## Guest Post: Tyler Widdison (USA Beach) – AVP Womenâ€™s Champions Cup EDA (exploratory data analysis)

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 [...]

## Step 11: Getting from VolleyMetrics to Expected Value Added (eV+). Step by Step.

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 - go ahead and click on the .pkg file and download that: on a Mac, you're looking for something that looks like this. 2.2 for Windows, you're looking for this [...]

## Step 10: Expected Value Added (eV+) for All Skills

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 [...]

## Step 9: Expected Value Added (eV+)

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 [...]

## Step 8: Input Situation Expected Value (eV)

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 [...]

## Step 7: Expected Value (eV) in Attacking

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 - [...]

## Guest Post: Brian Hurler (USAV Women) – How Set Speeds Affect Hitting Efficiency

Brian Hurler is currently the Performance Analyst for the USA Women's National Team. Brian was most recently the Technical Coordinator at Stanford and held a similar position at Creighton University when he wrote this in 2020. Not a terrible trajectory for the guy huh? (https://usavolleyball.org/story/hurler-bringing-talents-to-u-s-womens-nt-staff/) Outside Set Speeds Per Team Using match data from the [...]

## step 6: if he’s a good hitter, why doesn’t he hit good?

% 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 [...]

## step 5: if he’s a good passer, why doesn’t he pass good?

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 [...]

## step 4: passer ratings

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 [...]