Category: League-Wide Analysis

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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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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Who Are the Best Defensive Shooting Guards in the League?

The NBA changes as rapidly as the seasons, and the league is heading toward a greater and greater reliance on versatile perimeter players. The prevalence of 3-pointers, combined with the emergence of bigger primary ball handlers replacing some of the smaller “point guards” of previous eras, has resulted in a single mandate for NBA defenses: to find defenders who are big enough and quick enough to guard anyone.

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Who are the Best Defensive Point Guards in the NBA?

Rankings are among the most popular exercises for most NBA fans, and also among the least efficient ways to evaluate data. The enduring appeal of rankings and lists owes to their relationship, however tangential, with the big questions: Who is the best? Which player is better? How much better or worse would a team be if they replaced this player with that one? Every fan, analyst, scout, coach, and executive needs to be able to answer these questions.

The problem with rankings is not the questions they address, but the biases implicit in ordinal numbering. Our brains are trained to think that the difference between 12 and 17 is the same as the difference between 18 and 23, and that all these values are on a different order of magnitude from single digit numbers. When we transfer these assumptions to rankings of the “Top 50 Players in the NBA” or something similar, we start with false presuppositions. There is not an identical difference in value between each pair of contiguous players on any player ranking. Even a hypothetically perfect, pie-in-the-sky ranking that ranked players in the exactly correct order would still need something besides the ranking to indicate the players’ true value relative to their peers. Our habit of using ordinal numbers to rank players blinds us to the shape of the data.

As a result, both league insiders and outsiders spend an inordinate amount of time debating questions such as “Is Kevin Durant the second-best player in the league or the eighth-best?” “Is Paul George a top 5 player, a top 10 player, or a top 15 player?” What really matters is how much a player contributes to wins by helping put points on the board (on offense) and keep points off the board (on defense). If the 6th ranked player in the league and the 16th ranked player are nearly identical in terms of their contribution to team success, it makes little sense to lay so much weight on their difference in the rankings.

What a valuable ranking system can tell us is how much value a player generates, relative to the rest of the population. In answering the question posed in the title of this article, I will attempt to provide enough context for the reader to be able to comprehend the shape of the data. As such, let’s start with refining the question itself:

Who are the best defensive point guards in the league?

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Steals Don’t Mean Squat

If there’s one thing we know about defensive statistics in the NBA, it’s that steals don’t mean squat. James Harden was second in the league in steals per game last year, and Andre Drummond was eighth. This was not merely an illusion created by the two playing lots of minutes, as both ranked within the top 20 in the league in steals per 36 minutes. In 2016-17, Manu Ginobili and T.J. McConnell were first and second in the league, respectively, in steals per 36 minutes. Steph Curry and Nerlens Noel were both in the top seven in the league in 2015-16, while the same two players along with Pablo Prigioni all ranked in the top eight in the league the previous season. Andray Blatche was eighth in the league in steals per 36 in 2012-13. The illusion extends back as far as steals go in the statistical record.

Jason Getz-USA TODAY Sports

The Real Reasons Why Steals Don’t Mean Squat

          These examples are merely anecdotal evidence, though; what really makes steals unreliable indicators of defensive performance are the many different chains of events which can lead to a player being credited with a steal in the box score. Many of those sequences involve plays made by teammates of the player who gets credit for the steal. Any observer can recognize these plays when they happen: tipped passes, saves on balls headed out of bounds, instances in which an on-ball defender pokes the ball away from the dribbler and another defender grabs the loose ball, traps, double teams, errant passes caused by pressure on the ballhandler, etc. Sometimes a steal is the result of a phenomenal play by one defender, but oftentimes a steal is the result of one player disrupting the offense and another player recovering the ball. Steals create an immediate problem of attribution; the player who gets credit for the steal is not always the player who truly created the turnover.

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Matchup-Based Defense

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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The So-Called Disappearance of the Big Man, Part 2

In the first half of this study, I analyzed the conditions that would be necessary for a true “league without centers” – an ultimate small-ball paradise without any traditional big men. We found that the offensive value of high-efficiency finishers would be difficult to replace without an unimaginable increase in three-point shooting accuracy.

To this point, we haven’t yet analyzed the point at which most teams would not use a traditional big man for defensive purposes. While the foregoing analysis has laid out what I see as the necessary conditions for a big man to have no purpose on offense, the question remains as to what shape such conditions might take on the defensive end. When would it not make sense to have a big man on the defensive end?

Photo by Scott G Winterton, Deseret News
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The So-Called Disappearance of the Big Man

Photo by Andrew D. Bernstein/Getty Images

          Much ink has been spilled in the latter stages of the three-point revolution on the topic of the march of the traditional big man toward extinction. The low-post scoring, rebounding, bruising, shot-blocking center of previous generations seems to recede further and further from view with every passing season. As teams emphasize floor spacing more and more on offense, the low-post operator vanishes from offensive game plans. Modern offenses often replace the traditional center with a rim runner who sets a high ball screen and rolls to the rim, then gets out of the way or sets another screen.

Defensively, the league continues to transition toward switching on screens, and prizes players who can switch across positions. The big man who can only defend his position is now a liability. The traditional center was typically slower and bulkier than his teammates, which was good for matching up with his opposite number in the low post. Now that the low post game is out of fashion, however, there is little benefit to the added bulk of a traditional big man. Furthermore, because of the evolution of offenses leaguewide, a big man’s lack of quickness is a greater disadvantage on defense than it has ever been.

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2019 Offseason Crunch

Yesterday I outlined a method to accurately grade offseason moves based on an analysis of the cost of wins in the NBA, the relationship between performance and salary, and a rubric to help the grades make sense. Today, I’m presenting the first annual NBA Offseason Data Crunch, in which I evaluate every move made by every team this summer. Before you dig in, there are two caveats:

  • In what follows I will evaluate all acquisitions in terms of the player’s value relative to the value of his contract. This means that for trades, we are not interested (right now) in figuring out which team won or lost the trade. There is a time for evaluating trades in that manner, but today’s analysis will consider moves purely in terms of cost efficiency.
  • The data crunch will deal only with players who are likely to impact winning or losing NBA games this year, and players whose impact we are able to reliably estimate. Rookies and future draft picks, as they do not have any NBA data, are difficult to forecast with the same accuracy as existing NBA players, so I will leave them aside for now.

Los Angeles Lakers trade NOP for Anthony Davis

          Let’s start with the easiest transaction to grade. Acquiring AD was a home run for the Lakers. Davis is projected to make over a little over $27 million next season, followed by a player option for 2020-21. In the three seasons prior to last year, Davis averaged 12.6 wins per season. At that rate, we would anticipate AD to generate roughly $118.3 million worth of value, meaning that the Lakers are getting a 91 million dollar surplus from trading for AD. Of course, they did have to give up something to get him …

GRADE: A+

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