Category: Analytics Literature

The Best Defenders in the NBA this Season

What Have You Done for Me Lately?

The NBA is a right now league. Each game can change in a moment. Every season’s trade deadline brings about substantial team restructuring, and the offseason free agency and trade market has become an event unto itself. Change happens quickly, and the team that wins the championship is often the team that becomes the best version of itself at just the right moment.

Many of the most important questions deal with which player or team is the best right now. “What have you done for me lately?” is the unspoken question on the minds of everyone in and around the league. In previous posts, I’ve used the defensive matchup data at stats.nba.com to create a model for defensive performance since 2013-14. The entire dataset is now features prominently on the homepage, and you can pull any player card for any season from the Google Sheets tool.

But what about this season?

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A Prediction Model for Sports Match Results – Part 5

Introduction

I am quite happy to have arrived at the final post of the series – not because I disliked writing the previous posts, but because I have worked for four years with the models presented in this series. I have become accustomed to their weaknesses and strengths, as well as those of the methods used for comparison. This post, however, is new and fresh. Finally, I have the change to put on paper (sort of) all the ideas I have had over the years on how to improve the model.

I don’t think it will surprise anyone reading this to know that I love modeling data. One of the reasons I love it is that modeling is not an exact science, as one might expect it to be. Modeling is just as much an art as a science; there are no certainties when we work with Probability Theory. Dealing with probabilities means making your peace with the fact that you will always be uncertain about your conclusions.

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Identifying the Best Defenders in the NBA Using Matchup-Based Defense

Defense is half of the game of basketball, but it has been difficult to gather information about the defensive capabilities of all the players in the league until very recent seasons. Due to the paucity of data about defensive performance and the limitations imposed by television broadcasting contracts, it was practically impossible to truly know who the best defenders were, night in and night out.

Offense, by contrast, is well-documented. The box score provides a good deal of useful information on individual offensive output, and even more granular data has been available throughout the 21st century by virtue of play-by-play data.

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Beyond the Box Score

It is time to stop evaluating players based on statistical feats. The ubiquity of statistical data has brought with it a ready supply of historical comparisons. In today’s NBA, one can hardly turn around without running into a stat trumpeting a player as “the first player since MJ to have 18 consecutive games of 20-5-5”. Who can ignore the box score aggregators, with their perpetual lists of “top performances of the night” that are oriented mainly around points, rebounds, and assists? The basketball community is awash in counting stats-based evaluations, but it’s time to cut it out.

The comeuppance has been a long time coming, to be perfectly honest. Fans have long prized points, rebounds, and assists as basketball’s version of the “Triple Crown” – a trio of statistics that can combine to depict a player’s value. Such an outcome is to be expected, since those three statistics are usually the largest numbers in any box score; our eyes are drawn to big numbers. In The Book of Basketball, Bill Simmons concretized the method of measuring players by Points+Rebounds+Assists in constructing his all-time rankings, which certainly did nothing to cool the ardor of counting stat acolytes.

It Was Never a Good Idea

Adding together counting stats was a bad idea to begin with. Taking values that describe discrete events and adding them without modification is among the poorest methods of measuring a player’s impact. Every box score statistic (every statistic period, as a matter of fact) exists on a certain scale. While it is quite common for a player to score 10 points in a game, and somewhat common for a player to grab 10 rebounds in a game, it is decidedly uncommon for a player to register 10 steals in a game. The scale – or range of normal values – for steals is much smaller than the scale for points. Since points, rebounds, and assists each have their own scale, adding them is always going to privilege points above the other components.

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A Prediction Model for Sports Match Results – Part 3

by André Vizzoni

Introduction

Parts one and two of this series of posts were quite heavy with probabilistic jargon (even with me trying to lighten it), and I hope this third one won’t be like that. The main objective of this article is to show the readers how the outputs of this model fit within the current basketball analytics framework. With that in mind, I will be looking at the relationship between the parameters of every team and the following stats:

  • Team 2-Point Field Goal Percentage (T2FG%)
  • Opponent 2-Point Field Goal Percentage (O2FG%)
  • Team 3-Point Field Goal Percentage (T3FG%)
  • Opponent 3-Point Field Goal Percentage (O3FG%)
  • Team Effective Field Goal Percentage (TEFG%)
  • Opponent Effective Field Goal Percentage (OEFG%)
  • Team True Shooting Percentage (TTS%)
  • Opponent True Shooting Percentage (OTS%)
  • Team Offensive Rebounding Percentage (TOREB%)
  • Opponent Offensive Rebounding Percentage (OOREB%)
  • Team Defensive Rebounding Percentage (TDREB%)
  • Opponent Defensive Rebounding Percentage (ODREB%)
  • Team Free Throw Rate (TFT%)
  • Opponent Free Throw Rate (OFT%)
  • Team Turnover Percentage (TTO%)
  • Opponent Turnover Percentage (OTO%)

Since I’m using this Justin Jacobs’ article as a guide, it also makes sense to look at the Four Factors Adjusted Ratings, hosted on this website. First, I will lay out the Methodology for my analysis. Next, I will analyze the scatter plots of the model parameters versus the assorted stats in the Analysis section. On the basis of those results, I will use the Discussion section to make sense of all that the analysis unearths.

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Can You Give Me Some Stats to Prove that Russell Westbrook Steals Rebounds?

          Properly valuing and evaluating rebounds has become something of a hot topic in recent years with Russell Westbrook averaging a triple double for three consecutive seasons despite being one of the smallest players in the history of the league to average 10 rebounds per game. Much of the discussion about his incredible feat has centered on whether or not it is the case that Westbrook’s rebounding numbers are inflated due to Westbrook taking a disproportionate amount of defensive rebounds which could be collected by other members of his team.

          Naturally, a lot of people are looking for stats to support the conclusion which they’ve already reached (thus the tongue-in-cheek title). I think we can analyze this question, within the context of a useful estimation of rebounding value for the entire league.

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What is TPA?

by Alan Moghaddam

Chances are if you’re into sports you’ve seen the famed charts from @NBA_Math that feature overlapping pictures of NBA players in a conventional Cartesian plot with a line plotted on it (y=-x). Anything above the line is good, anything below is bad, and average values will tend to walk the line. The graph is a visual attempt to quantify some mystery statistic known as TPA.

This is a standard TPA graph from @NBA_Math

What is TPA?

This is something of a loaded question; the acronym TPA stands for “Total Points Added”. The basic idea behind it is that a player adds points on offense and defense. You then total these subsections to get TPA.

Unfortuantely, the definition above is rather incomplete. We now need to understand Offensive Points Added (OPA) and Defensive Points Saved (DPS), the components which make up TPA. The two subcategories are much more complex than TPA alone.

To get OPA and DPS, we need to use “Box Plus/Minus,” an all-in-one statistic created by Daniel Myers and hosted at basketball-reference.com. I promise we are almost at the bottom of the well here in terms of stat definitions. Box Plus/Minus is a relativistic stat that gauges a player’s impact on team performance when s/he is on the court. S/he again will have an impact on both defense and offense, so accordingly Box Plus/Minus can break down into two stats: Offensive Box Plus/Minus (OPBM) and Defensive Box Plus/Minus (DPBM). We can already see that TPA has the same structure as Box Plus Minus; both purport to measure a player’s impact on both ends of the floor. What is the difference between the two?

Equation 1. Calculation of TPA as a function of Defensive Points Saved and Offensive Points Awarded
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