Esports odds in Python, without scraping

Published 2026-10-08 · by KashRock

Player props from eight DFS books, consensus lines, and live scores — normalized onto one schema, one key, one pip install. This is the shortest path from zero to a working esports odds pipeline.

1. Install and make the first call

pip install kashrock

from kashrock import KashRock

kr = KashRock("kr_demo_swagger_public_readonly")  # read-only demo key
props = kr.props("lol")["props"]
print(props[0])

2. Compare a prop across books

Every prop carries one propId across every book — the over and the under share it. Grouping on it gives you the whole market in one pass:

from collections import defaultdict

board = kr.props("cs2")["props"]
by_prop = defaultdict(list)
for p in board:
    by_prop[p["propId"]].append((p["book"], p["direction"], p["line"]))

# find a prop priced by multiple books
for pid, offers in by_prop.items():
    if len({o[0] for o in offers}) >= 3:
        print(pid, offers)
        break

3. Add lines, matches, and live scores

kr.lines("cs2")        # consensus lines (Hobby+)
kr.matches("lol")      # schedule + results
kr.live_games("valorant")  # live scores + player stats
kr.research_board()    # graded prop research surface
kr.history_tape()      # line tape for backtesting

Next steps

Grab a free Sandbox key in the console (1,500 credits/UTC day, no card), read the SDK reference, or see the whole surface on the esports odds API page.