Chad Gordon
*blue is won matches, orange is lost matches - matchid is chronolnogical from start (1) to end of season (39)* So the other evening, I was starting to draw up my first practice of the 2019-2020 season for the Bay 16-1s. It's a team that has great potential, some fantastic athletes, good height, and just [...]
I've noticed that someone with a lot of nerdy friends who like volleyball has liked 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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
*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. [...]
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 [...]
