Sports Betting Odds Sample Dataset (SharpAPI)

Real sportsbook odds snapshots from SharpAPI, the real-time sports betting odds API. Free to use for research, coursework, backtesting experiments, and data journalism, with attribution (CC BY 4.0, see below).

Files

File	Rows	What it is
data/worldcup_2026_odds_snapshot.csv	6,178	Full odds board for the 2026 FIFA World Cup, captured 2026-07-13 (semifinals week: Argentina vs England, France vs Spain). 20 sportsbooks, 99 market types, from moneylines and Asian handicaps to correct score and outrights.
data/mlb_odds_snapshot.csv	3,673	Same-day MLB slate across the same books: moneylines, run lines, totals, and derivative markets.

Both files share one schema: each row is one price on one selection at one sportsbook at capture time.

Schema

Column	Type	Description
id	string	Unique row id from the API
sportsbook	string	Book slug (draftkings, fanduel, pinnacle, novig, kalshi, ...)
event_id	string	Stable event identifier, join key across books
sport / league	string	e.g. soccer / fifa_-_world_cup
home_team / away_team	string	Event participants
market_type	string	One of 99 market slugs (moneyline, asian_handicap, total_goals, ...)
selection	string	The specific outcome priced
selection_type	string	side, total, outright, ...
odds_american	int	American odds (+150, -110)
odds_decimal	float	Decimal odds
odds_probability	float	Implied probability (vig included)
line	float or empty	Handicap/total line where applicable
event_start_time	ISO 8601	Scheduled start
is_live	bool	Whether the price was in-play at capture
timestamp	ISO 8601	Capture time of this price

Quick start

Python:

import pandas as pd
wc = pd.read_csv("data/worldcup_2026_odds_snapshot.csv")
ml = wc[wc.market_type == "moneyline"]
best = ml.loc[ml.groupby(["event_id", "selection"]).odds_decimal.idxmax()]
print(best[["selection", "sportsbook", "odds_american"]].head())

R:

wc <- read.csv("data/worldcup_2026_odds_snapshot.csv")
ml <- subset(wc, market_type == "moneyline")
aggregate(odds_decimal ~ selection, ml, max)

Ideas: cross-book vig comparison, best-line analysis, implied-probability calibration against results, market-efficiency studies across 20 books, arbitrage detection exercises.

Want live or historical data?

This is a static snapshot. The live feed behind it: SharpAPI serves real-time odds from 45+ sportsbooks with sub-89ms SSE streaming, no-vig fair odds, +EV and arbitrage detection, plus historical odds and closing-line data on paid tiers. The free tier needs no credit card: sharpapi.io/pricing. SDKs: Python and TypeScript.

License and attribution

Data: CC BY 4.0. Use it freely, including commercially, with attribution:

Odds data from SharpAPI (sharpapi.io), Sports Betting Odds Sample Dataset, 2026.

BibTeX:

@misc{sharpapi2026odds,
  title  = {Sports Betting Odds Sample Dataset},
  author = {{SharpAPI}},
  year   = {2026},
  url    = {https://sharpapi.io},
  note   = {2026 FIFA World Cup and MLB odds snapshots, 20 sportsbooks}
}

Disclaimers

Odds are informational market data, not betting advice. Prices move constantly; this snapshot was accurate only at capture time. Sports betting involves financial risk, is legal only where permitted, and is restricted to those of legal age (21+ in most US states). Please wager responsibly.