pip install requests pandas

import requests
import pandas as pd

API_KEY = "your_api_key_here"
BASE = "https://api.fieldfunded.com/v1"
H = {"X-API-Key": API_KEY}

# Get Premier League events
events = requests.get(
    f"{BASE}/events",
    headers=H,
    params={"sport": "soccer", "league": "england_epl"}
).json()

# Build a flat list of all match winner odds
rows = []
for event in events["events"][:10]:
    odds = requests.get(
        f"{BASE}/events/{event['id']}/odds", headers=H
    ).json()

    for market in odds["markets"]:
        if "1x2" not in market["name"].lower() and \
           "winner" not in market["name"].lower():
            continue
        for sel in market["selections"]:
            rows.append({
                "match": f"{event['home_team']} vs {event['away_team']}",
                "kickoff": event["commence_time"],
                "selection": sel["name"],
                "odds": sel["odds"],
                "market_id": market["id"],
                "selection_id": sel["id"],
            })

df = pd.DataFrame(rows)
print(df.head(9))

                              match              kickoff    selection  odds
0  Manchester United vs Liverpool  2026-05-10T15:00:00Z   Man United  3.40
1  Manchester United vs Liverpool  2026-05-10T15:00:00Z         Draw  3.60
2  Manchester United vs Liverpool  2026-05-10T15:00:00Z    Liverpool  2.10
3        Arsenal vs Aston Villa  2026-05-10T17:30:00Z      Arsenal  1.55
4        Arsenal vs Aston Villa  2026-05-10T17:30:00Z         Draw  4.20
5        Arsenal vs Aston Villa  2026-05-10T17:30:00Z  Aston Villa  5.80

# Implied probability = 1 / decimal_odds
df["implied_prob"] = (1 / df["odds"] * 100).round(1)

# Group by match to calculate the margin (overround)
def calc_margin(group):
    total_prob = group["implied_prob"].sum()
    group["margin"] = (total_prob - 100).round(1)
    return group

df = df.groupby("match", group_keys=False).apply(calc_margin)

print(df[["match", "selection", "odds", "implied_prob", "margin"]].head(6))

                              match    selection  odds  implied_prob  margin
0  Manchester United vs Liverpool   Man United  3.40          29.4     5.9
1  Manchester United vs Liverpool         Draw  3.60          27.8     5.9
2  Manchester United vs Liverpool    Liverpool  2.10          47.6     5.9
3        Arsenal vs Aston Villa      Arsenal  1.55          64.5     5.1
4        Arsenal vs Aston Villa         Draw  4.20          23.8     5.1
5        Arsenal vs Aston Villa  Aston Villa  5.80          17.2     5.1

# Example: your model's probability estimates
your_model = {
    "Man United": 32.0,    # Your model says 32%, book says 29.4%
    "Draw": 26.0,          # Model says 26%, book says 27.8%
    "Liverpool": 42.0,     # Model says 42%, book says 47.6%
    "Arsenal": 68.0,       # Model says 68%, book says 64.5%
}

df["model_prob"] = df["selection"].map(your_model)
df["edge"] = (df["model_prob"] - df["implied_prob"]).round(1)

# Value bets: where your model gives higher probability than the book
value_bets = df[df["edge"] > 0].sort_values("edge", ascending=False)
print(value_bets[["match", "selection", "odds", "implied_prob",
                   "model_prob", "edge"]])

                              match    selection  odds  implied_prob  model_prob  edge
3        Arsenal vs Aston Villa      Arsenal  1.55          64.5        68.0   3.5
0  Manchester United vs Liverpool   Man United  3.40          29.4        32.0   2.6

import time
from datetime import datetime

def collect_snapshots(event_id, intervals=10, pause=60):
    """Collect odds snapshots over time."""
    snapshots = []

    for i in range(intervals):
        odds = requests.get(
            f"{BASE}/events/{event_id}/odds", headers=H
        ).json()

        timestamp = datetime.now().isoformat()

        for market in odds["markets"]:
            if "winner" not in market["name"].lower():
                continue
            for sel in market["selections"]:
                snapshots.append({
                    "timestamp": timestamp,
                    "selection": sel["name"],
                    "odds": sel["odds"],
                })

        print(f"Snapshot {i + 1}/{intervals} collected")
        if i < intervals - 1:
            time.sleep(pause)

    return pd.DataFrame(snapshots)

# Collect 10 snapshots, 1 minute apart
# history = collect_snapshots("event_abc123", intervals=10, pause=60)

import matplotlib.pyplot as plt

def plot_odds_history(history_df):
    """Plot odds movement over time for each selection."""
    fig, ax = plt.subplots(figsize=(10, 5))

    for name, group in history_df.groupby("selection"):
        ax.plot(
            range(len(group)),
            group["odds"].values,
            marker="o",
            label=name,
            linewidth=2,
        )

    ax.set_xlabel("Snapshot")
    ax.set_ylabel("Decimal Odds")
    ax.set_title("Odds Movement Over Time")
    ax.legend()
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig("odds_movement.png", dpi=150)
    print("Chart saved to odds_movement.png")

# plot_odds_history(history)

# Save to CSV
df.to_csv("epl_odds.csv", index=False)
print(f"Saved {len(df)} rows to epl_odds.csv")

# Save to Excel with multiple sheets
with pd.ExcelWriter("odds_analysis.xlsx") as writer:
    df.to_excel(writer, sheet_name="All Odds", index=False)
    value_bets.to_excel(writer, sheet_name="Value Bets", index=False)
    print("Saved to odds_analysis.xlsx")