Attacking Theory

koobsattacking

Hey Kolby.

So I’ve been throwing out the question to some friends about what kinds of stats they’d like to see but have never had access to. Attacking is naturally something that comes up as it’s the most common way to score points and thus a deeper understanding would be beneficial.

Above is my proposed progression for how we should view attacking. These are the 2016 Big Ten attacking stats for individuals with more than 100 swings – and I had to chop out the bottom 3 individuals, (Enners, Duffin, and Fletcher) who you’ll only be upset about if you’re affiliated with Rutgers, because they were skewing my charts. #science

The column on the left is straight attacking efficiency like we’re all accustomed to seeing it. Kills minus errors, divided by attempts. Many of us are also familiar with “KOBE” charts (Kill, Zero, Block, Error) for looking at attacking and the question often arises, what happens with those “zero” attacks – the non-terminal swings? Clearly chipping the ball at the libero is a worse choice than chipping it at the opposing setter, but is there a way to quantify the value of this decision?

When I arrived at Illinois, Hambly introduced me to “True Efficiency,” a way to look at how your opponent attacks in transition after your initial swing. The general idea being: take your normal attack efficiency and subtract (your opponent’s hitting efficiency multiplied by the percentage of times your opponent gets a swing after you attack). For most attackers, this lowers their overall efficiency, unless you happen to be “Magic Mo” Criswell – an outside with the 2014 Illinois team who spatched balls so awkwardly at opponents that her True Efficiency was actually higher than her season Attack Efficiency. This means that when Mo hits at you, you’re likely to hit negative in transition. Pretty crazy when you think about it.

True Efficiency is certainly an improvement over traditional attack efficiency because we can all agree that the outside whose swing management puts the ball on the setter rather than the libero should be rewarded for that choice – since most teams defend better against an out of system high ball than they do an in-system attack.

The next step in this progression is to look at what I’ve been calling Efficiency Change. It’s the idea that you factor in the circumstances an attacker is dealt (perfect pass, poor dig, etc) and you look at what type of output they get you (kill, error, poor dig, perfect cover, etc). As we’ve established previously, each of these inputs and outputs have specific “Win Efficiencies” per team. A perfect dig is worth X to Nebraska and Y to Michigan State and Z to Rutgers. To tug this thread some more, a perfect dig for Nebraska playing Rutgers has a specific value – so does a poor dig for Minnesota against Penn State. These all hold different values. Take it a step further and look at the output from the attack. Getting a kill on a high ball against Nebraska or Minnesota is tough. Getting a kill on a high ball against Rutgers is less challenging. These circumstances should be valued differently.

This is what Efficiency Change seeks to answer. It looks at 4 things (where Team X, Illinois, is attacking against team Y, Nebraska):

1. How well does team X typically do in this situation against all teams? (How well does Illinois hit Go’s in general off a perfect pass?)

2. How well do all teams in this situation do against team Y? (How well do all teams in general hit Go’s off a perfect pass when playing Nebraska?)

3. How well does team Y do against all teams with the output from the attack? (How well does Nebraska do against all teams when they get a perfect dig in trans?)

4.How well do all teams do against team X with the output from the attack? (How well do all teams do with a perfect dig in trans when playing Illinois?)

The first two seek to account for the offensive strength of the attacking team and the defensive strength of the opponent. These determine Input Efficiency. The latter two seek to account for transition strength of the opponent and the defensive strength of the attacking team. These determine Output Efficiency

Once we have our Input and Output Efficiencies, we take (Output Eff – Input Eff) to calculate Efficiency Change. Does our outcome leave us with a better chance to win the possession than the chance we had going in?

How well does an attacker handle the situation they are dealt, in the context of what their contact is worth to both teams. You can chip a ball to the Rutgers libero and still be ok. You can’t do that to Minnesota. That’s why we look at the output efficiency – and we’ve already established that different teams are more or less difficult to attack against. We must account for all these things.

For those that care about the details, I also account for whether each team is in a 3 hitter or 2 hitter situation – as well as the start zone of the attack. Ohio State using Sandbothe out of zone 3 on a perfect pass in a 3 hitter rotation is incredibly tough to slow down. Illinois using Davis on the pipe out of zone 8 off a poor pass in a 2 hitter situation is pretty easy to slow down…mainly because she’s one of our “wee ones” who comes in for RS Naya Crittenden in the backrow. That’s why we account for all these things…not just the blanket overall efficiencies of the teams.

Back to the viz really quickly. Haleigh Washington is a baller. She’s the highest in all 3 categories so whatever metric you subscribe to, you can’t tell me she doesn’t handle her business. But efficiency change allows us to find hitters who contribute the most value, even though they may not carry the heaviest load or score the most points. While Molly Lohman leads Minnesota in both Atk Eff and True Eff, it’s NPOY Sarah Wilhite who has the 4th highest Eff Change per attack in the Big Ten. Lohman does alright, but because she mostly receives sets under great circumstances, she’s expected to perform excellently each time. Looking from this alternate viewpoint, it’s actually Wilhite and her gnarly range that provides the greatest value in each situation she’s dealt.

Eff Change also helps us weed out Purdue’s Blake Mohler and Ashley Evans. Mohler’s numbers look good until you look at her Eff Change – where she actually loses value for her team when she swings – meaning she likely takes great situations for Purdue and doesn’t terminate like she should. Ashley Evans just needs to pump the brakes on the dump. Your front row is like 6’5″ across the board. There are better options…

Anyway, that’s where I’ll leave it for now. Keep nibbling on this food for thought.

One more thing: Should we only recruit middles named Haleigh?

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