Chad Gordon
for RMD version: https://rpubs.com/chadgordon09/step9 1. Expected Value Added (eV+) is simply: Results minus Expectations. 2. Building on the logic from the last two posts (Step 7 - Expected Value in Attacking, Step 8 - Expected Value of Input Situations) we merely combine the two to determine the value of each touch. Output eV comes from [...]
for RMD version: https://rpubs.com/chadgordon09/step8 1. No reason to bury the lede - all attacking situations are not created equal. 2. A middle attacking on a Front 1 after a perfect pass is expected to score at a better rate than an outside hitter receiving a high ball over her shoulder from 30 feet away. I [...]
for RMD version: https://rpubs.com/chadgordon09/step7 1. Above are the possible outcomes that result from any given attack, using VolleyMetrics' coding. The Expected Value is given for each output along with the count of each in parentheses (you'll notice we're not working with a small sample size...) 2. Block touches; if you're wondering where they are - [...]
Brian Hurler is currently the Graduate Manager/Technical Coordinator for the women’s volleyball program at Creighton University. (https://gocreighton.com/staff-directory/brian-hurler/290) Outside Set Speeds Per Team Using match data from the 2019 fall season, I took a sample of set speeds for the seven teams who have made the Elite Eight the most since 2015, plus Creighton. The speeds [...]
% split of Terminal vs. Non-Terminal attacks 1. Welcome to attacking: the most important skill in our game. I'd also argue it's the most interesting, least understood, and most dynamic of our game - making it the best place to pick some low-hanging fruit. 2. We need to understand the reality of attacking. As you [...]
If you haven't noticed, big Moneyball guy over here. But that one line really cuts to the core of the issue. If he's a good hitter, why doesn't he hit good? 1. In our case, the problem is not that there are rich teams and there are poor teams...but that we are not evaluating the [...]
So. Where do we start? We know that passing is important, but does it predict the future? Man...I hope so. That would save me some steps. 0.5 Just for reference, I've created a passer rating in the data that's built as follows: Top to bottom: perfect, pretty good, medium, poor, no attack/overpass, error 1. Let's [...]
To be fair, predicting the past is easier. It's already happened. We're just looking for which metrics help explain something that has already happened. To it's credit, attack efficiency differences between two teams in a single set is wildly indicative of who won that set. So yes, we do care about attack efficiency, definitely don't [...]
for the RMD version: https://rpubs.com/chadgordon09/step3 The quick answer is, no, attack efficiency doesn't help us predict the future either. (However, it does help explain the past). Let's follow the same steps we used for PS% and see why. 1. Find the attack efficiency average for each team 2. Grab all the different sets that were [...]
for the RMD version: https://rpubs.com/chadgordon09/step2 Unfortunately, just knowing the average point score % for two opponents doesn't really predict who will win the set. I honestly thought it would...or that it would fair better than it did. Here's what I did: 1. Find the point score % average for every team in the dataset 2. [...]
