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

Monthly Archives: January 2016

Schedule Strengths: Who’s Going to Win the Super Bowl?

(Warning: Math Heavy) The NFL has an adage: “Any given Sunday.” This phrase typically means that during any week, for any given match-up between two teams, either team has a relatively high probability of winning the game. This translates into parity within the league. Typically we should see teams win between 6 and 10 games…

January 31, 2016 in NFL, Rankings, Rankings.

NBA Power Rankings: Normalized Ranking Model

In this article, we come back from a month hiatus and introduce a normalized rankings model for ranking every team in the NBA. By normalized ranking, we set all rankings to a score between zero and one, where one is a perfect ranking and zero is the worst possible ranking. To build the model we consider…

January 27, 2016 in NBA, Rankings, Uncategorized.

NCAA March Madness – January 24th Edition

Over the course of the previous 15 days, there have been some serious upsets within the NCAA Division I Men’s Basketball landscape. On January 12th, #11 West Virginia ran #1 Kansas out of the arena with a 74-63 win. On the same day, #3 Maryland dropped a close 70-67 game to Michigan. One day later,…

January 24, 2016 in Basketball, March Madness, NCAA, Uncategorized.

March Madness Bracketology – January 5th Edition

Last night, Oklahoma (12-1) and Kansas (13-1) put together probably the best NCAA college game of the season in a thrilling 109-106 triple overtime that favored the Jayhawks at home. Billed as a match-up between #1 teams, the game brought everything to the table as far as high stakes competition, a challenge for the lead…

January 5, 2016 in Basketball, March Madness, NCAA.

March Madness Bracketology – January 3rd Edition

As of the morning of January 3rd, 2016, there have been a total of 2,571 games played by the 351 NCAA division I teams. This means that we are approximately 49% of the 2015-16 season completed. On December 29th, conference play started to take off across the 36 conferences as the American, ACC, Big East, Big…

January 3, 2016 in Basketball, March Madness, NCAA.

Recent Posts

  • Approximating Curves II: Assimilation of the Jump Shot Process
  • Approximating Curves I: Mechanical Process
  • Analytic Breakdown: 1963 Finals Game 6
  • Extending Possessions: Geometric Distribution
  • Offensive Crashing

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