## Guest Post: Dwight Wynne – Match Win Probabilities

Dwight Wynne teaches statistics at California State University, Fullerton and supervises student projects helping their women's volleyball team get more out of their data. Introduction One tool that has become increasingly common in the modern consumption of sports analytics is a win probability graph. A win probability graph shows, given the current score and time [...]

## Guest Post: Steve Aronson – The Impact of Point Differential on Winning Percentage

Introduction Having a lead in a set conveys confidence that we are more likely to win. We should be even more confident if that lead is near the end of the set, unless we are matched against a much better opponent. Using a lot of data and some modeling, we can quantify that confidence into [...]

## Season Trends

Madeline Gates - 2019 Stanford University Interactive Dashboard: https://public.tableau.com/views/SeasonTrends/HittingEfficiency?:language=en&:display_count=y&publish=yes&:origin=viz_share_link Is your team getting better or worse as the season progresses? Are your players developing and performing stronger as the year goes on? In the chart above, we take a look at Madeline Gates, the middle from Stanford. We're looking at a rolling average of her [...]

## Expectation vs. Reality

Interactive Dashboard: https://public.tableau.com/views/Expectationvs_Reality/Overall?:language=en&:display_count=y&publish=yes&:origin=viz_share_link This is the direction I think the sport of volleyball needs to go - getting all skills, all contacts, all situations, and all performance evaluation into the single language of points. The same way the NBA has expected points per shot based on the location on the floor + number of defenders, [...]

## Do Service Errors Matter?

Interactive Dashboard: https://public.tableau.com/profile/chad.gordon#!/vizhome/ServiceErrors/Service_Errors I'd argue, no. At least not in the way that most people think? The core issue here is that given a specific input, we see a wide range of outputs. As you can see from the rainbow..ish blob on the right, at any given range of Service Error Percentage, there's quite a [...]

## Step 13: Optimizing Before Prediction

So, admittedly it's been a while since I posted about Point Win Probability (PWP), which to be honest, I think is pretty sweet. But today, I'm mostly here to steal a good idea from Nate Ngo (whose blog is here and you should 1000% check out if you have not). His issue was that, like [...]

## 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 [...]