kennyhyder/sportsbookish-daily-odds

SportsBookISH Daily Kalshi vs Sportsbook Odds

Real-time pricing snapshot comparing Kalshi event-contract probabilities against US sportsbook consensus across nine sports.

Description

Daily-refreshed JSON / CSV export of every active Kalshi market alongside the de-vigged book median across 13+ US sportsbooks. Covers golf (PGA Tour), NFL, NBA, MLB, NHL, EPL, MLS, UEFA Champions League, and FIFA World Cup.

Source

Live data plane:

• JSON: https://hyder.me/api/data/daily-odds
• CSV: https://hyder.me/api/data/daily-odds-csv

Refreshed hourly server-side; this Hugging Face mirror is updated daily.

Schema

Column	Type	Description
source	string	"golf" or "sports"
league	string	One of: pga, nfl, nba, mlb, nhl, epl, mls, ucl, wc
event_title	string	Human-readable event name (e.g. "Lakers vs Celtics")
event_slug	string	URL-safe slug for the event on sportsbookish.com
season_year	integer	Season year (e.g. 2026)
start_time	timestamp	ISO 8601 event start, or empty for futures
side	string	Team name (sports) or player name (golf)
kalshi_implied	float	Kalshi implied probability (0.0000 - 1.0000)
owgr_rank	integer	Official World Golf Ranking (golf only, may be empty)
generated_at	timestamp	When this snapshot was generated

Usage

from datasets import load_dataset
import pandas as pd

# Load from Hugging Face
ds = load_dataset("kennyhyder/sportsbookish-daily-odds", split="latest")
df = ds.to_pandas()

# Or pull the live CSV directly from the source
df = pd.read_csv("https://hyder.me/api/data/daily-odds-csv")

# Top Kalshi probabilities across all sports
df["kalshi_pct"] = df["kalshi_implied"] * 100
df.sort_values("kalshi_pct", ascending=False).head(20)

# Per-league market counts
df.groupby("league").size().sort_values(ascending=False)

Citation

@misc{sportsbookish_dataset_2026,
  title  = {SportsBookISH Daily Kalshi vs Sportsbook Odds},
  author = {Hyder, Kenny},
  year   = {2026},
  url    = {https://sportsbookish.com/data},
  note   = {Hourly snapshot of Kalshi event-contract prices alongside US sportsbook consensus across nine sports}
}

APA: Hyder, K. (2026). SportsBookISH Daily Kalshi vs Sportsbook Odds [Data set]. SportsBookISH. https://sportsbookish.com/data

License

CC-BY-4.0. Free to use, redistribute, fine-tune models on, embed in research papers, or include in commercial products. Attribution to sportsbookish.com required.

Methodology

Kalshi implied probabilities are computed via bid/ask midpoint when both sides have real liquidity (yes_bid > 0, spread ≤ 10¢, ask < 1.00); otherwise the last-trade price is used. References older than 30 minutes are filtered out before computation.

Full methodology: https://sportsbookish.com/about/methodology

Maintainer

Kenny Hyder — hyder.me · @kennyhyder

For research-grade access (full historical archives, per-book price snapshots, sub-minute updates), use the contact form at https://sportsbookish.com/contact