PropLine Python SDK

Official Python client for the PropLine player props API — real-time betting odds from Bovada, DraftKings, FanDuel, Pinnacle, Unibet, and PrizePicks across MLB, NBA, NHL, soccer, UFC, and more.

Installation

pip install propline

Quick Start

from propline import PropLine

client = PropLine("your_api_key")

# List available sports
sports = client.get_sports()
# [{"key": "baseball_mlb", "title": "MLB", "active": True}, ...]

# Get today's NBA games
events = client.get_events("basketball_nba")
for event in events:
    print(f"{event['away_team']} @ {event['home_team']}")

# Get player props for a game
odds = client.get_odds("basketball_nba", event_id=events[0]["id"],
    markets=["player_points", "player_rebounds", "player_assists"])

for bookmaker in odds["bookmakers"]:
    for market in bookmaker["markets"]:
        for outcome in market["outcomes"]:
            print(f"{outcome['description']} {outcome['name']} "
                  f"{outcome['point']} @ {outcome['price']}")

Get Your API Key

1. Go to prop-line.com
2. Enter your email
3. Get your API key instantly — 1,000 requests/day, no credit card required

Available Sports

Migrating from the-odds-api? Their sport key names work as aliases (americanfootball_nfl, icehockey_nhl, soccer_spain_la_liga, mma_mixed_martial_arts, ...) so only the base URL changes. Aliases exist only where the competition is identical; anything else returns a structured 404 with did_you_mean rather than a silently-different feed.

Bookmakers

Every odds response returns a bookmakers array so you can compare lines across books in a single request — iterate the array to line-shop.

from propline import PropLine, Bookmaker

client = PropLine("your_api_key")

odds = client.get_odds("baseball_mlb", event_id=events[0]["id"],
    markets=["pitcher_strikeouts"])

# Filter to a specific book
for bk in odds["bookmakers"]:
    if bk["key"] == Bookmaker.DRAFTKINGS:
        ...

# Or iterate all books
for bk in odds["bookmakers"]:
    print(f"\n{bk['title']}")
    for market in bk["markets"]:
        for o in market["outcomes"]:
            print(f"  {o['description']} {o['name']} {o['point']}: {o['price']}")
# Bovada
#   Zack Wheeler Over 6.5: -130
# DraftKings
#   Zack Wheeler Over 6.5: -125
# FanDuel
#   Zack Wheeler Over 6.5: -135

Available Markets

MLB

pitcher_strikeouts, pitcher_outs, pitcher_earned_runs, pitcher_hits_allowed, batter_hits, batter_home_runs, batter_rbis, batter_total_bases, batter_stolen_bases, batter_walks, batter_singles, batter_doubles, batter_runs, batter_2plus_hits, batter_2plus_home_runs, batter_2plus_rbis, batter_3plus_rbis

NBA

player_points, player_rebounds, player_assists, player_threes, player_steals, player_blocks, player_turnovers, player_points_rebounds, player_points_assists, player_rebounds_assists, player_points_rebounds_assists, player_double_double, player_triple_double

NHL

player_goals, player_first_goal, player_goals_2plus, player_goals_3plus, player_shots_on_goal, player_points_1plus, player_points_2plus, player_points_3plus, goalie_saves, player_blocked_shots

Soccer (EPL, La Liga, Serie A, Bundesliga, Ligue 1, MLS)

anytime_goal_scorer, first_goal_scorer, 2plus_goals, goal_or_assist, player_assists, player_2plus_assists, player_cards, both_teams_to_score, double_chance, draw_no_bet, correct_score, total_corners, total_cards

UFC / Boxing

h2h, total_rounds, fight_distance, round_betting

Game Lines (all sports)

h2h, spreads, totals (includes alt lines and team totals)

A team total rides the same totals key as the game total, so one book can return several totals markets on one event. Read the market's team field to tell them apart — it carries the canonical event team name (matching home_team / away_team exactly) on a team total and is None on the game total:

game_total = next(m for m in book["markets"]
                  if m["key"] == "totals" and m.get("team") is None)

team is always None outside totals, and is present on odds, odds history, closing lines and movement. The book's own description is still there as the human-readable label, but every book words it differently (Bovada suffixes " - {team}", BetUS prefixes "Team Total - ", Smarkets and TAB say nothing), so prefer team over parsing that string.

Examples

Get MLB pitcher strikeout props

from propline import PropLine

client = PropLine("your_api_key")

events = client.get_events("baseball_mlb")
for event in events:
    odds = client.get_odds("baseball_mlb", event_id=event["id"],
        markets=["pitcher_strikeouts"])

    print(f"\n{event['away_team']} @ {event['home_team']}")
    for bk in odds["bookmakers"]:
        for mkt in bk["markets"]:
            for o in mkt["outcomes"]:
                if o["point"]:
                    print(f"  {o['description']} {o['name']} {o['point']}: {o['price']}")

Filter to game-period markets

Every odds endpoint (get_odds, get_odds_history, get_odds_closing, get_movement) also accepts a bookmakers= kwarg — a bookmaker key or list of keys, same parameter name as the-odds-api — to restrict the response to specific books:

# Only DraftKings + FanDuel lines
odds = client.get_odds(
    "baseball_mlb", event_id=12345,
    markets=["pitcher_strikeouts"],
    bookmakers=["draftkings", "fanduel"],
)

Every odds endpoint accepts a period= kwarg to scope results to first-quarter / first-half / first-period / first-N-innings markets. Omit it for full-game markets — the default behavior is unchanged.

Event-page links (click out to the book)

get_odds and get_event_best_line accept include_links=True — each bookmaker block (odds) or price row (best-line) then carries a link: that book's public event-page URL, so your UI can click out from a line straight to the book. Plain navigation, no affiliate tagging. Links ship for Bovada, DraftKings, FanDuel, BetMGM, Kalshi, Polymarket and Smarkets; other books return None. The same flag also adds app_link — a mobile app-open deep link that opens the book's native app on the fixture (app-store fallback otherwise), vs link = the desktop web page. ProphetX only today; None elsewhere.

bl = client.get_event_best_line(
    "baseball_mlb", 12345, include_links=True)
for line in bl["lines"]:
    for side, info in line["sides"].items():
        best = info["best"]
        print(f"{side}: {best['price']} @ {best['book_title']} -> {best['link']}")

Native book ids (join onto a book's own data)

get_odds accepts include_book_ids=True — each bookmaker block then carries a book_event_id and each outcome a book_outcome_id: that book's OWN identifiers for the event and the priced selection. Use them to join PropLine rows onto a book's native feed by id, instead of fuzzy-matching team names, player names and lines.

Kalshi ships both — the event ticker and the per-contract market ticker — which makes this the leg-level join key if you already pull Kalshi's own API. Most other books ship an event id; books without a stable public id return None.

event = client.get_odds(
    "baseball_mlb", event_id=12345,
    markets=["h2h"],
    include_book_ids=True,
)
for book in event["bookmakers"]:
    print(book["key"], book["book_event_id"])
    for m in book["markets"]:
        for o in m["outcomes"]:
            print("   ", o["name"], o["book_outcome_id"])

Note a two-sided market can share one book_outcome_id across both legs: a Kalshi contract is binary, so Over and Under are its YES and NO sides. The id identifies the contract; the outcome's name tells you which side.

# First-quarter NBA totals
q1 = client.get_odds(
    "basketball_nba", event_id=12345,
    markets=["totals"],
    period="q1",   # q1|q2|q3|q4 | h1|h2 | p1|p2|p3 | i1..i9 | f3|f5|f7
)

# Multiple periods in one call — pass a list or a comma-separated string
both = client.get_odds(
    "basketball_nba", event_id=12345,
    markets=["totals"],
    period=["q1", "q2"],
)

# Pass period="all" to include every period alongside the full-game row.

Every response row carries a period field so you can bucket client-side. Coverage today: Bovada / DraftKings / FanDuel / Pinnacle on NBA / NHL / MLB / soccer. Football period markets land at NFL preseason (August 2026). The same period= kwarg works on get_odds_history() and get_odds_closing() too.

Exchange liquidity (is the price actually bettable?)

ProphetX is a peer-to-peer exchange, so its best price is often a thin dangling offer with only a few dollars behind it. Every ProphetX outcome carries liquidity — the dollars you can actually stake at the quoted price — so you can filter or flag quotes that are only good for a buck. None for books without a resting-size signal. The same field rides every price row on get_best_line, where a thin exchange quote often wins the best slot on price alone.

event = client.get_odds("baseball_mlb", event_id=12345)
for book in event["bookmakers"]:
    if book["key"] != "prophetx":
        continue
    for m in book["markets"]:
        for o in m["outcomes"]:
            liq = o.get("liquidity")
            if liq is not None and liq < 25:
                print(f"thin: {m['key']} {o['name']} {o['price']} (${liq})")

Get game scores

scores = client.get_scores("baseball_mlb")
for game in scores:
    if game["status"] == "final":
        print(f"{game['away_team']} {game['away_score']}, "
              f"{game['home_team']} {game['home_score']}")

Get game context — pitchers, umpire, weather (free)

ctx = client.get_context("baseball_mlb", event_id=37464)
print(f"{ctx['away_probable_pitcher']} ({ctx['away_probable_pitcher_hand']}) @ "
      f"{ctx['home_probable_pitcher']} ({ctx['home_probable_pitcher_hand']})")
print(f"Umpire: {ctx['home_plate_umpire']}  Lineup set: {ctx['lineup_confirmed']}")
if ctx["weather"]:
    w = ctx["weather"]
    print(f"{w['temperature_f']}F, wind {w['wind_speed_mph']}mph {w['wind_direction']}, {w['conditions']}")

The conditions a prop settles under. For MLB: probable starting pitchers and their throwing hand (home_probable_pitcher_hand / away_probable_pitcher_hand, "L"/"R"/"S" — platoon-split context for every batter prop), a confirmed-lineup flag, the home-plate umpire, and first-pitch weather at outdoor / open-roof venues. For NFL & NCAAF: the venue and kickoff weather (pitcher/umpire/lineup fields are None for football). The same block is embedded in get_results(), so every graded prop carries its conditions — unique to PropLine. Free tier. Raises on 404 when no context is on file for the event yet.

Get line movement & steam (Hobby+)

mv = client.get_movement("baseball_mlb", event_id=37464)
for s in mv["steam"]:
    print(f"{s['name']} {s['consensus_direction']} "
          f"({s['books_moved']}/{s['books_quoting']} books, score {s['steam_score']})")

Line movement derived from our snapshot tick history. Per (book, market, outcome): opening line, latest line, implied-probability + point shift, direction. The steam array flags outcomes multiple books moved the same direction — the sharp-money signal across every book we poll. Unique to PropLine. Hobby+ full; free tier redacted.

Get resolution coverage summary (free)

s = client.get_resolution_summary(days=30)
print(f"{s['total_graded']:,} props graded across "
      f"{s['sports_covered']} sports in {s['days']}d")
for row in s["by_sport"][:5]:
    print(f"  {row['title']}: {row['graded']:,} ({row['events']} games)")

Get resolved prop outcomes (Pro only)

results = client.get_results("baseball_mlb", event_id=16,
    markets=["pitcher_strikeouts", "batter_hits"])

print(f"{results['away_team']} {results['away_score']}, "
      f"{results['home_team']} {results['home_score']}")

for market in results["markets"]:
    for outcome in market["outcomes"]:
        print(f"{outcome['description']} {outcome['name']} "
              f"{outcome['point']}: {outcome['resolution']} "
              f"(actual: {outcome['actual_value']})")
# Output: "Tarik Skubal (DET) Over 6.5: won (actual: 7.0)"

Get historical line movement (Hobby+)

history = client.get_odds_history("baseball_mlb", event_id=16,
    markets=["pitcher_strikeouts"])

for book in history["bookmakers"]:
    for market in book["markets"]:
        for outcome in market["outcomes"]:
            print(f"\n[{book['key']}] {outcome['description']}:")
            for snap in outcome["snapshots"]:
                print(f"  {snap['recorded_at']}: {snap['price']} @ {snap['point']}"
                      f" (book reported: {snap.get('book_updated_at') or 'n/a'})")

Each snapshot carries up to three change-detection signals: recorded_at (when our scraper saw the odds), book_updated_at (when the book itself reports the price was last set — Bovada today), and book_version (per-market monotonic counter — Pinnacle today). The gap between recorded_at and book_updated_at is per-book scraper latency; deltas in book_version between two snapshots tell you how many distinct market updates the book recorded between them, even when the visible price didn't change. See https://prop-line.com/docs#timestamps for the full semantic.

Period-historical query params

Combine any of these to scope, downsample, and de-noise:

# Just the last 30 minutes of moves before tip — and only the moments
# when the line actually changed.
moves = client.get_odds_history(
    "baseball_mlb", event_id=16,
    markets=["pitcher_strikeouts"],
    relative_from="-30m",
    relative_to="0",
    changes_only=True,
)

# One snapshot per minute for the 3 hours before commence — stable
# spacing for backtests / moving averages.
ts = client.get_odds_history(
    "baseball_mlb", event_id=16,
    markets=["pitcher_strikeouts"],
    relative_from="-3h",
    relative_to="0",
    interval="1m",   # 30s | 1m | 5m | 15m | 30m | 1h
)

• from / to: absolute ISO timestamps (from_ in Python — from is reserved).
• relative_from / relative_to: offsets relative to commence_time. Forms: -3h, -30m, -90s, 0. Mutually exclusive with the absolute counterpart.
• interval: downsample to one snapshot per bucket; latest snapshot in each bucket wins.
• changes_only: drop adjacent snapshots whose (price, point) match the previous one. Opening line is always kept.

Get opening & closing lines / CLV (Hobby+)

One call returns both ends of the move per (book, market, outcome): the last snapshot at or before commence_time (price / point / closing_at) and the first snapshot in the same 14-day pre-kickoff window (opening_price / opening_point / opening_at).

closing = client.get_odds_closing(
    "baseball_mlb", event_id=5885,
    markets=["pitcher_strikeouts"],
)

for book in closing["bookmakers"]:
    for m in book["markets"]:
        for o in m["outcomes"]:
            if o["description"] != "Bryan Woo" or o["name"] != "Over":
                continue
            print(f"{book['key']}: opened {o['opening_price']} @ {o['opening_point']}"
                  f" -> closed {o['price']} @ {o['point']} ({o['closing_at']})")
            # Compare to your entry: -110 → closing -130 = +CLV

Compare the points, not just the prices. On spreads and totals the number moves as much as the price (6.5 → 7.0), so a price-only comparison silently mis-measures those markets.

opening_age_seconds is how long before kickoff the opener was recorded. The archive starts April 2026, so for a book/sport PropLine began polling after a line was posted, opening_* means first observed by us rather than the book's true open — a value in minutes rather than hours is the tell.

Grade your bets against the close (Hobby+)

get_odds_closing gives you the closing line; grade_clv does the whole job — send the bets you actually placed and get CLV, the de-vigged closing fair, and the graded result back per bet. Stateless: nothing is stored.

res = client.grade_clv([
    {
        "ref": "b1",
        "sport_key": "baseball_mlb",
        "event_id": 150791,
        "market": "batter_hits_runs_rbis",
        "bookmaker": "lowvig",
        "selection": "Drake Baldwin",
        "side": "Under",
        "point": 0.5,
        "price": 145,
        "stake": 1,
    },
])

s = res["summary"]
print(f"{s['matched']}/{s['bets']} matched · "
      f"beat the close {s['beat_close_pct']}% · {s['profit_units']:+.2f}u")

for b in res["bets"]:
    if not b["matched"]:
        print(f"{b['ref']}: unmatched ({b['unmatched_reason']})")
        continue
    print(f"{b['ref']}: took {b['price']} vs close {b['closing_price']} "
          f"-> CLV {b['clv_pct']:+.2f}% · "
          f"vs de-vigged close {b['ev_vs_close_pct']:+.2f}% "
          f"({b['fair_source']}) -> {b['resolution']}")

Two CLV numbers, and they disagree on purpose. clv_pct is price-vs-price: familiar and quotable, but vig-blind, so it flatters a bet taken on the juicy side of a wide market. ev_vs_close_pct scores your price against the de-vigged close and is the honest one — a -110 taken into a -105/-115 close beat the price but not the fair line. On a real bet the two came out +6.52% and +0.08%.

The de-vig uses the sharpest book quoting that line at close (fair_source), not the book you bet at — de-vigging your own book always returns a negative number, because you paid its hold.

Bets whose event hasn't started carry closing_is_final: False, land in summary["pending"], and are excluded from the averages: before kickoff the "closing" price is just the latest price, so CLV is ~0 by construction.

Matching is fail-closed. A bet that can't be pinned to exactly one stored outcome comes back matched: False with an unmatched_reason (event_not_found, no_market_for_key, no_outcome_for_selection, ambiguous_selection, no_closing_snapshot) rather than a confident wrong match. Lines match by equality, never nearest-value — 0.5 and 1.5 are different bets. Max 500 bets per request.

Get player prop history (Pro full, Free redacted)

# "Did Bryan Woo go over/under his last 10 strikeout props?"
hist = client.get_player_history("baseball_mlb", "Bryan Woo",
    market="pitcher_strikeouts", limit=10)

for e in hist["entries"]:
    print(f"{e['commence_time'][:10]} {e['bookmaker_title']}: "
          f"line {e['line']}, actual {e['actual_value']} "
          f"-> Over {e['over_result']}, Under {e['under_result']}")
# Output: "2026-04-19 DraftKings: line 6.5, actual 6.0 -> Over lost, Under won"

Get a player's game log / head-to-head (free)

# Every raw box-score stat, per game, in one call — no fanning out one
# request per event. Build L5/L10/L20, season splits and charts from these.
log = client.get_player_games("baseball_mlb", "Aaron Judge", limit=10)

for g in log["games"]:
    where = "vs" if g["is_home"] else "@"
    print(f"{g['commence_time'][:10]} {where} {g['opponent']}: "
          f"{g['stats'].get('hits', 0)} H, {g['stats'].get('home_runs', 0)} HR")

# Head-to-head — accepts a name, nickname or abbreviation. The limit applies
# AFTER the filter, so this is the last 5 MEETINGS, not the Boston games
# among his last 5 games. Not capped to the current season.
h2h = client.get_player_games("baseball_mlb", "Aaron Judge",
    limit=5, opponent="BOS")

This reads the raw-stats archive, not graded-prop history — it covers every game with a box score on file, including games no sportsbook priced, so a "last 10 games" window here really is the last 10 games. It carries no line, price or grade; use get_player_trends for hit rates against a posted line.

Get player hit-rate trends (Pro full, Free redacted)

# "How often has Aaron Judge gone over his total bases line lately?"
# Rolling Over/Under splits over the last 5/10/20/50 graded games,
# plus current streak and most-recent line/actual. Omit `market` for
# trends across every market the player has graded games in. Pass
# `dfs_odds_type="standard"|"goblin"|"demon"` to compute the trend
# against that PrizePicks flavor's line only.
trends = client.get_player_trends("baseball_mlb", "Aaron Judge",
    market="batter_total_bases")

for m in trends["markets"]:
    l10 = m["last_10"]
    streak = m["current_streak"]
    print(f"{m['market']}: line {m['recent_line']}, avg {m['avg_actual']}, "
          f"L10 {l10['over']}-{l10['under']} ({l10['over_pct']}% over), "
          f"streak {streak['length']} {streak['result']}")
# Output: "batter_total_bases: line 1.5, avg 2.02, L10 3-7 (30.0% over), streak 2 under"

Cross-book +EV (Pro)

# Find +EV plays on a single event. A sharp book anchors the no-vig
# fair line; every other book's price gets an EV%, with +EV plays
# floated to the top of each line group.
#
# `bookmakers` narrows the PRICES to books you hold accounts at — never
# the anchor. This still measures DK and FD against Pinnacle. Read
# line["fair_source"] to see which book anchored each line; the anchor
# is picked per line, so one response mixes several.
ev = client.get_event_ev("baseball_mlb", 12345,
    markets=["pitcher_strikeouts", "batter_hits"],
    bookmakers=["draftkings", "fanduel"])  # optional

for line in ev["lines"]:
    plus = [o for o in line["outcomes"] if o["is_plus_ev"]]
    if plus:
        print(f"\n{line['market_key']} {line['description']} "
              f"line={line['point']} fair={line['fair_source']}")
        for o in plus:
            print(f"  {o['book_title']:11s} {o['name']:6s} "
                  f"{o['price']:+5d}  ev=+{o['ev_pct']}%")

Best line — cross-book line shopping (Hobby+)

# You've decided the bet — now find which book pays the most.
bl = client.get_event_best_line("baseball_mlb", 12345,
    markets="pitcher_strikeouts",
    bookmakers=["draftkings", "fanduel", "bovada"])  # only my books

for line in bl["lines"]:
    for side, info in line["sides"].items():
        best = info["best"]
        print(f"{line['description']:24s} {side:6s} {line['point']}: "
              f"{best['price']:+5d} @ {best['book_title']} "
              f"(of {len(info['all_prices'])} books)")

DFS pick'em books (PrizePicks, Sleeper, Dabble) are excluded — their quotes aren't independently bettable payouts; Underdog is included only at clean two-way lines. Each price carries last_update so you can discount stale quotes.

Bulk CSV export of resolved props (Pro)

# Save every resolved MLB strikeout prop since April 1st to disk.
client.export_resolved_props(
    sport="baseball_mlb",
    market="pitcher_strikeouts",
    since="2026-04-01T00:00:00Z",
    out_path="./mlb-strikeouts.csv",
)

# Or parse in memory with pandas for analysis.
import io
import pandas as pd
data = client.export_resolved_props(sport="baseball_mlb")
df = pd.read_csv(io.BytesIO(data))
hit_rate = (df.query("outcome_name == 'Over' and resolution == 'won'").shape[0]
            / df.query("outcome_name == 'Over'").shape[0])
print(f"Over hit rate across all MLB markets: {hit_rate:.1%}")

Every row carries both ends of the line move alongside the graded result — opening_price / opening_point / opening_at (first line in the 14 days before kickoff) and closing_price / closing_point / closing_at (last line at or before it) — so a full CLV study is one download rather than one /odds/closing call per event:

df = pd.read_csv(io.BytesIO(data))
df = df[df.closing_price.notna() & df.opening_price.notna()]
# Did the market move toward the Over after it opened?
moved_to_over = df.query("outcome_name == 'Over' and closing_price < opening_price")
print(moved_to_over.groupby("market").resolution.value_counts(normalize=True))

closing_point is distinct from line (the outcome's own current point); on spreads and totals they differ whenever the number moved. New columns are always appended immediately before customer_token, so positional parsers written against an earlier column set keep working.

Full line-movement history (Historical Backfill / Enterprise)

# Every recorded snapshot (price + line, per book) — not just the close.
# The raw tick history no subscription tier can bulk-pull; exclusive to
# the one-time Historical Backfill pass and Enterprise. Page month by
# month — a full archive runs to gigabytes per sport.
client.export_odds_history(
    sport="baseball_mlb",
    since="2026-04-01T00:00:00Z",
    until="2026-05-01T00:00:00Z",
    out_path="./mlb-line-history-apr.csv",
)

Webhooks (Streaming tier)

The Streaming tiers push line_movement, resolution, steam and market_suspended events to your URL in real time, with HMAC-SHA256 signing and automatic retries.

Register a subscription

wh = client.create_webhook(
    url="https://example.com/hooks/propline",
    filter_sport_key="baseball_mlb",
    filter_market_key="pitcher_strikeouts",
    min_price_change_pct=2.0,  # only fire on shifts of 2%+ (or any point change)
    batch_max=100,             # recommended: up to 100 events per POST
)

# Store wh["secret"] — this is the ONLY time it's returned.
SECRET = wh["secret"]
print(f"webhook id: {wh['id']}")

With batch_max set (1–500), events arrive as a signed envelope instead of one POST each: {"batch": true, "event_type": ..., "count": N, "events": [{"delivery_id": ..., "data": <per-event payload>}, ...]} with an X-PropLine-Batch: N header. Dedupe on each element's delivery_id. Use it for any high-volume subscription — sport-wide line_movement can exceed 1,000 events/min during a full slate, and one POST per event caps your delivery rate at your endpoint's response time. batch_max=0 reverts to per-event delivery. JSON format only (Discord stays per-event).

Verify incoming deliveries

Each POST carries these headers:

from propline import PropLine

# In a FastAPI/Flask handler:
ok = PropLine.verify_signature(
    secret=SECRET,
    timestamp=headers["X-PropLine-Timestamp"],
    body=raw_body_bytes,
    signature=headers["X-PropLine-Signature"],
)
if not ok:
    return 401

Line-movement payload

{
  "event_type": "line_movement",
  "sport_key": "baseball_mlb",
  "event": {"id": 5070, "home_team": "Seattle Mariners", "away_team": "Texas Rangers", ...},
  "market_key": "totals",
  "market_description": "Total 7.5",
  "player_name": null,
  "outcome_name": "Over",
  "dfs_odds_type": null,
  "payout_multiplier": null,
  "previous": {"price_american": -750, "point": 7.0},
  "current":  {"price_american": -300, "point": 7.5},
  "price_change_pct": 60.0,
  "timestamp": "2026-04-18T03:49:00Z"
}

Resolution payload

{
  "event_type": "resolution",
  "sport_key": "baseball_mlb",
  "event": {"id": 16, "home_score": 4, "away_score": 2, "status": "final", ...},
  "market_key": "pitcher_strikeouts",
  "market_description": "Total Pitching Strikeouts",
  "player_name": "Tarik Skubal (DET)",
  "outcome_name": "Over",
  "dfs_odds_type": null,
  "payout_multiplier": null,
  "point": 6.5,
  "resolution": "won",
  "actual_value": 9.0,
  "resolved_at": "2026-04-18T06:14:22Z"
}

market_description is where a DFS alt market's flavor + line live (e.g. PrizePicks "Rebounds (demon 12.5)"). dfs_odds_type is the PrizePicks flavor (standard / goblin / demon; null for every traditional book); payout_multiplier is Underdog's numeric boost/discount (PrizePicks publishes no numeric multiplier — the flavor is the signal). Same semantics as the identically-named fields on /odds outcomes.

Market-suspended payload

A book took a market off the board pregame. One delivery per (book, event, player) — a late scratch is ONE event carrying every key the book pulled, not one per key. books_agreeing is how many books have pulled the same subject on the same event; subscribe with min_books_agreeing=3 to hear only corroborated drops, or leave it unset to hear every one (the right choice if you price off a single book). Pull-side twin: suspended_at on every market in /odds, on every tier.

client.create_webhook(
    url="https://example.com/hooks/propline",
    events=["market_suspended"],
    filter_sport_key="baseball_mlb",
    min_books_agreeing=3,   # omit to receive every single-book drop
)

{
  "event_type": "market_suspended",
  "sport_key": "baseball_mlb",
  "event": {"id": 138811, "home_team": "Pittsburgh Pirates", "away_team": "Boston Red Sox", ...},
  "bookmaker_key": "draftkings",
  "bookmaker_title": "DraftKings",
  "subject": "Willson Contreras",
  "reason": "off_the_board",
  "markets": [
    {"key": "batter_hits", "description": "Willson Contreras Hits O/U", "period": null,
     "last_seen": "2026-08-16T14:03:45+00:00",
     "last_price": [{"name": "Over", "price": -115, "point": 0.5},
                    {"name": "Under", "price": -105, "point": 0.5}]},
    {"key": "batter_total_bases", "...": "..."}
  ],
  "books_agreeing": 7,
  "books": ["betmgm", "betrivers", "draftkings", "novig", "pinnacle", "prophetx", "underdog"],
  "suspended_at": "2026-08-16T14:07:30+00:00"
}

reason is "off_the_board" for a sportsbook and "no_offers" for an exchange whose resting offers went. There is no restore event: when the market returns, line_movement fires on the returning price.

Manage subscriptions

for wh in client.list_webhooks():
    print(wh["id"], wh["url"], "active" if wh["active"] else "paused")

client.update_webhook(wh_id, min_price_change_pct=5.0)  # change a filter
client.test_webhook(wh_id)                              # queue a test payload
client.list_webhook_deliveries(wh_id, limit=50)         # newest 50 attempts
client.list_webhook_deliveries(wh_id, limit=200, before_id=123456)  # page backwards
client.delete_webhook(wh_id)                            # cascades deliveries

Error Handling

from propline import PropLine, AuthError, RateLimitError, PropLineError

client = PropLine("your_api_key")

try:
    odds = client.get_odds("baseball_mlb", event_id=1)
except AuthError:
    print("Invalid API key")
except RateLimitError as e:
    # Daily-cap 429s include a pre-filled one-click upgrade URL
    print(f"Rate limited: {e.message}")
    if e.upgrade_url:
        print(f"Upgrade: {e.upgrade_url}")
except PropLineError as e:
    print(f"API error: {e.status_code} — {e.message}")

Gated and throttled endpoints return a structured error body (docs); its fields are exposed as attributes on every PropLineError:

Tracking Your Usage

Every authenticated response carries live quota headers; the client parses them into client.last_quota automatically:

client.get_sports()

q = client.last_quota
print(f"{q.used}/{q.limit} used today, {q.remaining} left")
print(f"Quota resets at {q.reset_at.isoformat()}")  # 00:00 UTC, hard reset

last_quota is None before the first request and refreshes on every call (including 429s), so a long-running poller can watch remaining and back off before hitting the daily cap.

Links

• Website: prop-line.com
• API Docs: prop-line.com/docs
• Recipes (code for common jobs): prop-line.com/recipes
• Odds API by sport and market (live line, books, graded hit rate): prop-line.com/odds-api
• Prop resolution (every prop graded against the box score): prop-line.com/prop-resolution-api
• Cross-book +EV: prop-line.com/ev
• Pricing: prop-line.com/pricing
• Dashboard: prop-line.com/dashboard
• OpenAPI reference: api.prop-line.com/docs
• Node SDK: npm install propline
• MCP server: npx -y propline-mcp

License

MIT