Think Blue Crew

In case you missed it, check out Parts 1, 2, 3, and 4 in this series on "The Anatomy of a Block": Introduction, By Shot Location, By Shot Type, and Repeatable Skill.

This is a long post coming up, but I hope you try to read it to the end, as I believe I uncovered the most interesting findings so far in this study. I mentioned last post that I would be doing a summary of my findings and concluding with improvements and possible future study ideas on the value of a block. Turns out that there is a lot more to work on and a lot more ahead, but for the time being, this will be the last post in this series on "The Anatomy of a Block," which will almost certainly be re-continued sometime in the future.

It's amazing what social media and the strong online basketball community has been able to help me with in terms of understanding the merits of this study, but much more so the limitations and areas for improvement. In the end, I believe the analysis on the value of blocks based on shot location and shot type may be worth it, but that it is limited in assessing the defensive value of players, and even assessing the value of a block itself. There are a host of other factors that go into determining the quantitative value of a block and its effects on the game, not just shot location and shot type. I suppose this is a consequence of every area of research, in that an examination of one part of the analysis will never be complete and always has room for improvement.

Before I go into the other components to take into account when evaluating the value of a block, let me first address a few problems from my previous posts, thanks to the critical evaluations of readers. My initial idea of assigning a number to a block based on shot location and shot type eventually came up with an average value of a block for each shot-blocker, and hence, my main analysis revolved around measures in the units of PPS (points per shot). To recall, I looked at points saved per block by shot location and points saved per block by shot type. I did not give credit to the actual quantity of blocks amassed by the Marcus Cambys and the Dwight Howards when discussing the PPS values, and I realize that I should not have neglected it. Even if Dwight Howard does not get as high value per block (based on location or type) as Amare Stoudemire (both with over 10,000 minutes played since 2007), it would be misguided to suggest that Stoudemire was more valuable from the shot-blocking perspective than Howard when Howard's 791 blocks since 2007 (2.43 per game) is much greater than Stoudemire's 413 blocks (1.40 per game).

Here's a tabular reproduction of the top 25 shot-blockers in total blocks since 2007 with additional columns of points saved per 36 minutes played for shot location and shot type as well as a look at the top and bottom shot-blockers in that category (minimum of 200 blocks and 1.00 blocks per game since 2007):








I sorted by shot location in the top and bottom points saved per 36 MP because it's generally the same as by shot type (and also because I found R2 values of 0.40 and 0.18 respectively for season-by-season fluctuations, meaning that shot location seems like the more reliable measurement for points saved by block). Factoring in the total number of blocks given the amount of minutes a player plays changes our evaluation of who attains the most value from blocks on a per unit time basis. Marcus Camby is one of the noticeable top shot-blockers in this measurement, as well as Alonzo Mourning and Chris Anderson, two players I talked about in previous posts. Take note that Dwight Howard, despite averaging 2.43 BPG, was 10th in points saved per 36 MP. Looking at the bottom guys, guys like Chris Bosh who had high value per block get penalized for only getting just over 1 block per game, while guys like Pau Gasol, Amare Stoudemire, and Elton Brand post low points saved per 36 MP while averaging around 1.40-1.70 BPG.

Now here's the real meat of this exercise, something I've only touched briefly on previously. To summarize what we've looked at thus far, we tried to figure out the value of a block based on shot location and on shot type, forming a "Points Saved" model. However, there are several problems and possible areas of future research I see:

• Probability of shot-blocker to commit a shooting foul
• Probability of shot-blocker to commit a goaltending violation
• Blocks per block attempt and/or block opportunity
• Altering a shot without recording a block
• Keeping a player away from the basket and forcing tough shots

The first two are definitely possible to take into account for each shot-blocker. The last three, however, must be noted when using the shot location and shot type models in this series. Some players may block low percentage shots precisely because it is better to force a tough shot in the first place. This includes forcing the shooter to take a shot out of position as well as altering the shot type (turning an open jump shot into a fade away, or turning an easy layup into a reverse layup).

Ideally, the numbers presented in this series should be taken with a grain of salt, and combined with what you see when you scout video with your own eyes. If a defensive player is very good at keeping the guard out of the paint, he is doing his job of forcing the guard to find other opportunities for points rather than taking the high percentage shot. And if the guard goes up for a difficult shot, and the player blocks it, this is the preferred defensive strategy than to block a high percentage shot, even though he is penalized by the shot location and shot type models.

The other side of blocks that I mentioned in the introduction of this series (and also a part of John Huizinga and Sandy Weil's work) is the "points created" part, which is the effect of a block on the shot-blocker's own team's expected points during the next possession. This part may be perhaps the most valuable side of a block that I haven't accounted for, in that blocks that lead to turnovers and create fastbreak points from transition baskets (known as "Russells") are more valuable than blocks that end up back in the offense's hands. Probably the part of the points created side that affects the value of blocks the most because of both its per block value and its total value is number of changes in possession, or possessions gained.

I'd like to take a preliminary look at points created by looking at who has possession after a block. The way I see it, the immediate post-block situation after any blocked shot falls into three categories in terms of possessions:

• Possessions gained by the defense (the shot-blocker's team)
  
• Possessions recovered by the offense
• Jump ball

If we consider that possessions gained as one possession, no change in possession as zero, and a jump ball as 0.5 possessions (50% chance for either team to get possession on a jump ball), we can formulate the average possessions gained per shot-blocker and per block.

Let's look at the top 25 block totalers as well as the top 15 and bottom 15 shot-blockers in possessions gained per block between 2007-2010, along with their season values to see if these numbers are consistent (consider that 57% is the average possessions gained per block):









Lots of revealing stuff going on here. Remember how I touted Chris Anderson for his points saved per block numbers? Looks like he'd be among those in the bottom of the league for a points created per block model, as his blocks resulted in a possession gained only 51.39% of the time, the worst value since 2007 for the players sampled. Lamar Odom tops it off with 65.84% since 2007, while the best season was Joel Przybilla in 2007 with 70.90%. Andris Biedrins had the worst season with a low value of 42.22% this past season, and was mainly average in his previous three seasons. The other player I noted who performed poorly in the points saved model was Brendan Haywood, and he's among the league leaders in possessions gained per block with 61.89% (again, compare this with the league average of 57%).

Finally, how do we combine the points saved model (using shot location and ignoring shot type) with the points created model, which is in the form of possessions gained? Using the league points per possession values for each season between 2007 and 2010, I calculated points created for each possession gained, then added it to the points saved. Let's take a look at some of these numbers since 2007 (minimum of 200 total blocks and 1.00 blocks per game since 2007):







Alonzo Mourning may be the best shot-blocker in the past 5 years, if not one of the best in our generation as his blocks are worth 6.528 points per 36 minutes played. Even though he averaged less blocks per game (2.16 BPG) in this last two seasons, he beat Marcus Camby (2.79 BPG) by over 1.5 points per 36 MP (6.528 pts/36MP vs. 4.920 pts/36MP). Chris Anderson is second, who more than makes up for his low possessions gained per block with 2.12 blocks per game, reaching a block value of 6.138 points per 36 MP. Guys like Joel Anthony and Ronny Turiaf also have high block value given their minutes. For the players sampled, the lowest points saved and points created yields players like Chris Bosh, Kevin Garnett, Amare Stoudemire, and Pau Gasol, players who averaged total block values less than 2.5 points per 36 MP since 2007.

This is my longest post yet in this series, but if you made it this far, I'm glad that you did and I hope you enjoyed what I found. I believe that this is only the starting point to analyze the value of blocks better, and this at least provides more information than total blocks or blocks per game in determining who are the best shot-blockers in the NBA. One thing of note is that I found very little season-by-season correlation in the possessions gained stat, which indicates that points created from blocks may fluctuate too greatly from year to year to be attributed fully to a player's shot-blocking skill. Still, these numbers can help us understand the value of blocks from past seasons better. I would still like to reiterate that numbers in measuring block value are not enough, and that they should be evaluated in conjunction with professional video scouting, especially in a dynamic team game like basketball where defense is many things other than blocks.

That's it for this series on the value of a blocked shot, at least, for the time being. If there is something you like (or didn't like), I welcome you to leave a comment or two. I'd like to put this project to rest (or on hold) for awhile, as there are other things in sports that I would like to write about. Nevertheless, I hope you enjoyed this series as much as I have.