Automating NCAAB Totals & Over/Unders

This tutorial demonstrates how to automate College Basketball (NCAAB) totals betting using Python. By utilizing the Terminal Software API, quantitative bettors can continuously scan hundreds of games to identify high Expected Value (+EV) discrepancies specifically in Over/Under markets.

On a Saturday in February, there are over 150 College Basketball games. Bookmakers simply cannot price every game perfectly. This script automates the detection of mispriced NCAAB Totals.

Environment Setup

pip install requests

Querying the NCAAB Feed

We will request the live telemetry for all active College Basketball markets.

import requests

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

Isolating the Over/Under

We specifically filter the ledger to only return edge cases in the "Totals" (Over/Under) market, as these are often slower to adjust to sharp action than moneylines.

for edge in cbb_data:
    ev = float(edge.get('ev', 0))
    market = edge.get('market', '').lower()
    
    if ev > 3.0 and 'total' in market:
        print(f"🏀 [CBB TOTALS] {edge['match_name']}")
        print(f"   Play: {edge['target']} | Book: {edge['sportsbook']} | EV: +{ev}%\n")