Chad Gordon
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, [...]
Interactive Dashboard: https://public.tableau.com/profile/chad.gordon#!/vizhome/Blocking-TheUnsungHero/BlockingAffectsHittingEff In my opinion, blocking is the most "shrugged off" skills in our sport. Guy gets up, bounces a ball in the middle of the court and blocker turns to the coach who promptly gives him the "thumbs up, move on" type of gesture. While you're unlikely to fix your team in the [...]
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 [...]
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 [...]
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 [...]
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 [...]
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 [...]
**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 - 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 [...]
Rethinking Volley 