Top Passers in NCAA Women's Volleyball (2022)

Top Passers on the women's side from the 2022 season are no surprise.

Nebraska's Lexi Rodriguez tops the chart, adding over 80 points, above expectation, to Nebraska's ability to sideout.

Similarly, PSU's Grimes and Stanford's Oglivie add tremendous value in the same fashion as Rodriguez.

Given the serves they saw in 2022, all 3 have expectations that their receptions will be worth 55% sideout

But after looking at their actual receptions, that value jumps closer to 59% sideout

This Actual - Expected gives us a +4% added value (above expectations) each time they pass the ball.

This can be translated to efficiency gains of +0.080  per pass.

Said another way, if the team is expected to hit 0.250 on first ball

Now they're expected to hit 0.330, way way better...

What's also interesting on this chart is Elan McCall of UCLA.

Watching her, you don't necessarily get the sense that she's as steady as a Rodriguez...

But looking at her reception error rate at 2.6%, it's noticeably lower than her peers.

So while she doesn't pass nearly as many good balls, her lack of getting aced makes up a ton of value

...and slots her at #18 on our passer ranking chart!

Similar deal with Maryland's Gomillion.

She comes across as a little bit of an outlier, but she stands out in 2 ways.

At the very low end of getting aced (3.4%) and also sees some of the toughest serves (54.2% Exp Exp SO)

Because of this combination, she ends up ranking in the Top 5, even though this may not pass the eye test as smoothly as the stars from Nebraska, Penn State, and Stanford.


How to evaluate passing? 

Who makes the most effort?

Who gets aced the least?

Who passes perfectly, most frequently?

My thesis: do you raise your team's likelihood to win the point?

Because all these other things, they're just proxies for what we consider "good" passing.

But really, rallies are just tug-of-war

And my job is to get the momentum on my team's side

So we will talk in terms of points

A few definitions:

Exp Exp Sideout

Expected Expected Sideout is:

given the specific individual server you are receiving against,

how well do passers do against them on average?

This refers to the quality of the receptions

and we use the value of those receptions to an average team

This way, we're excluding the power of the offense -

and instead, isolating the actual reception and how much value it would bring to an average squad

An Exp Exp SO of 55% here means that you are expected to win 55% of the points when you make a reception.

(This number is a little lower than you might assume, because by the time we get to a Reception, we know there has not been a service error, hence we are removing that piece of the equation when evaluating passing.)

Actual Exp Sideout

Actual Expected Sideout is, given the actual receptions you had, what is their value to an average team?

So again, we take the quality of the passes

And the value of those passes, to the average team.

Again, so we can remove the skill of the attackers, we look at the value relative to "average"

An Actual Exp SO of 59% here means that given your receptions,

an average team would be expected to win the rally 59% of the time

Expected Sideout Over Expected

Now we just take the difference between Expected and Actual from above ^

Exp SO OE = Actual - Expected.

This is the value the passer has created, above/below expectation

(Naturally, positive = above expected // negative = below expected)

In our example, Actual Exp SO - Exp Exp SO= 59% - 55% = +4%

This means that our passer is adding 4% to our expected sideout when he/she receives.

Eff Change

Efficiency Change and Expected Value Added can be thought of interchangeably

The only difference is the scale they are on.

When converting between Expected Value and Efficiency:

+4% in sideout = raising your offensive eff by 0.080

so if you usually hit 0.300 on First Ball,

this passer raises that to 0.380, which is obviously super helpful...

You'll also notice that their ranking, relative to Passer Ratings, are sometimes similar, sometimes way way different.

This is yet another reason why Passer Ratings are dumb and can blur the picture when evaluating actual value.

You can see more about their stupidity here: