Behind the Laptop: Week 3

Midweek matches are fun?

If you’re interested in watching any of our matches, all of them (except at Colorado State) are on ESPN+

Thursday, 9/12
It’s quiet in the office this morning because we played a midweek match last night, losing to Northern Colorado in 5 sets. Playing matches in the middle of the week is less than ideal, particularly when you’re also playing three tournament matches during the weekend. The benefit of playing a match like last night’s for us is in the likely RPI benefit. UNC will likely be at the top of the Big Sky conference this season, meaning that they are likely to win a bunch of matches. Since half of our RPI will be determined by our opponent’s win percentage, it’s good to play teams that you think are going to win a lot. When you’re positioning your team for an at-large bid to the NCAA tournament, RPI matters a lot so you do what you can to boost that ranking.

But, fitting a match into the schedule has its difficulties. For me, it increased and greatly compressed the scouting work I have to do. When I laid out my weekly schedule, I wrote that I’m working on scouting for the first two or three days of the week, with the expectation that matches will happen on day five. Instead of that relatively comfortable schedule, I had to have scouting for the first opponent ready by day 2 so that the associate head coach could have his video clips picked and his scouting report completed before our video session and practice on the afternoon of day 2. Also, I was preparing for four opponents instead of the usual two or three. After completing my work on UNC, I had to quickly move on to the weekend’s opponents because I still needed to complete that work by day three, as I usually would, even though we played a match that evening. As I mentioned before, all the opponents blur together at times in my mind. I don’t think that makes the work I do less accurate, it might make me less efficient, but most likely it just makes me busier.

Even with that extra work, I gave myself a tiny bit more work to do as well. I wrote about Chad Gordon’s eV this summer and, through that work, I made some worksheets to use in scouting and matches. I started keeping a folder on my desktop with files I’ll have open in the background during matches for reference until enough play has happened that I can shift to relying on in-game stats.

I have a reception eV sheet for each upcoming opponent in the folder, as well as a tool for figuring out how to create particular rotation matchups (more about that shortly). During matches, one of the main things I’ll discuss with the head coach is serving targets. So even though I have eV sheets for other skills, I only keep reception sheets handy during matches. As I mentioned previously, I download all the Volley Metrics scout files for all our opponents so the worksheets are built on a pretty decent data set, even by the third week. It’ll keep getting bigger and better as the season goes on, but there’s enough here already to be informative.

Because I believe that no single metric completely captures performance by itself, this sheet has three different reception metrics. I like GYR to describe in system/out of system performance, reception average as a reliable metric that coaches understand, and then eV/FBK percentages. As Chad explained in my eV series, expected value describes how frequently we should expect a team to get a first ball kill (FBK) when they pass. I compare that to the actual FBK% to see if they’re exceeding expectation or falling short. The VAR (Value Above Replacement, which is a convenient acronym borrowed from baseball) column is the difference between FBK% and eV.

Prior to matches, I’ll tell the head coach who I would target and why (notice the first column, where I rank my targets). It gives us an opportunity to discuss if there might be some other circumstances that are creating the FBK% we see. For instance, look at a team that relies on their opposite to score a lot in rotations 1, 2, and 3 but then subs her out for a DS in rotations 4, 5, 6. The FBK% for that DS might be a little lower than other players who pass when that opposite is in the front row and scoring. Again, I don’t rely on a single stat to select targets, so I would also look at how that DS rates in GYR and reception average. But, in these pre-match conversations, I’ll lean on FBK% a little more because it isn’t as obvious when watching a match who’s doing well or poorly in that area.

Once we get into matches, I’ll talk to the head coach about which passers are good targets, depending on what we’re trying to create. I’ll tell him who has low FBK% and who has low G% (same as IS%). If he is more interested in creating an out of system situation to make the other team’s offense more predictable, he might favor a target with a low G%. If he is more interested in keeping their likelihood of scoring lower regardless of how it may happen, he might favor a low-FBK% target instead. In those moments, he doesn’t expect me to decide the strategy, he expects me to provide him with actionable information. I don’t always know if there’s something he’s trying to exploit in a particular rotation, so I give him the data he needs, quickly and clearly, so he can use that data to make decisions that move us forward.

Sometimes, based on something our coaches saw in a previous set, they’ll want to create a particular matchup, for instance having a particular server/rotation serving at a particular opponent rotation. We have found that it can be difficult to clearly think through how to create that matchup during the three minutes between sets. To that end, I created a rotation calculator sheet. It isn’t actually calculating anything, it’s just a series of tables with possible rotation matchups. The sheet also contains a few questions to ask in order to figure out which table to look at. Lastly, I wrote a few reminders of how to deal with the different offsets that happen if our team is moving from serving in the previous set to receiving in the upcoming set or vice versa. The “calculator” doesn’t do the work, I do. I just laid out the information and the inputs I need to reason through the problem quickly and confidently so that the staff can move on to working with the team.

Next week, I plan to write about how I’m doing my part to help get more stats and analysis out into the broader community.