome_id, outcome in market[\"outcomes\"].items():\n q = outcome[\"players\"].get(\"0\")\n if not q or q.get(\"active\") is False:\n continue\n sides[OUTCOME_NAME[(m[\"marketId\"], int(outcome_id))]] = q[\"price\"]\n if sides:\n quotes[slug] = sides\n if quotes:\n both = sum(1 for s in quotes.values() if len(s) == 2)\n print(f\"corners {m['handicap']}: {len(quotes)} books ({both} two-sided)\")<\/code><\/pre>\ncorners 7.5: 6 books (5 two-sided)\ncorners 8.5: 6 books (5 two-sided)\ncorners 9.5: 9 books (8 two-sided)\ncorners 10.5: 7 books (6 two-sided)\ncorners 11.5: 6 books (5 two-sided)\ncorners 12.5: 4 books (3 two-sided)\ncorners 13.5: 2 books (1 two-sided)<\/code><\/pre>\n