How often does an MLB slugger hit a home run when it is priced?

The call

player_id comes from search_players. Over 0.5 home runs is the yes side: over_odds is the price for a home run.

import requests
import pandas as pd

API_KEY = "YOUR_API_KEY"

resp = requests.post(
    "https://mcp.fastballapi.com/v1/get_prop_history",
    headers={"x-api-key": API_KEY},
    json={
        "player_id": "39832",
        "market": "batter_home_runs",
        "from_date": "2026-03-01",
        "to_date": "2026-10-31",
        "limit": 200,
    },
    timeout=60,
)
resp.raise_for_status()
data = resp.json()["data"]

games = pd.DataFrame(data["games"])
s = data["summary"]
print(data["player_name"], data["stat"], "|", s["overs"], "overs,", s["unders"], "unders, over rate", s["over_rate"])
print(games[["game_date", "line", "over_odds", "under_odds", "actual", "result"]].tail(6).to_string(index=False))
# The price against how often he actually homered at that price.
games["band"] = pd.cut(games["over_odds"], [100, 200, 250, 300, 1000])
print(games.groupby("band", observed=True)["result"].apply(lambda r: (r == "over").mean()).round(3))

Replace YOUR_API_KEY with a key from your dashboard. Python needs requests and pandas (pip install requests pandas). JavaScript runs on Node 18 or newer, or in the browser.

Or use the official client: pip install fastballapi

What comes back

The data object of the response, trimmed to the first rows. Every field is real output from the run above.

{
  "player_id": "39832",
  "player_name": "Shohei Ohtani",
  "market": "batter_home_runs",
  "stat": "home runs",
  "bookmaker": null,
  "games": [
    {
      "game_id": "401814699",
      "game_date": "2026-03-26",
      "line": 0.5,
      "open_line": 0.5,
      "over_odds": 189,
      "under_odds": -262,
      "books": 4,
      "actual": 0,
      "result": "under",
      "margin": -0.5
    },
    {
      "game_id": "401814704",
      "game_date": "2026-03-27",
      "line": 0.5,
      "open_line": 0.5,
      "over_odds": 160,
      "under_odds": -225,
      "books": 4,
      "actual": 0,
      "result": "under",
      "margin": -0.5
    },
    {
      "game_id": "401814718",
      "game_date": "2026-03-28",
      "line": 0.5,
      "open_line": 0.5,
      "over_odds": 180,
      "under_odds": -248,
      "books": 5,
      "actual": 0,
      "result": "under",
      "margin": -0.5
    },
    {
      "game_id": "401814746",
      "game_date": "2026-03-30",
      "line": 0.5,
      "open_line": 0.5,
      "over_odds": 194,
      "under_odds": -275,
      "books": 3,
      "actual": 0,
      "result": "under",
      "margin": -0.5
    }
  ],
  "count": 141,
  "summary": {
    "games": 141,
    "graded": 139,
    "overs": 28,
    "unders": 111,
    "pushes": 0,
    "over_rate": 0.201,
    "avg_line": 0.5,
    "avg_actual": 0.2
  },
  "truncated": false,
  "next_from_date": null,
  "note": "2 of 141 games have no result yet (not played, or no box score or play-by-play of ours for the player)."
}

What to do next

• get_player_props (10 credits). Today's home run prices by sportsbook.
• get_plate_appearances (10 credits). Every plate appearance, with the batted ball.

More guides

• Where do I get MLB pitch-by-pitch data for a model?
• How do I get a pitcher's pitch mix and whiff rate in Python?
• How do I build an MLB spray chart in Python?
• How do I find MLB teams that over- or underperformed their run differential?

The same guide for other sports

• PuckAPI: How do I compare NHL player prop lines across sportsbooks?
• EndzoneAPI: How do I compare NFL player prop lines across sportsbooks?
• StrikerAPI: How do I get anytime goalscorer odds and results for a player in Python?
• DunkAPI: How do I compare NBA player prop lines across sportsbooks?