Blocking – to touch or not to touch?

To touch or not to touch, that is the question. There are a couple philosophies that I know of when it comes to blocking. First is the GMS, more hands are better than fewer hands. They know that hitting efficiency in the aggregate declines when you move from solo, to double, to triple blocks – therefore, defensively we’d like to have more hands in the attacker’s face whenever possible. The other stance coaches often take is that sometimes they’d prefer cleaner digging lanes so if a blocker knows she is late, just roll under for tips rather than throwing up hands to get tooled. For most of my time at Illinois, it was a block the line, dig the cross mentality. Perfectly valid with a libero like Brandi Donnelly back there. I’ve also popped into UCLA practices where Tony Ker is working with blockers/defenders how they’re going to handle the Go-Slide where they send the MB to the outside and go one on one with the slide to provide the cleanest look for the backcourt.

Here’s the men’s data from 2018 (touches are orange, untouched is blue, sorry).

men

**What you’re looking at is the efficiency of the defending team in terms of winning the rally on their 1st possession after the opponent’s attack was made *or earlier*. So if possession #1 is the opponent attacking at the defending team, your team’s block touch, dig, set, attack is possession #2. If you win or lose the rally in either possession #1 or #2, your data is counted here. So +1 for the defending team if they stuff block, dig to kill, or the opponent makes an error – naturally it’s -1 for the defending team if the opponent cleanly kills the ball, tools the block, or you dig the ball but make an error in transition.

The reason we include possession #2 here is because there might be a phenomenal touch by the blocker that leads to an easy transition opportunity for the defending team. We want to include that value in how we evaluate blocking. To be transparent however, better transition offense teams will naturally be better overall. If Long Beach State hits .300 on medium digs regardless of a block touch or not, that will lead to better efficiencies in the chart than Stanford who maybe only hits .150 in transition on a medium dig. Keep in mind that because we’re looking at the efficiency for the defending team, we basically live in the negative zone as the attacking team always has the advantage in winning the point, this logically makes a lot of sense. The defending team is more likely to lose the point than win it.

You’ll notice a decent sized gap between most team where they better overall after a block touch. This could result from a large number of stuff blocks, rarely getting tooled, or a nice job soft blocking. This also makes a lot of sense in the men’s game where strong attackers make digging clean swings consistently, very difficult. So below we’ll look at the women’s side of things from 2017.

Screen Shot 2018-09-08 at 10.37.51 AM

*Like many charts in the past, I’ve cut out Rutgers. But you likely didn’t notice anyway…

On the women’s side we do see two teams (Michigan St. & USC) who actually do better without touching the ball on the block. Perhaps they get tooled more frequently or just attack so aggressively in transition that losing the point on the block instead of getting a swing in trans is massively hurtful to their efficiencies. But either way, the overall trend holds for the women, with several teams doing a full 100 points better after a block touch than not. To dive a step deeper though, it would make sense to look at difference between the number of blockers on each attack.

Screen Shot 2018-09-08 at 11.41.41 AM.png

I know the efficiencies are tough to read without zooming – but here’s what the women’s side looks like when you split up solo, seam, double blocks. You’ll see the same chart as before in the all data section.

What we see here are huge differences primarily in solo and seam blocking when a touch is made versus not. For Stanford solo blocking last year there was a 270 point difference in their ability to win the point between touch and no touch. That’s crazy. Maryland was similar in that they were 300 points different in favor of touching the ball on the block in a solo situation. Even teams like Wisconsin who are only about 25 points in favor of touches overall, are a full 100 points better in the solo situation with a touch on the block.

The trend holds for most teams in the seam situation as well where a MB doesn’t quite close to a pin, but is in the air blocking. We still see differences of 100 points for many teams here.

Where the trend changes is in double blocks. This may be due to multiple factors. Attackers may make more errors when trying to avoid a double block – or hit easier shots to defend and transition with. The interesting thing here is that the difference between touch and no touch here is tiny, across the board. No team has greater than a negligible 8 points advantage between the two. No coach is going to freak out about .100 vs. .108 efficiency levels.

Anyway, this type of analysis has already affected the way I train my teams. We spend more time working on blocking footwork and eyework than I have previously devoted, with the goal of touching every single power attack we see. As always, thoughts and comments on this post are welcome and appreciated!

 

Advertisements

Blocking Responsibilities – Pac 12

pac12respblocker

Just to preface this post, it’s basically the same as the responsible blocker one I put up earlier using the Big Ten data. That post explains exactly how responsibilities are split between blockers against specific types of sets.

My Pac 12 knowledge is admittedly lacking and I had to google some of these kids. Schoenlein is a senior outside for WSU, Lutz you should know from Stanford (and who is apparently touching 11 feet at the moment), Willow Johnson is a freshman RS for Oregon (and Randy Johnson’s daughter, fun fact).  Woodford is an OH with WSU I believe and Plummer is the Stanford OH you’re likely familiar with.

Just a reminder that since there is no designation of position (OH, MB, RS, S) in DataVolley, I am relying on spacing from the setter. This is compromise that has to be made in order to do the analysis – but inherently fails us when we look at a team like Stanford who has Fitzmorris and Lutz sometimes floating between middle and rightside. Sorry…

What you see on the left is how attackers hit against the blocker in question, whether there is a registered block touch or not. So if you find Willow Johnson (just a cool name so I’m gonna keep using her), you’ll see that when hitters attack her down the line or into the portion of the seam she’s responsible for as a RS blocker, she holds them to one of the lowest Attack Efficiencies in the league.

What you see on the right is the Efficiency Change as a result of the block move. From earlier posts on Eff Change you may understand that this gives more credit to a blocker if they take a strong position for the attacker and neutralize it or turn it into a good position for the defense – for example, if the offensive team usually hits .500 on the Go and Willow Johnson gets a stuff block to terminate the rally, her Eff Change will be her output, minus the input. In this case the input is -.500 because your defense loses at that rate against the Go, but the output is 1.00 because your defense always wins when you get a stuff block. This gives WILLOW JOHNSON an Eff Change for this block move of 1.5, which is huge. It also hurts the blocker more if they do something stupid when the offense is already in trouble, like netting against an out of system attack. Don’t do that…

You’ll see from the EffChangeBlock column is that the best blockers add value each time an attacker targets the portion of the court they’re responsible for. This might be via good touches that help your transition offense or it might be just not getting tooled and leaving clean lines for your defenders to fill.

You can use EffChangeBlock to potentially find undervalued blockers. Jenelle Jordan, a senior MB from Cal for example. While attackers hit 0.268 against her, more than double what people hit against the top 3 in that category, she quietly has a positive impact on attacks into her zones. To be fair, these Eff Change stats account for the team you’re on as well as the team you’re playing, and Cal’s numbers are likely to be deflated relative to the rest of the league. So if they’re a subpar digging team, that increases the value of every block touch Jordan gets…

Anyway, just wanted to throw this out there and let people check it out.

Blocking Responsibilities – Big Ten

One of the things people have never been able to look at in DataVolley is the way to examine blocker responsibilities. The reason this is an issue is that if you don’t have a block contact coded in DataVolley, nobody knows that you were at the net when the attack happened.

The goal of uncovering the responsible blocker is to account for how attackers hit when they attack at an area of court you are “responsible” for blocking. If you’re the OH and your team is getting crushed by slide hitters taking swings down the line, just because you’re not getting tooled doesn’t mean you’re not blowing it. Or just because you’re not blocking balls doesn’t mean you aren’t forcing your opponent into bad swings or easy offspeed shots.

The way I solved this is to look at all players and their distribution around the team’s setter to identify the OH1/OH2, MB1/MB2, and RS. By knowing who is in each “slot” I also know who the front row blockers are if I know which rotation each team is in.

The immediate flaw that we have to deal with: I am making the assumption that the order is: S, OH1, MB2, RS, OH2, MB1. Some teams, like Stanford/Minnesota/Michigan/etc like to mess with this order and end up having middles blocking right, rights in the middle, etc. Or if you’re Purdue, you just have Cuttino doing whatever she wants and chasing the biggest hitter. The reason this is an issue however, is that against a Go set to the OH hitter, I make the assumption that the player in the RS “position” is the pin blocker in this situation. Because I only know the lineup and not the physical location of the blocker, we have to live with some error (until we get player tracking like SportsVU). So until we better this solution, players like Hannah Tapp, Danielle Cuttino, Audriana Fitzmorris, etc will be victims of this inadequacy. Sorry.

blockingresp

Here’s how I have the attack codes split up by blocker. I categorized attacks outside of zone 3 in 3 ways: at the pin, outside in, and straight in. At the pin are the top two courts, outside in are the next two (32s, inside sets to the RS, etc), and straight in is the third set (back 1, 3). The bottom set is for attacks in zone 3 or 8. I did my best to align blockers to sets they’re most likely to block – and have shaded in the area behind them as I saw fit.

The shading of the court is certainly open to discussion and can be altered. DataVolley has 9 zones and 4 subzones meaning there are 36 boxes to designate. Boxes in red are the responsibility of the pin blocker. Boxes in blue are the middle blocker.

If we look at blockers who were responsible for at least 150 attacks during the 2016 Big Ten season, here’s what we get:

blockatkeff

The first column is the actual attack efficiency when people hit at the responsible blocker or into a zone she is responsible for. Without being too biased, “Sporty J” Poulter absolutely handles her business. Carlini is alright too…

Personally I’m a little surprised to see so many pin blockers in the top echelon, seeing how I gave them a good chunk of court to be responsible for on almost all attacks.

*to clarify, regardless of where the ball lands, if there is a block touch recorded in the DataVolley file, that person making the touch is the responsible blocker. This way, if a good pin blocker dives huge into the angle and stuffs the ball, that pin blocker gets credit – not the middle who had better things to do…

The column on the right is the Eff Change for the responsible blocker. This means that getting stuffs and good touches when the opponent is in a more advantageous starting position nets the blocker more love. This also means that netting when your opponent is busy spatching a high ball out of bounds loses you a lot of love.

Would be interesting to break down where most of the Eff Change for the blockers comes from. Is it stuff blocks against in-system attacks? Not messing things up when your opponent is in trouble? Getting a ton of block touches in general? I’ll have to drill down on that one.