Sports Odds API Comparison 2026: Pricing, Coverage and Integration Scope

If you have ever started a sports prediction project, a trading bot, or a sports data product, you have probably done what every developer does on day one: type "best sports odds API" into Google. The results are a wall of affiliate review sites that all say roughly the same thing, in the same order, with the same conclusion. So you pick one, build for two weeks, and only then discover that the API you chose cannot do the thing you actually needed it to do.

The reason this keeps happening is that "best sports odds API" is the wrong question. There is no single best API because there is no single use case. The right question is what kind of data you need, at what cadence, with what guarantees, and at what budget. Once you frame the question that way, the landscape collapses into four distinct categories. Each category is best at a different thing. Picking the wrong category is the single biggest source of wasted weeks on this kind of project.

This is a documentation comparison; it does not establish that every provider was integrated, load-tested or evaluated on identical events. ZenHodl also operates the prediction API described below, so its role and prices are disclosed. If you want a wider survey that also covers model dashboards, live data feeds, and free utilities — not just APIs — see our roundup of sports prediction tools.

Documentation check: October 8, 2026. This is a source-based comparison, not a measured latency or integration benchmark. Three anonymous Polymarket GETs checked one NBA tag, one five-market page and one book. The remaining API examples, authenticated operations and provider service levels were not exercised in this review. Vendor prices, account eligibility, quotas and data licenses can change.

The Four Categories

The single most useful thing you can do before evaluating any specific vendor is decide which of these four categories you need. You may need more than one — a pipeline can combine scores, estimates and market prices. But the order in which you adopt them matters.

Category 1: Raw Odds Aggregators

The Odds API

The Odds API is the de facto starting point for many new projects. The current free tier includes 500 credits per month. Paid plans checked October 8, 2026 start at $30 for 20,000 credits, followed by $59 for 100,000, $119 for 5 million and $249 for 15 million. Credit cost depends on the requested markets/regions, not simply the number of HTTP requests. It covers major US and international sports and bookmakers; exact coverage depends on sport, region, and plan. The data format is clean JSON, and authentication is an API-key query parameter.

import requests

resp = requests.get(
    "https://api.the-odds-api.com/v4/sports/basketball_nba/odds",
    params={
        "apiKey": "YOUR_KEY",
        "regions": "us",
        "markets": "h2h",
        "oddsFormat": "american",
        "bookmakers": "draftkings,fanduel,betmgm",
    },
).json()

for event in resp:
    print(f"{event['away_team']} @ {event['home_team']}")
    for bm in event["bookmakers"]:
        for market in bm["markets"]:
            for outcome in market["outcomes"]:
                print(f"  {bm['key']}: {outcome['name']} {outcome['price']}")

Best for: building consumer-facing apps that display betting lines, or feeding sportsbook consensus into your own model as a feature.

The catch: raw odds are not probabilities. A line of -110 on both sides of a coin flip implies 52.4% per side, summing to 104.8%. That extra 4.8% is the bookmaker's vig baked into the price. To convert sportsbook odds into a usable probability, you have to devig:

def american_to_implied(odds):
    if odds > 0:
        return 100.0 / (odds + 100.0)
    return abs(odds) / (abs(odds) + 100.0)

def devig(home_odds, away_odds):
    home_raw = american_to_implied(home_odds)
    away_raw = american_to_implied(away_odds)
    total = home_raw + away_raw  # > 1.0 because of vig
    return home_raw / total, away_raw / total  # proportional no-vig estimates

The example produces proportional no-vig estimates from two prices. Other devigging methods differ; removing the quoted margin does not recover an independently known true probability or prove a model’s accuracy.

The Odds API currently refreshes featured moneyline/spread/total markets about every 60 seconds pre-match and 40 seconds in-play; props and alternate markets refresh about every 60 seconds. Those are published refresh intervals rather than a measured request-to-event latency or complete tick tape. Exchange feeds have separate 20-second pre-match/10-second in-play intervals; verify the requested product.

OddsJam

OddsJam’s API page advertises odds feeds and push access, with premium API pricing available through sales. Consumer subscription prices are not an API quote. Request the desired bookmaker/market coverage, refresh or push contract, licensing and a sample feed before comparing it with another provider; this review did not measure comparative latency.

Sportradar

Sportradar offers multiple sports, odds and probability products. Its account documentation distinguishes trials from production access; some products require a sales-issued trial. Confirm price, coverage, latency contract and redistribution rights for the exact product. No public starting price or cross-provider reliability ranking was verified here.

BetFair Exchange

Betfair is an exchange with account, geography and app-key requirements. Its official access-cost page distinguishes free delayed development access from approved live betting access: the Live App Key has a £499 one-off activation fee, and read-only live access is not permitted. The restricted-IP list includes the USA. Exchange prices do not by themselves prove superior forecast accuracy; check eligibility, commission and executable depth for the intended workflow.

Category 2: Sports Scores and Statistics

ESPN Scoreboard

The ESPN website scoreboard endpoint can return JSON without an API subscription key. That access does not establish a supported API contract, a safe polling rate, model-training permission or a commercial data license. Inspect the applicable terms and obtain the permissions your use requires. This review did not verify a current 11-sport/one-second production capture claim.

import requests

# Today's NBA games
data = requests.get(
    "https://site.api.espn.com/apis/site/v2/sports/basketball/nba/scoreboard"
).json()

for event in data["events"]:
    game = event["competitions"][0]
    teams = {team.get("homeAway"): team for team in game["competitors"]}
    home, away = teams.get("home"), teams.get("away")
    if home is None or away is None:
        continue
    print(f"{away['team']['abbreviation']} @ {home['team']['abbreviation']}: "
          f"{away['score']}-{home['score']}")

Endpoint pattern: site.api.espn.com/apis/site/v2/sports/{sport}/{league}/scoreboard. Replace with summary?event={event_id} for event detail; in-game win-probability fields vary by sport, event and response.

The catch: ESPN owes you nothing. They could rename fields, change response shapes, or shut off public access tomorrow. Mitigate by writing a thin adapter that normalizes the response to a fixed internal schema, and monitoring for parsing failures. When the inevitable schema change comes, you get a noisy alert and patch the adapter — rather than discovering it broke your bot at 3 AM.

Full walkthroughs: ESPN API Python tutorial.

League-Specific Official APIs

The NHL runs a public stats API at api-web.nhle.com. The MLB Stats API at statsapi.mlb.com is similarly open. The NFL has an unofficial endpoint that powers their app and rotates slightly each season. Excellent for league-specific projects, pointless if you need cross-sport coverage.

football-data.org

football-data.org’s pricing, checked October 8, lists a free plan with 12 competitions, delayed scores/schedules and 10 calls/minute. Its live-score plan starts at €12/month with 20 calls/minute. Competition coverage and freshness depend on the selected tier; assess them against the workflow rather than treating every plan as too slow for in-play.

Category 3: Direct Market APIs

Polymarket CLOB

Polymarket is not a data API first — it is an exchange that exposes data through its API. The data is the byproduct of letting you trade.

This retained public-read example targets py-clob-client-v2 1.2.0. The official unified SDK is recommended for new projects; token-backed and position-backed market identifiers must be routed correctly.

from py_clob_client_v2 import ClobClient

client = ClobClient(
    host="https://clob.polymarket.com",
    chain_id=137,
)

book = client.get_order_book("TOKEN_ID_HERE")
bids = book.get("bids", [])
asks = book.get("asks", [])
best_bid = max((float(level["price"]) for level in bids), default=None)
best_ask = min((float(level["price"]) for level in asks), default=None)
if best_bid is None or best_ask is None:
    raise ValueError("Two-sided book unavailable")

Public reads need no wallet credentials. Trading uses CLOB V2 credentials, a wallet signer, a matching wallet signature type/funder, and pUSD collateral. The legacy py-clob-client order-signing flow is unsupported in production; that is distinct from whether an old public-read method still returns JSON.

Rate limits are endpoint-specific rather than a single requests-per-second number. Fees are also per market: makers currently have a zero platform fee, while fee-enabled taker fills use a price-dependent curve selected at match time. Query the selected market with get_clob_market_info(condition_id) instead of hardcoding 2%. The market WebSocket at wss://ws-subscriptions-clob.polymarket.com/ws/market pushes orderbook and trade updates.

You do not need to deposit funds to call the public read endpoints. The orderbook is real money from real participants, so the prices reflect an actual market view rather than a bookmaker's posted line. Public read access does not establish complete historical coverage, every response’s quality or redistribution permission. Inspect observation clocks, gaps and the applicable terms.

Full guides: Polymarket API Python tutorial, practical API documentation. If you hit order_version_mismatch errors, see our debugging guide.

Kalshi REST

Kalshi is a CFTC-regulated event contract exchange. Public market REST reads require no key; authenticated requests use API-key IDs with RSA-PSS/SHA-256 or Ed25519, depending on the key type. The code below demonstrates only the RSA branch and was not executed in this review. Two important differences from Polymarket:

1. Venue eligibility. Check the current account and geographic rules for each venue; a regulatory label does not establish that a particular user or strategy is eligible.
2. A different market catalog and contract model. Both venues can list sports moneylines, spreads, and totals; the available leagues, lines, settlement rules, liquidity, and eligibility differ by venue and market.

import base64
import os
import time

import requests
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import padding

BASE_URL = "https://external-api.kalshi.com"
PATH = "/trade-api/v2/markets"

with open(os.environ["KALSHI_PRIVATE_KEY_PATH"], "rb") as key_file:
    private_key = serialization.load_pem_private_key(
        key_file.read(), password=None
    )

timestamp = str(int(time.time() * 1000))
message = f"{timestamp}GET{PATH}".encode()
signature = private_key.sign(
    message,
    padding.PSS(
        mgf=padding.MGF1(hashes.SHA256()),
        salt_length=padding.PSS.DIGEST_LENGTH,
    ),
    hashes.SHA256(),
)
headers = {
    "KALSHI-ACCESS-KEY": os.environ["KALSHI_API_KEY_ID"],
    "KALSHI-ACCESS-TIMESTAMP": timestamp,
    "KALSHI-ACCESS-SIGNATURE": base64.b64encode(signature).decode(),
}

markets = requests.get(
    BASE_URL + PATH,
    headers=headers,
    params={"series_ticker": "KXNBAGAME", "status": "open"},
).json()

Sign timestamp + HTTP_METHOD + full_path_without_query_parameters and send the result as base64 in KALSHI-ACCESS-SIGNATURE. The limits checked October 8 are token-based, with separate read and write budgets; the Basic tier provides 200 read and 100 write tokens per second, while most endpoints cost 10 tokens. Fees vary by contract and should be read from Kalshi's current schedule rather than modeled as one percentage of profit.

Polymarket vs Kalshi: compare the actual contract, not a venue-wide stereotype. Check eligibility, settlement rules, fee schedule, spread, depth, and executable size on both venues. Kalshi's defining distinction is its CFTC-regulated US exchange structure; Polymarket uses a crypto wallet and pUSD on Polygon. Either venue can have the better sports market for a particular game.

Category 4: Prediction APIs

Model estimates form a separate interface from executable market prices.

A prediction endpoint can return model estimates with version, observation time and evaluation metadata. Some models use market-derived inputs; the API category alone does not establish input independence or calibration. The JSON below is illustrative and is not an actual ZenHodl response:

{
  "sport": "NBA",
  "game_id": "401705412",
  "fair_prob": 0.617,
  "fair_prob_calibrated": 0.604,
  "ece": 0.029,
  "model_version": "wp_v3.4",
  "as_of": "2026-06-03T22:14:33Z",
  "features_used": ["score_diff", "time_remaining", "elo_diff", "pregame_wp"]
}

This is fundamentally different from raw odds. Sportsbook odds are prices, not probabilities — they include the vig and the bookmaker's positional bias. A model API’s output depends on its features, training and calibration; it does not automatically remove bookmaker bias or identify an independently known true probability. A prediction API supplies an estimate, not verified fair value. Inspect its dated evaluation and compare with executable prices and costs.

ZenHodl

ZenHodl is the prediction API we run. 8 sports on the public API — NBA, WNBA, NCAA men's and women's basketball, college football, NFL, NHL, and MLB — with separate soccer, tennis, Counter-Strike 2 and League of Legends research described in dated posts. Their current order-flow status is not established by this comparison.

import requests

resp = requests.get(
    "https://zenhodl.net/v1/edges",
    headers={"X-API-Key": "YOUR_KEY"},
)
edges = resp.json()
for edge in edges["signals"][:5]:
    print(f"{edge['sport']} | {edge['team']} | "
          f"fair={edge['fair_wp']:.1%} market={edge['market_ask']:.0%} "
          f"edge={edge['edge']*100:+.1f}c")

Pricing: free Developer tier (500 req/month, no card), Starter at $49 (30K requests/month), Pro at $149 (100K requests/month), Enterprise at $499/month with the displayed unlimited request allowance. The public paid plans include the course; check current pricing for terms.

What Actually Matters: Calibration

The most important thing about evaluating any prediction API is calibration. Anyone can train a model that returns a probability. Very few publish whether those probabilities are well-calibrated. Calibration is whether the model's stated confidence matches reality — when it says 70%, do those events actually happen 70% of the time?

You measure it with Expected Calibration Error (ECE). ECE depends on bins, sample size and distribution; 0.04 is not a universal quality or trading-safety threshold. Request held-out metrics with uncertainty and compare against relevant baselines on the same events. Forecast error also changes the sizing assumptions discussed in Kelly Criterion.

Always ask any prediction API provider for their numeric ECE per sport. Missing published evidence prevents verification; it does not prove the model is uncalibrated. Inspect the dated cohort, forecast timing, binning and uncertainty before using its probabilities. We audited 21 sports prediction sites earlier this year and exactly one published a calibration number. That linked audit is a dated publication sample, not a current inventory of every provider. Full deep dive: calibration beats accuracy.

Inpredictable’s About page describes NBA/WNBA analytics; this review did not verify a documented public prediction API. The FiveThirtyEight data repository says its sports forecasts stopped updating June 13, 2023. Neither observation establishes that ZenHodl is the only or closest free prediction API; compare specific currently supported products and licenses.

A Possible Hybrid Stack

After integrating all four categories, the obvious move is to use more than one. The roles below describe a possible integration; they do not certify current internal collection rates or feature sets:

1. A permitted score source for event state. ESPN’s example access does not establish reuse rights; source-event latency must be measured separately from polling cadence.
2. Versioned model estimates, with dated evaluation reports and feature provenance. The ZenHodl API exposes the documented pre-blend estimate; this comparison does not verify one universal training window or current quality level.
3. Polymarket WebSocket for near-real-time market data plus CLOB execution.
4. The Odds API as a confirmation layer (sportsbook prices polled every 2 min). Agreement can motivate investigation; shared information, timing and settlement differences mean it does not certify that an edge is real or that a disagreeing model is wrong.

The API-only stack can start at $0 with free tiers. If you need more sportsbook calls, the first displayed paid The Odds API tier is currently $30/month for 20,000 credits. The illustrated public endpoint requests do not require a subscription key; that does not establish permissions for commercial reuse. Infrastructure, provider license and trading costs are separate.

You do not have to start with all four. One possible prototype combines a permitted score source, a model and an eligible exchange. A prediction API can supply estimates without your own training run; no 2–3 month time-saving guarantee or complete $0 commercial cost was verified. Add The Odds API confirmation layer once you get tired of debugging cases where your model is wrong with no second opinion.

The Five Questions to Ask Any Vendor

1. Authentication model. API-key query parameters are simple. Kalshi requires per-request RSA-PSS signatures. Polymarket trading combines API credentials with wallet signing. Confirm secret storage and key rotation before choosing a vendor.
2. Rate limits and burst tolerance. Steady-state is the average. Burst is how much you can spike when several games finish at once. Ask separately about burst budgets, endpoint costs and throttling behavior.
3. Latency. Set a freshness requirement for the strategy and measure source-event, receipt and decision clocks. A 30-second cutoff is not a universal threshold, and age alone does not compute trading edge.
4. For prediction APIs: published ECE per sport, on what sample size. If they refuse to give a number, do not size positions against their probabilities.
5. Data license. Permissions differ by provider and use. Public access is not a license for collection, model training, commercial display or resale. The Odds API terms permit several derived/commercial uses while restricting standalone raw-data resale; do not transfer those permissions to ESPN or another source. If you are building a dashboard for paying customers, the license matters.

Decision Guide

Choose the Contract That Fits Your Workflow

Estimate the required events, fields, update age, quota cost and permitted uses before selecting a plan. Free credits can support a bounded prototype but may be inadequate for its request pattern. Public exchange reads do not require a funded trading account; they also do not guarantee eligible execution, complete coverage or a profitable strategy.

The hard part has shifted from finding data to integrating it well. The pipeline that turns API calls into a working bot — caching, edge math, position sizing, CLV tracking, drift monitoring — is where almost all the actual difficulty now lives. The four-category framework is just the starting map. The real work begins after you pick.

Get started: - ZenHodl API free Developer tier — 500 req/month, all 8 sports, no card required - ZenHodl Starter at $49/month — 30K requests + full bot course included - The Odds API — raw sportsbook lines, 500 free credits/month - Polymarket unified Python SDK — current recommendation for new projects - Full prediction market API guide — Polymarket, Kalshi, ESPN, The Odds API with scoped examples - How to leverage a sports prediction API — the 7-step integration workflow