Digging Profiles - Part 4

Converting the masses

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

Converting The Masses

I said last week that:

Not once in the history of volleyball has a referee blown the whistle in the middle of the play and given a point for a good dig. Ultimately, digs have to lead to opportunities to transition.

So… let’s see which teams were best at the thing that the referee does indicate a point for?

In the 3 profiles we’ve been discussing so far:

1. Teams that excel at keeping the ball off the floor

2. Teams that are good at creating opportunities on those digs

3. Teams that are good at converting those opportunities

These 10 teams are the converters. They were the teams most-reliant upon converting their transition opportunities.¹

Winning And Losing

In this sample, the correlation between CreatesConverted% and Win% was 0.26, which isn’t nothing, although it isn’t as high as a couple of the other digging metrics. For example, the correlation between Ace:Error Ratio was 0.33.

Let’s look at the other correlations I pulled that specifically relate to defense/transition:

0.48 - Dig%
0.52 - Create%
0.50 - Convert%
0.35 - Digs Created%
0.26 - Creates Converted%

The 3 that we’ve focused on the most are Dig%, DigsCreated, and CreatesConverted.

(This is because Create%, as Volleymetrics calculates it, contains Dig% as part of its formula, and so does Convert%, so you’re sort of double-counting. By using the derived formulas, we avoid double-counting and untangle some of the individual elements of defense/transition a bit more.)

I’ll admit, I was a little surprised to see CreatesConverted have the lowest correlation of the various indicators. Before I ran this study, I would have guessed that the correlation would have been higher than pure Dig %. While the correlation is still meaningful for converting, it’s not as high as just digging the ball.

Summary And Conclusions

For me, every time I go through these different profile analyses, I learn something. Sometimes just the act of compiling and looking at the data prompts me to wonder what Team X is doing and how they were able to be at the top of the leaderboard. Ultimately, the most important takeaway from any statistical study is, “okay so what should we do at practice today.”

My thoughts and takeaways from this multi-part series are:

1. Dig % is important, so invest time in that analytic. As I mention in that article, it’s worth assigning every opponent kill to either a blocker or defender as a primary responsibility for that kill. Doing this adds more data into your block/defense analysis, which can make you more accurate and better target your coaching efforts.

2. All aspects of defense/transition have fairly high correlations to winning, so as a large chunk of the game, we have to make sure we’re being good here. Like many things in the game of volleyball, it leads to the, “so we should just play this drill out, right?” conclusion. For any drill that you’re not playing it out with a defense and transition, the volley math better be in your favor and you need to be getting a lot of extra worthwhile reps in another skill.

3. I’ve probably been guilty in the past of coaching up “transition offense” more than just the ability to dig the ball. At lots of levels of volleyball, just being able to dig the ball goes a long way and this study indicates that level is higher than you might think.

4. Defensive analytics aren’t as easy or simple to work with as sideout analytics are. I’m excited to see what some technologies like Balltime can do in order to better quantify this. For example, the last time I spoke with their developers, I was imagining a scenario where a player’s defensive range could be calculated. Given that they were standing on a given place, how does their Dig % vary by the distance from where the ball was hit? Is it 80% when hit at them, 60% when 1m away, 30% when 2m away, etc?

Blocking Profiles are up next for the month of October. I’ll start rolling out that series next week. I’ll also keep you all updated on the Athletes Unlimited season as well as pull some additional case studies from the current NCAA women’s season. As always, I love to hear your input and feedback.

1. This is slightly different than just the 10 teams with the highest transition kill %. My goal here is to create a profile of a style of play, rather than just the teams that were the best at everything.