Pac12 Pass Rating and W/L Sets

B1G Pass Rating in W/L sets

 

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Figured I would build a similar chart for the Pac12 from 2016. It includes all conference matches, not just the big ones. As with the Big 10 numbers, there are certainly some teams where pass rating doesn’t necessarily differentiate won/lost sets. By including all sets however, this may introduce more noise than we’d like – as a good team may lower their performance to the level of their opponent and still win the set.

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Here’s the same descending correlation data from the Pac as well. Washington State clearly takes the cake – while both UC’s seem to be unaffected by their ability to pass, interesting.

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I’ve included the full Big Ten data – with all conference matches include as well. Overall there’s nothing super shocking, though I did have a chuckle with the Rutgers spike. You’re welcome to deduce why their curve has such slim deviation.

Here again is the correlation data for the Big when you include everything.

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Heatmap Intro

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*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.

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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.

Efficiency Change – Pac 12

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Here’s just a quick snapshot of Eff Change taken from a late September match between UCLA and USC this season.

I’ve explained the idea behind Eff Change in a couple posts but figured I’d throw it out in a Pac 12 post for fun. The idea plays off the FBSO and Service Error concept that different teams have different strengths/weaknesses. I don’t know enough about the Pac 12 to generalize, but let’s say USC is a stellar offensive team, but they struggle to dig balls. With that idea in mind, getting a stuff block against USC – or perhaps serving them out of their in-system offense might be incredibly valuable, given their offensive strength. But on the flip side, if USC is a team lacking great ball control defenders, then maybe failing to rack up kills against them is pretty bad, seeing how USC already has a lower defensive baseline in this example.

You should go read the original post about Eff Change if you haven’t since it explains in more detail how all of this is calculated…but the idea is that you account for the two teams that are playing – as well as the specific context within the rally (good block touch, perfect dig, poor reception, etc). So you look at both in the input and output contacts. For an attacker, you look at the pass or dig that precedes the attack as the input – and the output is the block touch, kill, error, or dig quality that follows the attack. You then subtract the efficiency of the output from the initial efficiency of the input to create Eff Change. So if you have an input of a poor reception (maybe that input eff is 0.100 since it’s tough to score with that pass quality) but you take that and tool the block for a kill (a kill has an output eff of 1.00 because that team always wins the rally). So you started at 0.100 and ended with 1.00; so that 1-0.1 for an Eff Change of .900. This is a big deal since you started with a weak position and improved the outcome drastically. That’s the gist of Eff Change.

For some of the touches in that above UCLA/USC match, you’ll notice that Eff Change is actually above 1 and below -1 for some touches. So for Frager’s ace, UCLA is actually expected to lose with that input contact more often than not. So UCLA starts with an Input Eff of -0.247 (not surprising as the receiving team is almost always favored) but because she serves an ace (Output Eff of 1.00) her net Eff Change is actually over 1.

Anyway, just some stuff to think about for those who haven’t cruised through all the posts. Let me know what you think / if you have questions. I can talk about this stuff all day.

Blocking Responsibilities – Pac 12

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Just to preface this post, it’s basically the same as the responsible blocker one I put up earlier using the Big Ten data. That post explains exactly how responsibilities are split between blockers against specific types of sets.

My Pac 12 knowledge is admittedly lacking and I had to google some of these kids. Schoenlein is a senior outside for WSU, Lutz you should know from Stanford (and who is apparently touching 11 feet at the moment), Willow Johnson is a freshman RS for Oregon (and Randy Johnson’s daughter, fun fact).  Woodford is an OH with WSU I believe and Plummer is the Stanford OH you’re likely familiar with.

Just a reminder that since there is no designation of position (OH, MB, RS, S) in DataVolley, I am relying on spacing from the setter. This is compromise that has to be made in order to do the analysis – but inherently fails us when we look at a team like Stanford who has Fitzmorris and Lutz sometimes floating between middle and rightside. Sorry…

What you see on the left is how attackers hit against the blocker in question, whether there is a registered block touch or not. So if you find Willow Johnson (just a cool name so I’m gonna keep using her), you’ll see that when hitters attack her down the line or into the portion of the seam she’s responsible for as a RS blocker, she holds them to one of the lowest Attack Efficiencies in the league.

What you see on the right is the Efficiency Change as a result of the block move. From earlier posts on Eff Change you may understand that this gives more credit to a blocker if they take a strong position for the attacker and neutralize it or turn it into a good position for the defense – for example, if the offensive team usually hits .500 on the Go and Willow Johnson gets a stuff block to terminate the rally, her Eff Change will be her output, minus the input. In this case the input is -.500 because your defense loses at that rate against the Go, but the output is 1.00 because your defense always wins when you get a stuff block. This gives WILLOW JOHNSON an Eff Change for this block move of 1.5, which is huge. It also hurts the blocker more if they do something stupid when the offense is already in trouble, like netting against an out of system attack. Don’t do that…

You’ll see from the EffChangeBlock column is that the best blockers add value each time an attacker targets the portion of the court they’re responsible for. This might be via good touches that help your transition offense or it might be just not getting tooled and leaving clean lines for your defenders to fill.

You can use EffChangeBlock to potentially find undervalued blockers. Jenelle Jordan, a senior MB from Cal for example. While attackers hit 0.268 against her, more than double what people hit against the top 3 in that category, she quietly has a positive impact on attacks into her zones. To be fair, these Eff Change stats account for the team you’re on as well as the team you’re playing, and Cal’s numbers are likely to be deflated relative to the rest of the league. So if they’re a subpar digging team, that increases the value of every block touch Jordan gets…

Anyway, just wanted to throw this out there and let people check it out.

Pac 12 – FBSO & Service Error

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Welcome to the Pac 12.

So here’s the same FBSO vs. SE% graphic I built for the Big Ten. Looks pretty similar, with a loose, positive correlation between the two metrics. If you don’t remember from the original post, these are the individual match performances of each team – meaning that UCLA in the bottom left there only missed 1.37% of their serves in a match where they also held their opponent (sorry Cal) to an FBSO Efficiency of -0.027. FBSO Eff again referring to the receiving team winning or losing the point on that first receive to attack possession, divided by the number of times they’re served to (which accounts for SE’s).

An interesting observation when comparing leagues is the correlation coefficient between the metrics in question . For the Big Ten, r = 0.45. For the Pac 12, r = 0.28. Translated, service error percentage has a stronger relationship with FBSO Eff in the Big Ten than in the Pac 12. What this suggests is that if you play in the Big Ten, missing your serves hurts you more than it does in the Pac 12.

But the next thought I had, which I think I’ve alluded to in most posts, is that the context matters – who you’re playing matters. Teams have different strengths, different weaknesses and maybe you just need to get them to 10 feet so they can’t set the quick – maybe you need to get them to 20 feet to really pressure them – or maybe you just need to serve in to pressure them…So we need to break this down by opponent.

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This is still Opponent FBSO Eff vs. Team SE% on a per-match basis, but the opponents are listed on the left. The linear regression lines you see going through each set of dots is a visual representation of how related the two metrics are. If you look at Cal or Washington State, you’ll see pretty strong, positive relationships. This means if you miss fewer serves, you’re likely to lower your opponent’s FBSO Eff.

But the surprising thing here is that for the majority of teams there is little to no relationship between the two. If you take UCLA for example, the almost horizontal regression line means that increasing your SE% does not result in a corresponding increase or decrease in FBSO Eff, it’s effectively a wash. The way I interpret this is that while you might be serving “tougher” the additional errors are offsetting the overall outcome. So you might be reducing FBSO Eff when your serve is in (since you’re serving tougher), but you’re also missing more often, essentially counteracting any benefit.

Colorado is the only opponent where an increase in SE% actually has the relationship with FBSO Eff that most coaches seem to think – and it’s a crazy weak relationship (r = -0.13).

So for some teams, the takeaway here is that missing serves doesn’t really matter – they’re going to have the same FBSO Eff either way. But for some, like Stanford (r = 0.49) or Cal (r = 0.68), you might want to tone down the aggressive serving as the more errors you make, the more likely your opponent is going to raise their FBSO Eff.

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Just for fun – and because I needed a break from my thesis – I ran this same type of thing using the Big Ten data again.

Playing against Ohio State has the strongest relationship between SE% and FBSO Eff at r = 0.68. Again, this means that you’re very likely to raise OSU’s FBSO Eff if you raise your SE% – so keep your freaking serves in play.

The Illinois chart is kind of funny. We had a running joke in the office that we shouldn’t pass perfectly if we could help it because our overall efficiency was better off a medium pass than a perfect one. This is likely because Jocelynn Birks was usually the best in-system choice but if the pass the perfect, our setter could make the mistake of involving someone else in the offense…Granted, Birks graduated last year and this data is from this season, but I’m glad to see the team sticking with tradition.

As you can see from the breakdown by opponent, the overall Big Ten relationship between FBSO Eff and SE% being stronger makes sense – as the majority of opponents show a weak to moderate relationship between the variables.

A complaint of this viewpoint might be that better teams serve better and that teams who miss a bunch of serves are less likely to stop an opponent in general. I think this is fair, Minnesota is a low-error team while Michigan State is often the opposite. One of these teams is also objectively better than the other, so should we compare them the same way? That’s where Efficiency Change comes into play. My next post will likely be Service Error % against Eff Change, broken down by team. This will help clarify the real value of serving against a specific opponent and take into account the overall context of the situation much better than FBSO Eff in general can.

Anddddd back to my thesis work…