Drip

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

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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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Matchup-Based Defense

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Defense is the unsolvable puzzle in NBA analytics. No matter how advanced the advanced stats get, defensive metrics continue to crash against the same conundrums. Better data often leads to better models, and recent years have seen a dramatic improvement in the quality of defensive data available for analysis. Tracking data, opponent shooting data, play-by-play data, and more have all played a hand in modern defensive analysis. In spite of the improvements, or perhaps in part because of the improvements, it is clear that defensive analysis is still not highly accurate.

Most defensive metrics which are currently extant are based on one of two schools of thought. In order to take stock of why defensive analysis is still frequently inaccurate, it will help to investigate the underlying assumptions behind most current models.

The Plus/Minus School of Thought

The most popular method by far is The Plus/Minus School, which counts BPM, RPM, RAPM, PIPM, and more among its adherents. The distinguishing precept of the Plus/Minus School is the belief that we can ascertain a player’s defensive value by evaluating the team’s performance with him on the court, if only we properly adjust for strength of opponent, the team’s talent level, the team’s performance with the player off the court, and the player’s performance level in seasons past. The adjustments made to raw plus/minus are attempts to extract reliable data by excising confounding variables.

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Robert Covington’s Seamless Adaptation to the Houston Rockets’ Defensive Scheme

 

After the Houston Rockets pulled off the deal for Robert Covington at this year’s trade deadline many wondered what the Rockets were doing. How would their team look, especially on the defensive end? In some respects, the Rockets performed as well after the trade as before, and one of the biggest factors in their defense has been the newest factor: Robert Covington.

Where did Covington come from? How did he land here, as a highly-prized trade target First, let’s go back to the beginning of Robert Covington’s career.

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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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A Prediction Model for Sports Match Results – Part 4

 

Introduction

The first three parts of this series of articles were focused on a static model for predicting match results. With that model, our measures of team quality (offensive quality, defensive quality, and home field advantage) were static, meaning that they were not allowed to change as time progresses. In other words, a team was considered as having the same quality whether you were trying to predict the team’s results in week 1 or its results in the last week of the season.

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Why Don’t We Care About NBA-All Star Weekend?

 

There is something fundamentally wrong with the NBA All-Star game and All-Star Weekend at large. The new NBA Jam-like rule change, while interesting, does not fix what plagues the NBA’s premier regular season event.

So What Is the Problem?

Give the NBA credit, they have shoehorned the All-Star event into the perfect time period. Those feeling hungover from the Super Bowl have something to watch, and those who are waiting for prime time NCAA basketball to come during March have an event to hold themselves together in the meantime. If you are an NBA fan, chances are you were already going to watch the All-Star activities. By picking mid-February, the league has found a period in the sports calendar where the NBA has center stage.

In spite of the logistical victory, fans do not care and players do not care. The NBA has seen a sharp decline in viewership numbers for the All-Star game from the ’90s leading into the 2000s, and then almost no growth since 2005 (Please note the Lockout of 1999 had the All-Star Game cancelled, all data from https://en.wikipedia.org/wiki/National_Basketball_Association_on_television). Viewership is not the best metric for fan engagement, but it does give a decent look at where fans stand with respect to watching the actual game (admittedly, it does not measure interest in the other festivities during All-Star Weekend). I also examined the voter numbers to assess engagement, but worried that the results might be unreliable. Due the constant changes in how and where fans could vote, any correlations might not be instructive.

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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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The Factors That Influence NBA Home Court Advantage

 

by Dashiell Nusbaum

Alternate link to story: https://medium.com/push-the-pace/the-factors-that-influence-nba-home-court-advantage-2a5a602f8c1f

Many see sports as the ultimate “equal playing field” — where the same rules, regulations, and opportunities exist for all, where the best team will emerge victorious. This isn’t always the case.

We find the existence of a “home court advantage” across sports, where teams are more likely to win at home than away. This is especially true of the NBA, which has the largest home team advantage among the four major American sports. The existence and magnitude of a home court advantage has been extensively studied; however, we lack an understanding of the factors that cause certain teams to have larger home court advantage than others.

I found the average regular-season home court advantage for all 30 NBA teams from 2008-09 through 2018-19. Home court advantage is a team’s winning percentage at home minus their winning percentage on the road. For example, from the 08-09 through 18-19 season, the Denver Nuggets won 75.62% of their home games and 46.52% of their away games. Therefore, Denver’s home court advantage = 75.62% – 46.52% = 29.10%.

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