Predictive Analysis of Player Performance in Sports Betting with Singular Value Decomposition
Research project utilizing SVD to identify latent factors affecting individual NBA player performance and predict future outcomes for sports betting applications.
Abstract
Sports betting relies heavily on predictive analysis of player performance. In basketball, where 82 games are played each season, there is ample data to assess player performance over time to predict future outcomes. This research focuses on player props for points (PTS), rebounds (REB), and assists (AST) over the course of a full game.
Various factors influence player performance including rest days, opponent strength, pace of play, and recent form. However, different players are affected by these factors to varying degrees. Instead of using the same predictive features for all players, this research utilizes Singular Value Decomposition (SVD) to identify patterns in player performance and weights these patterns to predict next-game performance.
Problem Statement
Traditional sports betting prediction models often use the same predictive features for all players, failing to account for individual differences in how players respond to various factors. Some players maintain consistent performance regardless of opponent strength, while others may struggle against strong defensive teams. Fatigue also affects players differently.
The challenge was to develop a methodology that could identify latent factors affecting individual player performance and use these patterns to improve predictive accuracy for betting decisions, specifically for player props in NBA games.
Technical Implementation
Data Collection & Features
- • NBA API data from 2023-24 and 2024-25 seasons
- • Historical patterns: PTSL5AVG, PTSL5STD, PTSL5TREND
- • Shooting efficiency: FG%, FG3%, TS%, FGA metrics
- • Usage/minutes: USG%, previous game minutes
- • Game context: Home game, rest days, back-to-back
- • Opponent context: Defensive rating, pace, team strength
SVD Methodology
- • Feature matrix X (m×n) with m games and n features
- • SVD decomposition: X = UΣV^T
- • Three principal components retained (r = 3)
- • U: game representation in reduced space
- • Σ: diagonal matrix with singular values
- • V^T: feature patterns matrix
Research Methodology
Pattern Recognition
- • Pattern 1: Game context and recent form
- • Pattern 2: Shooting efficiency and fatigue
- • Pattern 3: Game momentum and context
- • Individual player pattern analysis
- • Feature importance weighting
Prediction Process
- • Standardized feature vector construction
- • Projection onto V matrix: x' = x_new V
- • Weighting with Σ: x'' = x' Σ
- • Projection back to feature space: x̂ = x'' V^T
- • Conversion to original scale using player statistics
Technologies Used
Future Work
The SVD approach demonstrated that while it can identify patterns in player performance, it was not effective for making accurate betting predictions. The model's performance was insufficient for practical sports betting applications, indicating the need for a fundamentally different approach.
Future research should explore alternative methodologies such as Markov chains for modeling player performance transitions, ensemble methods combining multiple prediction techniques, or deep learning approaches that can capture more complex temporal dependencies. The insights gained from this SVD analysis can inform feature engineering for these alternative approaches.