Blocking Profiles - Part 1

Introducing The Data

Related:
Serving Profiles
Digging Profiles

Articles in this series:

Part 1 - Introducing The Data
Part 2 - Stuffmasters
Part 3 - The Funnelers
Part 4 - Touchy-Feely

The Data

I sample here is NCAA Women’s top-100 RPI teams from the 2023 season. There’s some pros and cons of using this as the data set. In previous years, I used just Power-5 conference play when running similar analyses. The upside with that is a bit more of a constrained data set. I had teams from the top and bottom of conferences and they were (mostly) all playing each other. The downsides are (a) fewer total teams and (b) you miss out on some relevant teams from outside the P5 conferences. Also, the term P5 doesn’t really exist anymore, so why not just go blanket top-100 RPI, amirite?

One thing to note is that these teams as a general sample are more successful than the median NCAA team, because these are the top 100 out of 300+. The median Win % in this sample is 74%, the equivalent of going 22-8 in a 30-match season. But… that might not be a bad thing. We’re trying to build profiles of winning teams and the full 300-team sample gets a little unwieldy.

The Stats

The stats I’m mining for the blocking portions are:

Stuff %
Touch %
Error %

Which then also gives you:

Block:Error Ratio

For this data-set, I’m using publicly-available data rather than my own block evaluation. I think Volleymetrics is reasonably good at this; there’s occasionally some balls that I might code as blocking errors that they don’t, but they get most of them.

Stuff% is (Stuff Blocks) / (Total Opponent Attempts)

Touch% is the % of opponent attacks that are touched in any way, negative or positive.

Error% is the % of block touches that are blocking errors; these could be the blocker in the net, but are most likely to be the blocker getting tooled.

Block:Error Ratio is simply (Stuff Blocks) / (Block Errors)

Top - Bottom - Middle

Let’s get a picture of what these numbers look like at the NCAA level.

Stuff %
10.4% - #1 (Pitt)
7.2% - #50 (High Point, Creighton, Liberty, UMBC, NC State, Georgia, Clemson)
4.9% - #100 (Villanova)

Touch %
47.7% - #1 (Pitt)
39.5% - #50 (South Carolina, Indiana, Delaware, Washington)
32.8% - #100 (Citadel)

Error %
21.3% - #1 (Rice)
27.9% - #50 (Auburn, UIC, Michigan State, UMBC)
33.8% - #100 (Colorado)

Block:Error Ratio
0.81 - #1 (Rice, UCSB)
0.65 - #50 (UNI, St John’s, SMU, Ohio)
0.41 - #100 (Villanova)

What’s Next

The goal of these profiles is less about “who is good” and more about the different shapes of strengths and weaknesses. No coach ever has enough time and no roster is a collection of perfect players. We make trade-offs when we plan a starting lineup: do you start the more powerful attacker with worse ball control or vice versa? We also make trade-offs with practice time: do you spend more time on block/defense or on offense? And finally, we make trade-offs with strategy and tactics. For example: do you commit block more to try to pick up an extra stuff block or two throughout the match or do you play a read and try to touch more balls?

The next 3 parts of this will divide digging into 3 different profiles:

1. Teams that stuff the most balls

2. Teams that get tooled the least

3. Teams that touch the most balls overall

Look for those parts in the upcoming weeks!