Chapter 17: Key Takeaways - Team Offensive Efficiency

Core Concepts Summary

1. Offensive Rating Fundamentals

• Offensive Rating (ORtg) = Points Scored / Possessions x 100
• Possessions = FGA + 0.44 x FTA - OREB + TOV
• Modern NBA league average: 110-114 ORtg
• Elite teams: 115+ ORtg; Poor teams: <108 ORtg

2. The Four Factors Framework

3. Play Type Efficiency Hierarchy

Most Efficient:
1. Cuts (1.28 PPP) - Requires defensive breakdown
2. Transition (1.12 PPP) - Defense not set
3. Pick & Roll Roll Man (1.10 PPP) - Creates mismatches
4. Putbacks (1.05 PPP) - Offensive rebounds

Least Efficient:
5. Spot-Up (0.96 PPP) - Contested catch-and-shoot
6. Pick & Roll Ball Handler (0.91 PPP) - Defense focused here
7. Isolation (0.88 PPP) - One-on-one without advantage
8. Post-Up (0.87 PPP) - Defense can collapse

4. Spacing and Ball Movement

• Optimal spacing: 4-5 players beyond three-point line creates driving lanes
• Ball movement correlation: Each additional pass adds ~0.05-0.08 PPP
• Touch time: Lower average touch time correlates with higher eFG%
• Network topology: Distributed networks harder to defend than hub-based

5. Shot Quality vs. Shot Conversion

• Shot Creation Value = xPTS generated - League average xPTS
• Shot Conversion Value = Actual PTS - xPTS
• Teams can excel at creation while struggling at conversion (or vice versa)
• Long-term success requires excellence in both

Essential Formulas

Offensive Efficiency

Offensive Rating = (Points / Possessions) x 100

Possessions (Basic) = FGA + 0.44 x FTA - OREB + TOV

Possessions (Advanced) = FGA + 0.44 x FTA - 1.07 x (OREB x (FGA-FGM))/(FGA-FGM+OREB) + TOV

True Shooting % = PTS / (2 x (FGA + 0.44 x FTA))

Effective FG% = (FGM + 0.5 x 3PM) / FGA

Individual Metrics

Usage Rate = (FGA + 0.44 x FTA + TOV) / (Team Possessions x Minutes Fraction)

Individual ORtg = (Points Produced / Individual Possessions) x 100

On-Off Differential = Team ORtg (Player On) - Team ORtg (Player Off)

Ball Movement

Passes Per Possession = Total Passes / Possessions

Potential Assist Rate = Passes to Shots / Total Passes

Assist Conversion Rate = Assists / Potential Assists

Ball Movement Score = w1(PPP) + w2(1-TouchTime) + w3(PAR) + w4(ACR)

Network Analysis

Entropy = -Sum(p_i x log(p_i))

Normalized Entropy = Entropy / log(n)

Centralization = Max Centrality - Mean Centrality

Implementation Checklist

Setting Up Offensive Analytics Pipeline

• [ ] Data Collection
• [ ] Acquire play-by-play data with possession indicators
• [ ] Gather shot location data with defender distance
• [ ] Collect passing and touch data (if available)

• [ ] Track play type classifications

• [ ] Basic Metrics Calculation

• [ ] Calculate team and individual possessions
• [ ] Compute offensive rating per game and season
• [ ] Derive Four Factors for all teams

• [ ] Generate eFG% and TS% by player and team

• [ ] Advanced Analysis

• [ ] Build play type efficiency breakdowns
• [ ] Calculate shot quality (xPTS) for all attempts
• [ ] Create passing network graphs

• [ ] Compute spacing metrics from tracking data

• [ ] Visualization

• [ ] Shot charts with expected value overlay
• [ ] Network diagrams for assist patterns
• [ ] Play type efficiency comparisons

• [ ] Four Factors radar charts

• [ ] Reporting

• [ ] Generate team offensive profiles
• [ ] Identify strengths and weaknesses
• [ ] Compare to league benchmarks
• [ ] Track changes over time

Common Pitfalls to Avoid

1. Ignoring Context

Problem: Comparing raw offensive ratings without accounting for pace, opponent quality, or era. Solution: Always use pace-adjusted metrics; compare to league average of the same season.

2. Over-interpreting Small Samples

Problem: Drawing conclusions from limited play type or lineup data. Solution: Require minimum sample sizes (100+ possessions for play types, 200+ for lineups).

3. Confusing Creation and Conversion

Problem: Attributing all credit/blame for shooting outcomes to the shooter. Solution: Separate shot quality (creation) from shot making (conversion) in analysis.

4. Neglecting Opportunity Cost

Problem: Optimizing one play type without considering what it replaces. Solution: Always analyze marginal changes - what are you giving up to get more of X?

5. Assuming Linearity

Problem: Thinking more of a good thing is always better (more threes, more pace). Solution: Recognize diminishing returns and optimal ranges for all metrics.

Quick Reference Tables

Offensive Rating Benchmarks (Modern NBA)

Shot Value by Zone

Play Type Frequency Guidelines

Application Scenarios

Scenario 1: Evaluating a Trade Target

1. Calculate player's individual ORtg and usage
2. Analyze on-off impact on team offense
3. Assess play type efficiency where they're used
4. Project fit with existing personnel (spacing, ball handling)

Scenario 2: Game Planning Against Opponent

1. Identify opponent's strongest play types
2. Find efficiency drops by zone/play type
3. Locate weak links in their passing network
4. Design defense to force lower-value actions

Scenario 3: Roster Construction

1. Ensure adequate floor spacing (3+ shooters)
2. Balance creation and finishing skills
3. Maintain playmaking at multiple positions
4. Consider pace preferences and transition ability

Scenario 4: In-Game Adjustments

1. Monitor real-time play type efficiency
2. Track shot quality trends
3. Identify which actions are working
4. Adjust rotation and play calls accordingly

Key Insight Summary

1. Pace matters: Always adjust for possessions when comparing offenses
2. Four Factors explain 90%: eFG%, TOV%, OREB%, and FTr capture most variance
3. Shot selection > Shot making: Creation drives sustainable success
4. Ball movement works: Extra passes create value across all play types
5. Spacing enables everything: Modern offense requires three-point threats
6. Context is crucial: Same action has different value in different situations
7. System over stars: Well-designed offense elevates all players
8. Measure what matters: Focus on efficiency per possession, not raw totals