Digging Profiles - Part 1
Introducing The Data
Related:
Serving Profiles
Blocking Profiles
Articles in this series:
Part 1 - Introducing The Data
Part 2 - Keepy Uppy
Part 3 - The Creator Economy
Part 4 - Conversions
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 digging portions are:
Dig %
Create % (CRT%)
Convert % (CNT%)
Which then gives you two additional derivations:
Digs Created %
Creates Converted %
For this data-set, I’m using publicly-available data rather than my own defensive evaluation. While I think Volleymetrics is sometimes questionable in how they assign dig errors, it’s the data set that everybody has access to, so I think it’s worth examining how it correlates to winning.
Dig % is simply the % of dig opportunities that didn’t result in a Dig Error and therefore were prevented from becoming an opponent kill.
Create % is the % of dig opportunities that led to a transition attack. So it’s a combination of Dig % and how often those digs lead to a transition attack. And therefore, you can figure out Digs Created % as well, which specifically targets how often a dig (not just a dig opportunity) is turned into a transition attack.
Convert % is the % of dig opportunities that led to a transition kill. So it’s a combination of Dig % and Transition Kill%. And therefore, you can figure out Creates Converted % as well, which specifically targets how often a create opportunity was killed.
Top - Bottom - Middle
Let’s get a picture of what these numbers look like at the NCAA level.
Dig %
82.8% - #1 (Northern Iowa)
79.0% - #50 (Auburn, Long Beach, Western Michigan)
73.5% - #100 (Colorado)
Create %
69.3% - #1 (Arkansas)
63.5% - #50 (Wichita State, Iowa State, Kansas State)
54.6% - #100 (Colorado)
Convert %
23.6% - #1 (Pitt)
19.7% - #50 (Pepperdine, Michigan State, Buffalo)
16.8% - #100 (Depaul)
Digs Created
85% - #1 (Arkansas)
80% - #50 (Lots of teams)
74% - #100 (Colorado)
Creates Converted
38% - #1 (South Carolina)
31% - #50 (Lots of teams)
27% - #100 (Cal, Depaul, Liberty)
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 put your best defender where they are going to get the most total chances to dig or do you put them where they’ll have the most challenging dig opportunities?
The next 3 parts of this will divide digging into 3 different profiles:
Teams that excel at keeping the ball off the floor
Teams that are good at creating opportunities on those digs
Teams that are good at converting those opportunities
#3 is arguably more about transition offense than pure digging, but I think it will be worth looking into.
Look for those parts in the upcoming weeks!