Squared Statistics: Understanding Basketball Analytics

A Blog by Justin Jacobs

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  • Tracking
    • The Potential Assist
    • Applying Role Alignment to Tracking Data
    • Skayton Ayton: A Look into Spacing and Putting Bigs on Skates
    • Modeling the Pass
    • Current Shooting Trends in the NBA
    • Game of Waveforms
    • The Components of Offense: Turning the Lurk into a Feature
    • Understanding Trends in the NBA: How NNMF Works
    • An Example in Kullback-Leibler Divergence
    • Stochastic Tracking II: Next Gen Solutions and Player Performance
    • Stochastic Tracking
    • Kinematics of Player Motion
    • Voronoi Tesselation and Rebounding Position: Defining Distance by Seconds
    • The Art of Sketching: Trajectory Analysis
    • Gravity: Introduction to Bodies
    • Gravity Example: 0.04s of Computation
    • Understanding the Spatial Tendencies of Assists, the K(t) Test, and the Orlando Magic
    • Measuring Attack Vectors of Ball-Handlers
    • Building a Simple Spatial Analytic: Passing Lane Coverage
    • Hammer Offense: Mechanics and Quantification
    • Identifying Player Possession in Spatio-Temporal Data
    • Bryant’s Role In the Lakers Offense
    • NBA Tracking Using Python: Warriors vs. Grizzlies
    • Building NBA Defenses Using the Convex Hull
    • NBA Data Science: Breaking Down NBA Data
    • NBA Shot Charts via Kernel Density Estimation
    • Spatio-Temporal Data In the NBA
  • Running Net Points
  • Analytics Analysis
    • Stop Rate
    • The “No Turnover” Turnover
    • True Shooting Percentage Part I: Introduction and Framework for Advancement
    • Random Manatees: The Art of Ranking Players
    • Regularized Adjusted Plus-Minus Part III: What Had Really Happened Was…
    • The “Wisconsin Stat”
    • Ranks and Percentiles
    • Second Chances and the Rebounding Specialist
    • Crediting Assists: A Fairly Risky Method
    • Offensive and Defensive Ratings
    • Usage and Efficiency
    • Making Blocks Count
    • Testing the Quality of a Binary Classifier: ROC Curves
    • Developing a Cross-Product Analytic: Kidd Score
    • Relationship Between TS% and eFG%
    • Deep Dive with Python: Offensive Ratings
    • Game Score: Focus on Scoring
    • Deep Dive on Regularized Adjusted Plus Minus II: Basic Application to 2017 NBA Data with R
    • Deep Dive on Regularized Adjusted Plus-Minus I: Introductory Example
    • Understanding FG% and Rebounding in Player Efficiency Ratings
    • Analyzing NBA Possession Models
    • Rebounding Rates: Good for Teams; Bad for Players
  • Miscellaneous
    • A Methodology for Qualitatively Comparing Games
    • Curious Tale of 3’s Versus 2’s in the NBA
    • Pistol: Disrupting the Defense
    • An Absurd and Effective Way to Combat Tanking and Make the Playoffs Insane
    • Minnesota Timberwolves Offense: Stability, Screens, and Mid-range Game
    • Distributional Analysis of Free Throws and the Denver Nuggets
    • Evaluating Assists with Python: Community Detection and the Brooklyn Nets
    • Analyzing Steals in the 2016-17 NBA Season
    • Basics in Negative Binomial Regression: Predicting Three Point Field Goal Percentages
    • Identifying Clutch Players in the NBA: 2016/17 Analysis
    • Applying Tensors to Find Optimal Match-Ups in the NBA
    • Using Random Forests to Forecast NBA Careers
    • How NBA Draft Lottery Probabilities Are Constructed
    • Is the NBA Draft Lottery Fixed? A Statistical Analysis of 1994 – 2017.
    • The Real 2017 NBA Draft Lottery Odds
    • Comparing West vs. East: If the NBA Playoffs Were Seeded Like the NCAA
    • Hypothesis Testing: Is NBA Scoring Up This Year?
    • Proximity of NBA Teams
    • Redefining NBA Divisions By Clustering
    • Quantifying NBA Hall of Fame Potential for NBA Players Using Random Forests
    • Score Flows of NBA Teams
  • March Madness
    • March Madness Bracketology: February 26th Edition
    • March Madness Bracketology: February 23rd Edition
    • March Madness Bracketology: February 20th Edition
  • Mathematics of Basketball Defenses
  • 1987-88 NBA RAPM
  • 1987-88 RAPM Interactive

Football

NCAA Football Rankings – November 22nd Edition

With 14 days remaining before selection day for the NCAA Division I Football Bowl Series, we decided to apply the Bradley-Terry model for identifying a ranking of the 128 NCAA Division I Football Bowl Series teams. All we considered for this model is whether a team won a particular match-up, and whether the location of the game…

November 22, 2015 in Football, NCAA, Uncategorized.

Recent Posts

  • Boston vs. The Field: Defensive 3PT%
  • Approximating Curves II: Assimilation of the Jump Shot Process
  • Approximating Curves I: Mechanical Process
  • Analytic Breakdown: 1963 Finals Game 6
  • Extending Possessions: Geometric Distribution

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