Learn from the Past.
Predict the Future.
Upside analyzes 500+ NBA draft outcomes from 2017-2024 to predict career trajectories. Input any player's stats and measurables to see their projected path to NBA stardom.
Career Trajectory Predictor
Enter a player's statistics and measurables to predict their professional basketball career path
Player Information
Statistics (Per Game)
John Smith
Duke University β’ 6'7" β’ 215 lbs β’ 19.5 years old
Predicted Career Path
Success Probabilities
Analysis
Strengths
Areas for Improvement
Similar Historical Players
How Our Model Works
Understanding the science behind NBA career trajectory predictions
Historical Data Foundation
Our model analyzes 500+ NBA prospects from the 2017-2024 draft classes, tracking their college/international statistics and how their careers actually developed.
- β’ Draft picks 1-60 across 8 recent draft classes
- β’ Pre-draft stats: scoring, efficiency, advanced metrics
- β’ Physical measurements: height, weight, age
- β’ Career outcomes: All-Star, starter, role player, bust
Key Analytics We Track
We focus on the metrics that best predict NBA success, weighted by position and historical correlation.
β’ BPM (20% weight)
β’ Scoring (15% weight)
β’ Shooting (13% weight)
β’ Physical (9% weight)
Proven Track Record
Our model correctly identified career trajectories for these notable players:
Luka DonΔiΔ
2018 Draft, Pick #3
Pre-draft: 16.0 PPG, 22.9 PER, 8.9 BPM
β Predicted: Superstar
Paolo Banchero
2022 Draft, Pick #1
Pre-draft: 17.2 PPG, 22.1 PER, 7.1 BPM
β Predicted: All-Star
James Wiseman
2020 Draft, Pick #2
Pre-draft: 19.7 PPG, limited sample size
β Predicted risk factors identified
Prediction Methodology
Statistical Analysis
We normalize stats across different leagues and competition levels, then apply position-specific weights.
- β’ Guards: Higher weight on assists and 3P%
- β’ Forwards: Balanced scoring and efficiency focus
- β’ Centers: Rebounding and FG% emphasis
Pattern Recognition
Machine learning identifies patterns between pre-draft profiles and career outcomes.
- β’ Similarity matching to historical players
- β’ Age-adjusted development curves
- β’ Multi-factor success probability modeling
Analyzing career trajectory...
Processing historical data patterns