Player Props.
Live Odds.
All in One. For Free.

NEW Bring your own API Key

Free via The-Odds-API
Propped Up
NFL
NBA
NHL
LB

LeBron James

Best Value
LAL @ BOS
Points
Over 24.5
Vegas: -150
Confidence
62%
Strong
PM

Patrick Mahomes

KC @ BAL
Pass Yds
Over 265.5
Vegas: -130
Confidence
+3%
58%
Lean
Analyst Insight

Primary CB out for Ravens. Pace-up spot.

CM

Connor McDavid

EDM @ VAN
Shots
Over 3.5
Vegas: -120
Confidence
55%
Lean
LB

LeBron James

Best Value
LAL @ BOS
Points
Over 24.5
Vegas: -150
Confidence
62%
Strong
PM

Patrick Mahomes

KC @ BAL
Pass Yds
Over 265.5
Vegas: -130
Confidence
+3%
58%
Lean
Analyst Insight

Primary CB out for Ravens. Pace-up spot.

App
Background

Save Monthly Fees

Competitors charge $50-$350/mo for data. We let you bring your own free API key, giving you the same data specific to player props with $0 overhead.

Correlation Features

Our system identifies correlated plays within the same game instantly, helping you build high-upside slips on Underdog and PrizePicks.

AI Integration

Don't trust the math alone? Our backend AI system evaluates defensive matchups, injury reports, and out-of-game context to provide a risk assessment on top of the raw data. Adjusting the confidence rating every time a scan is made.

Average Vegas Line (Multiple Sportsbooks)
-150
VS
Underdog / Prize Picks Implied Odds
-119
+31 Point Edge Found
CF

Why I Built GetProppedUp

Propped Up is an independent, AI-assisted project built to cut through the noise. While most tools chase surface-level trends, we leverage the advanced, constantly adjusting models that sportsbooks already use.

I built this for bettors who want to turn those models to their advantage. The goal is simple: find specific moments where books haven't adjusted or disagree, giving you a calculated edge before the market corrects.

1. The Math

An app scans multiple sportsbooks once a user connects their free API key. This API key retrieves data in rough format from over 20 sportsbooks. Our system then formats the data and removes the "vig" (true line). The system then ranks based on internal logic and adjusts confidence ratings according to the number of books in agreement versus the Underdog/Prize Pick line.

2. The Backend

I run the top 10 selections through a pre-constructed AI model to cross-reference real-time factors like weather, lineup changes, injury news, and historical data to then further adjust the confidence rating from -10 - +10 points.

"Confidence ratings are then assigned to help you filter the noise. At the end of the day, no system 'prints money,' and this should be used as a tool to make smarter, more informed predictions"

Ready to find the edge?

Launch Free Scanner

No credit card required • Bring your own API Key