Using Regression and Survival Analysis to predict substitutions in the NBA to optimize betting lines and payouts for player proposition bets

1. Predicting Substitutions
in the NBA
Charlie DeStefano, Ph.D.
Consulting for Unblockable, Inc. (CTO & Game Designer)
HotStreak

2. Motivation
Enormous Sports Betting Market
• $107B currently spent annually in U.S.
• $148B with legalization
Provide short-term, player proposition bets
• Substitutions not currently considered
• ~30% of all bets suffered from early substitutions
Goals:
1. Predict when substitutions will occur
2. Adjust bet lines and payouts accordingly

3. Solution
Analyze historical substitution patterns (Past 2
seasons)
1. Regression
• Predict time until next substitution
• Gradient Boosting Regression
2. Survival Analysis
• Predict probability of remaining on-court until a
substitution occurs, based on past observations
from similar scenarios
Deliverable: Updated
Bet Offerings

4. LeBron James:
• Current Time: 5:00 remaining
• Over/Under: 4 points
• End Bet Time: 0:00 remaining
• Next Sub Prediction  1:14 remaining (75% shorter)
• Survival Probability  61%
Updated Bet
1. New Line set to 3 points (.75x4)
2. “Under” Payout Reduced by 39% (100--61)
3. “Over” Payout Increased by 39%
Example Scenario

5. Feature Importance

6. Pipeline
Raw Game, Player,
& Bet Data
Feature
Engineering
Calculate
Regression
& Survival
Models
Compress
Models for
Deployment
Acquire
Real-Time
Game Data
CSV Files Pandas
SKLearn &
Lifelines
Pickle
Licensed Data
Sources
Calculate New
Bet Lines &
Payouts
Offer New
Bet to
User
Real-Time, During
Games
Pre-Processed Before
Games

7. Historical Bet Analysis
$1 Bets
Original Lines
and Payouts
New Lines and
Payouts
Player Wins 53,802 53,832
Player Losses 53,924 53,894
Paid Out $53,802 $45,916
Paid In $53,924 $53,894
Profit $122 $7,978
• 107,726 total bets
• 71% of all wins were “Under” bets
• Majority of wins were “Under” bets =
Majority of Payouts reduced

8. •Charlie DeStefano
•Ph.D. in Mechanical Engineering
About Me