Scout VB

NCAA Championship Volleyball

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!

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.

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!