Basketball Analytics Course

Lesson 1 • Data Sources and Collection Methods

Surveys play-by-play logs, tracking cameras, and wearable sensors as primary data sources. Students understand data provenance and its effect on reliability.

Lesson 2 • Introduction to Analytical Thinking in Basketball

Frames hypothesis-driven inquiry and sample-size awareness as core analyst habits. Prepares students to avoid common interpretive errors in sports data.

Lesson 3 • Core Statistical Vocabulary

Defines counting stats, rate stats, and efficiency metrics used throughout the course. Provides the shared language needed for all subsequent analysis.

Lesson 4 • Possession and Game Flow Concepts

Explains possessions as the fundamental unit of basketball analysis. Connects possession counting to offensive and defensive rating calculations.

Lesson 1 • Shot Location and Zone Analysis

Uses court zone data to identify high- and low-value shot areas. Connects location frequency to team offensive philosophy.

Lesson 2 • True Shooting and Effective Field Goal Percentage

Derives TS% and eFG% formulas and explains their superiority over raw FG%. Students apply both metrics to compare players across different shot profiles.

Lesson 3 • Expected Points and Shot Quality Models

Introduces expected points per shot (xPPS) models that incorporate distance, angle, and defender proximity. Students distinguish shot quality from shooter skill.

Lesson 4 • Contested vs. Open Shot Analysis

Quantifies how defensive pressure affects shooting efficiency using tracking data. Enables evaluation of shot creation quality beyond raw makes.

Lesson 1 • Assist Networks and Ball Movement

Maps assist relationships to reveal playmaking hubs and ball-movement patterns. Connects network density to offensive efficiency outcomes.

Lesson 2 • Transition Offence Evaluation

Measures transition frequency, speed, and efficiency relative to half-court offence. Identifies personnel and stylistic drivers of fast-break production.

Lesson 3 • Spacing and Floor Balance Metrics

Uses tracking coordinates to quantify floor spacing and its effect on drive efficiency. Demonstrates how personnel decisions alter offensive geometry.

Lesson 4 • Offensive Rating and Its Components

Defines offensive rating as points scored per 100 possessions and decomposes it into shooting, turnover, rebounding, and free throw factors. Builds a diagnostic framework.

Lesson 1 • Help Defence and Team Rotations

Analyses help-side positioning and rotation timing using spatial tracking data. Reveals how team coordination amplifies or undermines individual defensive skill.

Lesson 2 • Individual Defensive Metrics

Surveys DRTG, defensive win shares, and tracking-based matchup data for player evaluation. Addresses the difficulty of isolating individual defensive impact.

Lesson 3 • Perimeter Defence and On-Ball Coverage

Evaluates perimeter defenders using tracking matchup data and opponent shooting splits. Connects on-ball performance to team defensive scheme requirements.

Lesson 4 • Defensive Rating and Team Defence

Defines defensive rating as points allowed per 100 possessions and links it to the four defensive factors. Establishes a team-level diagnostic baseline.

Lesson 1 • All-in-One Metrics and Modern Frameworks

Introduces LEBRON, EPM, and similar modern all-in-one metrics that blend tracking and play-by-play data. Students evaluate their predictive validity.

Lesson 2 • Role-Based Player Profiling

Clusters players into functional roles using statistical profiles rather than traditional positions. Enables more precise roster construction analysis.

Lesson 3 • Box Score-Based Composite Metrics

Derives Player Efficiency Rating, Win Shares, and Box Plus-Minus from box score inputs. Highlights each metric's assumptions and known blind spots.

Lesson 4 • Contextual Adjustments in Player Valuation

Applies usage, pace, and teammate quality adjustments to raw player metrics. Produces fairer cross-team and cross-era comparisons.

Lesson 1 • Lineup Construction Principles

Identifies skill complementarity, spacing, and defensive versatility as lineup construction pillars. Connects analytical findings to practical roster decisions.

Lesson 2 • Net Rating and Lineup Performance

Defines net rating as the difference between offensive and defensive rating for a lineup. Establishes it as the primary lineup evaluation currency.

Lesson 3 • Clutch Performance and High-Leverage Lineups

Isolates clutch-time performance data to identify reliable high-leverage lineup combinations. Addresses sample size challenges in late-game analysis.

Lesson 4 • Rotation Optimisation Methods

Uses historical lineup data and simulation to test rotation patterns before game deployment. Reduces reliance on intuition-only substitution decisions.

Lesson 1 • Win Probability Models

Explains how score margin, time, and possession combine to produce real-time win probability estimates. Provides the foundation for all in-game decision analysis.

Lesson 2 • Timeout and Challenge Analytics

Evaluates the win probability impact of timeout usage and coach's challenge decisions. Provides a framework for conserving high-value resources.

Lesson 3 • Two-Point vs. Three-Point Decision Framework

Quantifies expected value differences between shot types in various game states. Guides coaches on when to prioritise volume three-point attempts.

Lesson 4 • Expected Value of Fouling Decisions

Calculates expected points from intentional fouling strategies in late-game scenarios. Compares hack-a-player tactics against alternative defensive approaches.

Lesson 1 • Combine and Physical Testing Analytics

Analyses combine measurements and athletic testing data as supplements to game film and statistics. Quantifies which physical attributes correlate with NBA-level performance.

Lesson 2 • Free Agency and Contract Valuation

Estimates player market value using performance metrics and salary cap context. Identifies over- and under-valued free agents relative to expected production.

Lesson 3 • Draft Model Construction

Builds predictive draft models using age, athleticism, skill metrics, and historical comparables. Identifies which inputs most reliably predict professional success.

Lesson 4 • Roster Construction and Team Fit Analysis

Evaluates how a prospect's statistical profile complements existing roster needs and system requirements. Prioritises fit-adjusted value over raw talent rankings.