Chad Gordon
**Steve Aronson is an assistant coach for girl's high school and club volleyball in Massachusetts. He is passionate about volleyball and now content to coach, analyze, and watch. With a background in statistics, data science, and machine learning, he strives to raise the analytics bar in volleyball and help others along this journey.** If we [...]
With the AVP Champions Cup over. Lets look at the stats! I did some web scraping from their results page. I won't go over how I web scraped this data. Additional resources for Beach Volley stats https://github.com/BigTimeStats/beach-volleyball/tree/master/data by Adam Vagner. For this post I didn't use Adams stats. Adams doesn't have Qualifier matches, the PASS [...]
1. Download the .dvw files from VolleyMetrics. 2. Download and install R. 2.1 for Mac, you're looking for something like this on that next page: on a Mac, you're looking for something that looks like this. 2.2 for Windows, you're looking for this (base), then this (actual download). 3. Download and install RStudio, an IDE [...]
RMD version: https://rpubs.com/chadgordon09/step10 1. The next logical step is to take the same methodology we used for attacking and dive deeper into the rest of the skills. If eV+ can help us better evaluate attacking, then perhaps it can be useful in other portions of the game. 2. Again, I'll attempt not to burry the [...]
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 [...]
