/markets

name: markets
description: |
  Markets orchestration — connects ESPN live schedules with Kalshi and Polymarket prediction markets. Unified dashboards, odds comparison, entity search, and bet evaluation across platforms.

  Use when: user wants to see prediction market odds alongside ESPN game schedules, compare odds across platforms, search for a team/player on Kalshi or Polymarket, check for arbitrage between ESPN odds and prediction markets, or evaluate a specific game's market value.
  Don't use when: user wants raw prediction market data without ESPN context — use polymarket or kalshi directly. For pure odds math (conversion, de-vigging, Kelly) — use betting. For live scores without market data — use the sport-specific skill.
license: MIT
metadata:
  author: machina-sports
  version: "0.3.0"

Markets Orchestration

Bridges ESPN live schedules (NBA, NFL, MLB, NHL, WNBA, CFB, CBB) with Kalshi and Polymarket prediction markets. Before writing queries, consult `references/api-reference.md` for supported sport codes, command parameters, and price normalization formats.

Quick Start

sports-skills markets get_todays_markets --sport=nba
sports-skills markets search_entity --query="Lakers" --sport=nba
sports-skills markets compare_odds --sport=nba --event_id=401234567
sports-skills markets get_sport_markets --sport=nfl
sports-skills markets get_sport_schedule --sport=nba
sports-skills markets normalize_price --price=0.65 --source=polymarket
sports-skills markets evaluate_market --sport=nba --event_id=401234567
sports-skills markets match_markets --sport=mlb --date=2026-06-06
sports-skills markets get_market_price --venue=kalshi --ticker=KXMENWORLDCUP-26-FR
sports-skills markets get_price_history --venue=kalshi --ticker=KXMENWORLDCUP-26-FR --interval=1d

Python SDK:

from sports_skills import markets

markets.get_todays_markets(sport="nba")
markets.search_entity(query="Lakers", sport="nba")
markets.compare_odds(sport="nba", event_id="401234567")
markets.get_sport_markets(sport="nfl")
markets.get_sport_schedule(sport="nba", date="2025-02-26")
markets.normalize_price(price=0.65, source="polymarket")
markets.evaluate_market(sport="nba", event_id="401234567")
markets.match_markets(sport="mlb", date="2026-06-06")
markets.get_market_price(venue="kalshi", ticker="KXMENWORLDCUP-26-FR", at_time="2026-05-01T12:00:00+00:00")
markets.get_price_history(venue="polymarket", token_id="<token_id>", interval="1h")

CRITICAL: Before Any Query

CRITICAL: Before calling any orchestration command, verify:

• A `sport` code is provided for sport-aware commands (`get_todays_markets`, `compare_odds`, `get_sport_markets`, `evaluate_market`).
• Price sources are identified correctly before normalization: `espn` = American odds, `polymarket` = 0-1 probability, `kalshi` = 0-100 integer.

Important Notes

• **Sport context is passed through.** `--sport=nba` maps automatically to the correct Polymarket sport code and Kalshi series ticker.
• **Both platforms use sport-aware search.** Polymarket uses `sport` → series_id; Kalshi uses `KXNBA`, `KXNFL`, etc.
• **Prices are normalized.** Everything is converted to implied probability for comparison.

Workflows

Today's NBA Dashboard

sports-skills markets get_todays_markets --sport=nba

Returns each game with ESPN info, DraftKings odds, matching Kalshi markets, and matching Polymarket markets.

Find Arb on a Specific Game

1. Get the ESPN event ID: `get_sport_schedule --sport=nba` 2. Compare odds: `compare_odds --sport=nba --event_id=<id>` 3. If arbitrage detected, response includes allocation percentages and ROI — computed from **reference** prices (`price_basis: "reference"`), so confirm both legs on each venue's order book before trading it. 4. `sources` and `completeness` say which venues actually priced this game; a provider error is reported as `error`, not as "no markets".

Full Bet Evaluation

1. `evaluate_market --sport=nba --event_id=<id> --outcome=0 --fee_per_contract=<dollars per $1 contract>` 2. Verifies the market is this game's full-game winner market for the chosen side, then reads the **ask** off the order book 3. Pipes ask + fee through `betting.evaluate_bet`: devig → edge → Kelly 4. Returns fair probability, edge, EV, Kelly fraction, and recommendation

Without `fee_per_contract` the ask is reported but `evaluation` is `null` — the net numbers are refused rather than assumed free. A ticker or `token_id` that names another team, another date, or a derivative (first-half, spread) market is refused, not substituted. Fees are not currently settable via the CLI; use the Python wrapper (`markets.evaluate_market(..., fee_per_contract=...)`).

Same Game on Both Venues

1. `match_markets --sport=mlb --date=2026-06-06` 2. Each match pairs the Kalshi event (with market tickers) and the Polymarket event (with moneyline token IDs) for the same game — joined deterministically on date + team codes, fuzzy title match as fallback. 3. Feed `kalshi.market_tickers[i]` and `polymarket.markets[i].token_ids[j]` straight into `get_market_price` to compare prices.

Price Movement Over Time

1. `get_market_price --venue=kalshi --ticker=<ticker> --at_time=2026-05-01` for a single point-in-time price (both `yes`/`no` sides, 0-1). 2. `get_price_history --venue=kalshi --ticker=<ticker> --interval=1d` for the full series — same `{timestamp, price}` shape on either venue.

Examples

Example 1: Today's games with prediction market odds User says: "What NBA games are on today and what are the prediction market odds?" Actions: 1. Call `get_todays_markets(sport="nba")` Result: Unified dashboard with each game's ESPN info and Kalshi/Polymarket prices

Example 2: Cross-platform team search User says: "Find me Lakers markets on Kalshi and Polymarket" Actions: 1. Call `search_entity(query="Lakers", sport="nba")` Result: All Lakers markets across both exchanges with prices and volume

Example 3: Odds comparison for a spec