To Block Or Not To Block
Sandy Sundays #4
This spring I’m doing 3 different running article series.
On Tuesday, I’m releasing articles aimed at giving you a small, specific tool to make your in-practice or in-match coaching more effective. On Fridays, I’m writing about ways to increase the physical capability of players. On Sundays, I’m releasing a more statistically-oriented beach article.
(Additional note: this one is gif-heavy so it might play better on your browser than inside your email.)
Last week’s Five-Play Friday Sunday format got some good feedback.
I’ll be adding more of those over the next few weeks, but I wanted to flip back to a more statistical format this week. Let’s talk about some defensive analytics. In particular, let’s look at blocking or not blocking and how to evaluate that. I’m going to use some data that’s collected from a dedicated statistician reviewing the video after the match. So not everything here is going to be doable by a high school or club coach. But the principles are going to apply.
Here’s part of the statistics I’m going to analyze, if I have the ability to collect all the data that I want.
(Note: Hat tip to Dani Santos who actually designed this nice fancy-formatted Excel sheet. As always, my goal with SmarterVolley is talk about this stuff and then have some of you smart folks go out there and create stuff better than what I was doing.)
So let’s see what we have here. We can see Kill% (and Efficiency) in a few different formats:
Overall → In-System, Out-of-System, Against Block, No Block, 2 Touches
First Ball → Same
Transition → Same
Hard Swings → InSys and OoS, vs Block or Not
Soft → Same
There’s a ton of data here and I could (and will, in time) produce 5 or 6 different articles breaking down the nuances of everything shown here. What I want to focus on today is the differences in performance against a block and not, and how that influences your decision on whether or not to block.
This is strong NCAA pair, and we see some numbers for them and their opponents that are pretty typical at the NCAA level for good pairs.
First Ball Kill% is 50ish
Transition Kill% is in the high 30s.
Overall Kill% is higher with a blocker than not.
Overall Kill% is higher on hard swings than soft shots.
The last 2 facts are influenced by the fact that:
The blocker blocks more In-System than Out-of-System.
The blocker stays to block more when the set is good than when it’s bad.
Hitters swing away more In-System than Out-of-System.
Hitters swing away more on good sets than bad sets.
These introduce some tricky variables when you’re trying to give your teams feedback on the effectiveness of their blocking tactics. Just taken at face value, you might say, “hey, why are you blocking so much?” AND ALSO: they actually might be blocking too much (or not enough). So what can we start to look at to determine this?
Let’s isolate a few things and see what they tell us:
Let’s look at Hard Swings that are In-System. For the analyzed team, their Kill% is 53% both against the block and agaisnt a pulling blocker. For their opponents, the Kill% is a bit higher against the block than against the pulling blocker.
Let’s look at Soft Shots that are In-System. For both the analyzed team and their opponents, Kill% are MUCH higher when they shoot agaisnt a block than against a pull. Which makes sense right? You’re going to kill the ball at a much higher rate shooting against 1 defender than 2.
For Hard Swings that are Out-of-System, we see an interesting difference. This team was better attacking hard against a blocker Out-of-System than against 2 defenders. But as defenders, they were better when they kept the blocker up.
For Soft Shots Out-of-System, we see a similar trend, but a huge difference in magnitude. Opponents really punished this team for staying and blocking in some Out-of-System situations. (It wasn’t a huge sample size, but not a small one either.)
So how do we turn this into feedback?
In-System, opponents are slightly better attacking hard when the blocker was up and much better at shooting when the blocker is up. That tells me that this team kept their blocker up a bit too often in In-System situations and might look for some opportunities to pull.
But the main takeaway is Out-of-System. Being able to keep the blocker up when the other team is looking to hit hard yielded benefits from this team, but blocking Out-of-System when the other team shot really hurt them.
Therefore, the main piece of feedback I’d have for this team is the critical Out-of-System situations where they need to delay their block or pull decision and make the best decision as often as possible.
At the NCAA level, there’s opportunities to scout. In what situations do the two playes on this team have shots they like to shoot when they’re in a tough situation? We can NOT get caught blocking in that situation and leave our defender out to dry. On the flip side, let’s be good at staying and blocking some of those situations when they are going to swing their way out of trouble.
And to go along with that, I’d be looking to design some practice situations to force this team into those situations where they have to make those reads and make the right decision. Something like a 1-way defensive drill where every ball (or every other ball) is an easy serve that the offensive team must pass medium-to-bad.
Okay, in the interest of length, we’ll end it there. Look for some videos coming next week and a flip back to another statistical topic the week after.