Blocking Responsibilities – Pac 12


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.


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:


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.