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.

Offensive Theory – Short Side Setting


Just so we’re all on the same page, what you see above is the attack efficiency when hitting a “Go” when the set originates from zones 4, 3, 2. Zone 4 making this is a “short side” set – and zone 2 making this a “long side” set…is that the right term? Without the alliteration it’s not as catchy. The team names are who you are hitting against. I’ve used the whole Big Ten season from 2016. I’ve filtered out those with fewer than 40 swings (eliminates Rutgers/Michigan Zone 4) to make sure we’re not generalizing from small sample sizes.

Offensive theory has a bunch of different tactics when it comes to setting, but they all revolve around the idea of moving attackers (and the ball) around put your offense in the best position. For example, a setter might run her middle on a 3 to overload the opposing right front blocker – or to isolate her right side attacker and hopefully get her a 1 on 1 situation if the blockers are fronting your middle. This is the basic idea.

To compound this is the concept of short side setting – meaning you set the hitter you are closer to. If the pass takes to forward into zone 4, you set the Outside. If the pass pushes you back into zone 2, you flip it overhead to the Rightside.

If you take a look at the viz, you’ll see there is pretty strong evidence to suggest you should short side the Go against Michigan State and Purdue. On the other hand, there’s also very strong evidence that you should NOT short side the Go against Northwestern, Ohio State, Minnesota, and Wisconsin. And for some teams it doesn’t seem to matter (damn you, Nebraska).


Here’s the same concept applied to the Slide. I’ve filtered out zones with fewer than 20 swings this time, but even with that, nobody really chucks the slide from zone 4 that often. Makes you wonder if you shouldn’t still be read blocking if they’re setting from zone 4…

Anyway, the majority of teams improve their attack efficiency as they move into zone 2 – with major improvements by Ohio State and Purdue when they short side the Slide. I could write more about these colorful bars, but I think you can decipher on your own.

The seed I would like to plant however relates back to the Optimal Choices / Optimal Choices 2 posts. With this information, why couldn’t you create the optimal offensive strategy? If you know you’re playing Minnesota, we’ve seen that you really really don’t want to short side the Go against Hannah Tapp and SSS, so why not prepare for that situation during practice and scouting? Maybe the better alternative is to flip a back 1 to the middle or the OH on the bic? Maybe the better choice is launch it back to your rightside attacker? Or if none of those are better than the 0.100 eff Minnesota typically holds short sided Go’s to, you better know where to tip that ball to get Minnesota in the worst possible position. Maybe it’s to the setter, maybe it’s to zone 4 as Wilhite and Hart roll under the block in zone 3. Maybe you try to recycle it off the block and get another swing on your side?

Regardless of what you choose, the principle is the same: why can’t we prepare to make the best possible choice once we recognize the situation? There is a statistically objective choice that is better than the rest. Why can’t we make the optimal choice when it comes to who we set?