I have been writing some posts on statistics for the current Plus Liga season, blocking, more blocking, even more blocking, setting, setting standards, serving and more and even some weird stuff. Most of the time I have tried to find different ways to look at things, and also to showcase how amazing the Science Untangled apps are. Today I will look just at outside hitters and the different areas of their games. For the record, I chose the cutoff of repetitions so that 25 players made each list so that the sample size for each is reasonable. In bold in the first column are the league averages for each skill and sub skill.
RECEPTION – I have to admit that some names here surprised me a little bit, but that is why we look at statistics in the first place. Interestingly, there are two players in the top 5 for both jump serve and float serve. Two notes, Szymanski is very close to the minimum, and the list of float serve receivers contains players who primarily overhead. Also interesting, the differences are small. 1% in expected sideout percentage corresponds to 1 expected sideout every 3-4 matches for the total, and 6-10 matches for the the sub skills. That is, the differences are tiny.
|REC TYPE||NAME||+ EXPECTED||SIDEOUT %|
|SZYMANSKI 64.8||SEMENIUK 64.3||SCHOTT |
|SEMENIUK 68.8||ROUSSEAUX 68.5||SZALPUK 68.3||FIRSZT |
SERVE – With serve I looked at two different measures, the expected breakpoint %, which includes all serves, and the opponent reception, which excludes service errors. There is less difference here than might be expected.
|SERVE||NAME||+ EXPECTED||BREAKPOINT %|
|LAPSZYNSKI 35.5||SLIWKA 35.4||SEMENIUK 35.2||LOUATI 34.2||CEBULJ 33.8|
|SERVE||NAME||+ OPPOSITION||EXPECTED||SIDEOUT %|
|CEBULJ 51.4||SEMENIUK 53.3||LAPSZYNSKI 54.3||LOUATI 56.2||LIPINSKI 57.3|
ATTACK – With attack, there are a lot of different ways of breaking things down. A couple of general notes… crunch time and pipe have generally low sample sizes which partially account for the appearances of different names. There are a lot of the same names. Lipinski is much better in transition than after reception. All of the players in the top 5 from position 2 are better from there than position 4.
|ATTACK||NAME||+ RALLY WIN||RATE|
|LIPINSKI 0.737||SLIWKA 0.737||ZALINSKI 0.726||SEMENIUK 0.692|
|SZALPUK 0.724||EBADIPOUR 0.724|
|ZALINSKI 0.731||SCHOTT 0.730||SEMENIUK 0.728|
|SZYMANSKI 0.821||GROBELNY 0.811||SZALPUK 0.804||WALINSKI 0.784||SEMENIUK 0.774|
|ORCZYK 0.739||SLIWKA 0.732||ZALINSKI 0.720||SZYMURA 0.708|
|ZALINSKI 0.769||SLIWKA 0.765||LIPINSKI 0.754||BUCKO |
|ORCZYK 0.660||KWOLEK 0.658||LIPINSKI 0.642||SZALPUK |
|MUAGATUTIA 0.955||MIKA |
|FORNAL 0.895||SZYMURA 0.842||CEBULJ |
Tagged Volleyball Analytics, Volleyball Statistics, Plus Liga, Science Untangled, 2020-21 Plus Liga
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Hi Mark – are you collecting this data from the teams/coaches themselves, having your scout(s) create the dvw files, or is there a database with league dvw files available (Volleymetrics)?
I’d like to run some analyses across leagues, but currently only have access to dvw files from within our league.
Thanks for all you share,
Hi Mark — if you don’t mind me asking, what is your process for collecting the dvw files / data for these analyses? From the teams themselves, your own scout(s), and/or via a database for each league?
I’d like to run some analyses across several leagues and I have multiple seasons worth of data from our league, but don’t have access to other league dvw files without scouting myself.
My other alternative is to scrape match reports from league sites and compile them, but it won’t be as valuable as having the raw dvw files, of course.
All the best and thanks for all you share,