Last updated 2026-06-08
Aggregate statistics from 50+ books in one call. Median, mean, min, max, and standard deviation for each outcome. See where books agree, where they disagree, and who the outliers are.
50
Books aggregated
Real-time
30s refresh
24
Sports covered
{
"event": "Lakers vs Warriors",
"market": "h2h",
"outcomes": [
{
"name": "Lakers",
"median_price": -142,
"mean_price": -140,
"min_price": -155,
"max_price": -130,
"std_dev": 5.8,
"books_reporting": 47
},
{
"name": "Warriors",
"median_price": +120,
"mean_price": +118,
"min_price": +108,
"max_price": +135,
"std_dev": 6.2,
"books_reporting": 47
}
]
}Median
The middle price across all 50+ books. This is the market's best estimate of the true line. If you're getting a price significantly better than median, you're likely on the right side of value.
Std Dev
High standard deviation means books disagree on pricing. Disagreement = opportunity. Low std dev means the market has converged — harder to find edges. Sort by highest std dev to find the most inefficient markets.
Min / Max
The widest spread between min and max tells you where to bet and where to fade. Max price = best available odds (bet here). Min price = worst odds (avoid this book). A 25-point min/max spread on a two-way market is highly unusual — investigate.
Pull consensus and find disagreement.
import requests
response = requests.get(
"https://api.bettingdataapi.com/intelligence/consensus",
headers={"x-api-key": "YOUR_KEY"},
params={"sport_key": "basketball_nba"}
)
events = response.json()["data"]
# Sort by highest disagreement (std_dev) — most edge potential
for event in sorted(events, key=lambda e: max(o["std_dev"] for o in e["outcomes"]), reverse=True)[:5]:
print(f"{event['event']}")
for o in event["outcomes"]:
spread = o["max_price"] - o["min_price"]
print(f" {o['name']}: median {o['median_price']}, spread {spread}pt, σ={o['std_dev']}")Use the 50-book consensus as a benchmark. If your model disagrees with consensus by more than 3%, investigate. Either your model sees something the market doesn't, or your model is wrong. Consensus keeps you calibrated.
Sort events by standard deviation. High σ = books disagree = edge opportunities. Focus your energy on events where the market hasn't converged. Low σ events are efficient — skip them.
If you don't have a proprietary model, the 50-book median is your best proxy for true probability. Bet any book offering significantly better than median. Combined with /intelligence/fair-odds (vig-removed), you get two independent fair-price signals.
We publish real, graded edge hits from the live feed every week.
Browse the edge case study archive →Business plan includes consensus, fair odds, value detection, arbitrage signals, and Pinnacle-anchored edge scanning. Raw odds cover 20+ sports; edge detection runs on the 16 sports where Pinnacle anchors the feed, concentrated in the leagues in season.