Databet Alternative: Free Esports & Sports Odds API (No Sales Call)

You Searched “Databet API.” Here’s the Catch Nobody Mentions.

You’re building an esports betting tool, a price scanner, or a model, and you found Databet — a slick B2B data and sportsbook platform that powers a lot of operators behind the scenes. Then you hit the wall every solo dev hits: there’s no “Sign up, copy your API key, make a call” button. Databet sells platforms and managed feeds to operators, not a self-serve API to developers. The funnel is a “Request a demo” form and a sales call.

If you just want live esports odds in JSON — CS2, Dota 2, League of Legends, Valorant — without becoming a licensed sportsbook first, you’re in the wrong queue. This guide shows you the developer-friendly alternative: OddsPapi, with a free API key, 370 bookmakers (including sharps like Pinnacle and prediction markets like Polymarket and Kalshi), and tested Python you can run in the next five minutes.

What Databet Actually Is

Databet is a B2B iGaming and esports technology company. Their core products are aimed at businesses launching or running a sportsbook: turnkey/white-label sportsbook software, managed trading and risk, and a data/odds feed bundled into that stack. That’s a genuinely strong offering — if you are an operator.

For a developer, scripter, or quant, three things make it a bad fit:

None of that is a knock on Databet. It’s just the wrong tool when all you need is a clean JSON odds endpoint you can hit from a Python script.

The Alternative: OddsPapi (Built for Developers)

OddsPapi is the “third option” between enterprise feeds (Databet, Sportradar, Genius) that need a sales call and generic odds APIs that only carry ~40 soft books. You get an instant free key and a flat REST API. Here’s the live coverage as of this writing (queried straight from the API, not a brochure):

For esports specifically, here’s what the catalog returned for the next 7 days when this post was written (summer is the off-window between major splits, so depth climbs sharply during LCK/Worlds/Majors):

Tutorial: Pull Live Esports Odds in Python

Four steps: authenticate, discover the sport, fetch fixtures, parse odds. Every snippet below was run against the live API. Auth is a query parameter (?apiKey=), never a header.

Step 1 — Authenticate

import requests

API_KEY = "YOUR_API_KEY"   # grab a free key at oddspapi.io
BASE_URL = "https://api.oddspapi.io/v4"

def get(path, **params):
    params["apiKey"] = API_KEY
    r = requests.get(f"{BASE_URL}{path}", params=params)
    r.raise_for_status()
    return r.json()

# Smoke test
sports = get("/sports")
print(f"{len(sports)} sports available")   # -> 69 sports available

Step 2 — Find the Esport You Want

Esports live under their own sport IDs. No need to guess — filter the catalog:

esports = [s for s in sports if "esport" in s["slug"]]
for s in esports:
    print(s["sportId"], s["slug"])

# 16 esport-dota
# 17 esport-counter-strike
# 18 esport-league-of-legends
# ...plus Valorant, King of Glory, Rainbow Six and more

Step 3 — Fetch CS2 Fixtures

The /fixtures endpoint takes a date range (max 10 days apart). Only fixtures with hasOdds: true will return a price payload, so filter on it:

from datetime import datetime, timezone, timedelta

today = datetime.now(timezone.utc).date()
frm = today.isoformat()
to = (today + timedelta(days=7)).isoformat()

fixtures = get("/fixtures", sportId=17, **{"from": frm, "to": to})
playable = [f for f in fixtures if f["hasOdds"]]
print(f"{len(playable)} CS2 fixtures with live odds")

f = playable[0]
print(f["participant1Name"], "vs", f["participant2Name"], "|", f["tournamentName"])
# Lavked vs Mana eSports | European Pro League Series

Step 4 — Parse the Odds (the nested part)

The /odds response is deeply nested. The top key is bookmakerOdds; the current price lives at players["0"]["price"]. Build a name lookup from /markets first so you get human-readable market and outcome names instead of integer IDs:

# Build {marketId: name} and {(marketId, outcomeId): name} lookups
catalog = get("/markets", sportId=17)
market_names = {m["marketId"]: m["marketName"] for m in catalog}
outcome_names = {
    (m["marketId"], o["outcomeId"]): o["outcomeName"]
    for m in catalog for o in m.get("outcomes", [])
}

odds = get("/odds", fixtureId=f["fixtureId"])
books = odds["bookmakerOdds"]
print("Books on this match:", list(books))
# ['bet365', 'kalshi', 'pinnacle', 'polymarket']

# Match Winner = market 171 ("Winner")
MARKET = "171"
for slug, data in books.items():
    market = data["markets"].get(MARKET)
    if not market:
        continue
    row = {}
    for oid, outcome in market["outcomes"].items():
        price = outcome["players"]["0"]["price"]
        row[outcome_names.get((int(MARKET), int(oid)), oid)] = price
    print(f"{slug:10} {row}")

Live output for that exact CS2 match:

pinnacle   {'1': 1.153, '2': 5.05}
bet365     {'1': 1.25,  '2': 3.75}
polymarket {'1': 1.0,   '2': 1.0}   # listed but inactive in off-window
kalshi     {'1': 1.0,   '2': 1.0}   # listed but inactive in off-window

Two things to notice. First, you got a sharp price (Pinnacle 1.153 / 5.05) and a soft price (Bet365 1.25 / 3.75) on the same esports match — that’s the line-shop edge no single-book feed gives you. Second, prediction-market venues (Polymarket, Kalshi) appear in the catalog but show 1.0 placeholders here because they’re inactive outside major tournaments. Always check the price before trusting it — parse defensively, skip 1.0 / active: false rows.

Bonus — The Line-Shop Edge

On the underdog (Mana eSports), Pinnacle pays 5.05 versus Bet365’s 3.75 — a 35% better price for the exact same bet. Multiply that gap across hundreds of matches and 370 books and you have the entire premise of line shopping and value scanning. A single-operator feed like Databet’s can’t surface it, because it’s one book’s view of the world.

# Best available price per outcome across every book
best = {}
for slug, data in books.items():
    market = data["markets"].get("171")
    if not market:
        continue
    for oid, outcome in market["outcomes"].items():
        price = outcome["players"]["0"]["price"]
        if price <= 1.0:          # skip inactive placeholders
            continue
        name = outcome_names.get((171, int(oid)), oid)
        if price > best.get(name, (0, ""))[0]:
            best[name] = (price, slug)
print(best)
# {'1': (1.25, 'bet365'), '2': (5.05, 'pinnacle')}

When to Pick Databet vs OddsPapi

If you’re comparing several screeners and feeds, also worth a look: jedibets.com runs a player-props odds screener with Discord alerts. For pure esports coverage, see our esports odds API guide, the PandaScore alternative, and the Valorant VCT odds walkthrough.

FAQ

Does Databet have a public API or free tier?

No self-serve developer API or public free tier. Databet sells data and sportsbook platforms to operators; access starts with a sales/demo conversation. For an instant free key, OddsPapi is the developer alternative.

Can I get CS2, Dota 2 and LoL odds from OddsPapi?

Yes. Each esport has its own sportId (CS2 = 17, Dota 2 = 16, LoL = 18, plus Valorant and others). Filter /sports for "esport" in the slug, then call /fixtures and /odds.

Why do Polymarket and Kalshi show odds of 1.0?

They’re listed in the bookmaker catalog but inactive for that fixture (common for esports outside major tournaments). Always skip prices of 1.0 or rows with active: false — parse defensively.

Is the esports odds data real-time?

Yes. The REST /odds endpoint returns live prices, and OddsPapi also pushes updates over WebSocket so you don’t have to poll.

Stop Booking Demos. Start Making Calls.

Databet is built for operators. If you’re a developer who just needs live esports odds across 370 books — sharps and prediction markets included — you shouldn’t have to sit through a sales call to get them. Get your free OddsPapi key and pull your first CS2 odds payload in the next five minutes.