Drip

Featured

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.

Continue reading “Matchup-Based Defense”

Who is Really the Best Defensive Center in the League?

 

As with the previous posts in this series which detailed the best defenders at each position, I start with the question “Who are the best defensive centers in the league?” In order to address the question properly when evaluating centers, however, it is necessary to answer a prior question. What is a center’s defensive role in the modern NBA? During some prior eras, the center could remain near the basket either defending a fellow behemoth on the low block or walling off the path of opposing drives. Rule changes constricted the scope of zone defense. Then, the 3-point shooting revolution caught even big men in its tantalizing web. For the first time in basketball history, forces conspired to pull centers away from the basket for good.

Photo by Rick Bowmer, AP

As always happens in games of strategy, however, there were side-effects to stylistic changes. The spacing afforded by stretch bigs has meant that dribble penetration continues to be an integral part of every team’s offensive attack. Having fewer big bodies in the way makes driving easier. As a result, the role of “rim protector” is still very much alive. Teams in today’s NBA still need interior defense from their center.

At the same time, teams also need their center to be able to cover quicker opponents on the perimeter. The center’s role now lies somewhere in between the inside and the outside. When we ask who the best defensive centers are, we are really asking two things: 1) Who is the best rim protector? 2) Who is the best at defending on the perimeter?

Continue reading “Who is Really the Best Defensive Center in the League?”

Failure to Launch

 

by Alan Moghaddam

The Houston Rockets are at a point of critical failure – or rather, they have reached a crux with more than one point of critical failure.

The Rockets’ record stands at 13-7 roughly a quarter of the way into the regular season. It has finally been long enough to shake off the “it’s too early to comment” crowd, and we can finally make some valid statement about this team long-term. To all appearances, things are not looking particularly spectacular for a team that entered the season with championship expectations.

Continue reading “Failure to Launch”

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
Continue reading “What is TPA?”

A Prediction Model for Sports Match Results – Part 2

 

Introduction

As outlined in the first post, this installment of the series will be about the model’s estimates and predictions. I will also touch on how to compare predictions between models. For all that to work, I will have to introduce a few mathematical definitions. In a similar way to the first post, I will give both an intuitive explanation of every concept and a mathematical explanation.


The Estimates section will begin with an explanation of how the model fits the data (with a few graphs to show the process of finding the estimates for last season’s NBA), followed by a brief introduction on Bayesian inference. Next, the Model Comparisons section will define the predictive likelihood of a model, and how to calculate it. I will also discuss a few different ways to compare predictions. Finally, in the Discussion section, I will offer critiques of the work presented in this post.

Continue reading “A Prediction Model for Sports Match Results – Part 2”

The Best Defensive Power Forwards in the NBA

 

Small ball is no longer the wave of the future – it’s the wave of the present. The 80’s and 90’s featured brawny, bruising power forwards who could soak up punishment in the post, clean the glass, and protect the paint as weak side shot blockers. In a game dominated by giants, power forwards were the centers’ sidekicks. Even the beginning of the 21st century saw the San Antonio Spurs’ “Twin Towers” follow the same frontcourt structure that had held sway throughout the league’s existence.

AP Photo/Eric Gay

Change, though, is persistent. Stylistic and strategic changes alter the face of the league regularly. In the last decade, those changes have eroded the physical profile of the standard power forward. Horace Grant has given way to Jerami Grant. As stretch 4’s have gradually replaced the 4’s of yesteryear, the function of power forwards on the defensive end has changed considerably. As with all changes related to lineup construction, the small ball revolution is an arms race. The more stretch 4’s there are who can shoot from outside, the more teams need their “power forwards” to defend on the perimeter. Today’s power forward must be able to stay in front of some opponents outside while still holding his own inside.

Continue reading “The Best Defensive Power Forwards in the NBA”

A Prediction Model for Sports Match Results

 

by André Vizzoni

Introduction

In this post, I will introduce a prediction model that was the product of a research project that spanned four years (winning a few awards) and was the final project for my degree in Statistics. For those interested in the final project, here it is – though I warn everyone in advance that it is in Portuguese. The objective of this post, then, is to translate the most central parts of that project to English while, at the same time, talking about applications of the model to basketball data, since the original project used soccer data.

First, I will give an intuitive explanation of the model, with no equations or mathematical concepts introduced. Next will come the methodology section, where there will be a lot more maths and formal definitions. As such, people who are interested only on the intuitive definitions might wish to skip the methodology section). The idea behind this structuring of the post is for it to be understandable, both by laypeople and by people well versed in statistics. Finally, in the discussion section there will be a few summary comments, as well as a preview of things to come on this site.

Continue reading “A Prediction Model for Sports Match Results”

The Best Defensive Small Forwards in the NBA

 

In the last installment of this series, we evaluated the best defensive shooting guards. We noted that the shooting guard group is crucial in the modern NBA due to the advantages gained by employing versatile defenders capable of stopping opponents of different sizes and skill sets. The same rationale applies for the players in the “small forward” bin using basketball-reference.com’s play-by-play position designations. When compared with the previous group, the main difference is that the small forwards are larger. (perhaps we should start calling them “big wings”?)

Wings, whether they are categorized as “shooting guards” or “small forwards,” exhibit greater spread in the defensive load they carry than other position groups do.

While the median values are relatively consistent across positions, wings have a wider distribution than other positions. Raw defensive load for wings can range from very high (>15 ppg) to very low (<6 ppg). Other positions, especially interior defenders, have much more compressed distributions.

Continue reading “The Best Defensive Small Forwards in the NBA”

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.

Continue reading “Who Are the Best Defensive Shooting Guards in the League?”

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?

Continue reading “Who are the Best Defensive Point Guards in the NBA?”

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.

Continue reading “Steals Don’t Mean Squat”