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.
Rethinking Volley 