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.Continue reading “Who Are the Best Defensive Shooting Guards in the League?”
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?Continue reading “Who are the Best Defensive Point Guards in the NBA?”
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.Continue reading “Matchup-Based Defense”
by Baltej Parmar
For this series, I will be posting research that I’ve looked into and posted on my Twitter but wanted to expand and add some more thoughts about the data. Recently I looked into shot selection for all teams based on league average shooting talent. In other words, how good would each team’s offense be if they made a league average percentage of the shots they took, given the type of shots they took? I created two tables which you can see below. The first table accounts for whether or not three-point attempts were contested by the defense, as well as the location of the shot. Doing should tell us both how “good” or “bad” a shot is in terms of location as well as openness.Continue reading “The Muses’ Notebook (Sep. 24)”
Rebounding has traditionally been the domain of big men throughout NBA history. As perimeter players have grown larger and more athletic, however, it has become more and more important for outside players to help out on the boards. Indeed, the last three seasons have even featured a guard (Russell Westbrook) averaging over 10 rebounds per game on his way to averaging a triple double.
Who are the best-rebounding guards in the league? To answer this question, I evaluated the relative difficulty of acquiring each rebound based on whether the rebound was contested or uncontested. By finding the success rates for the offense and the defense on each type of rebound and comparing the expected value with the observed value in each case, I was able to assign an appropriate value for each type of rebound – contested defensive rebounds, uncontested defensive rebounds, contested offensive rebounds, and uncontested defensive rebounds. The result gives us the total rebounding value added by each player. (For a more detailed description of the method for evaluating rebounds, consult The Basketball Bible)Continue reading “Top 25 Rebounding Guards in the NBA”
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+Continue reading “2019 Offseason Crunch”
At this time of year, NBA analysts, fans, and front offices are all concerned with cost efficiency. Free agent season stimulates near-constant conversation evaluating each new contract as a good deal, bad deal, or fair deal. What is the basis of all the conversation, though? To be more specific, what is the standard used to determine whether a player is overpaid, underpaid, or fairly paid? If the standard is subjective, then offseason “grades” merely reflect the degree of correlation between a team’s offseason moves and what I happen to think each player is worth. That correlation is not valuable to anyone aside from me. Nobody else can use grades like that, because the grades only reflect a subjective opinion.Continue reading “How to Do Offseason Grades the Right Way”
The 2016-17 season represented a major inflection point in “The Process,” the Philadelphia 76ers multi-season tank job engineered by Sam Hinkie and widely praised and criticized by nearly everyone with any stake in the league. The beginning of the 2016-17 season saw the Sixers enter with a young core of nine players all drafted or signed under the Hinkie regime. For reference, here is those players’ performance across the subsequent seasons:
|Nerlens Noel||3.7 Wins, 73% Eff., 1047 MP||0.8 Wins, 55% Eff., 472 MP||4.0 Wins, 56% Eff., 1056 MP|
|Joel Embiid||3.8 Wins, 55% Eff., 786 MP||8.1 Wins, 54% Eff., 1912 MP||10.6 Wins, 50% Eff., 2154 MP|
|Dario Saric||3.9 Wins, 46% Eff., 2129 MP||4.7 Wins, 50% Eff., 2310 MP||3.1 Wins, 47% Eff., 2022 MP|
|Jahlil Okafor||2.2 Wins, 45% Eff., 1134 MP||0.6 Wins, 46% Eff., 353 MP||1.8 Wins, 48% Eff., 935 MP|
|Ben Simmons||7.3 Wins, 54% Eff., 2732 MP||6. Wins, 53% Eff., 2700 MP|
|Timothe Luwawu-Cabarrot||1.6 Wins, 45% Eff., 1190 MP||1.1 Wins, 40% Eff., 806 MP||0.6 Wins, 52% Eff., 669 MP|
|Furkan Korkmaz||0.1 Wins, 36% Eff., 80 MP||1.3 Wins, 47% Eff., 679 MP|
|Jerami Grant||2.6 Wins, 45% Eff., 1531 MP||3.7 Wins, 51% Eff., 1647 MP||4.8 Wins, 48% Eff., 2612 MP|
|Richaun Holmes||2.7 Wins, 54% Eff., 1193 MP||1.8 Wins, 52% Eff., 746 MP||1.8 Wins, 49% Eff., 1184 MP|
|Robert Covington||4.9 Wins, 52% Eff., 2119 MP||5.6 Wins, 47% Eff., 2532 MP||2.4 Wins, 51% Eff., 1203 MP|
|T.J. McConnell||3.4 Wins, 56% Eff., 2133 MP||2.9 Wins, 49% Eff., 1706 MP||2.7 Wins, 50% Eff., 1470 MP|