Top Passers in NCAA Men's Volleyball (2023)

Top Passers on the men's side so far include some familiar faces.

PSU's Merk and UCLA's Gooch lead the charge.

While both liberos pass about 1/3 balls for their teams,

and both add about 100 points in hitting efficiency to their offenses

They arrive at these numbers very differently.

When Merk touches the ball, he's 68% good pass but > 5% error.

Gooch however is much lower GP at 56%, but only gets aced 3.5% of the time.

Because we're now evaluating performance using points, rather than any subjective passer rating scales

we are able to show that while they get there uniquely, they bring similar, high-level value to the floor.


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 56% here means that you are expected to win 56% 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 61% here means that given your receptions,

an average team would be expected to win the rally 61% 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= 61% - 56% = +5%

This means that our passer is adding 5% 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:

+5% in sideout = raising your offensive eff by 0.100

so if you usually hit 0.300 on First Ball,

this passer raises that to 0.400, 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: