Clayton Kershaw's Slider Is Sick-Nasty

[Edit 2: Since the original post, I found that some of the later plots had incorrect parameters, and therefore, should be ignored. They do not accurately represent Kershaw's swinging strike and contact percentages. You can take a look at the updated swinging strike percentages for his slider and curveball here.]

Sir Clayton Kershaw, he of the curveball nicknamed by Vin Scully "Public Enemy No.1," has been mighty magnificent this season with a 3.03 ERA and 163 K's through 157.1 innings (a side note: I would have linked the Youtube clip of Scully's call of Kershaw's huge curveball striking out a stunned Sean Casey during March 2008, but it was gone a week or so after I saw it over two years ago).

[Edit: Here's a grainy video of Scully's call at metatube, hat-tip to Bill Plaschke's sweater.]

But I believe that it's really been Kershaw's slider that has been helping him so much as an additional new toy for him to fool batters with. It's always great to add another pitch to a tandem arsenal like Kershaw's fastball/curveball combination, and the effectiveness of Kershaw's slider really shows how Kershaw has another weapon to induce swinging strikes. According to Fangraphs, Kershaw threw his slider on 7% of pitches in 2009 but 19% of pitches in 2010 so far.

Let's conduct a closer examination of Kershaw's pitch types since his ML debut by looking at the month-by-month table below to show when Kershaw introduced his slider and how much he's used it since then:



Keen Dodger fans have taken note of Kershaw's use of his new slider, but I didn't know he started using it so much, or significantly more than his famous curveball. If you check this out, you'll notice that he introduced his slider sometime during June 2009. Since then, he's gradually used his slider more and more and his curve ball less and less. This doesn't mean that his slider is more effective than his curve ball, as part of a pitcher's effectiveness is his ability to mix and match and locate different types of pitches given the previous pitch (for what it's worth, Kershaw's pitch type values at Fangraphs show that his above-average slider has been more valuable than his below-average curveball this season, attaining a 1.63 wFB/C and a -0.82 wCB/C, but please ignore those numbers if you don't know what they stand for).

Let's take a quick look at the pitch movement plots of Kershaw's curveball and slider:

Looks like Kershaw gets a lot of movement on both his curveball and slider, but it's the vertical movement of his curveball that sticks out here. That 12-6 break that everyone always talks about has really made his curveball into a filthy moving pitch. The slider isn't too shabby either, with huge horizontal movement to the left (from the catcher's perspective).

This brings up some interesting questions I have about Kershaw. Suppose I want to take a look at the effectiveness of Kershaw's curveball vs. Kershaw's slider (and yes, I do). For a few weeks now, I've been playing with my PITCHf/x database and learning to make pitch location plots, including the hexagonal binning plots you see above. However, it's difficult to compare between two types of pitches for the same criterion, say, swinging strikes on Kershaw's curveball vs. swinging strikes on Kershaw's slider, especially for normal scatter plots, because all you see are a bunch of dots (see my recent post about Jonathan Broxton and you'll know what I'm talking about). You can get information about where Kershaw locates his two pitches to induce swinging strikes, but you can't compare how well Kershaw does it with his curveball vs. his slider just by simple scatter plots. I suppose I could just show a table of Kershaw's swinging strike percentage by pitch and by month, but it's more telling and effective (and fun) to see the pitches in the strikezone for yourself.

Enter Dave Allen and Jeremy Greenhouse. I believe the idea of plotting PITCHf/x data in the form of heat maps was first popularized by Allen, and Greenhouse has also done fantastic work with heat maps of his own. With the help of both of these guys, I was able to figure out how to fit a surface model and plot them on filled contour plots. In this case, instead of plotting the actual curveballs that Kershaw pitched that caused a swinging strike, I ran a model to predict swinging strike percentage based on location, looking at all of Kershaw's curveballs and how often those pitches caused a batter to swing and miss. Creating a filled contour plot, much like what you see in infrared imaging and topographical elevation mapping, allows me to smooth out the data in order to see if there are any trends.

It's fitting that I make my first of these images for the Dodgers' best (read: best) starting pitcher, Clayton Kershaw. Let's take a look at the first of them, shall we? Here's Kershaw's swinging percentages on curveballs:



Note that there were 901 curveballs against RHH and 235 curveballs against LHH. I scaled the colors on the graph to range from 0% to 60% (shown as 0.0 to 0.6 in the legend) so that you can compare these with the slider graphs later, red being a very high swinging strike percentage, blue being near zero swinging strike percentage. I suppose the scale would be better read if I put them in percentages, but I'll discuss these values in 0-100% terminology rather than 0.0-1.0.

Let's discuss Kershaw's curveball. First of all, recall that Kershaw has great negative vertical movement as well as negative (left in the catcher's view) horizontal movement. As a left-handed pitcher, curveballs will go down and toward right-handed hitters and down and away from left-handed hitters. If you take a look at the bottom lefthand corner for left-handed hitters, you'll notice a green "hill," showing that Kershaw gets up to 30% of LH batters to swing and miss out of the zone when he throws his curveball there. The middle of the zone and low outside the zone actually get left-handed hitters to swing and miss the most. And as expected, LHH whiff more often overall than RHH on Kershaw's curveball.

However, the most interesting part is where you see a shade of green in the middle of the strike zone for right-handed hitters, where about 45-50% of RH batters whiff. But there's a blue spot to the immediate left of Kershaw's "hot spot," which looks like the spot where right-handed hitters get the sweet spot of the bat (or at least, contact). With a curveball as huge of a vertical break as Kershaw has, sometimes there's not much a hitter can do when he sees it coming toward the strikezone but to swing and hope it connects with the ball. In this case, that blue spot might just be where the batter swings and connects most often (and where Kershaw should avoid with his curveball).

Let's compare this with the swinging strike percentage of Kershaw's newest pitch, his slider, again looking at handedness splits:



Note that there were 473 sliders against RHH and 188 sliders against LHH. There's a lot more color in this plot, which tells us right away that Kershaw's sliders are getting more batters to swing and miss than his curveballs. The entire outside edge for lefties has up to 60% swinging strike percentage when batters are fooled and swing and miss, while the middle of the strikezone causes even more whiffs for righties. Low sliders also look like they fool both RHH and LHH.

Remember that these are models, not the actual pitches themselves, and that in order to get this graph to look smooth, the model may have overemphasized some hot spots. This is due to sample size. Still, it's very clear from these graphs that hitters swing and miss on a higher percentage of Kershaw's sliders than his curveballs.

Swinging and missing a lot means less contact. Let's take a look at hitters' contact percentage on Kershaw's curveballs and sliders, seeing if they agree with the previous swinging strike percentage plots:



Some good stuff here. Not sure what to make of the long red streak down the middle of the RHH graph, but perhaps that's where right-handed hitters make contact (fouling off pitches possibly?). Right-handed hitters make contact throughout much of the strikezone, around 30% to 45%, while left-handed hitters make contact mainly in the middle and/or up in the zone or out of it. I believe the red spot (50-60%) out of the zone is where many left-handed hitters foul off hanging curveballs. Either way, hanging curveballs that are up in the zone, even out of the zone, do not bode well for Kershaw, as they cause both righties and lefties to make decent contact.

Let's compare this with the contact percentage of Kershaw's slider, again with handedness splits:



At first glance, it looks like that hitters get more contact off Kershaw's sliders due to the red. But notice that most of the red for both RHH and LHH is out of the zone, likely meaning that although hitters make contact 60% of the time or more, they are probably mostly foul balls or pop-ups. Still, it's a vulnerable spot to leave a hanging slider. But notice that there is very little contact in most of the strikezone (the lucky ones who do get hits off Kershaw's best-placed sliders are drowned out by the many many more unlucky ones who don't). At the very least, Kershaw's sliders do not draw as much contact, and presumably hard contact from hitters as compared to his curveballs.

Remember how I mentioned earlier that Kershaw's curveball this season has been more valuable than his slider, according to Fangraph's run values? All of the plots seem to agree that Clayton Kershaw's newest pitch is even more effective than the one he is most known for. Not only does Kershaw's slider cause more batters to whiff than his curveball, but hitters are less likely to make contact off his slider as well. Maybe there is a new Public Enemy No.1 that less people are talking about. I prefer to call it Kershaw's sick-nasty slider.

What's Wrong With Jonathan Broxton?

Never mind that the Dodgers are 4th in the NL West and over 11 games behind the Padres. Jonathan Broxton, known early on as "The Ox," has lost his closer role due to three blown saves in July and August. He has been wild, uncharacteristically wild. While he's still striking out a ton at 11.4 per 9 IP, he has walked more batters than struck out since the All-Star Break, resulting in a post-All-Star 8.10 ERA and 2.10 WHIP.

Every pitch ends in five distinct outcomes: ball, called strike, foul, in play, and swinging strike. I wanted to take a month-by-month look at these five outcomes of Broxton's fastballs and sliders since 2007. If we take a look at the percentages that each outcome occurred by month, maybe we can glean some information about what's wrong with Broxton. But I realized that the sample size for each month got pretty small, so instead, let's take a look at the average velocity and result of Broxton's fastballs by two-month periods (October games included with August and September) since April/May 2007:



First of all, take note Broxton's fastball outcome breakdown is not the end-all, be-all of his troubles. Command issues and being hit hard can be separate things, as struggling with command could be due to injuries and the like while being hit hard could be the result of a high BABIP, just plain bad luck. Still, this information should tell us something about Broxton's recent struggles as well as the dominant months of his career. First thing to notice is that Broxton's average velocity is consistently down compared to recent years. His fastball was hovering around 95.6 MPH during the first two months of this season after consistently averaging above 97 MPH in the previous two seasons. It seems to have gotten worse as the season has gone on (and the walks and runs started piling up), and Broxton is averaging a flat 95 MPH in August. This is a significant loss of velocity for his standards of hitting 98 MPH on the gun consistently.

The second thing I notice is Broxton's swinging strike percentage vs. his ball percentage. It's particularly off so far in August, with Broxton throwing balls 55% of the time compared the 30-35% in previous two-month periods. His swinging strike percentage is low, as well as his foul ball percentage. Batters are actually making less contact off Broxton this month, but that is misleading due to the high number of balls he's been throwing outside the strikezone. Sure, Broxton has only allowed 9.1% of balls in play in August thus far, but compare this to the other period that he allowed less than 10% of pitches to be put in play, and you'll recall that during that period in April/May of 2009, Broxton had 39 K's in 20 IP with a 1.44 ERA and holding batters to a .096/.200/.145 line. This is very different from the 6.75 ERA and .259/.394/.426 line recently since the beginning of July. Broxton's high ball% on fastballs has allowed runners to get on base, and his loss in fastball velocity suggests that hitters are able to get more solid contact off his pitches when they do.

Let's look at the average velocity and % of outcomes of Broxton's sliders now:



Broxton throws about 2/3 less sliders than he throws fastballs, which decreases the two-month period sample sizes by a lot, returning the volatile results you see above. Still, it's plain to see that Broxton's slider velocity is also down, around 86-87 MPH rather than the norm of 88-89 MPH. This is concerning, because Broxton's slider is effective and fools hitters into swinging out of the zone when it has the most movement, and his decrease in speed might indicate that he's also lost movement. As a result, since June, Broxton's slider has resulted in swinging strikes only 15% of the time, compared to the usual 25-28% when he is on top of his game. It may be interesting to note that 30% of Broxton's sliders in August were put into play and only 5% were fouled off, but the sample size here only consists of 20 sliders, so take that with several grains of salt.

Finally, let's just take an overall look at the pitch locations and movements of Broxton's four-seam fastballs and sliders this year vs. previous years:

Lots to digest here and some interesting stuff going on. First of all, I'm definitely still in "experiment mode" with R plotting, as you can tell by the paint splatter all over the legend in the fastball location plot. Also still trying to figure out how to get hexbin plots on the same graph, in order to show a gradient color scale instead of just a blotch of singular-colored paint.

Aside from that, let's focus on the pitch movement plots instead of the pitch location plots (there are just too many pitches in the pitch location plots to glean any meaningful information from non-splits data except, maybe, that Broxton throws his slider low and away from right-handed hitters). The four-seam fastball movement plot is very very telling. I noted earlier that Broxton's velocity has decreased this season compared to previous seasons, and this plot shows that Broxton's fastballs are not moving as much as well. It's a pretty significant difference. If Broxton's loss in velocity can affect his "rising effect" movement on his fastball that much, it makes it that much easier for hitters to get around in time AND to make solid contact on this pitch.

The slider movement plot is similar. There is less vertical movement on the 2010 sliders (blue) than all of the other sliders from 2007 to 2009 but similar horizontal movement. It's clear to me that both Broxton's four-seam fastballs and sliders have been more ineffective this 2010 season than previous seasons, largely because of their loss in speed and hence movement. This resulted in the disparity we saw in swinging strike percentage this year and previous years, and is a precursor to the hard hits off Broxton. Along with the loss of control that Broxton has had this season, throwing more balls than ever before, this has resulted in Broxton's ineffectiveness this season (at least compared to the stellar campaigns of previous years).

Frankly, Broxton just hasn't been the same pitcher, and it's pretty blatantly showing up in both of his main pitches, his four-seamer and the slider (I didn't look at changeups because he doesn't throw that many). There are all sorts of reasons for why this has happened, and to be honest, I think it's on the coaches and pitching coach Rick Honeycutt to figure out what's wrong with Broxton's pitches. If there was an internal mechanics change from spring training or something, they have to recorrect it back if they want Broxton to be a top NL closer again. Maybe he's nursing an injury and Broxton and/or the Dodgers are hiding it. Sure, taking Broxton out of the closer role and putting him in the time-out chair might help him settle down, but fundamentally, Broxton has been a different pitcher as of late and needs to correct whatever it is that's wrong. I feel that these location and movement issues are what the Dodgers and the media should focus on, rather than his "makeup" or his "fear of Carlos Ruiz" or that "he isn't a winner" and "doesn't have what it takes" etc. etc. etc.

The Anatomy of a Block: Points Created (Part 5)

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.

The Anatomy of a Block: Repeatable Skill? (Part 4)

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

In this post, I'll take a look at whether or not value of blocks as measured by shot location and value by shot type is a repeatable skill. The premise is that if a skill in sports is measured effectively, then there should be reasonable expectations that the statistic measuring the skill will remain consistent from year to year, making the skill repeatable. One of the tests of the value of a statistic in objective evaluation is looking at how much that statistic varies with time for each player. Essentially, if a set of numbers fluctuates from year to year for not just one but most players, then that provides evidence that a certain skill (such as blocking certain shots based on location or type) may not be repeatable. This type of analysis looking at the correlation between what a player does one year with what a player does the next year is used in preliminary studies before determining the existence of the hot hand, clutch hitting in baseball, and how much control a pitcher has over the number of hits he allows.

Here's a look at the block value by shot location of the top 25 shot-blockers in total blocks since 2007 along with their season-by-season values to see if they fluctuate:



The color scale of the cells should give you an idea of how much points saved per block by shot location vary from season to season for these 25 players. For instance, if you look at the '2007' column and the '2010' column from left to right, you can see the colors of the cells change or stay the same, particularly for players with high value blocks such as Andrew Bogut, Emeka Okafor, and Josh Smith as well as players with low value blocks such as Andris Biedrins and Brendan Haywood. You can also notice some players with a little bit higher statistical fluctuation in their season-by-season value per block numbers, such as Ronny Turiaf. The standard deviation column on the far right is a measure of how spread out these numbers are, and captures a sense of how much the numbers fluctuate. A further discussion on the validity of this measurement gets into a statistical discourse covering other stats terms that I won't get into right now, but for the most part, these standard deviation values are relatively small and speak that the data is uniform from season to season (more likely that blocking shots based on location is a repeatable skill) more than it is volatile (less likely that the numbers are affected too greatly by random fluctuations).

Now here's a look at the block value by shot type of the same top 25 shot-blockers in total blocks since 2007 with their season-by-season values:



Again, the color scale of 2007, 2008, 2009, and 2010 shows mostly consistency from left to right, save for a few players. This time, Jermaine O'Neal has the most fluctuations in his values by season, ranging from 0.903 PPS in 2009 to 1.102 PPS in 2007. Other than O'Neal, however, it seems to me that shot-blockers with high value per block tend to have high values for all four seasons, and vice versa with shot-blockers with low values. Brendan Haywood did attain a 1.011 PPS by shot type in 2007, but followed that with some of the lowest PPS numbers with 0.839 in 2008 and 0.847 in 2009.

All of this brings me into another thought about whether or not block value by shot location is correlated with block value by shot type. Let's look at a scatter plot of value by shot location against value by shot type using all 122 players I sampled:



R2 value of 0.3952 is decent, which is a measure of how correlated the two sets of data are, ranging from 0 to 1 where 0 is completely unrelated. However, shot location and shot type may speak to different types of blocks and skills needed in order to attain those blocks, even if different shot types are precisely defined by where they are taken on the court (all dunks and layups are at rim and 3-pointers are at a distance, for instance). Also, I believe a more intrinsic evaluation on the interaction effects between types of shots and where they are on the court would need to be conducted in order to properly assess how the block value models affect one another and combine both models.

I'm sure many of you think a lot of this is just statistical blabber jabber, and I do too, so I'll just leave it at that for part 4. In my next and last post on this rather long series, I will be concluding with a summary (in words, not tables and charts and numbers) on what I found and detailing an exhaustive list I have compiled on the limitations of the study on blocks as well as possible future improvements and extensions. I hope to be getting back to doing more posts on what I've been working on with shot location heat maps, baseball PITCHf/x, and football play-by-play data, so in a sense, I'm rushing along to get this series done ;).

The Anatomy of a Block: By Shot Type (Part 3)

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

In this post, I'll take a look at the value of a blocked shot based on the shot type. The first thing to figure out is what type of shot types are recorded in the PbP dataset provided by Basketball Geek (I really can't stress enough how thankful I am that Ryan J. Parker provided this data). I grouped every shot with a recorded shot type from the 2007-2010 seasons and found the number of shots taken as well as the total points scored in order to figure out points per shot by shot type (there are 63 different types of shots in the PbP data). Let's look at several lists of shot types: 1) Most shots taken, 2) Highest points per shot, and 3) Lowest points per shot.

Big top 15 tables there. I've added effective field goal percentage (eFG%) which is just field goal percentage taking 3-pointers into account (however, I will be talking in terms of PPS rather than eFG% in order to remain consistent throughout this study). Most shots are categorized generically, for example, jump or layup or dunk instead of the more specific like jump bank hook or turnaround finger roll or putback reverse dunk. It's definitely interesting to see how many driving layups and reverse layups are distinguished from the generic layup.

In the second table, clearly dunks and layups of the eclectic variety return the most points per shot, with several thousand slam dunks averaging 1.956 points per shot (note here that an alley oop dunk averaged 1.814 PPS through four seasons of data, so next time your team messes up an alley oop dunk, it's alright to throw a tantrum). In the third table, generic jump shots (0.692 PPS) and hook shots (0.907 PPS) are the least efficient shots by this measure, but it's also important to note that the generic layup (0.914 PPS) is a close third. In addition to the 118,804 generic layups recorded, there are more than 69,000 other categorized layups as well, all of them being high value shots within 1.3-1.7 PPS.

Now let's take a look at which shot types result in or avoid being blocked the most: 1) Most shots blocked, 2) Highest % of shots blocked, and 3) Lowest % of shots blocked:







The second and third tables are the truly interesting ones here. 19.73% of all generic layups were blocked in 2007-2010, while 13.65% of all driving jump shots were blocked. Several other types of layups were also blocked more than 5% of the time. For these driving and reverse layups as well as the generic dunk, having efficient point values ranging from 1.345 PPS to 1.749 PPS are more important numbers than the block percentages. For instance, even though a guard driving in only to get blocked can be infuriating, keep in mind that a successful driving layup attempt goes in 73% of the time even if it is blocked 7.57% of the time (by successful, I mean successfully penetrating the defense up until the shot). Check out the end of this post to see the entire list of shot types.

Now let's go back to the players. Similar to the blocks by shot location post from yesterday, I took the points per shot for each shot type and multiplied that by the number of blocks of a particular shot type for each player. Summing it up, I found the total number of points saved by shot type and the points saved per block. Let's take a look at the top 25 shot-blockers in terms of total blocks since the 2007 season and see how they fared in points saved per block:



The values for points saved per block by shot type here actually vary more than the values by shot location I looked at yesterday. Here, Josh Smith (1.027 PPS) and Erick Dampier (1.022 PPS) saved the most points per blocked shot based on shot type in this top 25 list, while Andris Biedrins (0.860 PPS) and Brendan Haywood (0.875 PPS) saved the least, just like the table by shot location. Moving along, let's look at both the top 10 and bottom 10 shot-blockers in points saved per blocked shot based on the shot type (minimum of 200 blocks since 2007):





Chris Bosh (1.119 PPS), Joel Przybilla (1.112 PPS), and Chris Anderson (1.090 PPS) come out on top again, all three being among the top 10 in value by shot location. Andris Biedrins (0.860 PPS) and Andray Blatche (0.865 PPS) were also in the bottom 10 in value by shot location, but it's guys like Biedrins, Haywood (0.875 PPS), Roy Hibbert (0.876 PPS), and Shaq (0.879 PPS) who are noteworthy. The four appear in the bottom 10 of the other table, indicating that they are not as valuable to their teams for their shot-blocking abilities as we may have thought in the past by both measures.

Finally, and mostly just for fun, let's take a look at the players who tallied the most blocks since 2007 for different shot types:



Whew, hope you're still with me. Marcus Camby blocked the most generic layups, reverse layups, and running jump shots since 2007, second most for driving layups. To me, this says that Camby is very good at knowing when and where the shooter is just about to release his shot, whether it's off the dribble, a reverse, or a running jumper. Meanwhile, Dwight Howard blocked the most generic jump shots and hook shots since 2007. This tells me that Howard benefits greatly from a huge vertical leap in order to swat away these type of shots. (Diversion: A quick look online tells me that Howard has a 40-inch vertical jump. Current players with higher verticals include LeBron James, Shannon Brown, Vince Carter, Nate Robinson, and Gerald Green. Retired players with higher verticals include Spud Webb, Michael Jordan, Dee Brown, Shawn Kemp, and Dominique Wilkins. Definitely no surprises here.). Finally, Andre Iguodala, Tayshaun Prince, and Kevin Durant blocked the most 3-pointers since 2007.

Alright, that's part 3 of this series. As always, feel free to leave a comment if you found anything interesting or if you think there's a better way I can look at things. In my next post, I'll take a look at how block value by shot location relates with block value by shot type and whether or not these values fluctuate from season to season for different players.