Chad Gordon
I've noticed that someone with a lot of nerdy friends who like volleyball has linked my original Attacking Theory post on FB, so I figured I'd give you folks a little extra look on some data from this season. It's just a snapshot, but it's a pretty clean look at how these numbers get computed. This... Continue Reading →
Attacking is a little harder to nail down. The complexity outweighs serving & receiving, making honing in on the best approach very time-consuming. Here's where I've landed: The input for attacking is really the tough one - what factors help predict whether an attacker will be successful or not? In my experimentation, I've looked at... Continue Reading →
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... Continue Reading →
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... Continue Reading →
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... Continue Reading →
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... Continue Reading →
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... Continue Reading →
*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.... Continue Reading →
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... Continue Reading →
