Efficiency Change Refresher

So here's the concept: we want to evaluate each touch by the difference between expectations and results. Input Contact: this serves as the expectation - given the situation you've been dealt, how well do we expect you to do? Output Contact: this serves as the result - looking at both terminal & non-terminal results Serving, [...]

xP & POE – Serve (2017)

To break down serving, we must first identify the expectations and then figure out how to quantify the results. Some may immediately think that because it's a closed loop skill, that the expectations are simply 0. We have no expectations, there's no other force acting on your ability to hit the serve. But this isn't [...]

2018 Service Error (Men’s)

It's been about a year since I posted, so I figured my unpaid sabbatical from this blog should come to an end soon. I snatched the 2018 men's data, parsed it, and decided that the controversial (if you were to survey volleyball parents) topic of service error should be the first thing I dive into [...]

MPSF Service Error (2017)

Been waiting to dive into this for a while now, so let's get right to it. What you see above is the Service Error % in a given set and the opponent's FBSO efficiency: (opp. won in FBSO - opp. lost in FBSO)/total serves. The teams you see as labels are the serving teams, so [...]

Point Scoring% in the Big Ten

Not a sexy topic, but I just figured out how to do these 'joy division' charts in R so I'm kinda pumped to share. What you see is a histogram of each team's point scoring % in every individual set they played (only against the teams you see listed, so Purdue v. OSU but not [...]

Quick thoughts; serving

Was just messing around with some numbers this afternoon and wanted to share.I looked at a few things related to serving, specifically serve error%, point score%, and serve output efficiency. I ran some correlations between these stats and themselves as well as with winning the set overall.As with my last post, I'm only using data [...]

Attackers’ Trends + Visualizing Development

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 [...]

Which type of serve is best?

Bears. Beets. Battlestar Galactica. What you see above is the distribution of serving performances per player per match, broken down by type of serve. This chart is built using Big Ten and Pac 12 conference matches and serving performances with fewer than 5 serves in a match were excluded. 1st ball point score efficiency is [...]

Top Servers in the Big Ten

Coming back from the SSAC in Boston this past week, I've been putting more thought in player evaluation against the market they're situated in. Much like how baseball uses WAR (wins above replacement) to compare players' values against that of an average MLB player. That stat has of course evolved over the years with different measurements [...]

Pac 12 – FBSO & Service Error

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 [...]