altsportsdata 2.1.4

The data feed for alternative sports betting — odds, events, futures for 31 leagues

AltSportsData SDK

Alternative sports odds & data for sportsbooks, DFS platforms, and prediction markets.

One SDK. 30 leagues. Every market type. Moneylines, matchups, props, podiums, futures, and parlays — from surfing to Supercross to Formula Drift.

pip install altsportsdata

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Example 1 — Pre-Event Moneylines

from altsportsdata import AltSportsData

client = AltSportsData(api_key="your_key", league="spr")  # Supercross

events = client.list_events(status="upcoming")
odds = client.get_moneylines(events[0].id)

for o in sorted(odds["eventWinner"], key=lambda x: x["odds"])[:5]:
    ath = o["athlete"]
    print(f'{ath["firstName"]} {ath["lastName"]:20} {o["probability"]:6.2f}%  ({o["odds"]:.2f})')

Eli Tomac              49.42%  (2.02)
Hunter Lawrence        37.57%  (2.66)
Ken Roczen             23.49%  (4.26)
Cooper Webb            14.62%  (6.84)
Joey Savatgy            6.17%  (16.20)

Example 2 — Head-to-Head Matchups

matchups = client.get_matchups(events[0].id)

for m in matchups["headToHead"]:
    p1, p2 = m["eventParticipant1"], m["eventParticipant2"]
    a1, a2 = p1["athlete"], p2["athlete"]
    print(f'{a1["firstName"]} {a1["lastName"]} ({p1["odds"]:.2f})'
          f'  vs  {a2["firstName"]} {a2["lastName"]} ({p2["odds"]:.2f})')

Cooper Webb (2.31)  vs  Ken Roczen (1.57)
Eli Tomac (1.66)  vs  Hunter Lawrence (2.13)

Example 3 — Podium Probabilities

podiums = client.get_podiums(events[0].id)

for o in sorted(podiums["podiums"], key=lambda x: x["odds"])[:5]:
    ath = o["athlete"]
    print(f'{ath["firstName"]} {ath["lastName"]:20} {o["probability"]:5.1f}%  ({o["odds"]:.2f})')

Eli Tomac              87.7%  (1.14)
Hunter Lawrence        77.5%  (1.29)
Ken Roczen             63.7%  (1.57)
Cooper Webb            49.6%  (2.02)
Joey Savatgy           23.3%  (4.28)

Example 4 — Historical Results & Settlement

wsl = AltSportsData(api_key="your_key", league="wsl")  # World Surf League

completed = wsl.list_events(status="completed")
odds = wsl.get_moneylines(completed[0].id)

for o in sorted(odds["eventWinner"], key=lambda x: x["odds"])[:5]:
    ath = o["athlete"]
    tag = "✅" if o["settlement"] == "WIN" else "  "
    print(f'{tag} {ath["firstName"]} {ath["lastName"]:20} odds {o["odds"]:5.2f}  → {o["settlement"]}')

   Molly Picklum        odds  3.50  → LOSE
   Caitlin Simmers      odds  3.88  → LOSE
   Erin Brooks          odds  5.49  → LOSE
✅ Gabriela Bryan        odds  6.46  → WIN
   Bettylou Johnson     odds  9.90  → LOSE

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All Market Types

# Sportsbook — traditional markets
client.get_moneylines("event_id")         # event winner
client.get_matchups("event_id")           # head-to-head
client.get_totals("event_id")             # over/under
client.get_exactas("event_id", n=2)       # exacta (2, 3, or 4)
client.get_podiums("event_id")            # top-3 finish
client.get_shows("event_id")             # shows
client.get_heat_winners("event_id")       # heat winner
client.get_fastest_lap("event_id")        # fastest lap
client.get_dream_team("event_id")         # dream team

# DFS — player-level markets
client.get_player_props("event_id")       # prop bets
client.get_player_matchups("event_id")    # pick'em matchups
client.get_player_totals("event_id")      # over/under by stat

# Prediction markets — probability-first
client.get_market_probabilities("event_id")               # win probabilities
client.get_podium_probabilities("event_id")               # top-3 probabilities
client.get_top_finish_probabilities("event_id", top_n=5)  # top-N probabilities

# Same Game Parlay
client.calculate_parlay("event_id", picks=["odd_id_1", "odd_id_2"])

# Generic — any market by name or alias
client.get_odds("event_id", "moneyline")  # aliases: "h2h", "props", "totals", etc.

Events

client.list_events(status="upcoming")                     # pre-event
client.list_events(status="live")                         # in-progress
client.list_events(status="completed")                    # historical + settlement
client.list_events(status=["live", "upcoming"])           # combine filters
client.list_events(league="f1", status="upcoming")        # filter by league
client.get_event("event_id")                              # full detail with rounds/heats
client.get_participants("event_id")                       # athletes / roster
client.get_heat_scores("event_id", "heat_id")            # scores / lap times

Futures

client.list_futures()                                     # all available futures
client.get_futures(tour="tour_id", type="winner")         # season winner odds

Setup

from altsportsdata import AltSportsData

# All leagues
client = AltSportsData(api_key="your_key")

# Lock to a league — auto-filters every query
wsl = AltSportsData(api_key="your_key", league="wsl")
f1  = AltSportsData(api_key="your_key", league="f1")
spr = AltSportsData(api_key="your_key", league="spr")

Google Colab

!pip install altsportsdata

from altsportsdata import AltSportsData
from google.colab import userdata

client = AltSportsData(api_key=userdata.get("ALTSPORTSDATA_API_KEY"))

for lg in client.list_leagues():
    print(f"{lg.key:15} {lg.name}")

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