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Assistant Coaches Toolbox: Part 2

Joseph TrinseyComment

In Part 1 of this article, I shared a question from a Workshop participant:

I would be curious to hear your opinions on what you think the most important things would be for a young assistant coach to have in their toolbox or qualities that they should have. I’m getting ready to transition from my graduate assistant role into (hopefully) a full time assistant coach role after the 2020 season and looking for almost some direction.

I shared some thoughts on the statement, “it’s not what you know, it’s who you know,” as well as two skills to have in the toolbox. I have another to share today.

DataVolley, Analytics, Video

Learn DataVolley. If you want to be involved in high-level volleyball, and you don’t have a high-level playing background, this is one of the best ways to make yourself valuable. I played Division 3 volleyball and I’ve been part of an Olympic staff. DataVolley and analytics was the way I added value that my playing background didn’t bring.

It also makes you a better coach.

In 2002, all of the best poker players in the world were old guys with names like Lakewood Louie, Amarillo Slim, and Bones Berland. Actually, those guys were bigger in the 70s and 80s, but they have cooler names than Phil Ivey and Johnny Chan. Let’s move on. The point is, was that high-level poker had high-barriers to entry (like… money) and poker, like many things in life, takes practice to get good at.

Enter online poker. In 2003, the World Series of Poker Main Event was won by a guy named (I’m not making this up) Chris Moneymaker. Chris made a cool $2.5 Million dollars for that, but he made online poker companies a LOT more, because he was the first WSOP winner to qualify through an online tournament, rather than in-person, casino-hosted tournaments.

Within a few years, online poker’s takeover was complete. Almost all of the top players were playing online, and soon many of the top players were under 30 or even under 25. Why? Because online poker allows you to play FAR more hands than in-person poker. First of all, you don’t have to physically drive to the casino, park, leave the table to go eat, etc. Computers shuffle and deal way faster than human dealers. And many online players would play in several virtual tables at once. You can imagine the difference in learning curve when you go from playing 25 hands/hour in a live game to playing 4 simultaneous tables that are playing at a rate of 75 hands/hour. These players were literally 10xing their progress.

I think you can see the connection to DataVolley.

I grew up in Delaware. I played Division 3 volleyball. I saw a few Olympic Volleyball matches on television. I didn’t even know there was a World Championship or what a Grand Prix was. The best volleyball I could reliably see in-person was when somebody from the Pac 12 or Big 10 would travel to play the University of Delaware for strategic RPI reasons.

If I was relying on being able to be involved in-person in high-level volleyball… I’d still be waiting.

In February of 2012, I had seen close to 0 elite level volleyball. By June of 2012 (after a few months into an accidental volunteer position with the USA National Team… more on that another time) I had maybe 200 hours of live National Team practice under my belt…. and at least double that in elite level volleyball studied on video.

Every day I was going home and studying hours of video. It was to practice and learn coding, but I discovered I was also learning volleyball. The great thing about video is that you can rewind it. Over and over again. You can keep watching a play until you really understand it. You can watch it through and focus on the setter, and then watch it again and focus on the attacker, and then watch it again and focus on the blockers. You can really understand how each person contributes to the play.

The other thing about DataVolley and coding is that the nature of grading each play means that you start compiling this mental image of “this is what perfect passes look like… this is what bad passes look like.” It kind of tunes you in to that on a subconscious level.

The video end also gave me a lot of confidence interacting with elite players. I knew what I saw on video. I knew when I was scouting an opponent that I could watch them until I was 100% confident in what I was seeing. If I was just relying on experience and intuition, how could I be confident in what I was saying? But the video allows you to watch it until you’re sure. So I did!

There’s just too many benefits to being good with video to ignore it. Get out there and start studying it. And if you want to learn DataVolley even faster, try checking out this manual written by two guys who spent a lot of time with the program. :)

The Assistant Coaches Toolbox

Joseph TrinseyComment

I got a great follow-up question from a participant in the Coaches Workshop (featuring Luka Slabe) this past Saturday.

I would be curious to hear your opinions on what you think the most important things would be for a young assistant coach to have in their toolbox or qualities that they should have. I’m getting ready to transition from my graduate assistant role into (hopefully) a full time assistant coach role after the 2020 season and looking for almost some direction

If you prefer to listen on audio, I recorded a podcast episode and it’s also on my Youtube channel:

First of all, I love the way this coach is already thinking about the question. To be honest, she doesn’t even really need me to respond, because she’s already thinking about it in the right way. “What do I need to have in my toolbox?” (It’s one of the ironies of life that, once you start asking the right question, you don’t really even need to hear the right answer. But the people who never ask the question in the first place are the ones who most need to hear the answer.)

Because, the overriding principle of, “Build your skills, not your resume,” is how you need to think about this question. The statement, “it’s not what you know, is who you know,” is not only incredibly toxic and arrogant (OMG if only the right people could see how much I know I would have everything I so obviously deserve.), but it’s also dumb. The only way to know the WHAT is to find the WHO. If you want to find the value in the statement, you can read it is, “you can learn what you need to know without seeking out the right people to learn from.”

When you start getting serious about being a better coach, the only way to really learn is to start seeking out those better than you. As your skills raise, the amount of options you have in terms of learning resources start shrinking. When you’re in 9th grade, playing volleyball for the first time, you can learn a lot about passing from your JV coach. At a certain point there may not be much more that person can teach you. You need to keep seeking out higher and higher sources. At a certain level, that’s going to lead to somebody like Karch. There’s a reason he’s coaching the Olympic team.

So keep that in mind, and here’s what I consider to be the top-5 tools that young assistants need to put in their toolbox.


When you are a young coach, time is your ultimate superpower. You have more of it than anybody else. Odds are, you don’t have a family, you don’t have as much responsibility, you don’t have as many needs, and frankly, you just don’t have as much going on in life. Your 48 year-old head coach of a Big 10 team has a lot going on. They have a spouse, kids, maybe grandkids. They get asked to speak at alumni dinners and coaching conventions. Pro agents are calling them to get their opinions on what players to sign. Players call them to ask opinions on what job to take, or what city to move to. They have a lot going on.

You don’t.

Literally, your time isn’t very valuable. Just look at your paycheck if you ever forget. :)

But guess what, that also gives you a power. When I was volunteering at USA, I was the first person in the gym every day. Because… what else was I going to do? I didn’t have to make breakfast for the kids or enjoy a few extra minutes in a wonderful Orange County townhouse. I was sleeping on an air mattress in my Craigslist-rented “bedroom,” aka section of the living room that the person I rented from curtained off from the rest of the room. What was I going to do, linger over coffee and a croissant and read the morning paper?

I would jump at the chance to take video home and clean the DataVolley code until midnight because… what else was I going to do? I was 24 years old, broke, and I slept on an air mattress. It wasn’t like I was going on any dates!

Your goal as a young coach should be to find ways to spend 2 hours to save somebody else 20 minutes. That’s how you make yourself valuable. And by doing so, you start learning how to do that 2-hour task in 20 minutes, because your skill level rises.

Be Good In The Gym

First of all, if you’re a volunteer, being good in the gym might mean you get the nets set up and you wipe down the floors and maintain the volleyballs at the right pressure. Been there, done that, happy to still do it. But you need to have some volleyball skills.

When I coached at some high-level programs, I was coaching players that had a level of skill and/or physicality that would amaze me. I was definitely not as good of a volleyball player as them. But I could do some volleyball-related things that could help the practice. You need to be able to do the same.

  1. Can you run a passing tutor off your arm? Can you stand on the ground and hit consistent float serves to the locations your passer needs to work on? Can you stand on the ground or a box and hit power spins to replicate something of a spin serve?

  2. Can you hit balls with control to work on some defensive moves? Brittany talked about doing this every day with her Wisconsin players. Luka talked about the same. Your players need to get some defensive reps and you need to be able to put some balls in the right zone for them to practice diving, or sprawling, or overhand digging.

  3. Can you go beyond that and jump and block and attack? That was half of my role in my first college volunteer position. It’s not that I was some world-class player, but I could jump and hit with enough power and control that I could get our liberos reps digging. Or blockers reps blocking. And more importantly, I could jump and hit with enough power and control a couple hundred times a practice without much of a break. So get your body in shape.

Okay… check back soon for Part 2 where I discuss 3 more tools to have in your toolbox.

Quick and Easy Video Clips

Joseph Trinsey2 Comments

Quick follow-up question that a coach had after the Setting and Offense Coaches Workshop with Dani Busboom Kelly:

Quick question... how did you get volleymetrics clips into the video format that you played on VLC media player?  Being able to save montages from VM into a seamless video clip is huge.

I'm a bit of a novice when it comes to video editing, transfers, etc. but would love to learn how to do this.

Well, don’t fear, it’s really easy. Short-answer: I use a screen-capture program.

The one I use is Movavi Screen Recorder, but I can’t say it’s necessarily better or worse than other programs that might do the same thing. (Actually, if anybody from Movavi is listening, the ability to save a set window size would be super-convenient and save me 4 seconds every time I make a clip, which adds up.)

Anyways, I use a screen capture program, and just click and drag to capture the Volleymetrics window. Then I press “record” in the program, and roll the Volleymetrics clip, and then press “stop” in the capture program. I then save the clip. Then, you can get something like this:

If I’m making a montage of clips that are all from the same match, it’s pretty easy to just keep the capture program rolling and click on the different Volleymetric tags over on the right side.

Things are a little more annoying if you want to montage clips from different matches. In that case, I make a whole bunch of individual clips, then queue them all up in a media player (I use VLC, but that’s just personal preference), and use the capture program a second time to capture the running playlist of these separate clips into one clip.

Whew! A little bit of extra work! Volleymetrics, get your act together and create a montage function like DataVolley has!

Any other questions? Follow up in the comments.

How Does Player Ability Affect The Way You Coach (Part 2)

CoachingJoseph TrinseyComment

In Part 1 of this article series, we explored a follow-up question from the Jaylen Reyes Block/Defense Systems Workshop. If you want to hear the audio or see my beautiful face talk about this topic, you can check out the podcast I did on the same topic. The question was:

Based on your experience on coaching at different levels of women’s volleyball, how big of a difference in player ability are there between International vs D1 vs D2?

 I ask this because I had some good takeaways from this weekend but I am curious of what I need to take and put into the context of D2 volleyball?

The three aspects I discussed in Part 1 were:

  1. Principles don’t change, but applications do.

  2. Physical differences change tactics.

  3. Higher skill levels open up more options.

There are two more aspects of this topic I want to discuss today.

Technique Is Similar, But Not Always Relevant.

Even some U-12 kids can learn to pancake and dive and sprawl and overhand dig in very similar ways that an Olympic player do. Even some young kids can execute blocking moves or take approaches in basically the same way that an Olympic player does. But because of the difference in power of execution, some of these moves are relatively less important.

Let’s use the example of a Crossover-2 blocking move. This is a great blocking move for high-level blockers to close against a fast in-system ball. In Jaylen’s workshop, we saw video of Nebraska players executing this move at a high level.

This move is great for high-level blockers. Most collegiate middle blockers need to have this move in their toolkit. But it’s not very relevant for a high-school middle blocker.

First of all, a 5'8" middle with average athleticism is probably never going to block a ball with a crossover-2 move, because she can't get up over the net unless she has both feet underneath her. And second of all, even if she could, it's likely that the offense she's playing against isn't fast enough to require a crossover-2 move to defend it. So while the high school middle can execute a crossover-2 move with good technique, it’s just not relevant.

For an example in the back-row: a no-step sprawl is a really nice move for high-level defenders. Here’s Nebraska executing a few:

Younger kids actually can learn to do it pretty well from a purely physical standpoint if you rep it out with them. But it's kind of irrelevant, because U-14s almost never hit hard enough to require a no-step sprawl. So moves like a step and cut or a more extended diving move to help smaller kids cover more court are more relevant.

Reading And Autonomy Is Just As Important

Seeing and reading of the game is just as important at any level. I think as coaches we have to be aware that we can't fix problems as quick as we'd like to. There's always this desire to over-coach to make up for a skill deficit. When we have a, "good," team, there's always more trust and autonomy. And when we have a, "not as good team," we always feel like... "well, that's all well and good for [this team who's better than mine] to have this autonomy and freedom to read and react and make decisions, but my players aren't that skilled."

I’ve seen this at every level. And that’s why I know it’s important at every level.

I’ve coached at the U-12 level, and had coaches say, “wow, those kids can really just go out there and do it.” Well yeah… because they’re playing against other 12 year-olds. Throw them in with the high schoolers and it looks like autonomy is failing them. It’s not autonomy that’s failing them. It’s not that they can’t be trusted to read and react to the game, it’s that they just aren’t good enough. No system can compensate for lack of fundamental skill.

One year I was coaching a U-17 club team. We were a really nice team for our area. Nationally-competitive, had some players who would go on to play scholarship-level volleyball in college, etc. We were playing in our Regional Championship, and I was coaching the way I did at any tournament, which was to say we had a read-oriented system where I trained them hard in practice to see and react and in competition they were trusted to execute those responsibilities. For our region, we were a very strong team (we would win the Region Championship that day), so things appeared to be going well.

In one match, the other team called a timeout, and I did what I usually did: I let my team huddle up for the first part of the timeout to talk while I talked to my assistant coach for a few seconds by the timeout. We were standing by the work table, and the coach who was with his work team at the table said, “I guess with a team like that, you don’t need to say much, huh?”

And the implication is something like, “When you have great players, you can give them that autonomy, but MY players aren’t that good. I actually have to coach them.” And “coach them” means “tell them what to do.”

But here’s the thing: that’s just because this coach happened to see us playing a team we were better than. Of course this read/autonomy-based system was working well! Pick a different match, like when we played a team that would eventually go on to medal at JOs, and he have the opposite feeling.

And this keeps going up the scale. That U-17 would look out of sorts against the D2 team. While the D2 coach is thinking, “Well Nebraska can give their players autonomy but my players…” And the Nebraska coach could easily start thinking, “Well the National Team can give their players autonomy, because they’re the best in the world, but I’m just dealing with inexperienced freshman here…”

And keep in mind that a few months prior, that freshman at Nebraska might have been the player that the club coaches are all looking at like, “well sure, THAT girl can be trusted to read and react to the game, but MY players…”

Jaylen knows this, and I love how he’s teaching his players at Nebraska. It’s not that you just roll the ball out there and say, “do whatever you want.” But it’s the recognition that as a coach you have to train your players to see and process the game faster. Getting them to see what you can see (and ideally… beyond that) is the way toward having a team that can play the type of defense you see Nebraska playing.

What do you think? Leave a comment, tweet or message me on Instagram (@volleycast) or send me an email: [email protected]

How Does Player Ability Affect The Way You Coach? (Part 1)

Joseph Trinsey

In a follow-up email after the Jaylen Reyes Coaches Workshop, a participant asks:

Based on your experience on coaching at different levels of women’s volleyball, how big of a difference in player ability are there between International vs D1 vs D2?

 I ask this because I had some good takeaways from this weekend but I am curious of what I need to take and put into the context of D2 volleyball?

This is a great question and one I’ve been asked a lot, so I thought it was worth making into a blog post and podcast.

Principles Don’t Change, But Applications Do

Laws of physics still apply at any level of the game. The court is still the same size. The players get bigger between D2, D1, and International, but not THAT much bigger. As players get better, they tend to automatize certain things, so they may focus more on other details, but those basic fundamentals are still critical to their success.

In many case, coaching less skilled players forces you to be tighter with your principles, because the nonsense doesn’t work and they will immediately fail. On the flip side, more elite players can appear to, “make it work…” at least until you play a team with similar skill level and get smashed.

Physical Differences Change Tactics

We can see this in blocking. At the high school level, you might not even want your smaller setter (for example) to bother blocking, because she’s not big enough to ever block a ball. She can help the team more by just pulling off and digging tips. At the NCAA level, this may transfer into something like, “do I keep my smaller setter in a bunch to help on the quick, or should she just leave and worry about the outside, because she isn’t physical enough to help and recover and still put up a good block on the outside?”

Keep in mind that the physical capability of your opponents also dictate your tactics. I coached fairly high level club volleyball, and almost never blocked against back row attacking. Even players who would go on to become NCAA All-Americans weren’t very effective attacking out of the back row against us. Coaching at the mid-major D1 level at LMU, we almost never blocked back row attacks, and back row attacks had less than 0.100 efficiency against us. But in the NCAA Final 4, we see players every year that can score well out of the back row. It would be suicide to not block Yossiana Pressley or Kat Plummer.

Physical differences can also dictate offensive tactics. International teams run 4th-step quicks almost exclusively. Many NCAA teams, even Division 1, would be more effective with slower 3rd-step, or even 2nd-step attacks out of the middle, because their hitters aren’t high enough and their setters aren’t good enough to consistently connect on 4th-step quicks, and their opposing blockers aren’t good enough to shut down slower quick attacks.

High Skill Opens Up More Options

This can be both good and bad, because coaches of highly-skilled athletes can start fixating on all the options they can run and neglect fundamentals. But it’s clear that, as the level of play raises, you have more cards in your hand as a coach. At the high school level most teams are best served to just set high sets across the board, because all that matters is taking a good approach and taking a good swing in the court.

At lower levels of volleyball, you’re not really playing against the other team. Both teams are playing against the net. The U-14 team who serves in and hits in more than their opponent will almost always win.

The higher the level, the more you’re playing against the other team, because there is a base level of fundamental skill mastery. The net is less of an issue.

In the 2019 NCAA Division 1 National Championship, Stanford and Wisconsin combined for just 12 unforced hitting errors on over 200 attempts.

In 2018, when BYU played Texas in the Regional Final, they had 9 unforced hitting errors, compared to 15 Texas stuff blocks. That’s making the other team have to beat you. This applies at the highest levels of D2 volleyball as well. In the 2019 D2 National Championship, CSUSB had 11 unforced errors and 11 times blocked and Nebraska-Kearney had 9 unforced errors and 12 times blocked. So in high-level D2 ball, you have to beat the other team; you can’t let them beat themselves.

In contrast, at the high school level, it’s common to see teams with 5 times as many unforced errors as times blocked. It’s common to see high school matches where the winning team scored more unearned points from opponent errors (service, attacking, ballhandling) than earned points from aces, kills, and blocks. At the U-13 level, it’s rare when this ISN’T the case. That’s beating yourself.

So as the level gets higher, tactics become relatively more important. This isn’t to say that fundamentals aren’t important. They always are. But maybe U-13 is 100% fundamentals and 0% tactics. Maybe high school is 90% fundamentals and 10% tactics. Maybe high-level D2 is 70% fundamentals and 30% tactics. Maybe high-level D1 is 60% fundamentals and 40% tactics and maybe International is approaching 50/50. I think fundamental execution is always #1, but tactics can be a tipping point as you go to a higher level.

These are my initial thoughts. I’ll have some more in Part 2 soon.

Another Reason To Use KO %

Joseph TrinseyComment

Here’s an image to ponder:


Pts/Serve is ultimately what we care about the most. At the end of the day, we need to score points, so the servers that are scoring the most points are (barring something like an exceptionally-poor or good defensive rotation for the server) our best servers. All the statistics we take are basically attempting to say, “here’s the server that’s going to score the most points, assuming they have an equal team around them.”

We can see pretty clearly that Serve-In % has a pretty low correlation with Pts/Serve. We can see that Ace % has a better correlation, but it’s still not perfect. And we can see KO % has the best correlation.

This isn’t surprising. KO % is a more comprehensive stat (it measures Aces + Out-of-System passes and it contains a bit of Serve-In as well), so we expect it to have a higher correlation to success. What’s great about KO % is that it’s also easy to understand. It’s human scale. The best servers on this team (as tends to be the case in HS/club volleyball) are at about 67% KO, which is about, “2 out of 3.” That human scale makes it easy for players to understand and process.

This Week's Workshop - Jaylen Reyes On Defense

Joseph TrinseyComment

The Coronavirus Pandemic and associated stay-at-home orders have changed the lives of many people. In relatively unimportant, yet personally relevant terms, it means volleyball coaches have had way fewer chances to interact this spring- the time of year when many coaches are trying to learn and grow as coaches.

This Saturday will be the 11th session in my Coaches Workshop series. My first few didn’t have much of a plan behind them. I simply called up coaches that I knew and asked if they wanted to talk some volley… and apparently some other coaches were interested in paying to be part of that conversation.

As the month’s have gone by, I’ve settled into more of a formula and upped the professionalism… at least a little bit. We limit the sessions to less than 20 attendees and I provide a 10-page pdf packet with notes and images synthesizing the information from the session.

I’ve found that video-heavy sessions work well, and having a focus on either the offensive or defensive side of the ball works well. What I aim to replicate is what you would get if you could visit that coach’s gym and spend 2 hours with them… watching film closely and getting to pick their brain. That’s what I’ve always loved to do, and apparently I’m not the only one.

This week, I’ll be talking with Jaylen Reyes, Assistant Coach at University of Nebraska-Lincoln. Why?

Big 10.PNG

Pretty simple: lead the toughest volleyball conference in the country in Opponent Hitting (and Nebraska has done it multiple times) and I want to know what you’re doing on defense.

Jaylen is a really sharp guy who was also an outstanding player himself. I’ll post more about my prep work throughout the week, but the three things I’m most interested in exploring are:

  1. How specific their gameplans are. How do they balance the desire to get the perfect plan in place with not overloading players? And I know they use the specifics of a gameplan to teach players general concepts about the game. I want to know more about that.

  2. How they handle In-System vs Out-of-System plays. This links a bit into #1, but there are some differences in where balls get attacked In-System vs Out-of-System. How big of an adjustment do they ask players to make?

  3. What their training structure is like. How much is, “rep it out,” vs other methods of teaching. They make some big-time defensive plays, so I want to know what goes into the physical training of their defenders.

Evaluating Blocking: Part 2

Stats LessonsJoseph TrinseyComment

In Part 1 of our series on Evaluating Blocking, we noted a few of the challenges that make evaluating blocking difficult:

  1. The blocker doesn’t have full control. On most sets, the hitter can beat the blocker with the right shot. It’s possible to make a good block and still have the hitter kill the ball.

  2. Many attacks never touch the block. At some levels, over half of attacks will touch the block, but at lower levels (such as high school), more than 3/4 of the attacks will go clean past the block. It’s difficult to evaluate the effectiveness of a block when the blocker doesn’t touch the ball.

  3. There are lots of non-terminal blocks. At the NCAA women’s collegiate level, only about half of block touches are stuffs or tools/errors. It’s not always clear whether a “block touch” (that doesn’t result in a point for one team or the other) is a positive or negative play.

  4. It can be difficult to separate the performance of the player from the system they play in.

  5. The standard box score in the USA, the NCAA box score, is mediocre at best at giving you blocking information.

We talked about the last point, and we saw that adding block errors/tools to the equation gives you better information with which to evaluate your blockers.

Teach players that their number-one job as a blocker is to stuff the ball. But the next most important thing is to not give up an easy point by getting tooled! Understanding both sides of this equation makes blockers better.

But what about point #2? Many attacks never touch the block. In this match, Texas defended 112 BYU attacks. BYU made unforced errors on 9 of those balls, and the Texas block touched 50 of them in some way, which means 53 of 112 BYU attempts, or 47%, were untouched by the Texas block. On the other side of the net, BYU defended 90 Texas attacks. 6 were unforced errors and 37 were touched by the BYU block, which means 52% of Texas attempts were untouched by the BYU block.

What do we do about all of these non-touches?

First, let’s look at how I scored them on the GMS Stats app:

BYU Opponent Sideout Screen, GMS Stats App

BYU Opponent Sideout Screen, GMS Stats App

Texas Opponent Sideout Screen, GMS Stats App

Texas Opponent Sideout Screen, GMS Stats App

BYU is on top, and Texas is on the bottom. Both of these screens are displaying our favorite blocking stat: “Stuff to Tool Ratio” as the Blocking Efficiency stat. If you refer to how Volleymetrics graded their blocking in Part 1, you’ll notice two things:

  1. There is a bigger gap between Texas and BYU in my app than in the Volleymetrics grading.

  2. Both teams have a lower ratio in GMS Stats than in Volleymetrics.

Why is that?

The reason for this is that I assigned a Block Error or Dig Error on every opponent kill. I always grade block and defense in this way, and recommend everybody taking statistics on their team to do the same. So instead of 16 Blocking Errors, I gave Texas 18. Instead of 9 Blocking Errors, I gave BYU 15. In some cases, these Blocking Errors are plays where the block did not touch the ball at all, but I assigned them as the primary responsibility for the kill. For example:

In most grading systems, neither of these would register as actions by the blocker. But clearly, the blockers bear responsibility to stop these attacks. While both of these hits could have been dug, they would require a heroic effort by the defender. My judge is that if you had to decide, “which of these players is primarily responsible for that point being allowed,” you would choose a blocker in both instances. We’ll call this grading system, “primary responsibility grading.”

One of the great things about advanced programs like Volleymetrics or DataVolley is that you can drill down deep into things like this. When I was with the USA National Team, we had a system for evaluating Blocking Efficiency that we called, “Expected Attacking %.” In this stat, we had 6 different grades of block touch, including a tag for, “No Touch,” where the hitter hit a ball hard past a blocker and the blocker failed to get a touch on the ball. In this system, the “expected efficiency” of each touch (except stuffing the ball or getting tooled) is always some fraction of a kill; remember that even good touches that slow the attack down are mishandled by defenders sometimes and even poor deflected touches are dug sometimes as well.

Some other coaches might use a 3 or 4-point scale rating for blocking where they assign each touch a value and give the blocker a grade like 1.6 or 2.2 (there’s some other issues with this particular system that we’ll discuss in part 3). We’ll call systems like this or “Expected Attacking %,” “fractional grading.”

This allowed us to get precise values for how a blocker affected an opponent’s ability to score against her. Unfortunately, most high school and club coaches don’t have dedicated statisticians or the budget for advanced statistics software! That’s why I don’t use a fractional system for blocking in the GMS Stats app. A primary responsibility system is easier to keep track of, because blocking touches are only assigned when the rally ends- either with a kill or stuff block.

Sometimes there are judgment calls:

While this is not a straight-down kill, it’s also a difficult play for the defender. Many coaches would be tempted to just call it a “good hit” by the attacker and leave it at that, but I think you’re missing some information. I decided to assign responsibility to the left-back defender. Let me know what you think in the comments section.

In summary, there’s a “Goldilocks Zone,” for most volleyball statistics where getting the right data can get you 90% of the information you need, but getting that last 10% of information requires massive resources. For NCAA and professional teams, that can be a worthwhile expenditure of resources, but most high school and club coaches just need to get the most bang for the buck!

In my opinion, the Goldilocks Zone for blocking is:

Not Enough Info: Only keeping track of stuff blocks.

Too Much Time/Energy: Using a fractional grading system to capture the value of every blocking action.

Just Right: Track stuff blocks and blocking errors, and use a primary responsibility grading system to capture a few additional non-touches that the block was responsible for.

In Part 3 of our series on Evaluating Blocking, we’ll dive deeper into non-terminal block touches and try to find out how much they really matter.

Evaluating Blocking: Part 1

Stats LessonsJoseph Trinsey

Two of the most difficult skills in volleyball to evaluate statistically are blocking and digging. We’ll discuss defensive evaluation in another article; today we’ll focus on blocking.

Some of the challenges that make it difficult to stat blocking are the same that make it difficult to coach blocking:

  1. The blocker doesn’t have full control. On most sets, the hitter can beat the blocker with the right shot. It’s possible to make a good block and still have the hitter kill the ball.

  2. Many attacks never touch the block. At some levels, over half of attacks will touch the block, but at lower levels (such as high school), more than 3/4 of the attacks will go clean past the block. It’s difficult to evaluate the effectiveness of a block when the blocker doesn’t touch the ball.

  3. There are lots of non-terminal blocks. At the NCAA women’s collegiate level, only about half of block touches are stuffs or tools/errors. It’s not always clear whether a “block touch” (that doesn’t result in a point for one team or the other) is a positive or negative play.

  4. It can be difficult to separate the performance of the player from the system they play in.

  5. The standard box score in the USA, the NCAA box score, is mediocre at best at giving you blocking information.

There’s a lot to unpack here, and we’ll need more than one article to really discuss blocking. Let’s start with the last point first.

NCAA tournament box score: BYU vs Texas

NCAA tournament box score: BYU vs Texas

We’ll take a look at this NCAA tournament match between BYU and Texas. The blocking information here is contained in the columns BS (“block solo”) and BA (“block assist”). The first thing you realize is that these distinctions are almost meaningless in modern NCAA volleyball, where almost every team is in some sort of help-block system. Between the two teams, there were 24 stuff blocks, and only one was scored as a solo.

By the NCAA blocking criteria:

A block assist (BA) is awarded when two or three players block the ball into the opponent’s court leading directly to a point. Each player blocking receives a block assist, even if only one player actually makes contact with the ball.

That means that on these plays, both blockers received equal credit:


This isn’t to diminish the efforts of the Texas or BYU middle blockers. Their job is to try to get involved in the play as best they can. Middle blockers can and do block balls when they are this late by hitters who hit the ball low into the sharp angle. But on these particular plays, it’s difficult to say that both players should be credited with an equal impact on the play.

It also happens (although not in this match) that middle blockers will stuff a quick attack and one of the wing blockers will take part in the block enough to get credit for a block assist, although they were barely off the ground at hitter contact.

International blocking statistics do a bit better, as they only credit one player with the block.

Serbia box score, 2018 FIVB World Championships Gold Medal Match vs Italy

Serbia box score, 2018 FIVB World Championships Gold Medal Match vs Italy

The FIVB statistics also add an “attempts” category, which is every touch the blocker made in that match.

That gives us a little bit more information (2 out of 4 of Busa’s touches were stuffs, compared to 2 out of 7 for Ognjenovic, that seems to give her an advantage), but both box scores neglect very important information:

Both NCAA and FIVB box scores only provide information about the most positive blocking touch (stuff block) and neglect to include the most negative blocking touch- a tool or blocking error. This would be like recording only kills and aces and not hitting or serving errors. (Whoops, the FIVB box score does that as well? Well… at least it’s consistent!)

Just as hitting efficiency has a higher correlation to winning than just kill %, blocking efficiency has a higher correlation to winning than just recording stuff blocks.

The stat I really like for Blocking Efficiency is “Stuff to Tool Ratio.”

BYU vs Texas Blocking Statistics, courtesy of Volleymetrics.

BYU vs Texas Blocking Statistics, courtesy of Volleymetrics.

Although the traditional box scores might be lacking, using more advanced statistical programs like DataVolley or Volleymetrics can give us more insight into what happened on the blocking end.

Quite a few individuals see their blocking statistics change. For Texas, Butler was credited with 4.5 blocks (9 BAs, each worth 0.5 blocks) by the NCAA but 7 by Volleymetrics. On the flip side, Johnson dropped from 3.5 (7 BAs) to 3 and Gabriel and Eggleston dropped from 2 BAs each to 0. BYU also sees some similar changes based on who actually made the block on each play.

Where it really gets interesting is when blocking errors are factored in.

At first glance, one of the stories of this match was the blocking advantage Texas had. They outblocked BYU 15 to 8! (NCAA box score actually had Texas with 16 blocks, but it’s likely they counted a ball BYU attacked into the net as a block- that happens sometimes.)

However, look at the blocking error column. This includes the blocker being in the net, as well as the hitter tooling the block. Texas had 16 and BYU had 9. Now things don’t look so lopsided. In terms of a raw +/-, both teams gave up 1 more point via blocking error than they scored via stuff block.

We could also measure this in a ratio or efficiency rating, by dividing stuffs by tools. In this case, Texas was 15/16 = 0.94 and BYU was 8/9 = 0.89. I call this ratio blocking efficiency, stuff-to-tool ratio, or just, “Stuff to Tool.” This is a stat that few people use that automatically upgrades your understanding of blocking effectiveness, both on the individual and team level.

It’s reasonable to assume that stuff blocking correlates well with blocking efficiency, just as kill % correlates well with hitting efficiency. But just as some hitters are high kill and high error, some blockers are both high stuff and high tool. This statistic also helps us coach. Teach players that their number-one job as a blocker is to stuff the ball. But the next most important thing is to not give up an easy point by getting tooled! Understanding both sides of this equation makes blockers better.

This brings us back to another reason NOT to use block solos and block assists. Using the NCAA logic for assigning block assists to any blocker involved in the block, whether they touched the ball or not, we’d have to assign block errors to any block involved in the block, whether they touched the ball or not. This strikes most people as absurd, so why are we assigning block assists to both players when only one made the block?

To summarize:

  1. Block assists are almost meaningless. The FIVB method of assigning the block only to the player who blocked the ball gives more accurate information.

  2. Both NCAA and FIVB statistics neglect at least half the story by only listing stuffs and not blocking errors.

  3. Blocking efficiency (stuff-to-tool ratio) gives us a much clearer picture of a team or individual’s blocking effectiveness than just stuff blocks.

In Part 2, we’ll look at how the GMS Stats App analyzes blocking, why it’s different than Volleymetrics, and what to do about all those pesky non-terminal block touches!

What Is Point Differential And Why Does It Matter? - Part 2

Stats LessonsJoseph Trinsey

In our earlier post on Point Differential, we talked about different ways of expressing the score:

  • Total number of points (ex: 104 to 103)

  • Point Differential (ex: 50.2% to 49.8%)

  • Sideout Differential (ex: 56% to 52%)

While each of these has value, Sideout Differential probably tells us the most information, because, in addition to telling us the margin of victory (or defeat), it tells us a little bit about how that victory (or defeat) occurred. A 70% to 68% Sideout Differential tells us something different than a 50% to 48% Sideout Differential. But what else can Sideout (or Point) Differential tell us?

In terms of Sideout/Point Differential, there’s 6 scenarios that can happen:

  1. We have a large positive differential and win the match.

  2. We have a large negative differential and lose the match.

  3. We have a small positive differential and win the match.

  4. We have a small positive differential and lose the match.

  5. We have a small negative differential and win the match.

  6. We have a small negative differential and lose the match.

(There’s also the scenarios where the differential is zero and we either win or lose, but those are similar enough to scenarios 3 through 6 to lump them in together.)

Scenarios 1 and 2 aren’t too interesting. Well… maybe interesting in some ways, but in terms of statistical analysis, not so much. You were either much better or much worse than your opponent that day and there was a gap in fundamental skills. Small tactical adjustments aren’t going to erase a 7-point lead.

Scenarios 3 through 6 on the other hand, are a lot more interesting to us from a statistical perspective, and these scenarios are ones where little things can tip the balance of the match one way or the other.

These four scenarios recently played out in the NCAA Women’s Volleyball Final 4. Let’s take a look, using our handy GMS Stats app to produce the analysis.

In our post breaking down the Final Four match between Illinois and Nebraska, we saw scenarios 4 and 5 play out, depending on which side you were rooting for.

Illinois Point Differential Screen, GMS Stats app

Illinois Point Differential Screen, GMS Stats app

Illinois outscored Nebraska, but lost the match. Or, if you’re a Nebraska fan, Nebraska got outscored, but won the match. This is uncommon, but it does happen. In a 5-set match, the team that scores more points only wins about 78% of the time.(*) On the flip side, we have the Stanford - Nebraska Championship Match:

Stanford Point Differential Screen, GMS Stats app

Stanford Point Differential Screen, GMS Stats app

On average, the margin of victory in a 5-set match is about 5 points, so these two matches were close even by 5-set standards.

In the post on that match, we looked at some of the rotational matchups and how they played out. A lot of times, these matchups can be the difference in a close match. Rotational order, matchups, and clutch play are the three things I like to look at in close margins of victory.

The danger with looking at one match is that there’s a lot of randomness involved. Is that rotation in which you gave up a late-game run really a bad rotation, or did you just catch a hot server at the wrong time? This is why we like to take a multi-match view of things. In the GMS Stats app, we built that into the Wizard feature:

Point Differential Wizard Screen, GMS Stats app

Point Differential Wizard Screen, GMS Stats app

This is the Wizard screen I pulled from a 5-match sample. I was coaching a professional team and wanted to see how our performance translated into wins and losses. For a single match, this translation is simple: if you outscore your opponent, you expect to win the match. If you get outscored, you expect to lose.

But things get trickier over multiple matches. If your Differential is 2% over a half-season, what do you expect your record to be? How about 5% or 10%? Fortunately, the Wizard feature does these calculations for us!

In this 5-match sample, we had a small but meaningful 2% edge over our opponents and that resulted in a 3-2 match record. Intuitively, we probably sense that this is about right. Maybe, we figure, we might be able to go 4-1, but 5-0 feels like a stretch. On the flip side, since we’ve outscored our opponents, we probably feel like 2-3 would be a letdown and 1-4 would indicate that something is really wrong.

Over time, this Point Differential-to-record translation can give us coaching insight. We can now imagine the previously mentioned scenarios reducing down to:

  1. We are outscoring our record.

  2. We are underscoring our record.

If we were 5-0 with a differential of only 2%, this would tell us some things as coaches. The first thing it would tell us: don’t get too cocky just yet, we’re probably getting lucky! It would also indicate that, while the results are good now, we should still be looking to make some changes if we want to keep the winning streak alive. There’s always the temptation to not make changes when the team is winning, but in this scenario, changes might still be warranted.

If we were 2-3 or 1-4 with a positive point differential, it tells us something different. First of all, it tells us that we’re not doing quite as bad as we think! So, while some changes are probably necessary, they might not need to be as drastic as the record might indicate. It also tells us that we’re probably losing some close games, and we have an opportunity to close them out better.

In this case, we look at our three Close Game Factors:

  1. Rotation Order

  2. Matchups and Scouting

  3. Clutch Play

More on these factors in our next post!

(*) 5-set win % and margin of victory data provided by Volleytalk legend The Bofa on the Sofa.

2018 NCAA Championship Breakdown

Match AnalysisJoseph Trinsey3 Comments

In the 2018 NCAA Women’s Volleyball Championship, Stanford edged out Nebraska in a close 5-set match. We saw in this post that Nebraska won their semifinal match over Illinois despite being outscored by 1 point. The margin in the final was the same, but this time Nebraska wasn’t able to pull it out.

End of match screen. GMS Stats app.

End of match screen. GMS Stats app.

The first two sets were close, but sets 3 and 4 saw each team trading blowouts. In an eerie recall of the semifinal vs Illinois, a challenge decided a crucial point late in the 5th set, but this time it didn’t go Nebraska’s way. Stanford won the 5th and earned their 8th National Championship.

In a match this close, we expect the margins to be thin statistically, and that was the case in this match.

Stanford Point Differential Screen

Stanford Point Differential Screen

Both teams were at about 59% sideout for the match, which, while lower than their season averages, is also higher than either team typically allowed. This is common- seeing the overall sideout rate in a match between two top teams end up about halfway between what those teams sided out and what they allowed against most other opponents. Let’s break down the statistics more to see if they can tell us where the slim margins of victory came from. All screens and statistics courtesy of the GMS Stats App!

When I analyze a match, the first thing I do is look at the overall Point Differential, and see the Sideout level for the match as a whole. The next thing I like to do is look at the 3 Key Factors to Sideout. We’ll look at both the Stanford and Nebraska Sideout Key Factors to see the similarities and differences.

Stanford Sideout Key Factors

Stanford Sideout Key Factors

Nebraska Sideout Key Factors

Nebraska Sideout Key Factors

Plenty of similarities here. Both teams passed well. Nebraska hit significantly better In-System (attacking after a Good Pass) than Stanford, but Stanford was better Attacking Out-of-System- after a Bad/Medium Pass or in transition. So Nebraska was In-System a lot, and hit well when they were. That’s usually a recipe for success. Let’s look at the defensive side of the ball to find out a little more information.

Stanford Opponent Sideout Screen

Stanford Opponent Sideout Screen

Nebraska Opponent Sideout Screen

Nebraska Opponent Sideout Screen

Again, plenty of similarities. Nebraska dug a bit better while Stanford blocked better. Blocking can be a deceiving stat because while Stanford only outblocked Nebraska 10 to 9, they did so while only giving up 22 tools/block errors, while Nebraska gave up 34. Hitters on both teams scored off the block well, but Stanford was better here. However, this was compensated by Nebraska being the better defensive team. Again, percentages come in handy. Both teams had 69 digs, but Nebraska dug those 69 balls on 100 chances, while Stanford had 119 chances to dig.

Since Nebraska hit better on the match, we see, as we often do, that backcourt defense has a bit stronger of an effect (in NCAA Women’s volleyball) than blocking on the opponent hitting efficiency.

Finally, we see the serving. Both teams knocked the opponents Out-of-System at a similar rate. All told, the Key Factor statistics were close, as you might expect when the Sideout % (and thus, overall points scored) is so close.

So what was the difference?

If we walk it back to the first image in this post, we see there were 209 total points scored this match: 105 by Stanford and 104 by Nebraska. Since Nebraska out-hit Stanford, we’d expect them to be better within the rally, and that was true. If we take away service errors and aces, and isolate only the points where a rally took place (meaning at least one of the teams got a chance to attack), we see the following:

Total Rally Points: 182

Nebraska: 94 (72 Kills, 9 Blocks, 13 Stanford Errors)

Stanford: 88 (65 Kills, 10 Blocks, 13 Nebraska Errors)

So indeed, Nebraska was 6 points better within the rally. But now let’s look back at the No-Rally Points, where there was either an ace or a missed serve:

Total No-Rally Points: 27

Nebraska No-Rally Points: 10 (2 Aces, 8 Stanford Missed Serves)

Stanford No-Rally Points: 17 (9 Aces, 8 Nebraska Missed Serves)

So Nebraska was 6 points better within the rally, but Stanford was 7 points better when no rally happened at all! We find that this happens quite a bit- the team that wins the match was no better, or even slightly worse when, “volleyball happened,” but a substantial margin in the serve-pass game can often compensate for that.

With so much attention on Stanford’s size and power at the net, and the flashy digs by libero Morgan Hentz, it’s easy to forget that the serve-pass game so often dictates the winner and loser, even (especially?) at the highest levels.

What Is Point Differential And Why Does It Matter? - Part 1

Stats LessonsJoseph TrinseyComment

In a previous blog post we showed the Point Differential screen of the GMS Stats App.

Nebraska Point Differential Screen; 2018 National Semifinal vs Illinois

Nebraska Point Differential Screen; 2018 National Semifinal vs Illinois

But what is Point Differential? Why does it matter? How does it affect me as a coach?

Point Differential is simply the difference between how many points I score, and how many points my opponent scores. It’s a fancy way of saying, “the score.” Most of us intuitively grasp the following two equations:

Scoring More Points Than The Other Team = “Good”

Scoring Fewer Points Than The Other Team = “Bad”

“Great, Joe, score more points than the other team. I can see why the National Team hired you.”

Calm down, unnecessarily sarcastic imaginary reader.

This screen from the app actually shows Sideout Differential, which gives us a little bit more information. This screen shows Nebraska (“your”) Sideout and Illinois (“Opponent”) Sideout. What’s the difference between Sideout Differential and Point Differential? It’s really just a matter of perspective.

Quick Volleyball Stats 101 Lesson:

A “Sideout” is any time the other team starts the rally with a serve and we win the point, whether they miss the serve, or we win on the first chance to attack, or it’s a long rally that we win in the end. If the rally started with the opponent serving, and we win the point, that’s a Sideout. (So an Opponent Sideout is any time we start the rally with the serve and the opponent wins the point.)

“Sideout %” is the number of chances we had to sideout (also: the number of times the opponent serves) divided by the number of times we actually sideout. So if the other team serves 100 times, and we sideout 54 times, our Sideout % is 54%.

So Sideout Differential is just a way of looking at Point Differential from a different perspective. You cannot have a better Sideout % than your opponent, but a worse Point Differential. And you cannot have a better Point Differential than your opponent, but a worse Sideout %. So why use Sideout Differential instead of Point Differential?

The best reason is that Sideout % is a reliable indicator of level of play. In U12 volleyball, the serving team has an overwhelming advantage. Sideout % is well under 50%. At the professional level, the receiving team now has the advantage. Both teams will Sideout well above 50% in most matches. So imagine two different matches:

Match 1: Sideout 41%, Opponent Sideout 40%

Match 2: Sideout 61%, Opponent Sideout 60%

In both these matches, the Differential was 1%, but the style was different. Match 1 was a defensive battle where both teams were going on runs. Match 2 was more of a sideout battle, with the offenses at an advantage over the defenses. Knowing the differential as well as the overall sideout rate between the two teams allows you to get an idea of the overall level of play, as well as how your team fared.

In Part 2, we look at more uses of Point or Sideout Differential and go from looking at one match to multiple matches.

NCAA Women’s National Championship Semifinal Match Analysis

Match AnalysisJoe TrinseyComment

Nebraska vs Illinois… could it be any closer?

Today we’re going to dive deeper into one of the best matches of this year’s NCAA tournament: the Semifinal between Nebraska and Illinois. The teams met twice during the regular season, and each came away with a 3-1 victory (oddly, Nebraska won at Illinois and Illinois won at Nebraska) and the third matchup between them would determine who would advance to the National Championship. With so much on the line, fans expecting a close match would not be a disappointed!

We’ll be using the new GMS Stats app (available now in the iOS App Store!) to break down these matches. Right away, you can see how close the match was:

End of match screen. GMS Stats app.

End of match screen. GMS Stats app.

Not only was the margin of difference only 1 point between the two teams, Nebraska was actually outscored by Illinois, yet still won the match! As coaches, we love when this works out in our favor, but it’s heartbreaking for Illinois.

Illinois point differential screen. GMS Stats app.

Illinois point differential screen. GMS Stats app.

Illinois outscored Nebraska by 1 point, but the margin was actually a bit greater than that in Sideout % terms. Illinois was a full 1% better than Nebraska over 207 serves, because (due to coin flips and how the end of the games worked out), Nebraska actually had 105 chances to side out, while Illinois only had 102. At Gold Medal Squared we talk about how there are, “no little things,” because we can see how razor-thin the margins are.

When matches are very close, one of the things to look at is end-of-game play. What’s interesting about the end of games is that they mirror the beginnings. The reason the starting rotation is so important is not because points scored at the end of the game matter more than points scored in the middle, or that it’s important to get off to a, “good start.” They don’t, and it’s not- at least not any more than it is important to be good every other time of the game. No, the reason the starting rotation is so important is that teams will usually rotate around two full times, serving and receiving in each rotation twice. However, the first rotation will almost always get a third turn. (In a game where both teams are siding out a lot, the teams will rotate around faster and in a game where both teams are going on long runs, they will rotate slower.)

This third turn is critical because it means that the rotational matchup you start the game with will come up at the end of the game, where you either have the chance to win the game with a run, or lose it by giving one up.

In game 2, Illinois had the serve to start and they opted to start, as they usually do, with their setter, Jordyn Poulter, as the first server. Nebraska matched up against this by receiving with their setter in 1. This can be a tough rotation for many teams, because the outside attacker is on the right side of the court, and the opposite is on the left side. In this case, Nebraska had their outside, Lexi Sun, passing in the middle of the court and attempting to hit in the middle.

Poulter attacked the seam between Sun and libero Kenzie Maloney and gave Nebraska all sorts of trouble. The first serve was an ace between Sun and Maloney. Maloney passed the second serve well, but Illinois blocked Sun in the middle. The third serve was another ace between Sun and Maloney. On the next play, Nebraska then tried to pull Sun over to the left side and stack their attackers over on that side. They got a good pass, but Sun hit out. At this point, Illinois was up 4-0. On the next play, Illinois won a rally after picking up Sun’s tip and then digging a big swing by Nebraska opposite Capri Davis and scoring in transition. Nebraska shuffled Sun back to the middle of the serve receive and Poulter served another ace into some confusion on the Nebraska side. Finally, Nebraska shifted Sun over to the right side and had Maloney and Mikaela Foecke pass in a 2-person sideout, and they got the sideout.

By then, the damage was done, and Illinois cruised to a win in the second set.

After Nebraska won the third set, Illinois would start the fourth set with the serve. Since Illinois almost always elects to start with Poulter as their first server when they serve first, Nebraska could decide whether they wanted to change their rotation to create new matchups or stick with the matchup they had in game 2, and try to execute better.

As coaches, we face this dilemma all the time! Nebraska obviously planned to receive in rotation 1 because it’s a strong rotation for them. They aren’t dumb; they have the statistics about how their rotations have performed previously. Yet as coaches, we see the matches evolve in front of our eyes and we have to decide, “do we stick with what has worked in the past, or am I seeing something that needs to be adapted to in the present?”

Nebraska opted to make a change; they backed up one rotation, so that instead of Poulter serving at rotation 1, she served at Nebraska’s “Setter-2” rotation, with Nebraska setter in zone 2, and Lexi Sun and middle Callie Schwarzenbach in the front row. This was a bold move by Nebraska, because this had not been their strongest rotation; in fact, for the match as a whole, it ended up being their weakest sideout rotation!

Nebraska rotation screen. GMS Stats app.

Nebraska rotation screen. GMS Stats app.

But in game 4, it worked out just fine. With the change in rotation, Nebraska had an additional defensive specialist in the game, as well as Maloney and Foecke, two strong passers. Sun was also freed up to be out of serve receive and on the left side of the court, to do what she does best: hammer on the left side of the court. Nebraska passed the first serve well, Sun got a good swing and Illinois was only able to bring back a freeball, which Schwarzenbach killed on the slide. For bonus points, notice how Schwarzenbach stayed in front of the setter on the first ball (possibly because the pass came off the net a little), but then went on a wide slide on the freeball. Illinois OH Beth Prince was pulled in a little tight, possibly expecting Schwarzenbach to run a quick in front or worried about Foecke attacking out of the backrow. This subtle change helped get Schwarzenbach an open net and an easy kill.

Game 4 was off to a better start for Nebraska than game 2, but they still needed to close it out. At 21-19, both teams had rotated all the way around twice and now entered the critical “third turn” that is created by the rotation order. Poulter went back to serve and Nebraska was again in their Setter-2 rotation. Nebraska was unable to sideout on the first ball. They tried Schwarzenbach on the slide, but the Illinois block was ready for her this time and slowed her down enough for an easy dig, which Illinois turned into a kill out of the middle to cut the lead to 21-20. Poulter missed the next serve and Nebraska setter Nicklin Hames ran 3 points in a row to close out game 4 and send it to a 5th and deciding set.

5th sets present some new challenges for coaches. First, the dynamics of rotations are different. Instead of rotation all the way around twice and having 1 or 2 rotations come up for a third turn, you will generally rotate all the way around once and have 1 or 2 rotations NOT come up for a second turn. So, it may be less about maximizing a good rotation and more about minimizing a bad rotation. Additionally, many coaches like to start with the rotation that puts their best attacker in zone 4, wanting to maximize the number of sets they can get her in the critical 5th game. And some coaches like to stick with what they’ve been doing and start game 5 in the rotations that have been strongest all year or in that match.

Chris Tamas of Illinois had an option that could do two of these things at once. He opted to start with the Setter-6 rotation, which put his All-American OH Jacqueline Quade in zone 4 and was also a very strong rotation overall.

Illinois rotation screen. GMS Stats app.

Illinois rotation screen. GMS Stats app.

Nebraska, possibly expecting Illinois to start with Poulter serving and wanting the same matchup as game 4, started receiving with their setter in 2. The change by Illinois created matchups that had not played out earlier in the game, which causes both teams to adjust on the fly. And because a 15-point 5th-set doesn’t create the same “third turn” as 25-point games do, this meant there was less predictability in how the end of the game would play out.

Both teams traded points throughout the whole set. At 11-11, Illinois was back to serve with their Setter-4 rotation, which was good news for them. Throughout the match, they had been strong defensively in this rotation. With defensive specialist Taylor Kuper serving, they held Nebraska to under 50% sideout. Illinois fans could reasonably expect to score at least one point and gain a critical lead late in the game.

Unfortunately for Illinois, it was not to be. Nebraska was receiving in their Setter-6 rotation, and you can see in Nebraska’s rotation screen that this was a very strong rotation for them as well. National Semifinal matchup, coming down two of each team’s strongest rotations. It doesn’t get any better!

Nebraska sided out on their first chance on a big swing by opposite Jazz Sweet to go up 12-11. On the next rally, Nebraska dug a big swing by Quade, but Foecke appeared to hit out in transition. On replay, the smallest of touches was shown, reversing the call and giving Nebraska the point. So close! Kenzie Maloney served an ace for Nebraska to put them up 14-11, and on the final point, after some fantastic defense by both teams, Foecke found the Illinois end line to give Nebraska the match and send them to the National Championship.

What a match!

There’s lots of lessons coaches can take away from a high-level match like this. For me, the biggest lesson is:

Know your strongest rotations, but be prepared to adapt to what you see. Setter-2 rotation was not especially strong statistically for Nebraska, but coach John Cook saw a passing rotation that could handle Poulter’s serve better and give them a better shot to handle the matchup that the game presented as it played out. And sometimes the margins are thinner than you can imagine, two great teams playing dead even, with a fraction of a touch on replay making the difference.

I hope you enjoyed this analysis. If you want to do your own rotation analysis, check out the GMS Stats app on the iOS App Store!