Sniping NCAAF Spread Discrepancies

Unlike the NFL, College Football features extreme talent disparities, leading to massive 30+ point spreads. Sportsbooks frequently disagree on these obscure games, creating arbitrage windows.

Environment Setup

You will need a basic Python environment to parse the JSON responses from our high-frequency feeds.

pip install requests

Targeting NCAAF

We configure the matrix to strictly return College Football market data.

import requests

url = "https://api.terminalsoftware.online/v1/sports/edges?sport=football_ncaaf"
cfb_data = requests.get(url, headers={"Authorization": "Bearer term_live_YOUR_KEY_HERE"}).json()

Tracking Obscure Lines

With so many games running simultaneously on a Saturday, we look for extreme inefficiencies (+4.5% EV) that indicate an off-market line.

for edge in cfb_data:
    ev = float(edge.get('ev', 0))
    if ev > 4.5:
        print(f"🎓 [CFB SHIFT] {edge['match_name']}")
        print(f"   Play: {edge['target']} | Book: {edge['sportsbook']} | EV: +{ev}%\n")