Adding Nuance To The In-System Label
Time-intensive but possibly useful
Webinar Update: On June 30 I’ll host another subscribers-only webinar. This time we’ll be talking In-System Offense, to match the theme of this month’s posts. If you didn’t catch the previous one on Reception Systems, check it out. And if you’re not yet a Premium Subscriber, this is a great time to join!
As part of the summer emphasis on Offensive Profiles, I’ve been talking In-System Offense in June. The definitions of In-System are:
Good or Perfect Pass
R# or R+ in Datavolley terminology
3-pass on a 3-point scale
3 or 4-pass on a 4-point scale
“Setter has all 3 options”
These are all saying the same thing. We know that the offensive efficiencies, on average, are close enough on R# and R+ passes that it’s worth lumping them together in order to get a bigger dataset to analyze. That’s how things are displayed on the standard DataVolley Match Report:
However… from a coaching perspective, there are some reasons to differentiate between different types of In-System passing. I’ll demonstrate some techniques I’ve used to help refine your coaching lens.
3 Directions
If we visualize the perfect reception, we can also visualize that a setter can move off that spot in 1 of 3 directions:
Forward
Backward
Off the net
It seems reasonable to imagine that different setters, with different strengths and weaknesses, might locate the ball more accurately moving in one of those directions and less accurately in another direction. It’s also reasonable to imagine that the hitters on one team might do better with longer sets, shorter sets, etc.
My mind tends to read “variance” as “something a coach can affect,” so I’d love to have some information on this. Unfortunately, it’s not super-easy to do.
Here’s a jammed-together image of the sort that I used to make a while ago. This involved putting a custom code into DataVolley and then me making these crappy-but-kinda-informative graphics in Inkscape. The image on the left represented our efficiency on the “Go” (fast set to the left) and the image on the right represented our efficiency on the “Red” (fast set to the right).
In this case, you can see that we were better setting both balls while moving forward and that the efficiency of both sets dropped as the setter moved backward. There’s two things you can do with that information:
Technical Adjustment - Work with your setters and hitters on getting better at converting when the ball is passed toward zone 2.
Tactical Adjustment - Try to aim your passing target more into the center of the court.
The numbers won’t tell you which choice to make, but having the data is helpful.
You can also see from those images that we were relatively better setting the Red from off the net than the Go. And again, whether you choose to make Technical or Tactical adjustments from there is up to you.
Here’s another way to represent this sort of data:
Now, instead of taking one particular play-set and mapping the efficiency by different areas of the court, I’m taking one area of the court and mapping each play-set. I did the same for our defense as well.
So we can see here that we were excellent at running behind the setter when we came off the net, but we weren’t so good at forcing the ball to the middle or running the left-side from off the net. In contrast, we were quite good at stopping the Go when opponents were off the net, but not so good at defending Gap or Red.
Perhaps there’s a broader lesson there that volleyball teams in general tend to default to the left-side as the pass comes off the net and that players get habituated to stopping that play.
Coordinate Data
These charts are from the wonderful Untan, but similar versions to these are produced in lots of different ways.
The color-density here is defined by frequency of the set. So here we can see that the setter liked to set the middle moving forward, but would throw the ball long to the left-side when moving backward.
If you’re able to zoom in a bit, you can see the Kill % and Win % (Sideout %) for each choice as well. If you’re analyzing your own setter, this can help you dial things in tactically. From an ideal game theory standpoint, the most effective play-set should get set the most and the least effective should get set the least. We see here that this setter isn’t too far off. (Considering who this is a chart of, we shouldn’t be suprised.)
You can also display this by efficiency based on where the ball was set FROM. (Remember, the previous charts were based on where the ball was set TO.)
We can see that this team, in this data set, was quite efficient moving both forward and backward. Let’s parse it out by attack code.
The chart on the left is efficiency setting the left side from different locations on the court. The chart on the right is efficiency setting the right side (not including slides) from different locations on the court.
You could say as a general trend that this team tended to (a) prefer to set shorter-distance sets and (b) was more efficient when they set the closer hitter. In fact, there were no balls in this (fairly extensive) sample where the setter set the right side from zone 4.
Again, whether you choose to make a technical or tactical adjustment based off this information is up to you, but these are two of the tools that I like to use, if I have the resources to do so.