Long Beach State In-System Atk (Men’s)


Just got access to the men’s side of the data so I’m still playing around with a few things.

What you see above is a data-driven visualization of what coaches might term “the key guy to stop.” In recent years with a team like BYU, the common phrase was “Taylor Sander is going to get his kills, let’s focus on the other guys.” So what I’ve built is essentially a histogram of what players hit in any set they appear in – and then color code   lost sets in red and won sets in…turquoise? *only sets in which players have 3 or more in-system hitting attempts count – and players must appear in a minimum of 30 sets during the mpsf conference season to be counted.

LBSU cohensd

What you’ll notice from the visuals is that yes, TJ DeFalco certainly has an impact between won/lost sets, it’s actually Amir Lugo Rodriquez who’s hitting efficiency carries the most weight. The likely reason is that if Amir gets going, LBSU can get pin hitters 1 on 1’s much easier as opponents move to front the quicks. Another possibility is that this doesn’t actually prove causation – and that Amir hits better when LBSU passes better. This is also fair, but again, we include the data if Amir has at least 3 attempts in the set.

I like this visual because it makes sense to look at – coaches can see that shift between won and lost sets, but to also include the actual cohen’s d and magnitude levels supplies additional statistical weight to the problem. I’d like to use this approach more frequently moving forward – in both the men’s data from the spring and the women’s data currently coming in from the Big Ten and Pac 12 this fall once conference kicks off.


Offense/Defense by Shot Type


So what you see above is how well each team in the Big Ten attacks when they use a variety of shots. Originally I was interested in looking at how well teams both used and defended against the tip, so that’s why they’re ordered in this manner.

As you might expect when looking at full swings, teams like Minnesota, Wisconsin, and PSU really excel. The former two via speed and range while the latter team goes slower and hits harder. But overall, the order of teams in this category isn’t shocking.

Something I wasn’t expecting was how poorly teams score on off-speed rolls. It would be interesting to look at the percentage of these that are off the block for tools rather than just being slow, poor attempts at swings that go awry.

And leading the pack in tipping efficiency, the Fighting Illini. Not necessarily sure what to make of it, but there it is. If you’re playing Illinois, you should probably have your tip coverage figured out. If you’re playing Maryland, don’t worry about it.


Here’s the viz I was even more intrigued by – how well do teams defend against each speed of attack. Nebraska separates themselves in this field. They handle both hard driven attacks and tips at the best rate, and roll shots at the third best (behind PSU and Minnesota). Moral of the story. Don’t tip on Nebraska? People who have played them recently understand the scrappiness of their backrow, spearheaded by JWO who departed the program this spring for a summer in Anaheim.

One interesting takeaway from this is the mediocrity of Minnesota’s tip coverage. Personally, I expected a defense built with the understanding that the off blocker dedicates herself to rolling under to cover the tip to perform better than average.

You could of course expand of this concept to turn it into something more actionable. How does Ohio State handle the tip on the Go when you run Go-Slide at them. Or how successful is Wisconsin at tipping on the Slide when they throw Go-3-Slide at you and force your middle to front the quick. Or even from a personnel basis, what’s the average dig quality when you tip against their setter versus their opposite. Having these ideas in the back pocket might be a get out of jail free card if you find yourself stuck in a tough rotation.

Attackers’ Trends + Visualizing Development

attacker trends.jpg

Here are how four of the key outsides with the top teams in the Big Ten looked from the start of conference play until their respective seasons ended. Output Efficiencies are calculated using data from both the Big & Pac 2016 seasons and look at not only the kills/errors/attempts, but also the value of non-terminal swings. In this case OutputEff differentiates between a perfect dig by the opponent and a perfect cover by the attacking team – or a poor dig versus a great block touch by the opponent – etc. In this sense it’s better than traditional “true efficiency” in that it’s not just about how well your opponent attacks back after you attack – but it also appropriately weights different block touch, dig, and cover qualities as to their league-average value.

What you see above is the trends of these outsides over the course of the season. Foecke continuously improves as the season, as does Haggerty for Wisconsin. Frantti is interesting in that she actually declines up until early November then turns it on as PSU approaches tournament time. Classic Penn State. If Wilhite didn’t hit for over “.600” early in the season, she wouldn’t look like she’s trending down – but you have to keep in mind that her average (just north of .300) kinda blows people out of the water when you look at her consistency.

Personally, while I think this type of stuff is mildly interesting and you can definitely spin a story out of it, it’s not actionable in the sense that it’s going to help a coach make a better decision. However, this same principle could and probably should be applied on an in-season basis to look deeper at the development of players and specific skills. For example, high ball attacking:


You could build something like this for every day in practice. If you goal is to pass better, cool, let’s take your data from practice and graph it for the week and see if whatever changes we’re trying to implement have had their desired effect. Or let’s see if the team is improving as a whole as we make these specific changes:


*the asterisk on 10/29 is because volleymetrics coded both MN matches from that week on the same day, so the date on the file for both says 10/29. That’s why we use Avg. Output Eff.

Anyway, there are thousands of ways to implement something like this – and then turn it into some digestible and actionable for the coaching staff.

Coverage by Attack Zone


We looked at this a couple times at Illinois but I don’t think it was ever concrete enough to become actionable in terms of devoting practice time to it. What you see above is where coverage digs/errors were made by teams in the Big Ten and Pac 12 this season. What this does not show is the accurate percentage of where balls actually landed. But if there was a dig code after your attacker was blocked, this shows the location of those codes, broken down by zone. The start zone is where the attack took place, the end zone is where the cover took place.

The trend is pretty obvious: most coverage touches occur in the same zone that the hitter attacks in – makes sense. You’ll also see a similar trend into the middle third of court, where attackers in zone 2 are likely to be covered in zone 9 (between 2 and 1 down the line) and likewise for zone 3 and 4 attackers who see a slight rise in zone 7 and 8 coverage, respectively.

Naturally, you might want to play the percentages and make sure whatever zone you attack in, you have strong coverage for as well. What this chart doesn’t break down is the type of set the attacker is going after, nor does it account for the general tendencies of each hitter. If you have an attacker like Sarah Wilhite who likes to bounce balls down the line and attack deeper into the angle, then maybe you want to cover directly under her in zone 4 – and you might expect more deflections to land in zones 3 and 8 when she attacks angle.

I think coverage studies are interesting, but you lose way too much in the aggregate. Personally, when I’m thrown out there as an outside, I know I attack high down the line and low around the inside of the middle blocker’s inside hand. So if I was designing coverage around myself, I would make sure to have someone in zones 7 and 5 (deep down the line behind me) and then throw the rest of the team into zones 3 and 2 if that middle happens to have bigger hands that I’m expecting… Or pulls a Hannah Tapp and just dives hard into the cross because she knows closing the seam is overrated sometimes.

Anyway, that’s all I have to say about that.



Attackers above the norm


So I’m procrastinating on my thesis again and wanted to see how attackers stacked up relative to the league – on a per team basis. I’ve measured success via Eff Change in this case, built using league averages. For example, a Big Ten player who attacks in zone 2 after a perfect dig in transition is expected to have an efficiency of 0.274. So if you kill the ball on this perfect dig, your Eff Change is 1.00 minus 0.274 or 0.726. Or if you’re attacking out of zone 4 after a perfect pass (R#) you’re expected to hit 0.335. So if you take that perfect pass and hit into a “good” dig (an output eff of 0.011), you’ve actually dropped your efficiency by 0.324.

All of these individual input to output situations are accounted for – as well as the zone you attack out of. From there I can create an aggregate average Eff Change for each attacker based on these expectations.

Not surprising, the top teams are mostly in the green – and Michigan State holds on tightly to their stereotype of being super terminal in both directions. For the most part, setters, ease up on the dumping…Kelly Hunter…..


Here’s again what this looks like for the Pac 12. Of course the league averages aren’t the same between the two leagues, so just be aware of that.

I’ll just leave these here and let you come to your own conclusions, but the gist of these visuals is: how well do your hitters handle the situations they’re dealt, relative to the league. As you can see, this is a much quicker way to see who is terminal when they’re supposed to be and who understands swing management when they need to play it safe.

Heatmap Intro


*the net is at the top. the endline is closest to this text

In the land of cones and zones…and subzones, it’s easy to forget that these are merely representations of locations on the court. Equipped with that logic – and sparked by our friends at VolleyMetrics – I converted the zones to xy coordinates.

Yes, it’s annoying that the x-axis is above the heatmap, but I wanted to throw this up here anyway. It’s the hitting efficiency of attackers in the Big Ten in 2016 based on where the set came from – and it’s what you’d expect. Perfect passes lead to good things, moving your setter forward isn’t terrible, but moving your setter backwards is horrible. Below the hitting efficiencies in each grid are the frequencies.

One thing I think that gets overlooked is being content with medium passes. It goes back to the FBSO posts and why getting aced hurts. Yeah you’re hitting .100 instead of .300, but at least you aren’t losing the point 100% of the time like when you get aced or fail to dig a ball. That’s why keeping digs on your side is huge. As long as they don’t result in a set from the endline, you’ll be hitting positively – rather than defending against a perfect pass after you overpass.


Here’s the same thing for the Pac 12 from 2016. I’ve set the cutoff for both graphs between green and red at 0.150. But again, the frequencies are below the efficiencies so I would definitely not devise an offensive system that runs through zone 1D. Just thought I’d add it for those who were curious.

Anyway, looking forward to messing around with heatmap-type stuff to answer a variety of questions. The inherently great thing about heatmaps is that they convey ideas incredibly quickly. You glance at it and understand that you’d want to pass into the green rather than the red. Stuff like this for serving targets, setter tendencies, attacker tendencies, etc  can all be addressed in this fashion. Heatmaps aren’t a novel concept for scouting by any means – both VM and Oppia have employed them (I believe) – but I got curious if I could build something similar and it looks like it’s prety easy to do with either R + ggplot2 or Tableau. If you have suggestions for ways to employ this, I’m all ears.

Out of System Thought


It’s a pretty crude visual, but an interesting topic. So I was out in Boston this past weekend for the Sloan Sports Analytics Conference and got a chance to talk stats with the VolleyMetrics crew for what probably seemed like 48 straight hours to them. You should read their paper, it’s a great step forward for volleyball analytics. When I landed on Thursday, I grabbed a beer with Matt Jones, a guy I coached with in SB who has gone on to bigger and better things as an assistant for the men and women at Harvard.

One of the topics was around where you’d be better off setting a high ball from. My contention was that when I attack an out of system ball, if I can see the set and the block at the same time, this should make it easier to aim my swing. Biomechanically, this angle is also more similar to what an outside is used to seeing – rather than a ball coming over his shoulder from 15 feet off the net.

So what you see above is the court broken into zones/subzones and the hitting efficiency of attackers in the Pac 12 from the 2016 season when the set originates from the zone in question. This is only for high sets to the OH. It does not differentiate who is setting, just that these are classified as High and not Go sets to the left side. If there were fewer than 40 attempts from the specified subzone, I crossed them out.

I’ve circled subzones for which hitting eff is above .130, a completely arbitrary level. You can determine on your own what level you expect, but it offers an interesting conclusion that seems to support my hypothesis. Attackers in the Pac 12 hit better on this type of set when the set originates closer to the net/closer to the middle of the court. I’m admittedly posting this quickly rather than creating a pretty visual and running it for the Big Ten, but I hope it sparks a new thought for some readers.

What this might suggest is that rather than having your setter dig balls over to your libero in left back, you might want to dig to about 5-10 from the net and let the next set originate from the middle of the court. As you can see, zone 3 is a great spot for this set to come from and may influence how you handle easy digs by your setter.

Of course this is a little different for each team and if your go-to high ball hitter is your rightside, you’d run this looking in the other direction. But hopefully this at least makes you rethink casually shoveling setter digs to left back. If you look at Stanford over the last few years, often it would be Inky or Lutz setting out of zone 3 to the pins. Admittedly this takes time to get your middles to do this, but a bumpset by your libero or even using your BR outside to handset from zone 3 might be the way to go.

*edit* just kidding, ran it for the Big Ten – circled those above .130 again. Some subzones have a small number in the upper left corner, that is the number of attempts if they were on the lower end of things. So we might view the effs up around .290 with a grain of salt for zones 2C and 3B.


Just looking at the numbers and ignoring my circles, this viz might ask more questions than it answers. Couple thoughts just off the top of my head that are merely possibilities. Do the setters who set from tight to the net also put up sets that are too tight for most hitters to deal with – leading to those .009 and .071 effs? Are blockers in the Big Ten better overall than the Pac12? Are attackers in the Big Ten worse? Are defenders in the Big Ten better? Why are zones 8C/D and 5B better than others? Is it because teams train from 8c/d more often? Is this actionable if I’m a coach in the Big Ten? (probably not).

I need to convert my subzones to xy-coordinates so I can start running “heatmap” type visuals…

One idea is to create a setter cheatsheet showing how different set origins should be distributed to different players (perhaps even by rotation if you’re fancy). Created visually, a setter could see that “perfect” passes should go to Mary-Kate, while medium passes toward zones 4/7 should go to Ashley, while poor passes to zone 6 should go to some other Olsen sister – all in an attempt to help your setter distribute the ball more efficiently.