My friend Jim Dietz’ blog post
I approached the
idea from a point scoring standpoint. The initial explanation is basic and putting
the discussion and assumptions on common ground, so please be patient.
Volleyball scoring
is not very complex. The idea is to score 25 points before our opponent does,
with rally scoring; that is, there is a point scored with every ball whether
the team scoring the point is serving or receiving serve. The point scoring for
the good guys is split between points the good guys score on our own, and the
points the bad guys give us as errors they make.
The points earned
by a team are:
· Ace serves
· Kills
· Stuff blocks.
The points given to
the opponent by a team are errors:
· Service errors
· Blocking errors
· Attack errors
· Digging errors
· Passing errors
· Referee sanctions
A great way to track these numbers is the trendline
graphical representation of the point scoring invented by Dan Mickle. https://thecoachesmind.com/trend-line-stats/
The scoring for the entire set boils down to:
·
How we are scoring,
·
How our opponent is scoring,
·
How many errors we make, and
·
How many errors our opponent makes.
There are uncertainties and randomness that are associated
with each of the ways the points are scored. We do not know anything about the
uncertainties and randomness prior to the set because if we did, they would not
be uncertain and random. These uncertainties and randomness are what makes the
game interesting and what differentiates playing the game with humans from a computer
simulation.
Nassim Nicholas Taleb’s book Antifragile
In Chapter 7 of the book, titled Naïve Intervention,
he explains the idea of iatrogenics, which means “caused by the healer”.
The idea is the balancing of the benefits versus the losses caused by a cure:
does the benefit of the promised cure outweigh the losses caused by the cure,
either intended or unintended? It speaks
directly to the Hippocratic oath: Do No Harm. Iatrogenics is the situation
where more harm than good comes from an action, or that more losses than gains
result.
When we humans interact with a system, either simple or
complex, our urge is to correct those things that don’t immediately conform to
what we think is normal. We tweak and guide
the system to a stable result for that moment. This is because we believe in
the idea that the aggregation of stable subsystem reactions to a small disorder
will necessarily guarantee a stable complete system reaction from larger
disorders. Taleb’s contention is that in aggregate, it is necessary for the
small results to be undamped, minimally controlled, and random, because minimally
controlled, and random instabilities in a small scale will benefit the large-scale
complex system by attaining long term stability. Complex system can withstand and
survive the undesirable small perturbations to the system, the larger complex system
is better able to naturally damp out temporary instabilities in the system; in
addition, the larger complex system is better able to adjust and learn from the
small perturbations so that when a larger perturbation happens to the larger,
more complex system, it is able to withstand that larger perturbation because it
had absorbed and adjusted to the smaller perturbations. Indeed, this is the idea
behind antifragility. An antifragile system gains from disorder rather
than just survive.
This is not to say we should adapt a completely hands off
approach: the goal is not to let the system act and react open loop in a laissez
faire fashion. The difference is that it is necessary to have a strong understanding
of the system dynamics and behavior so that when we do intervene, we can
knowledgeably do so without causing harm. What is needed is a robust model of
how the system behaves and have some predictive ability before intervening.
This is where the word naïve comes in. Intervening
without a model which has consistently demonstrated controllability, observability,
and some order of predictability will create a rash of unintended consequences.
This would be naïve intervention.
I applied Taleb’s ideas to the case of volleyball scoring by
using the scoring modes I had defined earlier.
·
How we are scoring,
·
How our opponent is scoring,
·
How many errors we make, and
·
How many errors our opponent makes.
This is our system. There are no other ways of scoring as
far as I know. The four modes of scoring are interrelated because a change in
one affects the other.
Increased errors from our team means that the pressure on
the other team’s earned scoring mode is decreased, which results in our team
needing to score more in a timelier manner, or our team needing to put more
pressure on the opponents to make them make more errors. Every point shifts the
emphasis on each of the four modes. The game flow is affected with every point,
and the impact of each of the four scoring mode shifts with every point. Of
course, digging into that kind of granularity is an overkill, but most coaches
have an internal accounting of these four modes from point to point, if not
numerically then instinctively.
Looking at the components of the four scoring modes:
· Ace serves
· Kills
· Stuff blocks.
· Service errors
· Blocking errors
· Kill errors
· Digging errors
· Passing errors
· Referee sanctions
We see that none of these factors are sure things, as we can
see in the descriptive statistics teams take during sets and matches. The
numbers never duplicate itself completely and absolutely, there are always variations;
there are and always will be uncertainties and randomness built into all of the
scoring components, which are aggregated into the four point scoring modes. Another
key issue is that there is an element of time involved: we need to get to 25
points before the other team can get to 25.
As coaches, we are always asking our players to minimize our
errors and maximize our opponent’s errors, whether it is overall errors,
categorical errors like service errors, or individual errors. It is natural and
logical to do this. But, by stating that request generically does not affect
the uncertainties or randomness inherent in the scoring modes.
It is however, a small jump logically to go from speaking in
general terms — minimize or maximize, to
speaking in specific terms — only make 8 errors in a set or make X number of
service errors. This subtle shift is to make the scoring components that were allowed
to be variable to become deterministic. In the real world, randomness is
distributed throughout all the factors
which make up the results, in this case, the randomness is distributed to four
scoring modes. But, if we made one of the four modes non-random by setting it
to a specific number, the randomness that was distributed to four modes are now
concentrated on three modes. In addition, the causal relationship between the
four scoring modes plays a role in making the concentrated randomness impact each
of the random scoring modes more significantly.
For example: if a team is told to limit their total errors
to 9 errors: service, blocking, kill, digging and passing. If a team can
accomplish this by will, the emphasis is shifted to the opponent scoring. If the
randomness is a positive one for the opponent, if a team that was not able to
generate an offense which is capable of scoring 16 points on their own went on
a streak where they were scoring well, each of those points score affect the
final outcome more impactfully than if a hard limit was not placed on the
errors committed. In this case, a positive variation for the opponent
translates to us either hoping and forcing the opponents to make more mistakes
and/or hoping that we can score more to overcome the opponent’s positive variation.
Another way to think about this is to recognize that there
is randomness with our errors but that the allowable error limit includes those
random events that can happen. The question is then: what is a realistic error
limit to set which includes the randomness that cannot be predicted?
The opposite is also true. If we wanted our servers to be
more aggressive in serving the ball, we can tell the servers to have a maximum number
of service errors in mind as they served. This deterministically shifts the
burden from our service errors to all the other errors: blocking, kill, digging
and passing. It also shifts the burden to keeping the opponent from scoring
more, i.e. playing better defense; to scoring more than our opponents on a play
by play basis, i.e. we need to stay in synch with our opponent’s scoring; and to
force our opponents to make more errors. These are all metrics that any beginning
coach can recite, but the impact of each three options become more important
because we have conceded a set allotment of error points.
This is not to say that coaches won’t be tempted to set
maximum and minimum performance goals for their teams. The question to ask is whether
we have a predictable error model for each individual player and for the
individuals interacting as an aggregate to confidently know that we are NOT naively
intervening in the set? Do we know whether our intervention will result in
unintended consequences which could be a case of iatrogenic? Do we have a way
to ameliorate the situation if the variations work against us?
Works Cited
Dietz, Jim. "Error #9?: The breakpoint of a
set." Good, Bad, I'm the Guy with the Blog. January 16, 2022.
https://thinkingbeyondthebox2018.wordpress.com/2022/01/16/error-9-the-breakpoint-of-a-set/
(accessed January 17, 2022).
Taleb, Nassim Nicholas. Antifragile. New York
City: Random House, 2012.
