Tag Archive for volleyball stats

Looking at offensive performance by set and pass quality

In this previous post I shared some offensive and defensive numbers for the 2017 Midwestern State season. Part of what we shared with the team was the table below. It breaks our offensive performance down by set location/type and pass quality.

For the sake of clarity, let me explain the table.

The column labelled “Set to OH” includes any sets to the OH, including high balls, go’s, and 3s. For visual reference, those are the 4, hut, and 3/Rip on this set diagram (relatively few of the latter). They are broken down by pass or dig quality using the 3-point grading system. The first line for each group is Hitting Efficency, which is (Kills – Errors) / Total Attempts. The second and third lines are Kill % and Error % respectively. Basically, that just breaks the Hitting Efficiency number into its component parts.

I followed the same process for the right side attacks (“Set to RS”). For the middle and back row attacks, however, I did not use the pass rating splits. In the latter case there just weren’t enough observations to make it matter, especially since we did not have a focused back row attack. They were mainly out-of-system swings, which is probably pretty easy to guess from the poor numbers.

Collecting this information is relatively simple, if you have someone dedicated to doing so. I did it myself on the bench during matches using pen and paper. It would have been much more efficient and easier to manage if we had something like DataVolley, but we didn’t. So we made do.


As a staff we were quite surprised by some of the numbers above. For example, we would not have predicted that slides were our most efficient middle hitter attack. It was something that really ran hot and cold. It would go from unstoppable to you can’t buy a kill. No doubt the latter skewed our perceptions. That’s a real risk, which is why stats are so important for good analysis.

The other surprising thing was the effectiveness of our in-system outside sets, especially compared to our middle attacks. You expect middles to generally go for a higher Kill % than pin hitters. That was clearly not the case for us. Even worse, our middles had a higher Error % in places. On reason for the high OH effectiveness on the good passes and digs is that we did a good job running the Shoot-Go combination. That gave our OHs some really good, open swings.

The anemic performance of our RS attack came as no surprise. We had a tall player in that position who was a blocking force, but just didn’t generate the power in her swings she needed to be really effective in our conference. Plus, her confidence wasn’t very high, so she wasn’t as aggressive as she really needed to be.

Putting it to use

The MSU attack just didn’t produce kills at a high enough rate last season. The table above lets us identify the specific areas where improvement is needed. The right side is near the top of the list. We will likely continue to struggle if we can’t get at least to a Kill % comparable to that of our OHs.

The second big thing is the middle attack. The error rates for Slides and Shoots were too high, which is likely a combination of poor attacking and poor sets. The error rate for 1s was more reasonable, but the Kill % was relatively low. That should be north of 40% for a team like ours.

Address those two parts of our game, while keeping our OHs performing at about the same level, and we would be a very competitive team in our conference.

Looking at jump count

In 2014 when I spent three weeks with a pair of German professional teams, I had a conversation with one coach about player jump counts. He was starting to use the VERT device to track jumps in training. It gave him a guideline as to when to shut things down. I had a similar conversation during one of the Volleyball Coaching Wizards interviews. It became the basis for a podcast.

All of this came after Volleywood posted something which suggested what I saw as a ridiculously high average player jump count. They said, “Most volleyball players jump about 300 times a match.” With no supporting evidence, I should note. I posted a comment contesting that idea. As this article shows, however, that idea somehow spread.

So what’s the truth?

The folks at VERT published a set of figures based on NCAA women’s volleyball. The following comes from an email they sent out which I received.

So setters jump the most, followed by setters, then outside hitters (probably including right sides). Notice none of them are anywhere close to 300. Yes, these are averages, but I’m hard-pressed to imagine any player in even the longest match getting to 300. Maybe, maybe hitters got that high back in the sideout scoring days when matches could go very long. Even then, that would be on the very high end, not the norm.

And according to the article I linked above, research indicates the average is significantly lower for beach players than indoor ones. Though for them you have to factor in playing multiple matches per day.

Training implications

So what does this mean for us as coaches?

It means it doesn’t make a whole lot of sense to have players do 150 or 200 jumps a day in practice when they will do far fewer in matches. If we do, then we are likely over-training, which puts us at risk of injury as a result of either fatigue or overuse. And we shouldn’t just think about jumps in practice here. We also have to consider jumps from strength training as well. It all adds up.

Is offense or defense more important in volleyball?

Which do you think is more important to the success of a volleyball team – offense or defense?

Generally speaking, the answer could very well depend on whether you’re from the men’s or the women’s side of the sport. In my experience, women’s coaches tend to prioritize defense more than men’s coaches.

I probably would have been one of those women’s coaches who said “defense” once upon a time. I had a very clear demonstration of the limitations of that thinking, though.

The Exeter experience.

My first year coaching the Exeter women we had a pretty good defense. This was demonstrated in our playoff match against Loughborough, which was a good team at that time. We got into some really long rallies with them and constantly foiled their attackers. The problem was we couldn’t get kills going back the other way. Just didn’t have the fire power. We were good enough to compete, but not good enough to win.

That changed the following year. We had a major offensive upgrade. Now we could win those rallies we couldn’t the year before. The result was a trip to the national semifinal.

A more detailed example

In a moment I will share some figures with you. Let me first set the stage, though.

In the 2016 season, the Midwestern State (MSU) team I coached finished 8th in the Lone Star Conference, out of 11 teams. It was a meaningful improvement over the performance the year before (winless in conference). Our defense was really poor, though. We ranked 9th in opponent hitting efficiency (.221) and 10th in blocks/set (1.27).

Naturally, we made defense a big focus for improvement in the off-season. It paid off. In 2017 we moved up to 6th in opponent hitting efficiency (.183) and jump all the way to 4th in blocks/set (2.20). That means we moved up the standings, right?

Nope. In fact, we dropped a spot and finished 9th.

Were we more competitive? Absolutely! We took sets off teams in 2017 – including nationally ranked opponents – we didn’t get close to in 2016. We even had one more match win in conference.

What did not improve was our offense, and that made all the difference.

Here’s a look at the conference statistical rankings for key offensive and defensive areas.


We’ll start with the attacking side of things. Take note of how closely the final standing of each team matches its rank in terms of hitting efficiency. Only in the case of Kingsville and West Texas is there a variation. The two of them were reversed in terms of their offensive rank, though we can really say they tied. Kingsville had one more match victory than West Texas.

Of course hitting efficiency is calculated as (Kills – Errors)/Total Attempts. Thus, we can break it down and look at Kill % and Error % separately. Compare the Kill% and Error % ranks in the table above and you’ll notice something interesting.

Tarleton is clearly well ahead of everyone else with a Kill% about 39. Commerce and Angelo are very close in the 2 and 3 spots in the 37s. Then the next four teams are also very tightly bunched together in the 34s. After that you have a steady progression lower as you move down the ranks. Overall, there is about an 11% difference between top and bottom.

Things aren’t nearly so orderly when it comes to Error %. First of all, the spread from best (Commerce) to worst (Permian) is only about 4%. Most teams are in the 14%-15% range. The 10th worst team in terms of errors actually finished 7th in the standings.

When you see this it seems pretty clear that the kill side of things weighs more heavily on performance than the error side. That’s not to say errors don’t matter. Obviously, they do. But kills seem to matter more when it comes to winning and losing, and there’s a lot more variation.


Now let’s look at how teams did stopping their opposition from scoring. The opponent hitting efficiency gives us a general measure of that. The top three teams in the standings were also the top three teams in terms of defending. No doubt the strength of their offense is a factor there. After all, if you’re hitting is strong it makes for more difficult transition opportunities coming back the other way when you don’t get a kill.

Below the top three the rankings and the final standings position deviate quite a bit. MSU is a prime example. We had the 6th best opponent efficiency, but only finished 9th. Western New Mexico was four places below us in the defensive rankings, but won two more matches than we did.

Now compare the opponent Kill% rankings to those for the hitting efficiency ones. They are almost identical. That tells us that opponent hitting errors don’t really matter much. This really bears out when we look at the Block % figures. That’s the percentage of times that team blocked an opposing attack. They are all over the place! The bottom two blocking teams finished right in the middle of the standings, while the second best blocking side ended up 10th.

The edge to the offense

Based on the figures in the table above, it looks like offense correlates more closely to final league standings than defense. This, of course, is a narrow study. It features teams from one of the stronger conferences in NCAA Division II volleyball for just a single season. As such, it might not be fully representative. Even so, it at least gives us something to think about.

Here’s some further analysis along these lines.

Kill percentage off perfect pass

The following question came in from a reader:

What percent of kills should we expect on a perfect pass? Serve receive or free balls?

The answer to this is reliant very much on level of play. High school girls probably do not score at the same rate as college men, for example. Unfortunately, the mailer didn’t tell me what level they are at.

I honestly don’t have a specific answer in any case. I reached out to Mark Lebedew from At Home on the Court to see what he had to say, and he told me in the men’s PlusLiga in Poland (the top professional division) it’s a 62% kills rate, with a 47% hitting efficiency. This struck me as low, but that just goes to show that personal impressions aren’t always (or even often?) right. 🙂

Mark went on to say the PlusLiga sideout rate off perfect passes is 72%.

My analysis from the 2017 Midwestern State suggested our perfect pass kill rate was below 40%, which was definitely sub-optimal.

I’m curious to hear what folks with good figures say about kill % and sideout rates at their level. If you have any data, please share via a comment below.