$7 Study: Men's Backrow Attacking

Does the opposite's release point matter?

$7 Studies are a running item on SmarterVolley. I started them in spring 2023 when I took a break from analytic-heavy content on SmarterVolley to focus on more qualitative aspects of coaching. To make sure I still got you all your fix of stats I launched the $7 Study feature where once a month I included some statistical research. The goal is to give you a concise summary with a couple clear takeaways that you can digest in 10 minutes or less.

It’s also my pitch to you free subscribers! Hey, I put out a lot of content, and a lot of it is for free. If you’re a free subscriber and you read everything I put out and listen to all my podcast appearances, etc, you’ll pretty much get a feel for my way of approaching the game. But, if you’re busy and want to get right to some core takeaways, you’ll like these $7 Studies. In 10 minutes or less you’ll get at least one clear takeaway for your team, supported by evidence.

Previous $7 Study Topics
What attack patterns are most effective?
How effective are the blockers at each position?
How do setting choices affect opponent transition efficiency?
How does Make Them Play correlate to winning?
Does serve location influence where passers pass?
Do setters influence where passers pass?
Should passers consider where the setter is?
MTP Part 2
Coin Flips

Today we’ll look at some back-row attacking, particularly for men’s volleyball.

Zone 1 Attacking

It’s common knowledge that the opposite position is highly influential in men’s volleyball. For example, in the 2022 Men’s World Championship, 8 of the top-10 scorers were opposites:

Getting production out of the opposite is critical, as is slowing it down. In men’s volleyball, the opposite has to be productive out of the back-row, attacking from Zone 1- often called a “D-ball” in American volleyball lexicon.

Today I want to share some data, with the hope of answering a simple question: is opposite attacking efficiency influenced by their rotational alignment? Let’s pull up some data and see.

The Data

The sample I’m using here is from the 2024 Men’s NCAA season. It’s a 182-match sample from MPSF teams. Let’s take a look at the numbers and see what they say.