Attack By Pattern
Earlier this week, I shared a few quick examples of how to analyze Sideout By Pass Quality.
In this post, I want to zero in on In-System offense a bit more. Many of you are already analyzing your Sideout game by Attack Pattern or Setter Call, and this content might be familiar to you. If not, this might be a nice potential upgrade for you.
In-System Offense Is Based Off The Middle Pattern
When you start to analyze your in-system offense, consider what pattern your middle is running, whether that middle gets set or not. For example, here’s a side-by-side of Italy1 in the VNL with their offense differentiated by two different setter calls:
These charts contain both the Kill % and Sideout % (“Win %”) for the two most common Setter Calls: K1 and K7. Remember that, of course, you can rename your Setter Calls and patterns whatever you like in Datavolley, but K1 is the most common international usage for “Front Quick” and K7 is the most common for "Gap” “31” “C-Quick” or “7”… basically whatever your country/system likes to call the middle running to the border between zones 3 and 4.
You can also get the summary. In VNL, Italy had a 64% Kill rate and 78.7% Sideout % when running the K1, with a 64% Kill rate and 74.1% Sideout when running K7, regardless of which attacker was set.
Looking at your own offense through this lens is important. In a recent conversation with a college coach who is changing some things about their middle attack, we were breaking down some of their offensive distribution. Something we saw was that their middle attacks were transitioned at a less effective rate, making their overall Sideout % higher when setting middle than pure kill % or efficiency would suggest.2
If I’m breaking down these numbers, I want to know Kill% and Efficiency for my own team, but I also want to know overall sideout. For example, we see in this chart that Italy’s overall Sideout % when running K7 was 4% lower than when they ran K1. There could be quite a few reasons for that, but it definitely makes me think of non-terminal attack discussion from the most recent mailbag. Perhaps something there is causing more misconnects that lead to easy transition kills for the opponent.
The stats are rarely the direct answer, but they can lead you to the right aspects of your video to study. You might see this pattern and decide:
Let’s reduce the amount of times we run a certain pattern, and focus on our more effective calls.
Let’s train the weaker pattern to bring it up to the level of the stronger.
Let’s modify our setting choices within the patterns themselves. Do we need to isolate more? Overload more?
Which one you choose is up to you, that’s the judgment call.
To just take a top-level FIVB men’s team with a well-known setter.
There might be some smacks of that in the Italy chart above. K1 to set middle was 70% kill but 85% Sideout, while K7 to set left was also 70% kill but “only” 77% sideout. This is a general trend I seem to see, but this is also a small sample size warning.