altsportsdata 3.0.0

Production-grade alternative sports data feed — odds, events, futures, settlement for 30 leagues

AltSportsData SDK

Production-grade alternative sports data feed — odds, events, futures, settlement for 30 leagues.

Original model-generated probabilities for sports nobody else can price. Built for prediction markets, DFS platforms, and sportsbooks.

pip install altsportsdata

---

Quick Start — See Everything Available

from altsportsdata import AltSportsData

client = AltSportsData(api_key="your_key")
events = client.list_events(status="upcoming")
print(events)

Events

league      name                              start_date           status     id
──────      ────                              ──────────           ──────     ──
spr         Indianapolis                      2026-03-15T00:00     UPCOMING   spr:7b06fffb...
wsl         Pipe Masters                      2026-12-08T00:00     UPCOMING   wsl:57eb4d84...
f1          Australian Grand Prix             2026-03-06T00:00     UPCOMING   f1:aad1221d...
wsl         Peniche                           2026-10-15T00:00     UPCOMING   wsl:f9b9c621...
dgpt        Queen City Classic                2026-03-20T00:00     UPCOMING   dgpt:c4014c1d...
...

120 rows × 6 columns

Instant DataFrame (like Alpaca's bars.df)

df = events.df
print(df[["league", "name", "start_date", "status"]].head(10))

  league  name                        start_date            status
0 spr     Indianapolis                2026-03-15T00:00:00Z  UPCOMING
1 wsl     Pipe Masters                2026-12-08T00:00:00Z  UPCOMING
2 f1      Australian Grand Prix       2026-03-06T00:00:00Z  UPCOMING
3 wsl     Peniche                     2026-10-15T00:00:00Z  UPCOMING
4 dgpt    Queen City Classic          2026-03-20T00:00:00Z  UPCOMING

# Export
df.to_csv("upcoming_events.csv")

---

For Prediction Markets (Kalshi, Polymarket)

Probabilities as 0–1, ready for contract creation.

client = AltSportsData(api_key="your_key", odds_format="probability")
wsl = client.get_league("wsl")

odds = wsl.get_market_probabilities(event_id)
print(odds)

Market: eventWinner

athlete                probability  odds      settlement  status  outcome_id
───────                ───────────  ────      ──────────  ──────  ──────────
Eli Tomac              0.4950       0.4950                OPEN    spr:abc123
Hunter Lawrence        0.3760       0.3760                OPEN    spr:def456
Ken Roczen             0.2350       0.2350                OPEN    spr:ghi789
Cooper Webb            0.1460       0.1460                OPEN    spr:jkl012

# DataFrame — ready for contract pricing
df = odds.df
df[["athlete", "probability", "outcome_id"]]

Settlement for Contract Resolution

result = client.get_settlement(event_id)
for name, mkt in result["markets"].items():
    for o in mkt.get("winners", []):
        print(f"  ✅ {o['athlete']} — settled at {result['settled_at']}")

---

For DFS Platforms (PrizePicks, Underdog)

client = AltSportsData(api_key="your_key")

# Head-to-head matchups — ready for pick'em
odds = client.get_matchups(event_id)
print(odds)

Market: headToHead

player1          player1_odds  player2          player2_odds
───────          ────────────  ───────          ────────────
Cooper Webb      2.31          Ken Roczen       1.57
Eli Tomac        1.66          Hunter Lawrence  2.13

df = odds.df
df.to_csv("matchups.csv")

---

For Sportsbooks (DraftKings, Bet365, Stake)

# American odds
client = AltSportsData(api_key="your_key", odds_format="american")
odds = client.get_moneylines(event_id)
print(odds)

# Fractional odds (UK books)
client = AltSportsData(api_key="your_key", odds_format="fractional")

---

Odds Format Conversion

Set once on the client — all responses auto-convert:

client = AltSportsData(api_key="key", odds_format="probability")  # Kalshi
client = AltSportsData(api_key="key", odds_format="american")     # DraftKings
client = AltSportsData(api_key="key", odds_format="decimal")      # Stake (default)
client = AltSportsData(api_key="key", odds_format="fractional")   # Bet365

Or convert individual values:

from altsportsdata import convert_odds

convert_odds(2.50, "decimal", "american")     # → 150.0
convert_odds(2.50, "decimal", "probability")  # → 0.4
convert_odds(150, "american", "decimal")      # → 2.5
convert_odds("3/2", "fractional", "probability")  # → 0.4

---

Async Client (Production Infrastructure)

from altsportsdata import AsyncAltSportsData
import asyncio

async def main():
    async with AsyncAltSportsData(api_key="key", odds_format="probability") as client:
        wsl = client.get_league("wsl")
        events = await wsl.list_events(status="upcoming")
        
        # Batch fetch — all events concurrently
        ids = [e.id for e in events[:20]]
        batch = await client.get_odds_batch(ids, "moneyline")
        for eid, odds in batch.items():
            if "error" not in odds:
                print(f"{eid}: {len(odds.get('eventWinner', []))} outcomes")

asyncio.run(main())

Requires: pip install altsportsdata[async]

---

Enterprise Reliability

Built for production — automatic retry, rate limit handling, request tracing.

client = AltSportsData(
    api_key="key",
    max_retries=3,        # exponential backoff on 429, 5xx
    retry_backoff=0.5,    # base delay in seconds
    timeout=30,           # per-request timeout
)

• Automatic exponential backoff with jitter on 429/5xx
• Respects Retry-After headers
• Request IDs (X-Request-ID) for debugging
• Thread-safe session management
• Context manager support: with AltSportsData(...) as client:

---

Full API Reference

Setup

from altsportsdata import AltSportsData

# General client
client = AltSportsData(api_key="your_key")

# League-scoped — auto-filters everything
wsl = client.get_league("wsl")
f1  = client.get_league("f1")
spr = client.get_league("spr")

# With options
client = AltSportsData(
    api_key="your_key",
    league="wsl",
    odds_format="probability",
    max_retries=3,
)

Leagues & Market Types

leagues = client.list_leagues()
print(leagues)        # clean table
df = leagues.df       # pandas DataFrame

# League details
info = client.get_league_info("f1")
print(f"{info.name}: {info.market_count} markets — {info.market_types}")

# All market types
client.list_market_types()
# → ['eventWinner', 'exactasEvent', 'fastestLap', 'headToHead', ...]

# Market catalog with event counts
for lg in client.get_market_catalog():
    print(f"{lg['league']:12} {lg['name']:30} upcoming={lg['upcoming_events']}")

Events

events = client.list_events(status="upcoming")        # ResultSet with .df
events = client.list_events(status="live")
events = client.list_events(status="completed")
events = client.list_events(status=["live", "upcoming"])

print(events)        # clean table
df = events.df       # pandas DataFrame
events.to_csv("events.csv")

event = client.get_event("event_id")
participants = client.get_participants("event_id")

Markets (one call, prices included)

markets = client.get_markets()                              # upcoming (default)
markets = client.get_markets(status="live")                 # live
markets = client.get_markets(status="completed")            # settled
markets = client.get_markets(status=["live", "upcoming"])   # all active

print(markets)       # clean table
df = markets.df      # pandas DataFrame

Odds (per event) — all return OddsResult with .df

# Sportsbook
odds = client.get_moneylines("event_id")    # event winner
odds = client.get_matchups("event_id")      # head-to-head
odds = client.get_totals("event_id")        # over/under
odds = client.get_exactas("event_id")       # exacta
odds = client.get_podiums("event_id")       # top-3
odds = client.get_heat_winners("event_id")  # heat winner
odds = client.get_fastest_lap("event_id")   # fastest lap

# Prediction market
odds = client.get_market_probabilities("event_id")
odds = client.get_podium_probabilities("event_id")
odds = client.get_top_finish_probabilities("event_id", top_n=5)

# DFS
odds = client.get_player_props("event_id")
odds = client.get_player_matchups("event_id")
odds = client.get_player_totals("event_id", stat="points")

# Generic — any market by name or alias
odds = client.get_odds("event_id", "moneyline")

# Every OddsResult has .df
print(odds)          # clean table
df = odds.df         # pandas DataFrame
odds.to_csv("odds.csv")

Batch Operations

# Fetch odds for many events in parallel
events = client.list_events(league="spr", status="upcoming")
ids = [e.id for e in events]
batch = client.get_odds_batch(ids, "moneyline", max_concurrent=5)

Settlement

result = client.get_settlement("event_id")
# → {event_id, event_name, league, status, settled_at,
#    markets: {eventWinner: {outcomes, winners}, headToHead: {...}}}

Live Polling

# Generator-based live odds feed
for update in client.poll_odds(event_id, "moneyline", interval=5, max_polls=60):
    print(update)

Futures

client.list_futures()
client.get_futures(tour="tour_id", type="winner")

Odds Conversion (Static)

AltSportsData.convert(2.50, "decimal", "american")     # → 150.0
AltSportsData.convert(2.50, "decimal", "probability")  # → 0.4

---

30 Leagues

Code	League	Code	League
wsl	World Surf League	pbr	Professional Bull Riders
sls	Street League Skateboarding	bkfc	Bare Knuckle FC
f1	Formula 1	motogp	MotoGP
spr	Supercross	mxgp	MXGP
nrx	Nitrocross	jaialai	Jai Alai
fdrift	Formula Drift	nll	National Lacrosse League
masl	Major Arena Soccer	nhra	NHRA Drag Racing
powerslap	Power Slap	dgpt	Disc Golf Pro Tour
worldoutlaws	World of Outlaws	usac	USAC Racing
xgame	X Games	motoamerica	MotoAmerica
hlrs	High Limit Racing	byb	BYB Extreme Fighting
athletesunlimited	Athletes Unlimited	lux	LUX Fight League
raf	Real American Freestyle	mltt	Major League Table Tennis
motocrs	Motocross	spectation	Spectation
gsoc	Global Soccer	sprmtcrs	Supermotocross

---

Links

• API Docs
• Swagger

License

MIT