Measuring Psychological Effects

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A while ago I was playing around with one of the Science Untangled apps, mostly because they are a lot of fun to play around with, and also because while having fun I often find out interesting stuff.  While playing around the 2019 VNL data, I noticed something particularly interesting or at least something that piqued my attention.  Teams playing against Brazil made more service errors than teams playing against Australia.  That seemed to be bad luck for us, but I decided to dig a bit further.  What I found was that against Brazil teams made a lot of service errors, and against Iran a few less, and against France a few less than that, and against USA a few less again, and against China and Australia they made hardly any.  It wasn’t so much that teams made more errors against better teams, but that the number of errors was pretty closely related to exactly how strong the opponent was.

This was suddenly really interesting.  It is logical that better teams can be ranked by things that they do.  Serving, blocking, reception etc are obviously a function of the quality of a team and you would expect to see that quality reflected in various statistics.  But serving being a closed skill, the only impact an opponent has on the server is inside the server’s head.  And it seems that impact is quite sensitive.  Implicitly in last year’s VNL ‘everyone’ roughly agreed on the relative quality of each team.  Could the psychological effect of an opponent be measurable?

Ben Raymond with help from a few others analysed data from several leagues, both club and national team, men and women.  He found that the effect is visible widely, especially for men.  He also looked at attack errors after reception on the same basis, with the same basic result, this time also for women.

There could be many reasons for this observation.  Certainly coaches tactics can have some effect, together lineup decisions, and even the way a coach coaches during a match.  But none of these are obviously measurable to the degree that we see.  It seems players know exactly how strong their opponent is.

If you would like to read the whole article, the scientific version, click for English here and Spanish here.

** When I broke it down by team, I discovered that thirteen of the sixteen teams followed that pattern.  The exceptions were USA who were very consistent in their number of errors across different opponents, France who were completely random, and China who were exactly the opposite. That is they made fewest errors against better teams and progressively more against weaker teams.

Click for more Volleyball Analytics, Science Untangled, 2019 Volleyball Nations League

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