## Step 12: Point Win Probability (PWP)

Interactive Dashboard (click me) https://public.tableau.com/views/Step12-PointWinProbability/Stanford-Wisconsin?:language=en&:display_count=y&publish=yes&:origin=viz_share_link (Quick note: all PWP in the chart are for Stanford. Therefore if PWP hits 100%, Stanford won the point. If PWP hits 0%, Wisconsin won the point. Anything above 50% means Stanford has the advantage, anything below...you get the idea. I find it much cleaner to use a single line [...]

## Step 11.6: Using Score Differential Rather Than Binary Win/Loss to Strengthen Predictions

So this might be pretty obvious, but I'm going to spend 2 minutes posting this so we're all on the same page. Chris Tamas of Illinois reminds his team of the only goal: "three sets, by two points" and while this may hold true in a practical sense - from a statistical sense, beating a [...]

## Step 11.5: Probability to Win the Point, Expected Value, and Other Metrics. A Quick Pitstop.

You may be seeing terms like Expected Value (eV), Win Probabilities, or other terms floating around this blog. I'd just like to take a quick second to help translate what's being talked about. Expected Value (eV) - its notation appears similar to a standard hitting efficiency. It's shown as a rate, meaning we divide by [...]

## Guest Post: Steve Aronson – How Much Does an Opponent Affect a Team’s Performance?

**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 [...]

## 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: 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 [...]

## 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 - [...]