Automating NFL Player Prop Discrepancies

This tutorial demonstrates how to automate NFL player prop betting using Python. By connecting to the Terminal Software API and utilizing Pandas, quantitative bettors can filter out highly-efficient moneyline markets to isolate high-value Expected Value (+EV) discrepancies specifically on player derivatives like passing yards and receptions.

The NFL spreads are hyper-efficient. The true algorithmic goldmine lies in derivatives: Passing Yards, Receptions, and Anytime Touchdowns. Here is how to automate NFL prop detection.

Environment Setup

We'll utilize Pandas to handle the heavy lifting of sorting and filtering player data.

pip install requests pandas

Querying the NFL Matrix

Pass your token to the API and convert the response directly into a Pandas DataFrame for analysis.

import requests
import pandas as pd

API_KEY = "term_live_YOUR_KEY_HERE"
url = "https://api.terminalsoftware.online/v1/sports/edges?sport=football_nfl"
df = pd.DataFrame(requests.get(url, headers={"Authorization": f"Bearer {API_KEY}"}).json())

Prop Filtration

We explicitly filter out Spreads and Moneylines to strictly return Player Props with a high Expected Value threshold.

# Isolate player props
player_props = df[(df['market'].str.contains('player', case=False, na=False)) & (df['ev'] > 2.5)]

for _, prop in player_props.iterrows():
    print(f"🏈 [NFL PROP] {prop['match_name']}")
    print(f"   Target: {prop['target']} | Book: {prop['sportsbook']} | EV: +{prop['ev']}%")