🏀 NBA Analytics
Before MLB, before soccer — this model was built on basketball first. Every player, every night, run through a real multi-factor prediction pipeline: recency-weighted form, matchup and pace context, schedule fatigue, live injury impact, and market calibration — not a single flat average dressed up as a prediction.
20+
Live Factors
11
Props Tracked
5
Signal Groups
82-26
Games/Season
ML Predictions
Full factor-by-factor breakdown for any player — probability curves & confidence scores
Best Bets
ML-ranked best plays across Underdog, PrizePicks, and sportsbooks
Trends
Hot streaks, cold spells, and over/under splits across the league
Game Winners
AI-predicted outcomes with win probability and full math breakdown
$10K Streak
Log your Underdog Fantasy picks and track your 11-game streak progress
How It Actually Works
Five Signal Groups, One Number
Every prediction starts from a season/career baseline, then moves through these five groups in order — each one a real, capped adjustment, not a black box.
Player Form
How good is this player right now, not just this season?
- Season + career baseline, dynamically blended
- Median-based outlier trimming (IQR) so one 40-point night doesn't skew the read
- Last-3-vs-prior momentum detection (hot/cold streak)
- Role-change detection when recent usage diverges sharply from history
Matchup Context
Who are they facing, and how fast will the game actually be played?
- Composite opponent defense rating vs. league average
- Shot-zone matchup analysis — their shot profile vs. what the defense allows there
- Primary defender-specific adjustment
- Real possessions-based pace factor (FGA − OREB + TOV + 0.44×FTA)
Schedule & Fatigue
Tired teams and cold benches produce different numbers.
- Back-to-back discount (0 days rest)
- 3-games-in-4-nights fatigue, distinct from the back-to-back check
- Blowout + coach-tendency risk, from real point-differential history
- Home/away performance split
Injury Impact
Missing teammates and missing defenders both move the number.
- Live ESPN injury report, both teams
- Historical with/without-teammate performance, not a flat guess
- Opponent's missing defenders factored the same way
- Return-from-injury minutes regression for the player themselves
Market Calibration
The sportsbook line already prices in things we don't see — respect that.
- Stacked ML ensemble blended with the statistical model
- Tiered regression toward the line when our number diverges sharply
- Team possession ceiling — can't exceed a player's realistic share of the scoring pie
- Confidence capped 5–80%, widened for real fat-tail/outlier risk
What confidence actually means
A 65% confidence pick is expected to hit roughly 65% of the time — not 95%, not a lock. Real basketball has real variance; the model widens its own uncertainty band when the signal is genuinely volatile instead of pretending every pick is a sure thing.
Sample Factor Breakdown
Illustrative example only — a hypothetical points prop, not a live prediction or a real player.
Baseline → Final
24.6→25.1
Props Covered
Run It On Tonight's Slate
Pick a player and see every one of these factors broken out line by line.
Open ML Predictions