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863 terms from Professional Basketball Analytics and Visualization
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(2 points)
1960s player: z = (30 - 20) / 8 = 10/8 = 1.25 - Modern player: z = (28 - 15) / 6 = 13/6 = 2.17 - **The modern player (z = 2.17) was more exceptional relative to their era** because their z-score is higher, indicating they stood out more from their contemporaries. → Chapter 5 Quiz: Descriptive Statistics in Basketball
+0.13
Turnover: 0.52 - 0.65 = **-0.13** - Made FT: 0.73 - 0.70 = **+0.03** - Missed shot: 0.48 - 0.55 = **-0.07** - Assist: 0.50 - 0.40 = **+0.10** → Chapter 21: Quiz - In-Game Win Probability
+1 if we have possession
**-1 if opponent has possession** - **0 if neutral (between plays)** - Captures ~1 point expected value of possession → Chapter 21: Quiz - In-Game Win Probability
1. Load Management:
Reduced regular season minutes (32 vs. 35) - More rest games (currently ~0 planned rest) - Minutes limits in blowouts - Back-to-back management → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
1.11 points per shot
### Question 24 A player has 340 assists on the season. If the average value of an assisted shot is 2.15 points and the player accounts for 35% of the assist value (the rest goes to the scorer), what are the player's assist points produced? → Chapter 17: Quiz - Team Offensive Efficiency
1.47 points
### Question 23 Calculate the weighted transition defense efficiency: - Transition possessions: 18% of total - Transition PPP allowed: 1.18 - Half-court possessions: 82% of total - Half-court PPP allowed: 1.05 → Chapter 18: Quiz - Team Defensive Analytics
103.5
### Question 22 A rim protector contests 7.5 shots per 36 minutes at the rim and holds opponents to 55.2% shooting. League average rim FG% is 65%. Calculate their points saved per 36 minutes. → Chapter 18: Quiz - Team Defensive Analytics
105.4
Projected Opp PPG: 104.1 * (91.1/99.3) = **95.5** → Case Study 1: The Pace-and-Space Revolution of the 2015-18 Golden State Warriors
107.3 DRtg
### Question 24 A defender's on-off differential shows the team has a 106.2 DRtg when they're on court (2,200 possessions) and 111.8 when off (1,600 possessions). Calculate the weighted team DRtg and the player's defensive impact. → Chapter 18: Quiz - Team Defensive Analytics
108.6
### Question 25 Calculate the expected points from second chances: - Opponent ORB: 12 per game - Second chance conversion rate: 1.15 PPP - Team DRB%: 74% → Chapter 18: Quiz - Team Defensive Analytics
115.3
### Question 22 A team's effective field goal percentage is 54.5% on 85 FGA with 11 three-pointers made. How many total field goals did they make? → Chapter 17: Quiz - Team Offensive Efficiency
13.8 points per game
## True/False Questions → Chapter 18: Quiz - Team Defensive Analytics
15%
0.2-0.4: 52/150 = **34.7%** - 0.4-0.6: 102/200 = **51%** - 0.6-0.8: 132/180 = **73.3%** - 0.8-1.0: 108/120 = **90%** → Chapter 21: Quiz - In-Game Win Probability
1987-88 Michael Jordan:
35.0 PPG, 5.5 RPG, 5.9 APG - 53.5% FG, 84.1% FT - 82 games, 40.4 MPG - League Pace: ~100 possessions/game - League PPG: ~108 → Chapter 13 Exercises: Win Shares and Wins Above Replacement
1990s Player:
BPM: +6.0 - League pace: 92 possessions/game - League TS%: 0.520 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
2. Biomechanical Intervention:
Landing mechanics training - Neuromuscular training programs - Jump landing assessment - Fatigue monitoring → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
2012-13 Season (Age 28):
28.7 PPG (Scoring champion) - 6.9 RPG - 2.6 APG - 56.0% TS - 5.4 Win Shares - 3.0 BPM → Chapter 22: Case Study 2 - The Decline of Carmelo Anthony: Projecting Aging Superstars
2013-14 Season (Age 29):
27.4 PPG - 8.1 RPG - 3.1 APG - 56.1% TS - 7.1 Win Shares - 3.0 BPM → Chapter 22: Case Study 2 - The Decline of Carmelo Anthony: Projecting Aging Superstars
2014-15 RAPM:
Total: +6.8 (top 15 in NBA) - O-RAPM: +2.1 - D-RAPM: +4.7 (top 5 in NBA) → Case Study 1: The Discovery of Draymond Green's True Value
2015 Contract Negotiation:
Traditional stats suggested ~$10M annually - RAPM suggested elite value (~$18-20M) - Green signed 5-year, $82M extension - Market value exceeded contract; Warriors benefited → Case Study 1: The Discovery of Draymond Green's True Value
2015-16 Statistics:
16.9 PPG, 7.7 RPG, 4.3 APG - PER: 18.8 - Win Shares: 4.0 - Age: 21 → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
2015-16 Stephen Curry:
30.1 PPG, 5.4 RPG, 6.7 APG - 50.4% FG, 90.8% FT, 45.4% 3PT - 79 games, 34.2 MPG - League Pace: ~96 possessions/game - League PPG: ~106 → Chapter 13 Exercises: Win Shares and Wins Above Replacement
2017-18 Season:
Expected FG%: 48.2% - Actual FG%: 49.1% - Shot-making above expected: +0.9% → Case Study 1: Building a Shot Quality Model for the Houston Rockets' Three-Point Revolution
2019-20 with Portland (Age 35):
15.4 PPG - 52.8% TS - 1.3 Win Shares (per 82 games pace) → Chapter 22: Case Study 2 - The Decline of Carmelo Anthony: Projecting Aging Superstars
2020s Player:
BPM: +6.0 - League pace: 100 possessions/game - League TS%: 0.570 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
24.96
Player B: 25.71 * (99 / 96) = **26.52** → Chapter 7 Quiz: Rate Statistics and Pace Adjustment
25.71
Player B: (1,500 / 2,100) * 36 = **25.71** → Chapter 7 Quiz: Rate Statistics and Pace Adjustment
255.85 assist points produced
### Question 25 Calculate passes per possession and ball movement efficiency for a team with: - Total passes: 312 per game - Possessions: 104 per game - Average touch time: 3.1 seconds - Potential assist rate: 27% - Assist conversion: 48% → Chapter 17: Quiz - Team Offensive Efficiency
3. In-Game Management:
Real-time fatigue monitoring - Mandatory rest in decided games - Substitution based on load metrics - Acceleration/deceleration tracking → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
30 points
**Player B:** (12 x 2) + (0 x 3) + (7 x 1) = 24 + 0 + 7 = **31 points** - **Player C:** (4 x 2) + (6 x 3) + (2 x 1) = 8 + 18 + 2 = **28 points** - **Player D:** (6 x 2) + (4 x 3) + (8 x 1) = 12 + 12 + 8 = **32 points** → Appendix G: Answers to Selected Exercises
40.0 PPG equivalent
Assists: 10.4 * (126/99.6) = **13.2 APG equivalent** → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
40.825 (approximately 41 field goals made)
### Question 23 Calculate the expected value of the following shot distribution: - 30 rim attempts at 65% probability = 2 points - 20 mid-range attempts at 42% probability = 2 points - 35 three-point attempts at 37% probability = 3 points → Chapter 17: Quiz - Team Offensive Efficiency
63.1
## True/False Questions → Chapter 17: Quiz - Team Offensive Efficiency
85% improbable
This means historically, only 15% of teams in that situation win - The team overcame 85% odds against them → Chapter 21: Quiz - In-Game Win Probability
97.1 PPG
107.0 ORtg at 93.9 pace = **100.5 PPG** → Case Study 2: Mike Conley and the Mid-2010s Memphis Grizzlies Pace Outliers
|
7.9** | **+15.3** | - | → Case Study 1: The 2019-20 Milwaukee Bucks - Elite Net Rating and Giannis Antetokounmpo's On/Off Dominance
A
A) 1
EVENTMSGTYPE 1 represents a made shot; 2 represents a missed shot. → Chapter 2 Quiz: Data Sources and Collection
A) 3PT: 1.08, Mid-range: 0.96
Calculate as (point value × percentage): 3×0.36=1.08 and 2×0.48=0.96. → Chapter 1 Quiz: Introduction to Basketball Analytics
a) Calculations:
100 PPG: 0.32 × 100 = 32.0 marginal points per win - 110 PPG: 0.32 × 110 = 35.2 marginal points per win - 120 PPG: 0.32 × 120 = 38.4 marginal points per win → Chapter 13 Exercises: Win Shares and Wins Above Replacement
Above Break 3 (245)
bimodal distribution. → Chapter 5: Exercises
ACM KDD Sports Analytics Workshop
Data mining approaches to sports → Chapter 27: Computer Vision and Video Analysis - Further Reading
Acquiring Player Profile:
Traditional stats: 15 PPG, 45% FG - xFG%: 43.5% (takes difficult shots) - Actual FG%: 45.0% - Shot-Making: +1.5% (above average) → Case Study 2: Evaluating Shot-Making Ability - Separating Skill from Shot Selection
ACSM Annual Meeting
American College of Sports Medicine - Sports science and medicine research presentations → Chapter 24: Injury Risk and Load Management - Further Reading
Action Classification
Automatic play type identification - Pick-and-roll detection - Post-up recognition → Case Study 1.2: The Evolution of Player Tracking Data
Adjusted Value (accounting for risk):
Fultz: 54 × (1 - 0.32 × 0.5) = 45.4 risk-adjusted WS - Tatum: 55 × (1 - 0.22 × 0.5) = 49.0 risk-adjusted WS → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Advanced Analysis
[ ] Build play type efficiency breakdowns - [ ] Calculate shot quality (xPTS) for all attempts - [ ] Create passing network graphs - [ ] Compute spacing metrics from tracking data → Chapter 17: Key Takeaways - Team Offensive Efficiency
Advanced Metrics:
True Shooting Percentage (TS%) - Player Efficiency Rating (PER) - Usage Rate (USG%) - Win Shares - Box Plus/Minus (BPM) → Case Study 1: Building a Comprehensive Player Statistics Pipeline
Advanced Topics
Bayesian concepts - Time series basics - Classification methods → Prerequisites
Advantages over OpenPose:
Runs efficiently without GPU - Simpler installation and setup - Built-in hand and face detection - Active development and support - Mobile device compatibility → Chapter 27: Computer Vision and Video Analysis
Advantages:
Bounded range, easy to interpret - Preserves zero values → Chapter 5: Descriptive Statistics in Basketball
After Green (Analytics Model):
Centers valued for switching ability, passing, versatility - Defense measured by overall impact (RAPM, DRTG) - Small-ball lineups deployed strategically for advantages → Case Study 1: The Discovery of Draymond Green's True Value
Against clutch as a distinct skill:
What statistical evidence suggests clutch is mostly noise? - How do sample size issues affect conclusions? → Chapter 20: Quiz - Game Strategy and Situational Analysis
Age (22)
Old for a prospect - Limited development runway expected → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
All-Star Probability:
Fultz: ~35% - Tatum: ~30% → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Also practice:
Recovering deleted files with `git checkout` - Undoing the last commit with `git reset` - Viewing the reflog to find lost commits → Chapter 3 Exercises: Python Environment Setup
American College of Sports Medicine (ACSM)
https://www.acsm.org/ - Research and practical guidelines → Chapter 24: Injury Risk and Load Management - Further Reading
Analysis:
Together they were dominant (+10.2) - Individually, similar impact (Paul +5.4, Harden +4.8) - Paul's differential appears lower because Harden-only lineups were still good → Case Study 2: The Impact of Point Guards - Chris Paul's On/Off Excellence Across Teams
Andrew Ng's Machine Learning (Coursera)
https://www.coursera.org/learn/machine-learning - Classic introduction to ML concepts → Chapter 26: Machine Learning in Basketball - Further Reading
Annual proceedings available at:
https://www.sloansportsconference.com/ → Chapter 22: Player Performance Prediction - Further Reading
Application Rules:
Stronger regression for smaller samples - Less regression for more stable statistics (e.g., free throw percentage) - More regression for volatile statistics (e.g., three-point percentage) - Less regression for players with longer track records → Chapter 22: Player Performance Prediction - Key Takeaways
Apply model scoring
Calculate value score - Flag prospects above threshold (e.g., 70+) → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Arguments against clutch as a distinct skill:
Small sample sizes make clutch statistics highly variable - Year-to-year correlations in clutch performance are weak - Regression to the mean explains most apparent clutch performers → Chapter 20: Game Strategy and Situational Analysis
Arguments for BPM Accuracy:
Jokic's teams consistently outperform expectations - On/off splits support elite impact - Advanced tracking metrics confirm playmaking value → Case Study 1: Nikola Jokic and the Evolution of Center Value
Arguments for clutch as a skill:
Some players consistently perform better in high-pressure situations - Psychological factors like composure and experience should matter - The "killer instinct" narrative has face validity → Chapter 20: Game Strategy and Situational Analysis
Arguments for Potential Overvaluation:
Defensive metrics may be too generous - Position adjustment may over-credit center playmaking - Playoff sample shows some regression → Case Study 1: Nikola Jokic and the Evolution of Center Value
Assist Quality:
Potential assists per game: 18.2 - Assists per 100 possessions: 14.9 - Assist-to-turnover ratio: 2.0 → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Assist rate
Limited playmaking creation → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Assists
higher z-score indicates more exceptional relative to league. → Chapter 5: Exercises
Athletic Profile Concerns
Not a traditional NBA athlete - Lateral quickness questions - Vertical explosiveness below typical NBA center → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Athletic Testing:
Standing vertical leap - Maximum vertical leap - Lane agility time - Three-quarter court sprint - Bench press repetitions → Chapter 23: Draft Modeling and Prospect Evaluation
Automated highlight generation
AI-selected key moments 2. **Broadcast augmentation** - Real-time graphics from tracking 3. **Democratization** - Cheaper tracking for all levels 4. **Privacy-preserving analytics** - Anonymized player data → Chapter 27: Computer Vision and Video Analysis - Key Takeaways
Award Recognition:
2017 Defensive Player of the Year - Multiple All-NBA and All-Defensive Team selections - Recognition lagged RAPM indicators by ~2 years → Case Study 1: The Discovery of Draymond Green's True Value
B
B) 1 request per 2 seconds (0.5 requests/second)
The chapter recommends approximately 0.5 requests per second to avoid rate limiting. → Chapter 2 Quiz: Data Sources and Collection
B) 12 minutes
NBA regulation periods are 12 minutes; overtime periods are 5 minutes. → Chapter 2 Quiz: Data Sources and Collection
B) 2013-14
SportVU cameras were installed league-wide that season. → Chapter 1 Quiz: Introduction to Basketball Analytics
B) 25 frames per second
The tracking system captures positions at 25 fps. → Chapter 2 Quiz: Data Sources and Collection
B) 27.4
(32 + 24 + 28 + 31 + 22) / 5 = 137 / 5 = 27.4 → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) 50th percentile
By definition, the median is the 50th percentile. → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) `%matplotlib inline`
This magic command configures matplotlib to display plots directly in the notebook. → Chapter 3 Quiz: Python Environment Setup
B) `conda create --name nba_analysis python=3.11`
The `--name` flag specifies the environment name, followed by package specifications. → Chapter 3 Quiz: Python Environment Setup
B) `df.info()`
`info()` shows column names, data types, non-null counts, and memory usage. `describe()` shows statistical summaries for numeric columns. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) `df['position'].value_counts()`
`value_counts()` returns a Series with counts of unique values, ideal for categorical data. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) `git add .` then `git commit -m "message"`
Files must be staged (added) before they can be committed. → Chapter 3 Quiz: Python Environment Setup
B) `git init`
`git init` creates a new .git directory and initializes the repository. → Chapter 3 Quiz: Python Environment Setup
B) `pip install -r requirements.txt`
The `-r` flag tells pip to read requirements from the specified file. → Chapter 3 Quiz: Python Environment Setup
B) `python --version`
The double-dash format `--version` is the standard way to check version information. → Chapter 3 Quiz: Python Environment Setup
B) `python -m venv venv`
The `-m venv` runs the venv module, and the second "venv" is the directory name. → Chapter 3 Quiz: Python Environment Setup
B) `venv/`
The virtual environment folder contains local copies of packages and should not be committed. → Chapter 3 Quiz: Python Environment Setup
B) `venv\Scripts\activate`
Windows uses backslashes and the Scripts folder; macOS/Linux use forward slashes and bin. → Chapter 3 Quiz: Python Environment Setup
B) A strong positive relationship
r = 0.85 indicates strong positive correlation (typically r > 0.7 is considered strong). → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) Add Python to PATH
This allows you to run Python from any command prompt location without specifying the full path. → Chapter 3 Quiz: Python Environment Setup
B) BeautifulSoup
BeautifulSoup (from bs4) is commonly used with pandas for HTML parsing. → Chapter 2 Quiz: Data Sources and Collection
B) Center court
The coordinate system places the origin at center court with axes to sidelines and baselines. → Chapter 2 Quiz: Data Sources and Collection
B) Conda can manage non-Python dependencies
Conda can install system-level libraries, compilers, and other non-Python software. → Chapter 3 Quiz: Python Environment Setup
B) conda-forge
conda-forge is a community-maintained channel with a wide variety of packages. → Chapter 3 Quiz: Python Environment Setup
B) Create new columns in the working DataFrame
This maintains the original data, allows reproducibility, and keeps derived features accessible for analysis. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) Difficulty measuring leadership and chemistry
These intangibles remain hard to quantify. → Chapter 1 Quiz: Introduction to Basketball Analytics
B) Field goal percentage must be between 0 and 1
This is a logical constraint. The other options are not universally true (players can score 100+, starters don't always play 48 minutes, etc.). → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) Hexbin plot with shot counts
Hexbin aggregates shots into bins, showing frequency through color intensity. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) Higher scoring rate
A steeper slope in cumulative points means more points accumulated per game. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) It accounts for teammate and opponent quality
APM uses regression to isolate individual impact. → Chapter 1 Quiz: Introduction to Basketball Analytics
B) M
In command mode, 'M' changes a cell to markdown; 'Y' changes it back to code. → Chapter 3 Quiz: Python Environment Setup
B) Mode < Median < Mean
In right-skewed distributions, the long right tail pulls the mean higher than the median. → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) nba_api
The nba_api library, maintained by Swar Patel and contributors, provides a Pythonic interface to the NBA Stats API. → Chapter 2 Quiz: Data Sources and Collection
B) Points per game
Scoring distributions are typically right-skewed with most players scoring lower and few high scorers. → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) Position in tenths of feet from the basket
LOC_X and LOC_Y are measured in tenths of feet (decifeet) from the basket location. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) Referer
Requests must include headers like Referer (stats.nba.com) to receive valid responses. → Chapter 2 Quiz: Data Sources and Collection
B) Second Spectrum
Second Spectrum became the NBA's tracking partner in 2017, replacing SportVU. → Chapter 2 Quiz: Data Sources and Collection
B) Shift + Enter
Shift + Enter executes the current cell and moves to the next; Ctrl + Enter stays in place. → Chapter 3 Quiz: Python Environment Setup
B) SportVU cameras
These cameras were installed in all NBA arenas for the 2013-14 season. → Chapter 1 Quiz: Introduction to Basketball Analytics
B) Standard Deviation / Mean x 100%
CV = (s / x-bar) * 100% → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) The data significantly deviates from normality
p < 0.05 leads to rejection of the null hypothesis that data is normally distributed. → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) The distribution is right-skewed
When mean > median, the distribution has a longer right tail. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
eFG% = (FGM + 0.5×3PM) / FGA. → Chapter 1 Quiz: Introduction to Basketball Analytics
B) The player was traded mid-season
Traded players often have separate rows for each team they played on during the season. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
B) The proportion of variance in Y explained by X
R-squared = r^2 represents explained variance. → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) The robots.txt file and terms of service
These documents specify what automated access is permitted. → Chapter 2 Quiz: Data Sources and Collection
B) The time remaining in the current period
PCTIMESTRING shows time remaining (e.g., "8:34") in the current period. → Chapter 2 Quiz: Data Sources and Collection
B) There are likely outliers in the data
Large range with small IQR suggests extreme values at the tails not affecting the middle 50%. → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) They outscore 85% of NBA players
Being at the 85th percentile means their scoring exceeds 85% of the comparison group. → Chapter 5 Quiz: Descriptive Statistics in Basketball
B) To track when the most recent request was made
This timestamp allows calculation of elapsed time to enforce minimum intervals between requests. → Chapter 2 Quiz: Data Sources and Collection
B) Values more than 1.5 x IQR beyond Q1 or Q3
This is the standard definition used in box plots. → Chapter 5 Quiz: Descriptive Statistics in Basketball
Background:
Serbian center, 19 years old - Playing in Serbian KLS (second-tier league) and Adriatic League - Limited exposure to NBA scouts - Considered a draft-and-stash prospect → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Ball Pressure:
Forces 15% more turnovers in matchups - Opponent shooting -8% vs. expected → Case Study 1: Measuring Defensive Impact with Tracking Data
Base Bust Rates by Position:
Picks 1-5: ~25% - Picks 6-10: ~35% - Picks 11-20: ~45% - Picks 21-30: ~60% → Chapter 23: Draft Modeling and Prospect Evaluation - Key Takeaways
Based on Points Produced
Credits scoring at given efficiency - Adds value from assists - Subtracts costs from missed shots and turnovers → Chapter 13 Key Takeaways: Win Shares
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 → Chapter 17: Key Takeaways - Team Offensive Efficiency
Basic Rules
Scoring (2-point, 3-point, free throws) - Possessions and turnovers - Basic violations and fouls - Game structure (quarters, overtime) → Prerequisites
Basic Statistics:
23.2 PPG, 5.7 RPG, 5.9 APG - 47.6% FG, 41.3% 3PT, 64.9% FT - Minutes: 35.7 per game (25 games) - Age at draft: 19.1 years → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Basketball Reference API/Scraping
https://www.basketball-reference.com/ - Historical game data for model training → Chapter 25: Game Outcome Prediction - Further Reading
Basketball Reference:
Limited tracking metrics - Shot location data → Chapter 15: Player Tracking Analytics
Basketball-Reference
https://www.basketball-reference.com/ - Comprehensive historical statistics essential for projection model building → Chapter 22: Player Performance Prediction - Further Reading
Basketball-Reference CSV exports
**PBP Stats data access** → Chapter 10 Further Reading: Plus-Minus and On/Off Analysis
Basketball-Reference Player Projection Framework
Sports Reference's approach to projecting player statistics, documented through their methodology pages - Demonstrates practical implementation challenges → Chapter 22: Player Performance Prediction - Further Reading
Bayesian interpretation
Ridge equivalent to Normal(0, τ²) prior on coefficients - λ = σ²/τ² controls how strongly we trust prior vs. data - With limited data, estimates shrink toward prior mean → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
Bayesian Logistic Regression
Provides uncertainty estimates for predictions - Naturally handles rare game states through priors - Computationally more intensive → Chapter 21: In-Game Win Probability
Before Green (Traditional Model):
Centers valued for height, post scoring, rebounding - Defense measured by blocks and steals - "Small ball" considered gimmicky or emergency-only → Case Study 1: The Discovery of Draymond Green's True Value
Benefit:
10 extra games from star = ~2 extra wins - Reduced injury = ~$15M avoided in salary for injured player - Playoff availability = championship contention → Case Study 2: Load Management Using Physical Tracking Data
Best Practices:
Select 5-15 comparables for statistical stability - Weight by similarity score - Account for era differences - Include only comparable contexts (age, role, situation) → Chapter 22: Player Performance Prediction - Key Takeaways
Bias-variance tradeoff
Ridge introduces bias (shrinks true effects toward zero) - But dramatically reduces variance (more stable estimates) - Optimal λ minimizes total mean squared error → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
BigDataBall
Play-by-play and tracking data products → Chapter 28: Building a Basketball Analytics Career - Further Reading
Biometric Integration
Heart rate and exertion monitoring - Sleep and recovery tracking - Injury risk prediction → Case Study 1.2: The Evolution of Player Tracking Data
BJSM Blog
https://blogs.bmj.com/bjsm/ - British Journal of Sports Medicine blog with accessible sports science content → Chapter 24: Injury Risk and Load Management - Further Reading
Both players:
TS%: 0.590, USG%: 25 - AST%: 30, TOV%: 12 - ORB%: 4.0, 3PAr: 0.35 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Box Out Metrics:
Box out frequency (how often player initiates box out) - Box out duration (time maintaining position) - Box out effectiveness (rebound outcomes) → Chapter 15: Player Tracking Analytics
BPM (Box Plus-Minus):
Derived from box score statistics - Coefficients chosen to predict RAPM - More stable in small samples - Available for historical analysis → Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
BPM components
OBPM: Offensive contribution from points, assists, offensive rebounds, spacing - DBPM: Defensive contribution from steals, blocks, defensive rebounds, team adjustment - Total BPM = OBPM + DBPM → Chapter 12 Key Takeaways: Box Plus-Minus (BPM) and VORP
BPM estimates plus-minus from box scores
Uses regression coefficients derived from historical RAPM data - Combines scoring efficiency, playmaking, rebounding, and defensive statistics - Expressed as points per 100 possessions above/below league average → Chapter 12 Key Takeaways: Box Plus-Minus (BPM) and VORP
BPM interpretation scale
+10.0 and above: All-time great season - +8.0 to +10.0: MVP-level season - +6.0 to +8.0: MVP candidate - +4.0 to +6.0: All-Star level - +2.0 to +4.0: Quality starter - 0.0 to +2.0: Average starter - -2.0 to 0.0: Below average - Below -2.0: Replacement level or worse → Chapter 12 Key Takeaways: Box Plus-Minus (BPM) and VORP
Broadcast Video Analysis
Converting broadcast footage to tracking data → Chapter 27: Computer Vision and Video Analysis - Further Reading
Bust Probability:
Fultz: ~32% (elevated by FT%, small sample) - Tatum: ~22% (standard for top-3 pick) → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
But what if:
Player A takes mostly wide-open catch-and-shoot threes - Player B creates contested pull-up jumpers off the dribble → Case Study 2: Evaluating Shot-Making Ability - Separating Skill from Shot Selection
By Score Differential
Leverage peaks in tie games or 1-possession games - Leverage approaches zero in blowouts - The "leverage cliff" occurs around 15-20 point margins → Chapter 21: In-Game Win Probability
By Time Remaining
Leverage increases exponentially as the game nears its end - Final minute often sees LI > 5.0 - First quarter rarely exceeds LI of 1.5 → Chapter 21: In-Game Win Probability
C
C) (x - mean) / std
This is the standard z-score formula. → Chapter 5 Quiz: Descriptive Statistics in Basketball
C) -250 to 250
The valid X range is -250 to 250 (representing -25 to 25 feet from center). → Chapter 2 Quiz: Data Sources and Collection
C) 1996-97
Most endpoints provide data back to the 1996-97 season, with box score data available from earlier eras. → Chapter 2 Quiz: Data Sources and Collection
C) 2017
Second Spectrum replaced SportVU in 2017. → Chapter 1 Quiz: Introduction to Basketball Analytics
C) 25 times
Tracking systems capture 25 frames per second. → Chapter 1 Quiz: Introduction to Basketball Analytics
C) 3-5 seconds
A delay of 3-5 seconds is recommended to avoid imposing undue server load. → Chapter 2 Quiz: Data Sources and Collection
C) 429
HTTP 429 "Too Many Requests" indicates rate limit exceeded. → Chapter 2 Quiz: Data Sources and Collection
C) `jupyter lab`
JupyterLab is started with the `jupyter lab` command. → Chapter 3 Quiz: Python Environment Setup
C) `pip install pandas==2.0.3`
The double equals sign specifies an exact version requirement. → Chapter 3 Quiz: Python Environment Setup
C) A highly right-skewed distribution
Positive skewness indicates a long right tail; |skew| > 1 is considered highly skewed. → Chapter 5 Quiz: Descriptive Statistics in Basketball
C) A strong negative relationship
|r| = 0.65 is strong, and the negative sign indicates an inverse relationship. → Chapter 5 Quiz: Descriptive Statistics in Basketball
C) API keys and passwords
Credentials should never be in version control; use environment variables or secret managers. → Chapter 3 Quiz: Python Environment Setup
C) Contested shot percentage
This is derived from raw positions; it's computed, not directly measured. → Chapter 2 Quiz: Data Sources and Collection
C) Dean Oliver
His book "Basketball on Paper" (2004) established foundational concepts. → Chapter 1 Quiz: Introduction to Basketball Analytics
C) Excessive data availability
This is not a data quality issue; the others are common problems discussed in the chapter. → Chapter 2 Quiz: Data Sources and Collection
C) Grouped box plots or violin plots
These allow direct comparison of distributions across multiple categories in a single plot. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
C) Homebrew
Homebrew is the most popular package manager for macOS and simplifies Python installation and updates. → Chapter 3 Quiz: Python Environment Setup
C) JSON
The NBA Stats API follows RESTful architecture and returns JSON-formatted responses. → Chapter 2 Quiz: Data Sources and Collection
C) Leave as NaN/missing
A percentage based on zero attempts is mathematically undefined (0/0), not zero. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
C) Mean
The five-number summary is: Min, Q1, Median, Q3, Max. The mean is not included. → Chapter 5 Quiz: Descriptive Statistics in Basketball
C) Mode
The mode identifies the most frequently occurring value/location. → Chapter 5 Quiz: Descriptive Statistics in Basketball
C) More variable/inconsistent performance
Higher CV means higher relative variability compared to the mean. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
C) Offensive rebound
Offensive rebounds extend the possession; the other events end it. → Chapter 2 Quiz: Data Sources and Collection
C) pandas
pandas provides the DataFrame class, essential for working with tabular sports data. → Chapter 3 Quiz: Python Environment Setup
C) Parquet
Parquet's columnar format provides significant advantages for analytical workloads. → Chapter 2 Quiz: Data Sources and Collection
C) Plus-Minus
The Four Factors are: eFG%, TOV%, ORB%, and FT Rate. → Chapter 1 Quiz: Introduction to Basketball Analytics
C) Prescriptive analytics
Prescriptive analytics recommends optimal actions based on analysis. → Chapter 1 Quiz: Introduction to Basketball Analytics
C) Python 3 runs faster than Python 2 in all cases
This is not universally true; performance varies by use case. The other statements are valid reasons. → Chapter 3 Quiz: Python Environment Setup
C) Python 3.10
Python 3.10 and 3.11 provide the best balance of compatibility and modern features for data science work. → Chapter 3 Quiz: Python Environment Setup
C) Q3 - Q1
IQR measures the spread of the middle 50% of data. → Chapter 5 Quiz: Descriptive Statistics in Basketball
C) Restart kernel and run all cells before sharing
This ensures the notebook runs correctly from start to finish, verifying reproducibility. → Chapter 3 Quiz: Python Environment Setup
C) seaborn
seaborn's `boxplot()` function creates statistical box plots with minimal code. → Chapter 3 Quiz: Python Environment Setup
C) The distribution is right-skewed
When mean > median, there are high values pulling the mean up, indicating right skew. → Chapter 5 Quiz: Descriptive Statistics in Basketball
C) Three dimensions of data
X-axis (TS%), Y-axis (Points), and bubble size (Minutes) represent three dimensions. → Chapter 4 Quiz: Exploratory Data Analysis for Basketball
C) Weighted mean using attempts as weights
Different seasons have different sample sizes; weighting by attempts gives appropriate influence to each season. → Chapter 5 Quiz: Descriptive Statistics in Basketball
c) z = (8.8 - 15.2) / 6.4 =
1.00** d) z = (35.0 - 15.2) / 6.4 = **+3.09** → Chapter 5: Exercises
Calibration
[ ] Create calibration curve (predicted vs actual) - [ ] Calculate Expected Calibration Error - [ ] Apply Platt scaling if needed - [ ] Verify calibration across all probability bins → Chapter 21: Key Takeaways - In-Game Win Probability
Camera Configuration:
5-6 cameras positioned above the court - Resolution sufficient to track players within approximately 4 inches of accuracy - Ball tracking includes z-coordinate (height) for shot trajectory analysis - System operates in real-time during games → Chapter 15: Player Tracking Analytics
Candidate A: James Robinson (SG)
Age: 27, Seeking $18M/year - RAPM: +2.4 (O-RAPM: +3.1, D-RAPM: -0.7) → Case Study 2: Building a Real-Time RAPM System for Player Evaluation
Candidate B: Michael Chen (SF)
Age: 25, Seeking $12M/year - RAPM: +1.6 (O-RAPM: +0.9, D-RAPM: +0.7) → Case Study 2: Building a Real-Time RAPM System for Player Evaluation
Candidate C: Andre Williams (PF)
Age: 29, Seeking $8M/year - RAPM: +0.8 (O-RAPM: +0.3, D-RAPM: +0.5) → Case Study 2: Building a Real-Time RAPM System for Player Evaluation
Cap Situation:
Salary cap: $140M - Current committed salary: $88M - Cap space: $52M - Luxury tax threshold: $170M → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
Career (through 2023-24):
3× MVP (2021, 2022, 2024) - 6× All-Star - 5× All-NBA - NBA Champion (2023) - Career Win Shares: 100+ and climbing - Projected final career WS: 200+ → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Career milestones projection:
Probability of 2nd MVP: 55% - Probability of Championship: 45% (5-year window) - Probability of Hall of Fame: 95% - Expected remaining career Win Shares: 85-110 → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Career Outcome Probabilities:
All-Star probability: ~8% - All-NBA probability: ~3% - Out of league within 5 years: ~35% → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Career Win Shares Lost:
Pre-injury trajectory (ages 23-32): ~75-90 WS - Actual production (ages 23-32): ~25 WS - Estimated career impact: 50-65 WS lost → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Catapult Sports Blog
https://www.catapultsports.com/blog/ - Practical articles on load monitoring and wearable technology → Chapter 24: Injury Risk and Load Management - Further Reading
Centers (Traditional):
Historically highest WS due to rebounding credit - Kareem, Wilt, Shaq dominated Win Shares → Case Study 2: Win Shares for Career Evaluation - Comparing All-Time Greats
Centers:
Historically most efficient (close to basket) - TS% advantage has decreased as perimeter players optimized - Modern centers must shoot threes to remain valuable → Chapter 8: Shooting Efficiency Metrics
Challenges in Basketball:
Different movement patterns than soccer - Limited practice time for prevention programs - Player buy-in for additional training - Measurement and tracking difficulties → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Championship Years:
2015: PER 16.1 (slightly above average) - 2017: PER 16.8 (modestly above average) - 2018: PER 15.4 (barely above average) - 2022: PER 14.1 (below average) → Case Study 2: Draymond Green and the Limits of Box Score Evaluation
Characteristics:
Elite rim finishers - Corner three specialists - Primary beneficiaries of playmaking - Often undervalued by traditional metrics → Case Study 2: Evaluating Shot-Making Ability - Separating Skill from Shot Selection
Chasing a Deficit:
Maximize offensive firepower - Three-point shooting for quick scoring - Gambling on defense acceptable → Chapter 19: Lineup Optimization
Classification:
Accuracy (only with balanced classes) - Precision, Recall, F1 Score - AUC-ROC (probability calibration) - Log Loss (probabilistic predictions) → Chapter 26: Machine Learning in Basketball - Key Takeaways
Cleaning the Glass
https://cleaningtheglass.com/ - Advanced basketball analysis → Chapter 26: Machine Learning in Basketball - Further Reading
Cleaning the Glass (Ben Falk)
https://cleaningtheglass.com/ - Professional-quality analysis including player evaluation. Subscriber content demonstrates practical application of projection concepts. → Chapter 22: Player Performance Prediction - Further Reading
Cleaning the Glass Podcast
Ben Falk's analytical draft discussions - Methodology insights → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Cluster 1: Primary Ball Handlers (n=45)
Profile: High usage, high assists, moderate scoring - Exemplars: Luka Doncic, Trae Young, Ja Morant - Traditional equivalent: Point Guards → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Cluster 2: Scoring Wings (n=52)
Profile: High scoring, moderate efficiency, low assists - Exemplars: Devin Booker, Jaylen Brown, Zach LaVine - Traditional equivalent: Shooting Guards/Small Forwards → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Cluster 3: Three-and-D Wings (n=68)
Profile: High 3PT%, low usage, positive defense - Exemplars: Mikal Bridges, OG Anunoby, Herb Jones - Traditional equivalent: Role player wings → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Cluster 4: Stretch Bigs (n=42)
Profile: High rebounding, moderate 3PT attempts, interior defense - Exemplars: Brook Lopez, Myles Turner, Karl-Anthony Towns - Traditional equivalent: Modern centers/power forwards → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Cluster 5: Traditional Bigs (n=38)
Profile: Highest rebounding, rim protection, low perimeter involvement - Exemplars: Rudy Gobert, Clint Capela, Mitchell Robinson - Traditional equivalent: Classic centers → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Cluster 6: Playmaking Bigs (n=35)
Profile: High assists for position, versatile scoring - Exemplars: Nikola Jokic, Domantas Sabonis, Bam Adebayo - Traditional equivalent: No traditional equivalent → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Clustering:
Silhouette Score - Within-cluster variance - Domain expert validation → Chapter 26: Machine Learning in Basketball - Key Takeaways
COCO (Common Objects in Context)
https://cocodataset.org/ - Standard benchmark for detection and pose estimation → Chapter 27: Computer Vision and Video Analysis - Further Reading
College Statistics (2015-16):
20.3 PPG, 11.6 RPG, 1.8 APG - 53.8% FG, 17.6% 3PT, 68.6% FT - Age at draft: 22 → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Combination Analysis
[ ] Identify all two-man combinations - [ ] Calculate three-man core performances - [ ] Compute synergy scores - [ ] Rank combinations by Net Rating → Chapter 19: Key Takeaways - Lineup Optimization
Commercial Systems:
Catapult (GPS/accelerometry) - Second Spectrum (optical tracking) - Kinexon (RFID tracking) - Whoop (wearable recovery monitor) → Chapter 24: Injury Risk and Load Management - Further Reading
Common Confounders in Basketball Data:
Playing time (affects all counting statistics) - Team pace (affects per-game statistics) - Era effects (rule changes, playing style evolution) - Role/position (different expectations by role) - Sample size (small samples inflate correlations) → Chapter 5: Descriptive Statistics in Basketball
Common Pitfalls:
Over-relying on small comparable sets - Ignoring context differences - Treating unprecedented players as having valid comparables → Chapter 22: Player Performance Prediction - Key Takeaways
Common Statistics
Points, rebounds, assists - Steals, blocks, turnovers - Field goals and free throws - Basic box score interpretation → Prerequisites
Comparable criteria:
Age 21-22 in Year 3 - 15-20 PPG, 6-9 RPG, 3-5 APG - Positive Win Shares trajectory → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Comparison to VORP:
Both measure cumulative value - Win Shares uses different methodology (marginal offense/defense) - VORP tied to point differential; Win Shares tied to wins → Chapter 12 Further Reading: Box Plus-Minus (BPM) and VORP
Competition Level
Adriatic League not considered elite - Limited games against top EuroLeague competition → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Competitive Cost:
Estimated wins lost due to rest: 2-3 - Seeding impact: Minimal (comfortable 2nd place) - Playoff home court: Not affected → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Conference (WAC)
Low major conference - Conference factor: ~0.75 → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Conference Adjustment Factors:
Power conferences (Big Ten, Big 12, etc.): 1.03-1.08 - Mid-majors: 0.85-0.92 - Low-majors: 0.65-0.75 → Chapter 23: Draft Modeling and Prospect Evaluation - Key Takeaways
Conference Finals vs. Warriors:
3PA: 44.8/game - 3P%: 30.4% → Case Study 1: Building a Shot Quality Model for the Houston Rockets' Three-Point Revolution
Conference-adjusted scoring
ACC production against superior competition - Tournament success (Elite Eight) → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Confidence vs. Prediction Intervals:
Confidence intervals: Uncertainty about the model/parameters - Prediction intervals: Uncertainty about specific future observations (always wider) → Chapter 22: Player Performance Prediction - Key Takeaways
Consensus momentum
Once Fultz was established as #1, confirmation bias dominated - Mock drafts create feedback loops → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Considerations for Live Data:
Rate limiting is critical - WebSocket connections for streaming - Data latency varies by source - Commercial licenses typically required → Appendix D: Data Sources and APIs
Construction Methods:
Delta method: Track same players across seasons - Cross-sectional: Compare players of different ages (biased) - Mixed methods: Combine approaches with statistical corrections → Chapter 22: Player Performance Prediction - Key Takeaways
Contest Data:
Shots contested: 12.5 per game - Opponent FG% on contested shots: 38% - Altered shots: 8.2 per game → Case Study 1: Measuring Defensive Impact with Tracking Data
Context matters
team role, opponent quality, and sample size affect interpretation 4. **EDA informs but doesn't decide** - analysis supports but doesn't replace basketball judgment 5. **Communication is key** - findings must be accessible to non-technical stakeholders → Case Study 2: Player Performance Profiling for Trade Deadline Analysis
Context Matters Enormously
Shaq's Finals scoring (26.6 PPG) looked respectable - But efficiency (51.8% FG) and team collapse (1-4) told different story → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
Context Matters:
Game 7 dynamics differ from regular season - Playoff experience may be underweighted in models → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Context:
Team Record: 52-30 - Team Net Rating: +4.1 - Player's backup is a second-round rookie → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
Contextual Features:
Score differential - Quarter - Home/away → Case Study 2: Building an EPV Model from Tracking Data
Contextual Information:
Play calls not captured - Coaching instructions unknown - Injury status not always reflected → Chapter 15: Player Tracking Analytics
Contextual Red Flags:
Statistical production dependent on superior teammates - Limited experience against high-level competition - System-dependent production (e.g., specific offensive schemes) → Chapter 23: Draft Modeling and Prospect Evaluation
Converting to OWS:
Team marginal points per win ≈ 30 - Curry OWS ≈ 450 / 30 = 15.0 (approximate) → Case Study 1: The 2016 Warriors - Win Shares in a Historic Season
Converting to wins
Wins ≈ RAPM × (Minutes / 200) / 2.7 - A +5.0 RAPM player playing 2,500 minutes ≈ 23 wins added → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
Counter-Argument:
Mid-range shots can be easier to generate - Some mid-range specialists (Chris Paul, Kawhi Leonard) shoot >50% - Eliminating options makes defense easier → Case Study 2: The Houston Rockets' "Moreyball" Era (2017-2020) - Team-Level Efficiency Optimization
Coursera - Machine Learning (Stanford/Andrew Ng)
https://www.coursera.org/learn/machine-learning - Classic introduction to ML concepts → Chapter 28: Building a Basketball Analytics Career - Further Reading
Coursera: Visual Perception for Self-Driving Cars
Good coverage of detection, tracking, depth estimation - University of Toronto → Chapter 27: Computer Vision and Video Analysis - Further Reading
Covered in detail in Chapter 14
## Contextual Factors to Consider → Chapter 10 Key Takeaways: Plus-Minus and On/Off Analysis
Crucially excluded:
Shooter identity (to measure difficulty, not ability) → Case Study 2: Evaluating Shot-Making Ability - Separating Skill from Shot Selection
Cumulative projection (Age 30-35):
Total Win Shares: 21.7 - Expected remaining All-Star selections: 2-3 - Probability of productive career past 35: 30% → Chapter 22: Case Study 2 - The Decline of Carmelo Anthony: Projecting Aging Superstars
Curry-Draymond Pick-and-Roll:
Curry's gravity created open shots for others - Draymond's passing from short roll created layups - Win Shares credits Curry for his points, Draymond for his assists - But the system created more value than individuals alone → Case Study 1: The 2016 Warriors - Win Shares in a Historic Season
CVPR (Computer Vision and Pattern Recognition)
Premier computer vision conference - Annual, proceedings available online → Chapter 27: Computer Vision and Video Analysis - Further Reading
CVPR Sports Workshop
Workshop at CVPR focused on sports vision → Chapter 27: Computer Vision and Video Analysis - Further Reading
D
D) Daryl Morey
As Houston Rockets GM, his analytical approach became known as "Moreyball." → Chapter 1 Quiz: Introduction to Basketball Analytics
D) Field goals, three-pointers, and free throws
TS% = PTS / (2 × (FGA + 0.44×FTA)). → Chapter 1 Quiz: Introduction to Basketball Analytics
D) Shooting (40%)
Oliver found shooting efficiency is the most important factor for winning. → Chapter 1 Quiz: Introduction to Basketball Analytics
d) Tradeoffs:
**Player X:** Higher ceiling, more impactful per minute, but durability/availability concerns. Ideal as a high-impact starter or closer. - **Player Y:** More reliable availability, consistent production, but lower peak impact. Better for overall team stability. → Appendix G: Answers to Selected Exercises
Daily Monitoring:
Pre-game wellness survey - Tracking load from prior games - Practice load integration → Case Study 2: Load Management Using Physical Tracking Data
Data Available Through NBA Stats:
Player speed and distance - Touches and time of possession - Contested/uncontested shot classifications - Closest defender distance - Catch-and-shoot vs. pull-up classifications → Appendix D: Data Sources and APIs
Data Categories:
Four Factors (eFG%, TOV%, ORB%, FT Rate) - Lineup data - On/Off statistics - Zone shooting percentages → Appendix D: Data Sources and APIs
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 → Chapter 17: Key Takeaways - Team Offensive Efficiency
Data Engineer (James)
Needs: nba_api, requests, sqlalchemy for data pipelines - "I pull data from multiple sources daily." → Case Study 1: Setting Up a Team Analytics Environment
Data Infrastructure
Centralized data warehouse integrating all sources - Real-time data pipelines for tracking and wearables - Secure storage meeting health data regulations - APIs for model serving and alerts → Chapter 24: Injury Risk and Load Management
Data preparation requirements
Play-by-play data transformed to stint-level observations - Each stint has 10 player indicators (+1 home, -1 away) - Response: point differential per 100 possessions - Weights: number of possessions per stint → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
Data Standards:
Review vendor documentation for data formats and export options → Chapter 24: Injury Risk and Load Management - Further Reading
Data Structures
Lists and dictionaries - Working with files - String manipulation → Prerequisites
DataCamp
https://www.datacamp.com/ - Interactive courses including sports analytics tracks → Chapter 26: Machine Learning in Basketball - Further Reading
Dataset:
5 seasons of NBA regular season games (6,150 games) - Opening lines, closing lines, and results - Point spreads and totals (over/under) → Chapter 25: Case Study 2 - Evaluating the Efficiency of NBA Betting Markets
Death Lineup Statistics (2015-16)
Net Rating: +25.4 per 100 possessions - Offensive Rating: 123.8 - Defensive Rating: 98.4 - Minutes: 281 → Case Study 1: The Golden State Warriors' Offensive Revolution (2015-2019)
Death Lineup:
With Green at center, team posted +28.7 net rating - Individual Win Shares don't fully capture lineup-specific effects - System value partially attributed to individuals → Case Study 1: The 2016 Warriors - Win Shares in a Historic Season
Decision Categories
[ ] Shot selection (2 vs 3, quick vs normal) - [ ] Fouling strategy (when to foul, who to foul) - [ ] Timeout usage (call vs save) - [ ] Pace manipulation (speed up vs slow down) - [ ] Defensive approach (foul vs defend) → Chapter 20: Key Takeaways - Game Strategy and Situational Analysis
Deep Contribution:
10 players with positive Win Shares - Bench contributed ~16 total Win Shares - Role players all with positive WS/48 → Case Study 1: The 2016 Warriors - Win Shares in a Historic Season
Deep dive on flagged prospects
Additional video study - Interview coaches/teammates - Medical evaluation → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Defending (0.83) > Fouling (~0.80)
but close. Depends on parameters. → Chapter 20: Quiz - Game Strategy and Situational Analysis
Defense
Defended field goal percentage - Shots defended per game - Defensive win shares (tracking-enhanced) → Case Study 1.2: The Evolution of Player Tracking Data
Defense:
Steals per 75 possessions - Blocks per 75 possessions - Defensive Box Plus/Minus → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Defensive Effort
Westbrook's steals (1.6 per game) appeared positive - However, he frequently gambled on passing lanes - Defensive rating with him on court (105.4) was actually better than off (109.8), complicating the narrative → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Defensive Evaluation:
Matchup data against elite wings - Closeout speed and shot contest rates - Help defense frequency - Defensive versatility (positions guarded) → Chapter 15: Player Tracking Analytics
Defensive Metrics:
Opponent eFG% - Opponent Turnover Rate - Defensive Rebounding Rate - Opponent Free Throw Rate → Chapter 19: Lineup Optimization
Defensive Rating (On/Off):
Points allowed per 100 possessions with player on court - Points allowed per 100 possessions with player off court → Chapter 10: Plus-Minus and On/Off Analysis
Defensive Specialist Example:
ORtg On: 108.0, ORtg Off: 110.0 (Offensive Diff: -2.0) - DRtg On: 102.0, DRtg Off: 112.0 (Defensive Diff: -10.0, meaning much better) - On/Off: -2.0 - (-10.0) = +8.0 → Chapter 10: Plus-Minus and On/Off Analysis
Demonstrate technical competence
Show you can handle the work 2. **Reveal basketball understanding** - Prove you know the game 3. **Highlight communication skills** - Present work clearly 4. **Differentiate yourself** - Stand out from other candidates → Chapter 28: Building a Basketball Analytics Career - Key Takeaways
Deployment
[ ] Build prediction API - [ ] Handle real-time updates - [ ] Monitor calibration drift - [ ] Document model assumptions → Chapter 21: Key Takeaways - In-Game Win Probability
Descriptive Statistics
Measures of central tendency (mean, median, mode) - Measures of variability (variance, standard deviation, range) - Percentiles and quartiles - Basic data visualization (histograms, scatter plots, box plots) → Prerequisites
Detectron2
https://github.com/facebookresearch/detectron2 - State-of-the-art detection and segmentation → Chapter 27: Computer Vision and Video Analysis - Further Reading
Deterrence Has No Box Score
Ben Wallace's impact included shots never attempted - Opponents changing behavior left no statistical trace → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
Difficulty Levels:
★ Basic: Reinforce fundamental concepts - ★★ Intermediate: Apply concepts to new situations - ★★★ Advanced: Extend methods or combine multiple concepts - ★★★★ Challenge: Open-ended research questions → Chapter 1 Exercises: Introduction to Basketball Analytics
Director of Basketball Strategy
Owns game preparation analytics - Manages relationship with coaching staff - Leads player development measurement - Oversees in-season analysis workflow → Chapter 28: Case Study 1 - Building an NBA Team Analytics Department from Scratch
Director of Data Engineering
Manages all technical infrastructure - Ensures data quality and availability - Leads tool development for analysts - Maintains security and access controls → Chapter 28: Case Study 1 - Building an NBA Team Analytics Department from Scratch
Director of Player Evaluation
Owns all player evaluation models and processes - Manages draft board creation - Coordinates with scouting department - Leads trade and free agency analysis → Chapter 28: Case Study 1 - Building an NBA Team Analytics Department from Scratch
Disadvantages:
Sensitive to outliers - New maximum values require rescaling → Chapter 5: Descriptive Statistics in Basketball
Discord Servers
Various basketball analytics communities - Search for "basketball analytics discord" → Chapter 28: Building a Basketball Analytics Career - Further Reading
Distance:
Total distance per game - High-intensity running distance (>18 mph) - Distance by game segment → Case Study 2: Load Management Using Physical Tracking Data
Distributed (Motion Offense):
Relatively even centrality - Many interconnections - Harder to defend but requires high IQ → Chapter 17: Team Offensive Efficiency
Do not commit secrets to version control!
### Exercise 34: Logging Implementation → Chapter 3 Exercises: Python Environment Setup
Don't Over-Trust Single Games:
Even well-calibrated models have significant single-game variance - 22% events happen 22% of the time → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
DraftExpress.com (Archive)
Historical prospect reports - Pre-draft analysis archive - Methodology documentation → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Drop Coverage:
Defender sags to protect rim - Concedes pull-up jumpers (EPV ~1.10) - Best against poor-shooting ball handlers → Case Study 1: EPV Analysis of the Pick-and-Roll
Dunc'd On
Combines analytics with salary cap and league coverage → Chapter 28: Building a Basketball Analytics Career - Further Reading
Durability Assessment:
Workload history and trends - Speed/explosion consistency over season - Injury history relative to workload → Chapter 15: Player Tracking Analytics
F
Factors that predicted extended improvement:
Entry age (18) meant more development runway - High basketball IQ enabling skill translation - Organization investment in development - Work ethic and dedication → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
False
Hollinger created PER while writing for ESPN, before joining the Grizzlies. → Chapter 1 Quiz: Introduction to Basketball Analytics
Fast.ai - Practical Deep Learning for Coders
https://www.fast.ai/ - Free, practical deep learning course - Top-down teaching approach → Chapter 28: Building a Basketball Analytics Career - Further Reading
Fast.ai Practical Deep Learning
https://www.fast.ai/ - Practical, code-first approach to deep learning → Chapter 26: Machine Learning in Basketball - Further Reading
Favored archetypes:
High-usage scorers with good efficiency - Point forwards with versatile stats - Shot-blocking rim protectors → Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
Feature Engineering
[ ] Score differential (primary feature) - [ ] Time remaining transformations (log, sqrt) - [ ] Possession indicator (-1, 0, 1) - [ ] Score-time interaction terms - [ ] Quarter/period indicators - [ ] Clutch situation flag - [ ] Optional: team strength adjustment → Chapter 21: Key Takeaways - In-Game Win Probability
Feature Selection
[ ] Shot location (x, y or distance + angle) - [ ] Defender distance at release - [ ] Shot clock - [ ] Touch time / dribbles - [ ] Shot type classification → Chapter 16 Key Takeaways: Shot Quality Models
FIBA Basketball
https://www.fiba.basketball/ - International competition statistics → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Film Remains Essential
Box scores showed the Pistons won - Film showed how and why they won → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
Finals Adjustment:
Moved Tucker into starting lineup against Suns - Reduced Lopez minutes against smaller Phoenix lineups - Holiday-Middleton-Tucker-Giannis core in crunch time → Chapter 19: Lineup Optimization
Findings:
Williams is significantly underpaid (RFA risk) - Jackson is significantly overpaid (potential release/trade) - Thompson is moderately overpaid (expiring—tradeable) - Johnson is providing surplus value → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
First Edition
*A comprehensive textbook for undergraduate students* *in sports analytics, data science, and sports management* → Professional Basketball Analytics and Visualization
First Half (Games 1-41):
Role: Sixth man - Minutes per game: 24 - Net Rating On: +6.5 - On/Off: +8.2 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
First Quarter:
Minutes 0-6: Starting five - Minutes 6-12: First substitution wave (typically two bench players) → Chapter 19: Lineup Optimization
FiveThirtyEight
https://fivethirtyeight.com/sports/nba/ - Statistical sports journalism → Chapter 26: Machine Learning in Basketball - Further Reading
FiveThirtyEight - NBA
https://fivethirtyeight.com/sports/nba/ - Data-driven NBA analysis and prediction models → Chapter 28: Building a Basketball Analytics Career - Further Reading
FiveThirtyEight CARMELO Projections
Documentation: https://fivethirtyeight.com/features/how-our-nba-projections-work/ - Silver, N. & Fischer-Baum, R. (2015). "How Our NBA Projections Work." - CARMELO uses player similarity and aging curves to generate probabilistic projections. The methodology documentation provides practical insight → Chapter 22: Player Performance Prediction - Further Reading
FiveThirtyEight RAPTOR
Documentation: https://fivethirtyeight.com/features/how-our-raptor-metric-works/ - Successor to CARMELO incorporating player tracking data. Documentation of the metric provides context for projection inputs. → Chapter 22: Player Performance Prediction - Further Reading
Floor General:
PTS/100: 20, TS%: 0.560 - AST%: 45, TOV%: 16 - USG%: 22, 3PAr: 0.30 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
For Basketball Beginners
NBA.com beginner's guide - YouTube tutorials on basketball fundamentals - Watch several games with commentary → Prerequisites
For Binary Predictions:
**Accuracy:** % correct predictions - **Brier Score:** Mean squared probability error (lower = better) - **Log Loss:** -log(predicted probability of actual outcome) → Chapter 25: Game Outcome Prediction - Key Takeaways
For centers (Position = 5):
Higher expected BLK% and DRB% - Blocks weighted slightly less (expected production) - Steals weighted more (exceeds expectations) → Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
For clutch as a skill:
What evidence supports this view? - What psychological factors might enable clutch performance? → Chapter 20: Quiz - Game Strategy and Situational Analysis
For fans:
Understanding game dynamics - Historical comparisons → Chapter 21: Quiz - In-Game Win Probability
For guards (Position = 1-2):
Lower expected BLK% - Blocks weighted more heavily (rare production) - Steals weighted at baseline → Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
For media:
Broadcast graphics - Storytelling - Fan engagement → Chapter 21: Quiz - In-Game Win Probability
For MVP-Level Players:
Higher premiums (injury risk more impactful) - Career-ending coverage options - Performance decline provisions rare → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
For Spread Predictions:
**MAE:** Mean absolute margin error - **RMSE:** Root mean squared error (~11-12 points typical) - **ATS %:** Against-the-spread accuracy → Chapter 25: Game Outcome Prediction - Key Takeaways
For Teams Needing Immediate Contributors:
Weight floor over ceiling - Focus on athletes who can defend - Be cautious of "project" prospects → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
For Teams with Multiple Second-Round Picks:
Use model-identified prospects as primary targets - Accept higher bust rate for upside - Draft-and-stash internationals when possible → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
For teams:
In-game decision making - Player evaluation - Game planning → Chapter 21: Quiz - In-Game Win Probability
For Those Wanting More Context
*The Book of Basketball* by Bill Simmons: Entertaining history - *Basketball on Paper* by Dean Oliver: Analytics foundation - The Thinking Basketball YouTube channel → Prerequisites
Forgetting to activate venv
Packages install globally instead of in project 2. **Not pinning versions** - Builds become non-reproducible 3. **Committing venv to Git** - Repositories become bloated 4. **Storing credentials in code** - Security vulnerability 5. **Using Python 2** - End of life, no longer supported 6. **Ignoring → Chapter 3: Key Takeaways
Format:
Multiple choice questions: Select the best answer - True/False questions: Determine if the statement is correct - Short answer questions: Provide a brief response → Chapter 1 Quiz: Introduction to Basketball Analytics
Forwards:
Moderate historical efficiency - "Stretch four" revolutionized power forward position - Small forwards increasingly resemble guards in shot selection → Chapter 8: Shooting Efficiency Metrics
Franchise Impact:
Lost championship window (2012-15 Bulls were contenders) - Ticket/merchandise revenue decline - Diminished national television value - Draft capital used on injury replacements → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Free throw percentage: 64.9%
Well below the 70% threshold - Risk multiplier: 1.30 - This was THE critical data point that models should have flagged → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Free throw percentage: 84.9%
Elite FT% strongly correlates with NBA three-point shooting - Projected shooting improvement likely → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
G
Game Context:
Date: June 19, 2016 - Teams: Cleveland Cavaliers vs Golden State Warriors - Series: Tied 3-3 - Stakes: NBA Championship - Final Score: Cleveland 93, Golden State 89 → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Game Preparation
Detailed opponent tendencies - Matchup-specific strategies - Real-time adjustments based on tracking feeds → Case Study 1.2: The Evolution of Player Tracking Data
Game Style Differences:
Shorter three-point line (until recently harmonized) - Different foul rules - FIBA-style play tends to be slower, more structured - Less isolation-heavy offense → Chapter 23: Draft Modeling and Prospect Evaluation
Game-to-Game Recovery:
Back-to-back game adjustments - Travel distance considerations - Practice intensity modifications → Chapter 15: Player Tracking Analytics
Games Rested:
Home games: 8 (estimated lost ticket/concession revenue: $800K) - National TV games: 3 (league fines possible) - Fan disappointment: Unquantified → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Garbage Time Effects
Some statistics accumulated in blowouts (both directions) - Did not distinguish high-leverage from low-leverage performance → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
General Pattern:
Most skills peak in mid-to-late 20s - Athletic attributes decline earlier than skill-based attributes - Different positions and playing styles age differently → Chapter 22: Player Performance Prediction - Key Takeaways
Generative Models
Ho, J., Jain, A., & Abbeel, P. (2020). "Denoising Diffusion Probabilistic Models." NeurIPS. → Chapter 27: Computer Vision and Video Analysis - Further Reading
Glassdoor
https://www.glassdoor.com/ - Salary reports and company reviews → Chapter 28: Building a Basketball Analytics Career - Further Reading
Google AI Blog - Sports
https://ai.googleblog.com/ - ML/CV research with sports applications → Chapter 27: Computer Vision and Video Analysis - Further Reading
Google Machine Learning Crash Course
https://developers.google.com/machine-learning/crash-course - Free, quick introduction with TensorFlow → Chapter 26: Machine Learning in Basketball - Further Reading
Grade Scale:
A: 61-68 points (90%+) - B: 54-60 points (80-89%) - C: 47-53 points (70-79%) - D: 41-46 points (60-69%) - F: Below 41 points → Chapter 6 Quiz: Box Score Fundamentals
Gradient Boosted Trees (XGBoost):
Handles non-linear relationships - Feature importance interpretable - Fast inference for real-time applications → Case Study 2: Building an EPV Model from Tracking Data
Gradient Boosted Trees (XGBoost, LightGBM)
Automatically capture interactions and non-linearity - Often achieve better predictive accuracy - Require careful calibration to produce true probabilities → Chapter 21: In-Game Win Probability
Gradient Boosting (XGBoost, LightGBM):
Often best performance - Handles missing values - Requires careful tuning - Risk of overfitting → Chapter 26: Machine Learning in Basketball - Key Takeaways
Grading Rubric:
Beginner exercises: Completion and correctness - Intermediate exercises: Code quality, documentation, and best practices - Advanced exercises: Comprehensive solution, professional quality, innovative approaches → Chapter 3 Exercises: Python Environment Setup
Greek League Statistics (2012-13):
Games: 26 - Minutes per game: 24.7 - Points per game: 9.5 - Rebounds per game: 5.0 - Assists per game: 1.4 - Field goal percentage: 50.5% - Free throw percentage: 68.2% → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Green's RAPM by Lineup Type:
Traditional (with standard center): +4.2 - Small ball (Green at center): +8.1 - Difference: +3.9 points per 100 possessions → Case Study 1: The Discovery of Draymond Green's True Value
Guards with sub-65% FT in college:
45% bust rate (vs. 25% baseline) - Average NBA career WS: 18 (vs. 35 for guards with 80%+ FT) - Three-point shooting translation: Very poor → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Guards:
Historically least efficient (most difficult shots) - TS% has increased most dramatically - Three-point shooting has elevated guard efficiency → Chapter 8: Shooting Efficiency Metrics
I
ICCV (International Conference on Computer Vision)
Biennial major conference → Chapter 27: Computer Vision and Video Analysis - Further Reading
Identify production anomalies
Young age with production - Extreme improvement - Position-unusual skills → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
If Jackson opts in:
Portland pays $22M for projected ~1.2 VORP - $/VORP: ~$18M (well above market) - Limits flexibility for other moves → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
If Jackson opts out:
Creates $22M in cap space - Allows pursuit of replacement options - Risk: May need to overpay for replacement → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
Ignoring rate limits
Results in IP blocking and unreliable data collection 2. **Not validating data** - Garbage in, garbage out applies strongly to sports analytics 3. **Assuming data completeness** - Missing values are common and meaningful 4. **Comparing raw statistics across eras** - Context is essential 5. **Relying → Chapter 2: Key Takeaways
Impact Metrics:
Second chance points generated - Transition opportunities surrendered → Chapter 15: Player Tracking Analytics
Impact:
Accuracy: 65.8% → 66.4% - Brier Score: 0.219 → 0.214 - Spread RMSE: 11.8 → 11.2 points → Chapter 25: Case Study 1 - Building an Elo Rating System for NBA Prediction
Important Caveats:
ACWR can be misleading after extended rest (low chronic load) - Individual baselines vary significantly - Context matters (type of load, not just volume) → Chapter 24: Injury Risk and Load Management - Key Takeaways
Important Guidelines:
Respect robots.txt and rate limits - Add delays between requests (3+ seconds recommended) - Cache responses to avoid repeated requests - Include proper User-Agent header - Consider using their data export feature for large datasets → Appendix D: Data Sources and APIs
Important Notes:
The API is undocumented and subject to change without notice - Requires specific headers to avoid being blocked - Rate limiting may apply; be respectful with request frequency - For production use, consider caching responses → Appendix D: Data Sources and APIs
In Non-Triple-Double Games:
Record: 14-26 (35.0%) - Average margin: -4.1 points → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
In Triple-Double Games:
Record: 33-9 (78.6%) - Average margin: +8.2 points → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
In-Game Analytics
Live shot quality assessment - Lineup impact monitoring - Fatigue indicators → Case Study 1.2: The Evolution of Player Tracking Data
In-Season:
Workload monitoring - Neuromuscular maintenance training - Fatigue-based substitution guidelines - Recovery optimization → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Indeed
https://www.indeed.com/ - Aggregates many job postings → Chapter 28: Building a Basketball Analytics Career - Further Reading
Inferential Statistics
Hypothesis testing concepts - p-values and significance - Confidence intervals - Correlation and causation → Prerequisites
Information Asymmetry
Limited video available - Few scouts with expertise in Serbian basketball - Not a household name internationally → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Information available to OKC:
Three consecutive years of declining efficiency - Clear defensive liabilities - High usage rate/iso-heavy style - No demonstrated ability to play complementary role → Chapter 22: Case Study 2 - The Decline of Carmelo Anthony: Projecting Aging Superstars
Information Factors:
Injuries (varies by player impact) - Lineup changes - Back-to-back games → Chapter 25: Game Outcome Prediction - Key Takeaways
Input features:
Shot location (x, y coordinates) - Defender distance at release - Shot clock time - Touch time before shot - Dribbles before shot - Shot type (catch-and-shoot, pull-up, etc.) - Player shooting ability → Case Study 1: Building a Shot Quality Model for the Houston Rockets' Three-Point Revolution
Input variables:
Current production: 27.7 PPG, 12.5 RPG, 5.9 APG, 14.4 WS - Age: 24 (prime years ahead) - Durability: Excellent injury history - Skill trajectory: Continued improvement - Physical profile: Elite size/athleticism → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Interaction Features:
Physical profile combinations - Style matchup indicators → Chapter 26: Machine Learning in Basketball - Key Takeaways
Interactive Practice
LeetCode (easy problems) - HackerRank Python challenges - Exercism Python track → Prerequisites
International Leagues
Less scouting coverage - Age-based production often overlooked - Different playing styles undervalued → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Interpretation:
**Skewness = 0**: Symmetric distribution - **Skewness > 0**: Right-skewed (positive skew), long tail to the right - **Skewness < 0**: Left-skewed (negative skew), long tail to the left → Chapter 5: Descriptive Statistics in Basketball
Investigate information gaps
Why is this player undervalued? - Is it correctable or fundamental? → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Irving's 3-Pointer WPA:
Pre-shot: GSW 50% - Post-shot: CLE 85% - WPA: +0.35 (one of highest single-play WPA in Finals history) → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Isolation Defense
Points per possession allowed - Shooting percentage allowed - Turnover rate generated - Frequency defended → Chapter 18: Team Defensive Analytics
L
Late Bloomers
Traditional models penalize age - Improvement trajectory can indicate higher ceiling → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Late Start in Basketball
Only 7 years of organized basketball - Seen as lacking fundamentals → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
League Context:
League leading scorer (Tracy McGrady): 28.0 PPG - Average team leading scorer: 20.4 PPG - Detroit's leading scorer ranked 35th in the NBA → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
League-wide shot distribution (2010):
Mid-range: ~35% of shots - Three-pointers: ~22% of shots - Restricted area: ~28% of shots - Other paint: ~15% of shots → Case Study 1: Building a Shot Quality Model for the Houston Rockets' Three-Point Revolution
LeBron Advantages:
More total seasons at high level - Will likely pass Kareem's total - More consistent (never fell below 5 WS until very late career) → Case Study 2: Win Shares for Career Evaluation - Comparing All-Time Greats
Less reliable than OWS
Box scores miss most defensive value - Good teams have more DWS to distribute - Individual attribution is approximate → Chapter 13 Key Takeaways: Win Shares
Levels.fyi
https://www.levels.fyi/ - Tech company compensation data → Chapter 28: Building a Basketball Analytics Career - Further Reading
LightGBM
https://lightgbm.readthedocs.io/ - Fast gradient boosting from Microsoft - Good for large datasets → Chapter 26: Machine Learning in Basketball - Further Reading
LIME
https://github.com/marcotcr/lime - Local interpretable explanations → Chapter 26: Machine Learning in Basketball - Further Reading
Limitations of the Box Score Era:
No context for when or how statistics occurred - Defensive contributions largely invisible - Team effects confounded individual evaluation - Pace differences made era comparisons difficult → Chapter 1: Introduction to Basketball Analytics
Limitations:
May overvalue players with limited minutes (survivorship bias) - Doesn't capture the value of durability and availability - Can be misleading for players in specialized roles → Chapter 13: Win Shares and Wins Above Replacement
Linear/Logistic Regression:
Interpretable coefficients - Fast training - Assumes linear relationships - Good baseline → Chapter 26: Machine Learning in Basketball - Key Takeaways
Lineup Overlap Analysis:
Curry minutes: 2,846 - Green minutes: 1,730 - Shared minutes: 1,520 (88% of Green's time) → Case Study 1: The Discovery of Draymond Green's True Value
LinkedIn
https://www.linkedin.com/jobs/ - General job board with sports listings → Chapter 28: Building a Basketball Analytics Career - Further Reading
Load and Inspect
Understand what data you have 2. **Clean and Validate** - Address quality issues 3. **Explore Distributions** - Understand individual variables 4. **Analyze Relationships** - Discover variable interactions 5. **Visualize Patterns** - Create informative graphics 6. **Document Findings** - Record disc → Chapter 4: Key Takeaways
Load Indicators:
Player Load (proprietary composite) - Deceleration events - Change of direction frequency → Case Study 2: Load Management Using Physical Tracking Data
Load Management
Monitor total distance - Identify fatigue patterns - Optimize rest schedules → Case Study 1.2: The Evolution of Player Tracking Data
Load Metrics:
Maximum consecutive games: 5 - Target games played: 55-65 - Minutes per game: 32-34 (reduced from career average) - Back-to-backs played: 0-2 → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
log_seconds = log(seconds_remaining + 1)
Captures diminishing importance as time decreases - +1 prevents log(0) error → Chapter 21: Quiz - In-Game Win Probability
Low Risk (normal play):
ACWR 0.8-1.3 - Subjective fatigue < 5/10 - HRV > 85% of baseline - Adequate rest between games → Chapter 24: Injury Risk and Load Management - Key Takeaways
Low-Major Conferences
Heavy discounting creates opportunities - Look for players with extreme physical tools → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Lower Pace Eras (1990s-2000s):
Fewer possessions reduced Win Shares opportunities - Jordan's 214 WS in 15 seasons was in slower era → Case Study 2: Win Shares for Career Evaluation - Comparing All-Time Greats
Lower scoring volume
Shared touches with other lottery picks (Giles, Kennard) - Usage-adjusted scoring was competitive → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
M
Machine Learning Engineer (Priya)
Needs: scikit-learn, xgboost, tensorflow for predictive models - "My models need to be reproducible for auditing." → Case Study 1: Setting Up a Team Analytics Environment
Machine Learning Mastery
https://machinelearningmastery.com/ - Practical tutorials by Jason Brownlee → Chapter 26: Machine Learning in Basketball - Further Reading
Marginal Calculation:
Replacement level: ~0.92 points/possession - Marginal rate: 1.17 - 0.92 = 0.25 points/possession above replacement - Marginal points: 0.25 × 1,800 = 450 marginal points → Case Study 1: The 2016 Warriors - Win Shares in a Historic Season
Marginal offense calculation
Marginal = Above replacement level (~0.92 pts/poss) - Higher efficiency = more marginal points - Converts to wins using team context → Chapter 13 Key Takeaways: Win Shares
Markelle Fultz:
Diagnosed with thoracic outlet syndrome affecting shooting motion - Career (through 2023-24): ~8 Win Shares total - Never developed into projected star - Currently: Rotation player (Orlando Magic) → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Market Assumptions:
Win value: $3.5 million - Points per win: 30 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Matchup Data:
Primary matchup PPP: 0.82 (league avg: 0.95) - Iso defense PPP: 0.75 - Post defense PPP: 0.88 → Case Study 1: Measuring Defensive Impact with Tracking Data
Mathematical explanation:
Two-point efficiency naturally declines with volume (more difficult shots) - Three-point efficiency can remain stable because the skill is less context-dependent - Shot creation ability maintains open look rates → Case Study 1: Stephen Curry and the 2015-16 Season - Redefining Shooting Efficiency
Measuring Gravity:
Average distance of nearest defender - Frequency of help defense drawn - Impact on teammates' shot quality when player is nearby → Chapter 15: Player Tracking Analytics
MediaPipe
https://mediapipe.dev/ - Real-time ML solutions for pose, hands, face → Chapter 27: Computer Vision and Video Analysis - Further Reading
Meta AI
https://ai.facebook.com/ - Video understanding research → Chapter 27: Computer Vision and Video Analysis - Further Reading
Metrics:
Paint touches per possession - Time spent in paint (offense) - Number of players in paint simultaneously - Paint entry frequency and success rate → Chapter 15: Player Tracking Analytics
Minimum Requirements
Any modern computer (Windows, Mac, Linux) - 8 GB RAM - 10 GB free disk space - Internet connection for data access → Prerequisites
Minimum sample sizes:
Single game: Game Score appropriate - Weekly/monthly: 200+ minutes for trends - Season analysis: 500+ minutes or 25+ games - Career analysis: 2,000+ minutes → Chapter 9 Key Takeaways: Advanced Box Score Metrics
Minimum thresholds:
Per-36 stats: 500+ minutes - Per-100 possession stats: 500+ possessions - Pace calculations: Full season preferred → Chapter 7: Rate Statistics and Pace Adjustment
Missing Data Patterns:
Combine data is missing for approximately 30% of draft picks (players who opt out or are not invited) - International players often lack complete college statistics - Early career exits create censored outcome data → Capstone Project 2: Create a Draft Model
MIT Sloan Sports Analytics Conference
Annual conference with injury analytics presentations - https://www.sloansportsconference.com/ → Chapter 24: Injury Risk and Load Management - Further Reading
MMDetection
https://github.com/open-mmlab/mmdetection - Comprehensive detection toolbox → Chapter 27: Computer Vision and Video Analysis - Further Reading
MMPose
https://github.com/open-mmlab/mmpose - Pose estimation toolbox → Chapter 27: Computer Vision and Video Analysis - Further Reading
Model Considerations
[ ] Exclude shooter identity for pure difficulty model - [ ] Include shooter for prediction model - [ ] Use temporal train-test splits - [ ] Evaluate calibration across probability bins → Chapter 16 Key Takeaways: Shot Quality Models
Model Evaluation:
**Accuracy:** Proportion of correct predictions - **Precision:** $\frac{TP}{TP + FP}$ - **Recall (Sensitivity):** $\frac{TP}{TP + FN}$ - **F1 Score:** $2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}}$ - **AUC-ROC:** Area under the Receiver Operating Characterist → Appendix A: Mathematical Foundations
Model extensions
O-RAPM and D-RAPM: separate offensive and defensive models - Multi-year RAPM: pool seasons for larger sample size - Prior-augmented RAPM: use box scores as informative priors → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
Model inputs:
Shot distance - Defender distance - Shot type (catch-and-shoot, pull-up, etc.) - Shot clock - Touch time - Dribbles → Case Study 2: Evaluating Shot-Making Ability - Separating Skill from Shot Selection
Model Limitations Revealed:
Couldn't capture fatigue, pressure, or "clutch gene" - Momentum effects possibly underweighted - Individual matchup dynamics not fully captured → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Model specifications:
Elo + efficiency differentials + situational factors - Trained on 3 seasons, tested on 2 → Chapter 25: Case Study 2 - Evaluating the Efficiency of NBA Betting Markets
Model Training
[ ] Choose algorithm (logistic regression recommended for interpretability) - [ ] Use time-series cross-validation - [ ] Apply regularization (L2) to prevent overfitting - [ ] Calculate Brier score on held-out data → Chapter 21: Key Takeaways - In-Game Win Probability
Model Validation
Prospective testing before deployment - Regular recalibration as data accumulates - External validation across seasons - Comparison to baseline (injury rates before analytics) → Chapter 24: Injury Risk and Load Management
Models Captured Momentum Shifts:
Q3 swing from 78% GSW to 50% accurately reflected changing dynamics - Models responded appropriately to scoring runs → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Moderate Positive Correlations:
Assists and turnovers (r ≈ 0.55): Ball-handling role creates both - Win shares and salary (r ≈ 0.45): Market partially values production - Height and blocks (r ≈ 0.50): Physical attributes enable rim protection → Chapter 5: Descriptive Statistics in Basketball
Moderate Risk (monitor closely):
ACWR 1.3-1.5 - Subjective fatigue 5-7/10 - HRV 70-85% of baseline - 3 games in 4 nights → Chapter 24: Injury Risk and Load Management - Key Takeaways
Modern Positionless:
LeBron, Giannis blur categories - Can accumulate both OWS and DWS → Case Study 2: Win Shares for Career Evaluation - Comparing All-Time Greats
Modifiable:
Training load and progression - Sleep and recovery - Strength and conditioning - Movement quality - Game schedule management → Chapter 24: Injury Risk and Load Management - Key Takeaways
Monitoring:
Daily load metrics - Weekly biomechanical check - Monthly hamstring-quad ratio testing - Continuous self-reported fatigue/soreness → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Movement Patterns
Cuts and off-ball screens - Pick-and-roll coverage - Transition speed → Case Study 1.2: The Evolution of Player Tracking Data
O
Odds Portal
https://www.oddsportal.com/ - Historical odds comparison → Chapter 25: Game Outcome Prediction - Further Reading
Off Court:
Minutes: 1,560 - Team Points: 2,800 - Opponent Points: 2,960 - Estimated Possessions: 2,800 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
Off-Ball Movement
Box scores cannot capture whether Westbrook set screens or moved without the ball - His high usage meant limited off-ball contributions → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Offensive Evaluation:
Off-ball movement quality (cuts, relocations) - Catch-and-shoot percentages by contest level - Gravity metrics (defensive attention drawn) - Transition frequency and effectiveness → Chapter 15: Player Tracking Analytics
Offensive Identity:
Elite three-point shooting (highest volume and percentage) - Motion offense generating open looks - Transition excellence (1.18 PPP) - Stephen Curry/Klay Thompson gravity creating space → Chapter 17: Team Offensive Efficiency
Offensive Metrics:
Effective Field Goal Percentage (eFG%) - Turnover Rate - Offensive Rebounding Rate - Free Throw Rate → Chapter 19: Lineup Optimization
Offensive Rating
points produced per 100 possessions: → Chapter 13: Win Shares and Wins Above Replacement
Offensive Rating (On/Off):
Points scored per 100 possessions with player on court - Points scored per 100 possessions with player off court → Chapter 10: Plus-Minus and On/Off Analysis
Offensive Specialist Example:
ORtg On: 115.0, ORtg Off: 105.0 (Offensive Diff: +10.0) - DRtg On: 112.0, DRtg Off: 108.0 (Defensive Diff: +4.0, meaning worse) - On/Off: +10.0 - 4.0 = +6.0 → Chapter 10: Plus-Minus and On/Off Analysis
On Court:
Minutes: 2,400 - Team Points: 4,800 - Opponent Points: 4,560 - Estimated Possessions: 4,400 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
On/Off Defensive Metrics (2021-22):
Defensive Rating On: 108.2 - Defensive Rating Off: 113.4 - Differential: -5.2 (team is 5.2 points/100 better with Jokic) → Case Study 1: Nikola Jokic and the Evolution of Center Evaluation
Online Courses
Khan Academy Statistics and Probability - Coursera: Statistics with Python Specialization - edX: Introduction to Probability and Statistics → Prerequisites
OpenCV
https://opencv.org/ - Essential for video I/O, basic vision operations - Python and C++ interfaces → Chapter 27: Computer Vision and Video Analysis - Further Reading
OpenPose
https://github.com/CMU-Perceptual-Computing-Lab/openpose - Multi-person pose estimation → Chapter 27: Computer Vision and Video Analysis - Further Reading
OpenPose Documentation
https://github.com/CMU-Perceptual-Computing-Lab/openpose - Comprehensive implementation details and tutorials → Chapter 27: Computer Vision and Video Analysis - Further Reading
Optimal parameters:
n_estimators: 200 - max_depth: 4 - learning_rate: 0.1 - min_samples_leaf: 10 - subsample: 0.9 → Chapter 26: Case Study 2 - Building a Draft Success Prediction Model with Gradient Boosting
Optimal Solution:
Sign James Miller: $22M, 3.8 VORP - Sign Andre Smith: $16M, 2.8 VORP - Sign Chris Lee: $8M, 2.0 VORP - Remaining: ~$25M for roster filling → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
Optimization
[ ] Define player minute constraints - [ ] Build rotation simulation - [ ] Evaluate stagger strategies - [ ] Identify optimal closing lineups → Chapter 19: Key Takeaways - Lineup Optimization
Option A (from contender):
Receive: Young player (BPM -0.5, 22 years old) + 1st round pick - Send: Thompson → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
Option A: Quick shot (2-pointer)
Time to execute: 8 seconds - Success probability: 48% - If successful: Overtime with 50% win probability - If unsuccessful: Defensive rebound gives opponent ball with 22 seconds → Chapter 14 Exercises: Expected Possession Value (EPV)
Option B (from rebuilder):
Receive: Expiring bad contract + 2 second-round picks - Send: Thompson → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
Option B: Run clock and shoot three
Time to execute: 25 seconds - Success probability: 35% - If successful: Win - If unsuccessful: Offensive rebound 20% of time with 5 seconds left → Chapter 14 Exercises: Expected Possession Value (EPV)
Option C (keep Thompson):
Maintain current production - Lose for nothing in free agency → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
Option C: Run clock and shoot two
Time to execute: 25 seconds - Success probability: 52% - If successful: Overtime with 50% win probability - If unsuccessful: Game over (no time for offensive rebound) → Chapter 14 Exercises: Expected Possession Value (EPV)
Overrated by BPM:
High-usage scorers with marginal efficiency - Players with inflated assist rates (system dependent) - Shot blockers on poor defensive teams → Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
P
Pace and Transition:
Possessions per minute - Transition frequency - Fast break points per possession → Chapter 19: Lineup Optimization
Pace Manipulation
Thunder's pace (99.6) was above league average - This created additional counting stat opportunities → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Passing
Passes made and received - Potential assists - Assist points created → Case Study 1.2: The Evolution of Player Tracking Data
Payscale
https://www.payscale.com/ - Salary benchmarking → Chapter 28: Building a Basketball Analytics Career - Further Reading
PBP Stats
https://www.pbpstats.com/ - Detailed play-by-play derived statistics → Chapter 22: Player Performance Prediction - Further Reading
PCA:
Linear dimensionality reduction - Preserves global structure - Components may be hard to interpret → Chapter 26: Machine Learning in Basketball - Key Takeaways
Per-100-Possession Rates:
Points: 32.8 - Rebounds: 8.1 - Assists: 8.4 → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Performance:
2015-16: +25.4 net rating in 281 minutes - 2016-17: +16.9 net rating with Durant replacing Barnes - Regularly deployed to close games and swing momentum → Chapter 19: Lineup Optimization
Phase 1 (Months 1-3): Leadership
Hire VP of Basketball Analytics - Hire Director of Data Engineering → Chapter 28: Case Study 1 - Building an NBA Team Analytics Department from Scratch
Phase 2 (Months 4-6): Core Team
Hire Director of Player Evaluation - Hire Director of Basketball Strategy - Hire 2 Data Engineers/Developers → Chapter 28: Case Study 1 - Building an NBA Team Analytics Department from Scratch
Phase 3 (Months 7-12): Build Out
Hire Senior Analysts (2) - Hire Analysts (2) - Hire Cap Analyst → Chapter 28: Case Study 1 - Building an NBA Team Analytics Department from Scratch
Physical Measurements:
Height: 6'9" (but still growing) - Wingspan: 7'3" - Weight: 196 lbs - Age: 18.5 years → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Physical Profile:
Height: 6'10" (listed, likely 6'11" without shoes) - Weight: 232 lbs - Wingspan: ~7'1" - Standing reach: Excellent for position - Athletic testing: Did not attend combine → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Physical Red Flags:
Below-average wingspan for position - Poor athletic testing numbers - Injury history, particularly to knees or feet - Older age relative to draft class → Chapter 23: Draft Modeling and Prospect Evaluation
Physical-adjusted projection:
Finishing at rim: Expected significant improvement - Post scoring: Viable addition to game - Defensive impact: MVP-caliber potential → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Pick-and-Roll Ball Handler Defense
Points per possession allowed - Ability to navigate screens - Switching success rate - Help requirements generated → Chapter 18: Team Defensive Analytics
Pin your dependencies
Unpinned versions lead to reproducibility failures 2. **Validate before running** - Catch environment issues before they cause pipeline failures 3. **Use virtual environments consistently** - Isolation prevents dependency conflicts 4. **Handle headless environments** - Configure backends for server → Case Study 2: Debugging Environment Issues in a Production Analytics Pipeline
Pinnacle Sports Blog
https://www.pinnacle.com/en/betting-resources/ - Educational content on betting markets, line movement, market efficiency → Chapter 25: Game Outcome Prediction - Further Reading
Play A (Horns Pick-and-Roll):
Average starting EPV: 1.00 - Average EPV at first pass: 1.06 - Average EPV at shot decision: 1.14 - Points per possession: 1.08 → Chapter 14 Exercises: Expected Possession Value (EPV)
Play B (Motion Offense):
Average starting EPV: 1.00 - Average EPV at first pass: 1.03 - Average EPV at shot decision: 1.09 - Points per possession: 1.12 → Chapter 14 Exercises: Expected Possession Value (EPV)
Play-by-Play Derived:
Shot distribution by zone - Scoring by quarter - Performance in clutch situations → Case Study 1: Building a Comprehensive Player Statistics Pipeline
Player A (Consistent):
Q1 = 16 points, Q3 = 24 points - IQR = 8 points → Chapter 5: Descriptive Statistics in Basketball
Player A (Shot Blocker):
BLK%: 5.5, STL%: 1.0, DRB%: 23 - Poor help defense, limited lateral movement → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Player A's Plus-Minus Calculation:
Q1 (first 6 min): +4 (14-10) - Q2 (first 4 min): +2 (10-8) - Q3 (first 8 min): +2 (16-14) - Q4 (full): +4 (22-18) → Chapter 10: Plus-Minus and On/Off Analysis
Player A:
100 two-point attempts, 50 made (50% from two) - 0 three-point attempts - Total: 100 points on 100 shots - FG%: 50% → Chapter 8: Shooting Efficiency Metrics
Player Actions:
Shooting (jump shot, layup, dunk, free throw) - Passing (chest pass, bounce pass, overhead pass) - Dribbling (crossover, between legs, behind back) - Defensive movements (slide, contest, block attempt) - Rebounding (box out, jump, secure) → Chapter 27: Computer Vision and Video Analysis
Player B (Help Defender):
BLK%: 1.5, STL%: 2.0, DRB%: 18 - Excellent rotations, strong communication → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Player B (Streaky):
Q1 = 10 points, Q3 = 28 points - IQR = 18 points → Chapter 5: Descriptive Statistics in Basketball
Player B:
0 two-point attempts - 100 three-point attempts, 40 made (40% from three) - Total: 120 points on 100 shots - FG%: 40% → Chapter 8: Shooting Efficiency Metrics
Player Contract:
Salary: $25 million/year - Expected BPM: +5.0 - Expected minutes: 2,500 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Player features:
Historical shooting percentage by zone - Three-point specialist indicator - Rim finishing grade → Case Study 1: Building a Shot Quality Model for the Houston Rockets' Three-Point Revolution
Player Profile:
DeAndre Jordan, 30 years old - Seeking 4-year, $85M contract - RAPM: +1.8 (O-RAPM: +0.5, D-RAPM: +1.3) → Case Study 2: Building a Real-Time RAPM System for Player Evaluation
Player Stats:
Minutes: 2,850 (34.8 mpg) - Raw Plus-Minus: +412 (season total) - On/Off Differential: +9.8 - Net Rating On: +7.2 - Net Rating Off: -2.6 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
Player X (on Team A currently):
Net Rating On: +5.5 - On/Off: +7.2 - Team Net Rating: +3.0 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
Player X (Volume Three-Point Shooter):
15 PPG on 12 FGA and 2 FTA - Makes 5 threes per game → Chapter 8: Shooting Efficiency Metrics
Player X:
BPM: +9.0 - Minutes: 1,800 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Player Y (on Team B currently):
Net Rating On: +4.0 - On/Off: +9.5 - Team Net Rating: -1.0 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
Player Y (Rim Attacker with FT Drawing):
15 PPG on 10 FGA and 6 FTA - Makes 0 threes per game → Chapter 8: Shooting Efficiency Metrics
Player Y:
BPM: +5.5 - Minutes: 3,100 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Playing Style
Unorthodox offensive game - Heavy reliance on skill over athleticism - Didn't fit traditional center molds → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Playing Style Characteristics:
Explosive first step (among fastest in league) - Frequent hard stops and direction changes - High vertical leap (40+ inches) - Heavy contact absorption on drives - Played through minor injuries → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Playmaking:
Assists per 75 possessions - Assist % - Turnover Rate → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Playoff Defense
Small ball lineups were exploited by teams with dominant bigs - Three-point shooting became less reliable under playoff pressure → Case Study 1.1: The Houston Rockets' Analytical Revolution
Playoff Statistics:
Games played: 24 of 24 (100%) - Minutes per game: 39.1 (increased from regular season) - Points per game: 30.5 - FG%: 49.0% - Win Shares: 5.3 (in 24 games) → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Point Guards:
Often have high offensive on/off due to playmaking influence - May show team-wide effects (assists create teammate scoring) → Chapter 10: Plus-Minus and On/Off Analysis
Position adjustment
Different positions have different statistical expectations - A center with 6 assists is exceptional; a point guard with 6 assists is average - Position estimated from height, BLK%, AST%, rebounding rates → Chapter 12 Key Takeaways: Box Plus-Minus (BPM) and VORP
Position Scarcity
Unique skill sets (e.g., passing big men) - Versatile defenders with length → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Positioning Data:
Average distance to assignment: 4.5 feet - Help defense rotations: 8 per game - Closeout speed: 14.2 mph avg → Case Study 1: Measuring Defensive Impact with Tracking Data
Positions and Roles
Five positions (PG, SG, SF, PF, C) - General responsibilities of each position - Offensive and defensive concepts → Prerequisites
Positive Residual (Seth Partnow)
https://positiveresidual.com/ - Former Bucks analyst providing analytical perspective including player evaluation frameworks. → Chapter 22: Player Performance Prediction - Further Reading
Post-Decision Analysis:
Expected VORP: 14.5 over 4 years - Contract cost: $84M - Break-even VORP: 15.3 (at $5.5M/VORP) - Risk: Moderate (injury, regression) - Upside: All-Star development → significant surplus → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
Post-Injury Performance (2013-present):
Win Shares per 48: .082 (60% decline) - PER: 15.2 (35% decline) - All-Star selections: 0 → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
PPG alone is insufficient
consistency measures add important context 2. **Multiple metrics capture different aspects** of consistency (CV, IQR, game counts) 3. **Statistical significance** helps distinguish real differences from noise 4. **Visualization communicates** consistency effectively to general audiences 5. **Context → Case Study 2: Analyzing Scoring Consistency for MVP Voting
Practice Design
Identify movement pattern inefficiencies - Optimize defensive rotations - Track player exertion for load management → Case Study 1.2: The Evolution of Player Tracking Data
Pre-1973-74:
Win Shares estimated with less precision - Some statistics unavailable - Wilt's numbers involve assumptions → Case Study 2: Win Shares for Career Evaluation - Comparing All-Time Greats
Pre-Game Odds Were Reasonable:
62% home favorites lost 38% of the time historically - Model appropriately uncertain despite Warriors' season record → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Pre-Injury Performance (2010-12):
Win Shares per 48: .206 - PER: 23.5 - MVP Award (2011) → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Pre-Season:
Baseline biomechanical assessment - Movement screening (FMS, Y-balance) - Strength testing (especially hamstring-quad ratio) - Individual risk profiling → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Pre-Toronto Career:
2011-17: Played 330 of 492 possible regular season games (67%) - 2017-18: Played only 9 games - Concern: Chronic condition vs. acute injury → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Predictability
Opponents could game-plan specifically for the Rockets' tendencies - Lack of mid-range threat made defense easier → Case Study 1.1: The Houston Rockets' Analytical Revolution
Prerequisites:
Basic statistics (mean, variance, hypothesis testing) - Introductory Python programming - Interest in basketball and data analysis → Professional Basketball Analytics and Visualization
Prevention Program:
Bi-weekly neuromuscular training - Landing mechanics review monthly - Real-time deceleration monitoring - Recovery protocol after high-load games → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Primary Ball Handlers:
Essential for offensive initiation - Should overlap minimally with secondary ball handlers - Critical for closing lineups → Chapter 19: Lineup Optimization
Primary Factors:
Team offensive/defensive efficiency - Home court advantage (~3-4 points) - Recent performance trend → Chapter 25: Game Outcome Prediction - Key Takeaways
Primary Goal: Championship
Result: Achieved - Assessment: Complete success → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Primary Sources:
Play-by-play data from official NBA feed (via Stats LLC) - Player tracking data (Second Spectrum) - Historical box scores (10 years) → Case Study 2: Building a Real-Time RAPM System for Player Evaluation
Privacy-Preserving Analytics
Federated learning for distributed analysis → Chapter 27: Computer Vision and Video Analysis - Further Reading
Probability Fundamentals
Basic probability rules - Random variables - Expected value - Normal distribution basics → Prerequisites
Problems with DWS:
Steals and blocks are overweighted relative to their defensive value - Positioning and help defense are not captured - Elite perimeter defenders are often undervalued - Big men who protect the rim are sometimes overvalued → Chapter 13: Win Shares and Wins Above Replacement
Process Integration
Daily readiness reports for coaching staff - Pre-game injury risk assessments - Post-game load analysis - Season planning optimization → Chapter 24: Injury Risk and Load Management
Profile:
20 seasons - Career WS: 273.4 - Never had an exceptional WS/48 season (peak 0.292) - But maintained high level (>10 WS) for 15 consecutive seasons → Case Study 2: Win Shares for Career Evaluation - Comparing All-Time Greats
Programs with Evidence:
FIFA 11+ (soccer): 50% reduction in ACL injuries - Sportsmetrics: 60-70% reduction in female athletes - NBA-specific: Limited published evidence → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Projected Impact:
In new system with better shot creation: xFG% may increase to 47% - If shot-making holds: Actual FG% could reach 48.5% - Projected PPG increase: 15 to 17-18 PPG → Case Study 2: Evaluating Shot-Making Ability - Separating Skill from Shot Selection
Properties of the Inverse:
$(\mathbf{A}^{-1})^{-1} = \mathbf{A}$ - $(\mathbf{AB})^{-1} = \mathbf{B}^{-1}\mathbf{A}^{-1}$ - $(\mathbf{A}^T)^{-1} = (\mathbf{A}^{-1})^T$ → Appendix A: Mathematical Foundations
Properties:
Shrinks coefficients toward zero - Does not set coefficients exactly to zero - $\lambda = 0$ reduces to OLS - As $\lambda \to \infty$, $\hat{\boldsymbol{\beta}} \to \mathbf{0}$ → Appendix A: Mathematical Foundations
Prophet (Facebook)
https://facebook.github.io/prophet/ - Time series forecasting applicable to season predictions → Chapter 25: Game Outcome Prediction - Further Reading
Protecting a Lead:
Prioritize ball security and free throw shooting - Defensive versatility to prevent easy baskets - Clock management ability → Chapter 19: Lineup Optimization
Publicly Available (NBA.com/stats):
Aggregated speed and distance metrics - Touch statistics and time of possession - Catch-and-shoot vs. pull-up shooting splits - Defensive matchup data - Rebounding opportunity metrics → Chapter 15: Player Tracking Analytics
PyImageSearch
https://pyimagesearch.com/ - Practical computer vision tutorials → Chapter 27: Computer Vision and Video Analysis - Further Reading
Python Basics
Variables and data types - Control structures (if/else, loops) - Functions and modules - Basic error handling → Prerequisites
Python libraries:
`scikit-learn` - Machine learning - `lifelines` - Survival analysis - `pandas` - Data manipulation → Chapter 24: Injury Risk and Load Management - Further Reading
PyTorch
https://pytorch.org/ - Facebook's deep learning framework - Preferred for research → Chapter 26: Machine Learning in Basketball - Further Reading
PyTorch Tutorials: Video Classification
https://pytorch.org/tutorials/ - Official tutorials for video understanding → Chapter 27: Computer Vision and Video Analysis - Further Reading
PyTorchVideo
https://pytorchvideo.org/ - Video understanding library from Facebook → Chapter 27: Computer Vision and Video Analysis - Further Reading
R
R packages for sports analytics:
`survival` - Survival analysis for injury prediction - `caret` - Machine learning framework - `mgcv` - Generalized additive models → Chapter 24: Injury Risk and Load Management - Further Reading
Random Forest:
Handles non-linearity - Feature importance built-in - Less prone to overfitting than single trees - May struggle with very high-dimensional data → Chapter 26: Machine Learning in Basketball - Key Takeaways
RAPM (Regularized Adjusted Plus-Minus):
Derived directly from play-by-play data - Uses regression to isolate individual impact - Requires regularization to handle multicollinearity - Represents "ground truth" for on-court impact → Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
RAPTOR Documentation
https://fivethirtyeight.com/features/how-our-raptor-metric-works/ - Player-level ratings that feed into game predictions → Chapter 25: Game Outcome Prediction - Further Reading
Rate Statistics:
Per-36 or per-100 possessions normalizes for playing time - Adjust for team pace → Chapter 26: Machine Learning in Basketball - Key Takeaways
Real-Time 3D Reconstruction
Volumetric capture of sports action → Chapter 27: Computer Vision and Video Analysis - Further Reading
Real-Time AI
In-game strategy recommendations - Optimal substitution patterns - Live performance prediction → Case Study 1.2: The Evolution of Player Tracking Data
Real-Time Applications:
Broadcasting enhanced by WP displays - Coaching decisions informed by leverage index → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
RealGM International
https://basketball.realgm.com/international/ - International league statistics - European prospect tracking → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Rebounding Breakdown:
Offensive rebounds: 1.7 per game - Defensive rebounds: 9.0 per game → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Rebounding:
Rebounds per 75 possessions - Offensive Rebound % - Defensive Rebound % → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Recency bias
Strong late-season performances - Shooting slump early in season attributed to "adjusting" → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Recommended
16 GB RAM for larger datasets - SSD for faster data loading → Prerequisites
Recommended (Free)
VS Code or PyCharm (Community Edition) - GitHub account → Prerequisites
Recovery Metrics:
Required sleep: 8+ hours - HRV threshold: Individual baseline - Subjective wellness: Daily assessment → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Reddit - r/nba
https://www.reddit.com/r/nba/ - General NBA discussion, occasional analytics content → Chapter 28: Building a Basketball Analytics Career - Further Reading
Regression Analysis
Simple linear regression - Multiple regression - Interpretation of coefficients - R-squared and model fit → Prerequisites
Regression isolates individual contributions
Each stint is an observation; outcome is point differential - Player indicators as predictors allow simultaneous estimation of all effects - Coefficients represent marginal contribution controlling for who else was on court → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
Regression:
MAE (interpretable error) - RMSE (penalizes large errors) - R-squared (variance explained) → Chapter 26: Machine Learning in Basketball - Key Takeaways
Regular season vs. Warriors:
3PA: 42.3/game - 3P%: 36.2% → Case Study 1: Building a Shot Quality Model for the Houston Rockets' Three-Point Revolution
Regular Season:
Games played: 60 (73% of season) - Games missed to "load management": 22 - Minutes per game: 34.0 → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Regularization parameter selection
Cross-validation: train on folds, evaluate on held-out data - Typical range: λ ∈ [500, 5000] for single-season data - GCV provides efficient leave-one-out approximation → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
Replacement level concept
+2.0 adjustment converts from league average to replacement level baseline - Replacement level ≈ -2.0 BPM - Represents freely available player (end of bench, G-League call-up) → Chapter 12 Key Takeaways: Box Plus-Minus (BPM) and VORP
Reporting
[ ] Generate team offensive profiles - [ ] Identify strengths and weaknesses - [ ] Compare to league benchmarks - [ ] Track changes over time → Chapter 17: Key Takeaways - Team Offensive Efficiency
Required (Free)
Python 3.9 or higher - Jupyter Notebook or JupyterLab - Git (for version control) → Prerequisites
Research findings suggest:
500 minutes: Extremely noisy, not reliable for individual comparison - 1,000 minutes: Still substantial noise, broad conclusions only - 2,000 minutes: Moderate reliability, clear patterns emerge - 3,000+ minutes: Reasonable stability, suitable for analysis → Chapter 10: Plus-Minus and On/Off Analysis
Resources for Historical Context:
Basketball-Reference historical data (pre-1979) - APBR metrics community research - Academic papers on ABA three-point line → Chapter 8 Further Reading: Shooting Efficiency Metrics
Results (2000 games):
ATS Accuracy: 51.8% - Not statistically significant (p = 0.21) - Conclusion: No edge against the market → Chapter 25: Case Study 1 - Building an Elo Rating System for NBA Prediction
Results:
75 games played (vs. 65 prior season) - No significant injuries - Peaked physically for playoffs → Case Study 2: Load Management Using Physical Tracking Data
Return-to-Play:
Extended timelines (9-12 months minimum) - Psychological readiness assessment - Graded return protocols - Ongoing monitoring → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Ridge solution: (X'WX + λI)^(-1)X'Wy
Adding λI to X'X increases all eigenvalues by λ - Guarantees invertibility and improves condition number - Shrinks coefficients toward zero (or toward prior mean) → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
Rim Protection Without Blocks:
Alters shots through positioning - Forces difficult angles - Opponents shoot worse at rim with Green nearby → Case Study 2: Draymond Green and the Limits of Box Score Evaluation
Rim Protectors:
Essential for defensive integrity - May overlap with switchable defenders - Often staggered with offensive-minded bigs → Chapter 19: Lineup Optimization
Risk Assessment:
Probability Leonard is healthy: 60-70% - Probability he leaves in free agency: 50-60% - Expected championship probability if healthy: ~15-20% → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
Robust z-score
the distribution is right-skewed, so median/IQR better represent typical values. The robust score correctly shows this player earns well above typical. → Chapter 5: Exercises
Role Considerations:
Young international players often play limited roles on veteran teams - Per-minute production may be more relevant than total production - Performance against other NBA-level talent in EuroLeague is especially predictive → Chapter 23: Draft Modeling and Prospect Evaluation
Role player targets:
Prioritized floor-spacing shooters - Targeted corner three specialists - De-emphasized mid-range scorers → Case Study 1: Building a Shot Quality Model for the Houston Rockets' Three-Point Revolution
Rookie BPM Expectations by Pick:
Picks 1-5: Average BPM of +1.0 - Picks 6-10: Average BPM of -0.5 - Picks 11-14: Average BPM of -1.5 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Rose's Actual Timeline:
Cleared to play: Early 2013 - Actual return: October 2013 (17 months post-surgery) - Reason for delay: Psychological readiness, caution → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Rule Changes:
Three-point line (1979) - Zone defense legalization (2001) - Pace variations across eras → Case Study 2: Win Shares for Career Evaluation - Comparing All-Time Greats
Rules of Thumb:
|Skewness| < 0.5: Approximately symmetric - 0.5 ≤ |Skewness| < 1.0: Moderately skewed - |Skewness| ≥ 1.0: Highly skewed → Chapter 5: Descriptive Statistics in Basketball
S
SABR (Society for American Baseball Research)
Despite name, includes basketball content - Basketball research committee → Chapter 28: Building a Basketball Analytics Career - Further Reading
SABR Analytics Conference
Baseball-focused but methodology applicable to basketball → Chapter 25: Game Outcome Prediction - Further Reading
Sample size concerns worked both ways
25 games gave limited FT attempts - Evaluators may have dismissed it as noise - But small samples should increase uncertainty, not dismiss warnings → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
scikit-learn
https://scikit-learn.org/ - Essential Python ML library - Comprehensive documentation and tutorials → Chapter 26: Machine Learning in Basketball - Further Reading
scikit-learn Documentation
https://scikit-learn.org/stable/documentation.html - Essential reference for machine learning implementations in Python → Chapter 22: Player Performance Prediction - Further Reading
Scoring Breakdown:
Two-point field goals: 7.2 made per game (14.4 attempts) - Three-point field goals: 1.8 made per game (7.2 attempts) - Free throws: 8.2 made per game (10.4 attempts) → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Scoring Guide:
Multiple Choice: 2 points each - True/False: 1 point each - Short Answer: 3 points each - Calculations: 4 points each → Chapter 6 Quiz: Box Score Fundamentals
Scoring:
Points per 75 possessions - True Shooting % - Usage Rate - % of points from 3PT, 2PT, FT → Chapter 26: Case Study 1 - Discovering Player Archetypes Through Clustering
Screen for physical outliers
Extreme length - Elite athletic testing - Unique combinations → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Screen Metrics:
Screens set per game - Screen assists (passes leading to scores off screens) - Slip frequency (rolling to basket before contact) - Pop frequency (moving away after screening) → Chapter 15: Player Tracking Analytics
Season-Long Pacing:
Minute management strategies - Rest game decisions - Playoff preparation → Chapter 15: Player Tracking Analytics
Second Half (Games 42-82):
Role: Starter - Minutes per game: 34 - Net Rating On: +3.2 - On/Off: +4.8 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
Second Quarter:
Minutes 0-6: Second substitution wave - Minutes 6-12: Return of starters, closing lineup → Chapter 19: Lineup Optimization
Second Screen Integration
Detailed play breakdowns - Advanced stats during stoppages → Case Study 1.2: The Evolution of Player Tracking Data
Second Spectrum
https://www.secondspectrum.com/ - Official NBA tracking provider - Limited public access → Chapter 26: Machine Learning in Basketball - Further Reading
Second Spectrum Engineering Blog
Insights from NBA tracking provider → Chapter 27: Computer Vision and Video Analysis - Further Reading
Second Spectrum Products:
Coaching tablets with annotated video - Automated play tagging - Real-time broadcast graphics - Custom analytical applications → Chapter 15: Player Tracking Analytics
Second Spectrum Public Releases:
Occasional academic partnerships - Published research papers → Chapter 15: Player Tracking Analytics
Selection criteria:
25+ PPG - Primary scoring option - Similar playing style (mid-range focused, iso-heavy) - Perimeter-oriented forward → Chapter 22: Case Study 2 - The Decline of Carmelo Anthony: Projecting Aging Superstars
Self-Supervised Learning
He, K., et al. (2020). "Momentum Contrast for Unsupervised Visual Representation Learning." CVPR. → Chapter 27: Computer Vision and Video Analysis - Further Reading
SHAP
https://github.com/slundberg/shap - Model interpretation library - Essential for explaining predictions → Chapter 26: Machine Learning in Basketball - Further Reading
Shooting
Shot distance - Closest defender distance - Touch time before shot → Case Study 1.2: The Evolution of Player Tracking Data
Shooting (17.6% 3PT, 68.6% FT)
Poor shooting indicators - Traditional stretch-4 role unlikely → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Shot Quality Created
Assists counted equally regardless of shot difficulty - Did not capture screens set or gravity created → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Should Rose have been playing?
Bulls were up 17 points with 1:22 remaining - Game was effectively decided - Playoff series context: Game 1 of 7 → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Signal 3: Athletic Profile
Lane agility: Top 10% for forwards - Vertical: Above average - Sprint: Elite - Combined with length = switchable defensive profile → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Signal 3: Free Throw Percentage
75.7% FT in 2013-14 - Improved to 80%+ the following year - Indicated shooting touch that would translate → Chapter 23: Case Study 2 - Finding Gems: Jokic, Siakam, and Identifying Late-Round Value
Silver, N. et al. "How Our NBA Predictions Work."
https://fivethirtyeight.com/methodology/how-our-nba-predictions-work/ - Documentation of FiveThirtyEight's NBA prediction methodology - Combines Elo, RAPTOR ratings, and travel/rest factors → Chapter 25: Game Outcome Prediction - Further Reading
Simpler models often suffice
always establish baselines and question whether complexity is warranted. → Chapter 26: Machine Learning in Basketball
Situational Factors:
Rest days differential (~1-2 points) - Travel distance (minor) - Altitude (Denver: +1-2 points) - Schedule density → Chapter 25: Game Outcome Prediction - Key Takeaways
Situational Responses
[ ] Protecting lead playbook - [ ] Chasing deficit playbook - [ ] Tie game playbook - [ ] Two-for-one recognition → Chapter 20: Key Takeaways - Game Strategy and Situational Analysis
Size/length profile
6'7" with 6'11" wingspan - Ideal wing dimensions → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Skeletal Tracking
Body pose estimation - Hand and arm positioning - Shooting form analysis → Case Study 1.2: The Evolution of Player Tracking Data
Skill Assessment
Quantify improvement areas - Compare movement to elite players - Track physical development → Case Study 1.2: The Evolution of Player Tracking Data
Skipping inspection
Always look at your data before analyzing 2. **Ignoring missing values** - Understand why data is missing 3. **Inappropriate imputation** - Don't fill undefined values (0/0 percentages) 4. **Over-interpreting correlations** - Correlation is not causation 5. **Using wrong visualization** - Match char → Chapter 4: Key Takeaways
SlowFast
https://github.com/facebookresearch/SlowFast - Video recognition models → Chapter 27: Computer Vision and Video Analysis - Further Reading
Small sample size: 25 games
Washington went 9-22 - Limited high-quality competition → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Small Sample Warning Signs:
Large confidence intervals - Inconsistent with career norms - Extreme percentile rankings - High volatility in rolling averages → Chapter 5: Descriptive Statistics in Basketball
SoccerNet
https://www.soccer-net.org/ - Large-scale soccer video understanding benchmark → Chapter 27: Computer Vision and Video Analysis - Further Reading
Spacing Metrics (2015-16)
Average player spacing: 14.2 feet (league avg: 12.8 feet) - Players beyond 3PT line on average possession: 3.2 (league avg: 2.4) - Drives per game: 52.3 (league avg: 46.8) - Points in paint: 47.2 per game → Case Study 1: The Golden State Warriors' Offensive Revolution (2015-2019)
Spatial Analysis
Defender distance on shots - Court coverage on defense - Rebounding positioning → Case Study 1.2: The Evolution of Player Tracking Data
Spatial Features:
Ball distance to basket - Closest defender distance - Spacing metrics (team spread) - Lane penetration indicators → Case Study 2: Building an EPV Model from Tracking Data
Speed and Distance
Average speed (mph) - Distance traveled per game (miles) - Speed differential: offense vs. defense → Case Study 1.2: The Evolution of Player Tracking Data
Speed:
Average speed - Maximum speed - Acceleration count (hard accelerations) → Case Study 2: Load Management Using Physical Tracking Data
Sports Analytics Meetups
meetup.com has groups in many cities - Search "sports analytics [your city]" → Chapter 28: Building a Basketball Analytics Career - Further Reading
Sports Analytics World
Community and resources for sports analytics professionals → Chapter 28: Building a Basketball Analytics Career - Further Reading
Sports Info Solutions
College and NBA data (subscription) → Chapter 28: Building a Basketball Analytics Career - Further Reading
Sports Innovation Lab Summit
Various sports technology and analytics topics → Chapter 28: Building a Basketball Analytics Career - Further Reading
Sports Odds History
https://www.sportsbookreview.com/betting-odds/ - Historical betting lines (limited free access) → Chapter 25: Game Outcome Prediction - Further Reading
Sports Performance Bulletin
https://www.sportsperformancebulletin.com/ - Practical sports science content including injury prevention → Chapter 24: Injury Risk and Load Management - Further Reading
Sports Reference / Basketball-Reference
https://www.basketball-reference.com/ - Historical NBA statistics - College basketball statistics (Sports-Reference) - Draft combine measurements → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Sports-1M
Million sports videos for recognition research → Chapter 27: Computer Vision and Video Analysis - Further Reading
Staffing
Data scientists with sports medicine background - Biostatisticians familiar with survival analysis - Sports scientists understanding workload physiology - Coordination with medical staff and coaches → Chapter 24: Injury Risk and Load Management
Staggering Pattern:
James and Davis rarely rested simultaneously - Rondo provided secondary playmaking when James sat - Defensive units featured Caruso anchoring bench stretches → Chapter 19: Lineup Optimization
Standard ACL Recovery:
Surgery: May 2012 - Initial rehabilitation: 6-9 months - Full clearance: 10-12 months → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Standard Injury Insurance:
Coverage: 80% of salary for games missed beyond threshold - Premium: 2-5% of salary - Deductible: Usually 20-40 games → Chapter 24: Case Study 2 - Predicting and Managing ACL Injuries: Derrick Rose and the Cost of Catastrophic Injury
Standardization reduces friction
A consistent setup process helps new team members become productive quickly 2. **Tiered requirements** support diverse workflows without bloating everyone's environment 3. **Virtual environments** isolate dependencies and ensure reproducibility 4. **Documentation is crucial** - Good README and onboa → Case Study 1: Setting Up a Team Analytics Environment
Stanford CS231n: CNNs for Visual Recognition
http://cs231n.stanford.edu/ - Premier course on deep learning for vision - Lecture videos and assignments available → Chapter 27: Computer Vision and Video Analysis - Further Reading
Starting Center (Player C):
On Court Net Rating: +4.5 - Off Court Net Rating: -8.2 - On/Off: +12.7 → Chapter 10 Exercises: Plus-Minus and On/Off Analysis
Stat Quest (YouTube)
https://www.youtube.com/c/joshstarmer - Clear explanations of statistical concepts → Chapter 28: Building a Basketball Analytics Career - Further Reading
State Tracking
[ ] Current score differential - [ ] Time remaining (game clock) - [ ] Possession status - [ ] Timeout inventory (both teams) - [ ] Foul situation (bonus status, personal fouls) - [ ] Shot clock status → Chapter 20: Key Takeaways - Game Strategy and Situational Analysis
Statistical Analyst (Marcus)
Needs: pandas, scipy, statsmodels for hypothesis testing - "I'm running regression models on player performance data." → Case Study 1: Setting Up a Team Analytics Environment
Statistical Projection (Year 1):
Points: 4-8 PPG - Rebounds: 3-5 RPG - Win Shares: 0.5-2.0 - Wide confidence intervals due to data limitations → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Statistical Red Flags:
Poor free throw percentage (< 70%) for guards - High turnover rate relative to usage - Low steal rate for guards - Low block rate for bigs without elite athleticism - Declining production from freshman to sophomore/junior year → Chapter 23: Draft Modeling and Prospect Evaluation
StatQuest with Josh Starmer
Logistic Regression - Accessible introduction to logistic regression - Machine learning fundamentals → Chapter 16 Further Reading: Shot Quality Models
statsmodels
https://www.statsmodels.org/ - Statistical modeling including logistic regression → Chapter 25: Game Outcome Prediction - Further Reading
Stops
the number of times a player ends an opponent's possession: → Chapter 13: Win Shares and Wins Above Replacement
Subject to:
Each player plays within their sustainable minutes range - Position/skill requirements are met at all times - Rest constraints are satisfied - Substitution frequency limits → Chapter 19: Lineup Optimization
Supplementary Sources:
Player biographical data (height, weight, position, age) - Team schedule and travel data - Injury reports → Case Study 2: Building a Real-Time RAPM System for Player Evaluation
Switch Coverage:
Possessions tested: 140 - Ball handler shot frequency: 52% - Ball handler shot value: 0.94 - Roll man shot frequency: 8% - Roll man shot value: 1.25 - Other shot frequency: 40% - Other shot value: 1.00 → Chapter 14 Exercises: Expected Possession Value (EPV)
Switch:
Screener's defender takes ball handler - Creates potential mismatches (EPV impact varies) - Best with versatile defenders → Case Study 1: EPV Analysis of the Pick-and-Roll
Switches everything
No defensive liability 2. **Protects the rim** - Despite 6'6" height 3. **Initiates offense** - Acts as point center 4. **Spaces floor** - Enough shooting threat 5. **Covers mistakes** - Elite help defense → Case Study 2: Draymond Green and the Limits of Box Score Evaluation
Switching Capability:
Can guard positions 1-5 - No matchup forces lineup change - Enables Warriors' switching schemes → Case Study 2: Draymond Green and the Limits of Box Score Evaluation
Synergy Sports
Play-type data and video (subscription) → Chapter 28: Building a Basketball Analytics Career - Further Reading
T
t-SNE:
Non-linear visualization - Preserves local structure - Not for preprocessing (use PCA) → Chapter 26: Machine Learning in Basketball - Key Takeaways
Tankathon
https://tankathon.com/ - Draft order and pick value - Trade simulator → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Target Audience:
Undergraduate students in sports analytics programs - Data science students with interest in sports applications - Sports management students seeking quantitative skills - Self-learners pursuing careers in basketball analytics → Professional Basketball Analytics and Visualization
Team A
similar win margin but much more consistent performance suggests reliability in playoffs. → Chapter 5: Exercises
Team A Receives:
Player X: BPM +5.5, expected 2,200 minutes - Player Y: BPM +1.0, expected 1,500 minutes → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Team Actions:
Pick and roll execution - Fast break - Zone defense rotation - Inbound plays → Chapter 27: Computer Vision and Video Analysis
Team B
negative kurtosis means fewer extreme outcomes, more predictable totals. → Chapter 5: Exercises
Team B Receives:
Player Z: BPM +4.0, expected 2,800 minutes → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
Team Defense > Individual Defensive Stats
The Pistons ranked 10th in steals but 1st in defensive rating - Collective excellence transcended individual counting stats → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
Team Defensive Rebounding:
Opponent OREB%: 25.2% (2nd lowest allowed) - Detroit DRB%: 74.8% (2nd highest) → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
Team Defensive Statistics:
Steals per game: 8.1 (10th in NBA) - Blocks per game: 6.6 (4th in NBA) - Opponent turnovers forced: 15.8 per game → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
Team Rebounding:
Team Rebounds: 44.8 per game (4th in NBA) - Opponent Rebounds: 40.2 per game - Rebounding Differential: +4.6 (3rd in NBA) → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
Team Strength:
Warriors set regular season record (73-9) - Cavaliers were 3-1 underdogs before series - Warriors had home court throughout playoffs → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Team-based allocation
Team defensive success distributed by playing time - Individual adjustments for STL%, BLK%, DRB% - ~70% from team defense, ~30% from individual stats → Chapter 13 Key Takeaways: Win Shares
Team-Only Access:
Raw coordinate data (x, y, z positions) - Frame-by-frame tracking files - Custom metric calculations - Historical tracking data archives → Chapter 15: Player Tracking Analytics
TeamWork Online
https://www.teamworkonline.com/ - Primary sports industry job board → Chapter 28: Building a Basketball Analytics Career - Further Reading
Technical Specifications:
25 frames per second - X-Y coordinates for all 10 players - Ball tracking in 3D (X, Y, height) - Positional accuracy within inches → Case Study 1.2: The Evolution of Player Tracking Data
Temporal Features:
Shot clock remaining - Time in half-court - Possession phase indicators → Case Study 2: Building an EPV Model from Tracking Data
TensorFlow
https://www.tensorflow.org/ - Google's deep learning framework - Good production support → Chapter 26: Machine Learning in Basketball - Further Reading
Textbooks
*OpenIntro Statistics* (free online): Excellent introduction to statistical concepts - *Statistics* by Freedman, Pisani, and Purves: Classic undergraduate text - *Naked Statistics* by Wheelan: Accessible introduction for non-technical readers → Prerequisites
The Athletic Draft Coverage
Sam Vecenie's draft analysis - Detailed prospect reports - Statistical projections → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
The Best Defense May Not Gamble
High steal totals often indicate gambling - Detroit's moderate steal total reflected disciplined positioning → Case Study 2: The Defensive Dominance of the 2004 Detroit Pistons
The Block WPA Calculation:
Pre-play: GSW 62% (expected make gives them lead) - Post-play: GSW 50% (still tied, Cavs ball) - WPA for LeBron: +0.12 (conservative estimate) → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
The collinearity problem
Players appear in correlated patterns (starters with starters, bench with bench) - When players always appear together, their individual effects cannot be separated - This makes the design matrix nearly singular → Chapter 11 Key Takeaways: Regularized Adjusted Plus-Minus (RAPM)
The Lowe Post
ESPN's Zach Lowe with analytical perspective → Chapter 28: Building a Basketball Analytics Career - Further Reading
The Lowe Post Draft Shows
Zach Lowe's interviews with evaluators - Industry perspective on draft process → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
The Mathematical Argument Against:
Expected value analysis assumed normal distribution - Game 7 psychology may affect shooting percentages - Shot quality likely declined due to defensive pressure → Case Study 2: The Houston Rockets' "Moreyball" Era (2017-2020) - Team-Level Efficiency Optimization
The Mathematical Argument For:
15.9% was 2+ standard deviations below expected - Over 27 missed threes, expected value still: 0.159 * 3 * 44 = 21 points - Alternative mid-range attempts at 40%: 0.40 * 2 * 44 = 35 points → Case Study 2: The Houston Rockets' "Moreyball" Era (2017-2020) - Team-Level Efficiency Optimization
The Quadriceps Injury:
Initially injured in January 2017 (right quadriceps) - Played only 9 games in 2017-18 season - Diagnosis: Quadriceps tendinopathy - Traditional recovery timeline proved inadequate - Relationship with San Antonio Spurs deteriorated over treatment approach → Chapter 24: Case Study 1 - The Toronto Raptors' Kawhi Leonard Load Management Strategy (2018-19)
The Ringer Draft Coverage
Kevin O'Connor's prospect analysis - Draft guides and big boards - Methodology discussions → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
The Stepien
https://www.thestepien.com/ - Draft analysis and projections - Historical draft data → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Thinking Basketball
https://www.youtube.com/c/ThinkingBasketball - Video analysis combining stats and film → Chapter 28: Building a Basketball Analytics Career - Further Reading
Thinking Basketball (Ben Taylor)
https://www.youtube.com/c/ThinkingBasketball - Video analysis demonstrating integration of statistical and film-based evaluation relevant to projection. → Chapter 22: Player Performance Prediction - Further Reading
Thinking Basketball Draft Episodes
Ben Taylor's analytical approach to prospects - Integration of statistical and film analysis → Chapter 23: Draft Modeling and Prospect Evaluation - Further Reading
Thinking Basketball Podcast
In-depth analytical discussions → Chapter 28: Building a Basketball Analytics Career - Further Reading
Third Quarter Summary:
Score: CLE 33, GSW 27 - End Q3: Tied 76-76 - Q3 End WP: GSW 52% → Chapter 21: Case Study 1 - The 2016 NBA Finals Game 7: Win Probability in Action
Threshold Alerts:
3-game cumulative distance >9 miles: Consider rest - Sprint count >150 in 3 games: Reduce practice - Declining top speed trend: Mandatory rest → Case Study 2: Load Management Using Physical Tracking Data
Tie Game:
Balanced approach - Last-shot capability - Defensive stops equally important → Chapter 19: Lineup Optimization
Tier 1 (Strong Translation):
EuroLeague (top teams) - Spanish ACB - Turkish BSL → Chapter 23: Draft Modeling and Prospect Evaluation
Tier 2 (Moderate Translation):
Italian Serie A - French Pro A - German BBL - EuroLeague (lower-tier teams) → Chapter 23: Draft Modeling and Prospect Evaluation
Tier 3 (Weaker Translation):
Other European leagues - Australian NBL - Chinese CBA → Chapter 23: Draft Modeling and Prospect Evaluation
Time yourself and aim for under 15 minutes.
### Exercise 39: Debugging Environment Issues → Chapter 3 Exercises: Python Environment Setup
Touches
Touches per game - Time of possession - Dribbles per touch → Case Study 1.2: The Evolution of Player Tracking Data
Towards Data Science
https://towardsdatascience.com/ - ML tutorials and applications → Chapter 26: Machine Learning in Basketball - Further Reading
Tracking Insights:
Led league in defensive versatility (ability to guard multiple positions) - Elite at disrupting passing lanes without getting steals - Exceptional help defense coverage → Case Study 1.2: The Evolution of Player Tracking Data
Tracking-Based Defensive Metrics:
Matchup statistics - Contest data (shot quality allowed) - Defensive distance metrics - Help defense frequency → Chapter 15: Player Tracking Analytics
Trade Candidates:
Player A: Score-first guard averaging 18 PPG on a rebuilding team - Player B: Defensive wing averaging 8 PPG with elite steal rates - Player C: Stretch four averaging 14 PPG on 40% from three → Case Study 2: Player Performance Profiling for Trade Deadline Analysis
Traditional Defensive Metrics:
Defensive Rating (points allowed per 100 possessions) - Steal and block rates - Defensive Rebound Percentage → Chapter 15: Player Tracking Analytics
Traditional Statistics:
Per-game averages (points, rebounds, assists, steals, blocks) - Shooting splits (FG%, 3P%, FT%) - Minutes played - Games played/started → Case Study 1: Building a Comprehensive Player Statistics Pipeline
Trajectory model projection:
Year 4: 19-21 PPG - Year 5: 21-24 PPG - Peak (Age 26-28): 24-28 PPG → Chapter 22: Case Study 1 - Projecting Giannis Antetokounmpo's Career Trajectory
Transition (first 8 seconds):
Frequency: 18% of possessions - Average EPV at start: 1.18 - Average points scored: 1.15 → Chapter 14 Exercises: Expected Possession Value (EPV)
Transition Offense:
Defense not set - Numerical advantages possible - Quick decision-making required - Higher efficiency potential - Limited play calling → Chapter 17: Team Offensive Efficiency
True
Pace has increased significantly, from around 90 to over 100 possessions per 48 minutes. → Chapter 1 Quiz: Introduction to Basketball Analytics
Turnover Context:
5.4 turnovers per game (led league) - 7.7 turnovers per 100 possessions - Turnover percentage: 15.8% → Case Study 1: The 2016-17 Triple-Double Season of Russell Westbrook
Follow prominent analysts - Engage with analysis discussions → Chapter 28: Building a Basketball Analytics Career - Further Reading
Two components: OWS and DWS
OWS: Offensive Win Shares from Points Produced - DWS: Defensive Win Shares from team defense allocation - Total WS = OWS + DWS → Chapter 13 Key Takeaways: Win Shares
Typical age curves by position:
Guards: Peak at 26-28, gradual decline - Wings: Peak at 27-29, slower decline - Centers: Peak at 25-27, faster decline → Chapter 12: Box Plus-Minus (BPM) and Value Over Replacement Player (VORP)
Typical Costs:
Star player game missed: $200-500K - Average player game missed: $50-100K - Significant injury (20+ games): $3-15M - Season-ending injury: $10-40M → Chapter 24: Injury Risk and Load Management - Key Takeaways
V
Validation
[ ] Temporal validation (train on past, test on future) - [ ] Stratified evaluation (by quarter, score differential) - [ ] Compare to baseline models - [ ] Check edge cases (overtime, large leads) → Chapter 21: Key Takeaways - In-Game Win Probability
Validation Metrics
[ ] Log loss (calibration) - [ ] Brier score (accuracy) - [ ] AUC-ROC (discrimination) - [ ] Calibration plots by decile → Chapter 16 Key Takeaways: Shot Quality Models
Variance in Shooting
Three-point shooting has high variance - In Game 7 of the 2018 Western Conference Finals, the Rockets missed 27 consecutive three-pointers → Case Study 1.1: The Houston Rockets' Analytical Revolution
Versatility:
Defended all 5 positions - Effective against PG-PF (positions 1-4) → Case Study 1: Measuring Defensive Impact with Tracking Data
Video Analyst (Sarah)
Needs: matplotlib, opencv-python for frame analysis - "I spend hours each week extracting shot clock data from video." → Case Study 1: Setting Up a Team Analytics Environment
Video scouting contradiction
Fultz's shooting looked smooth on film - Led to cognitive dissonance between eye test and statistics → Chapter 23: Case Study 1 - The 2017 NBA Draft: Quantifying the Tatum vs. Fultz Decision
Vision Transformers
Dosovitskiy, A., et al. (2021). "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale." ICLR. → Chapter 27: Computer Vision and Video Analysis - Further Reading
Visualization
[ ] Shot charts with expected value overlay - [ ] Network diagrams for assist patterns - [ ] Play type efficiency comparisons - [ ] Four Factors radar charts → Chapter 17: Key Takeaways - Team Offensive Efficiency
Volume Scorer:
PTS/100: 35, TS%: 0.590 - AST%: 15, TOV%: 10 - USG%: 34, 3PAr: 0.40 → Chapter 12 Exercises: Box Plus-Minus (BPM) and VORP
VORP converts rate to total value
Formula: VORP = (BPM + 2.0) × (Minutes / 3936) - Measures cumulative value above replacement level - Rewards both quality (BPM) and quantity (minutes) → Chapter 12 Key Takeaways: Box Plus-Minus (BPM) and VORP
VORP to wins conversion
Approximately 2.7 VORP ≈ 1 win - Based on ~2.7 points of differential per win relationship - Allows dollar valuation: win value × (VORP / 2.7) → Chapter 12 Key Takeaways: Box Plus-Minus (BPM) and VORP
VORP-Based Valuation:
Projected VORP over 4 years: ~14.5 - At $6M/VORP: $87M fair value - At $5M/VORP: $72.5M fair value → Case Study 2: Using VORP for Roster Construction and Salary Cap Management
VP of Basketball Analytics
Strategic leadership of department - Integration with GM, coaching staff, and ownership - Budget management and resource allocation - Final approval on major methodological decisions - External representation (media, league meetings) → Chapter 28: Case Study 1 - Building an NBA Team Analytics Department from Scratch