## Migrating to volleydork.com

Hey all - Just a heads up that I'm slowly migrating (and revamping) what I consider to be the better posts from this blog over to the new, fancy version over at http://www.volleydork.com The .blog site has served me well when I was a volunteer assistant and had no following. But now I've got like [...]

## 2022 NCAA Tournament – The Finals

Many thanks as always to Professor Dwight Wynne for the win probability math and match charts!! national championship match: Texas opened the match with a 72% chance to win. After taking the first set, this improved to 84%. After going ahead 2-0 after a second set beatdown of 25-14, Texas had a 95% win probability. [...]

## Computer Vision – Updated July 10, 2022

Original Post --- Update #1 Time for a quick recap of everything we've been working on and the progress we've made thus far!! Player Tracking So to start, step 1 was just to be able to detect and track all the players on the court. Not quite as simple as it sounds, as you can [...]

## Step 14.1 Computer Vision (update)

Quick update on our original post. We've mostly solved the "players switching sides while jumping" problem and have also jumped into the rabbit hole of ball tracking as well. I'm not going to get into the technical details here - more just give a glimpse into what we've been working on and where we are [...]

## Bonus Post – NCAA Final Four / Stivrins Impact

Figured we'd throw a super quick Final Four + Stivrins post together just to wrap up the 2021 season as everyone shifts their attention to the transfer portal and freaks out about the seasonal coaching reshuffle. Starting with the semifinals, below are the Match Win Probability charts - visually describing a team's likelihood to win [...]

## Volleydork Ratings / Predictions Recap

So...what was the point again? Honestly, we didn't set out to make a ratings system. We were building our xPWP model (expected Point Win Probability) and needed a reasonably accurate estimate of the initial chance of each team winning the point (ex: Nebraska serving to Wisconsin, before the serve happens, how likely is Nebraska to [...]

## Step 14 – Computer Vision Intro

Computer vision already powers self-driving cars, the gaming world, and numerous other products. Within sport, this technology has already made its way into basketball, football, baseball, soccer, etc. - and has further fueled their respective analytics movements. One of my favorite articles that encapsulates what this work allows for is from Brian Macdonald. Published in [...]

## 2021 NCAA Tournament Predictions

December 18th: Updated predictions moving into the Finals are live Had a great week in Columbus - and now we're down to the final two. Our model likes Wisconsin over Nebraska 58/42 But personally...I think Nebraska is on a heater and they're gonna take this thing tonight...we shall see. December 14th: Updated predictions moving 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, [...]