The Anatomy of a Block: Introduction (Part 1)

Blocks are a fundamental statistic in basketball. Along with steals, the number of blocks is often recorded and cited by fans and writers in order to evaluate a basketball player's defense. Generally, fans attribute steals to small and fast guards with quick hands, while blocks are a contribution by tall, high-flying centers and power forwards who can get off the ground quickly. No doubt, such qualities are assets on the defensive end of the basketball court, and racking up steals and/or blocks force the worst kinds of turnovers for the opposing offense, as many of them result in fast break opportunities for the defensive team in transition.

Yet, the number of blocks a player gets is but a summation of a general defensive weapon, and says nothing of the value that the actual block gave to the defense by preventing a basket opportunity on a shot attempt. Sure, players like Hakeem Olajuwon racked up hundreds, even thousands of blocks in their careers to make cases for themselves as one of the best defensive centers in NBA history. And this study is not trying to take away from those exceptional centers who were able to gain much for their teams by swatting away multiple balls on a nightly basis.

However, when it comes down to it, fans look at the number of blocks in a given season and use that as a ranking basis for the best defensive big men in the league. This is under the incorrect assumption that all blocks are the same. Are all blocks created equal? Does blocking a lay-up bring the same value as blocking a jump shot?

A study by a professor at the University of Chicago Booth School of Business named John Huizinga (you may know him as Yao Ming's agent) shows that, no, not all blocks are equal. At this year's MIT Sloan Sports Analytics Conference, Huizinga presented a paper titled "The Value of a Blocked Shot in the NBA: From Dwight Howard to Tim Duncan." In it, Huizinga explains how he and Sportsmetricians Consulting's Sandy Weil developed a database called Chances, using data provided by STATS, LLC. to compile the context of each event for the past 7 years, events such as blocks. The idea for this database is simple: instead looking at box scores, look at play-by-play accounts of the game in order to ascertain individual offensive opportunities. As Sandy Weil explains at his website, the two believe that "chances" are a very useful unit of account for many types of analysis of basketball. It allows you to easily sort and filter the context of an event, for instance, what happened before a shot attempt or what happened after a steal.

One of the key concepts that Huizinga presented about in order to understand the value of blocked shots was the preblock situation. This is basically what happened before the block occurred. As Sebastian Pruiti explains at NBA Playbook, this allows the analyst to differentiate between a block of a layup coming off a fast break opportunity vs. a block of a long off-balanced two-point jump shot, understanding that the former is more valuable than the latter. The idea that these two types of blocks are different comes from the fact that all shots taken have its own values, whether the shot was a slam dunk or a turnaround jumper. This leads us to expected point value, and since every block is attributed to a shot, the value of a block is naturally related to the expected point value of the shot attempt.

Looking at Pruiti's recap of the presentation, Huizinga closed his thoughts by going over what he calls "block value." To quote Pruiti, "to determine block value, Huizinga used the formula Points Saved + Points Created where Points Saved equals the effect of a Block on Opponents Expected Points during this possession and Points Created equals the effect of a Block on Own Team’s Expected Points during the next possession." This formula allowed Huizinga to determine overall block value, a better indicator of who was the best "shot blocker" in any given season.

Without having the benefit of the same database and viewing of Huizinga's paper (I can't seem to find it on Google, if it is online), I decided to take the idea Huizinga hatched and to do my own analysis with Basketball Geek's PbP data from the past four seasons. Thanks to Ryan J. Parker's hard work, there is a wealth of shots data in this PbP dataset, from the location of each shot to the shot type (ranging everything from turnaround fade away to driving reverse layup to putback dunk).

Whereas Huizinga looked at both points saved and points created (blocks that lead to fast break points, for instance), I looked at only points saved. I've developed two models for estimating the value of blocked shots (I will dedicate one post to each):

1. Points saved per block by shot location
2. Points saved per block by shot type

For the first model, this distinguishes blocked shots at rim (layups or dunks) vs. blocked 3-pointers or long 2s. Based on shot location, we can look at the value of a shot in any X and Y coordinate on the basketball court based on four seasons of data, and attribute each block to its corresponding value based on shot location. Adding this all up, we can determine which players saved the most points per block based on their shot location.

For the second model, this distinguishes blocked shots based on shot type, so dunks from layups, turnaround jumpers from pullup bank shots, driving reverse layups from tip-ins. Each block is attributed to a corresponding shot type value for which the shot was blocked. Adding this all up, we can determine which players saved the most points per block based on their shot type (with the added bonus of which players were the best at blocking dunks, layups, 3s, or mid-range jumpers).

In my next few installments of (at least) two parts, I will look at some of the findings I found from each block value model.

Finally, as an ode to the work that has been done by John Huizinga and Sandy Weil, here are some of their findings from what I could gather up that I may refer back to in my next posts (paraphrased from NBA Playbook and Peter Keating on ESPN Insider):

• A jumper has an expected point value of 1.04.
• A layup has an expected point value of 1.54.
• 69% of Brendan Haywood's blocks were jumpers (31% layups).
• 91% of Jermaine O'Neal's blocks were layups (9% jumpers).
• Tim Duncan saved 1.12 points per block in 2008 (best season).
  
• Dwight Howard saved 0.53 points per block in 2008 (worst season).
  

This introduction post is dedicated to Huizinga's work with Weil. Hopefully my findings will agree with and add on to theirs.