This document critiques the concept of optimization in decision-making for soccer, arguing that assumptions behind optimization are often unrealistic in field settings due to varying factors such as time pressure and uncertainty. It highlights barriers to achieving optimal choices and suggests a shift towards building expertise through experiential learning rather than strict optimization models. The text emphasizes the importance of understanding different types of problems and adapting decision-making strategies accordingly.

1. A Decision/Action Model for Soccer
– Pt 11
The Fiction of Optimization and Deliberate Practice
Removing Barriers to Expertise
“The perfect is the enemy of the good” - Voltaire
“A good plan violently executed now is better than a perfect
plan executed next week.” - George Patton
“Optimization refers to the attempt to find the best option out of potential courses of
action… (Simon) defined optimization as the selection of the best choice, the one with
the highest expected utility.” Gary Klein [14]
“The concept of optimization relies on a number of assumptions. These assumptions
are very restrictive. I have not met any decision researcher or analyst who believes
that these assumptions will be met in any setting, with the possible exception of the
laboratory or the casino… In the majority of field settings, there is no way to
determine if a decision choice is optimal owing to time pressure, uncertainty, illdefined goals, and so forth.” Gary Klein [14]
1

2. The boundaries of an optimization process
“This chapter lists a number of barriers to selecting an optimal course of action and further
asserts that optimization should not be used as a gold standard for decision making.” [14]
1.
“The goals must be well defined in quantitative terms.
If the goals cannot be measured, then there are no metrics for use in
comparing courses of action.” [14]
1.
•
2.
This barrier eliminates qualitative and emotional assessments. No gut feelings.
“Best” has to have measurable qualities i.e. touches, score, tackles, turns,
processes, records and times. Youth tryouts are famous for having mystery
numbers.
“The decision makers values must be stable.
Optimization assumes that tastes and values are absolute, relevant, stable,
consistent and precise.” [14]
1.
•
3.
This barrier assumes that expectations and standards will not change. What
held yesterday will hold today, tomorrow, forever. Furthermore these values
are shared by all of the stakeholders involved in the action.
“The situation must be stable.
In practice, if feedback from the outcome of the decision sequentially affects
the implementation, this moves the task outside the boundary conditions for
decision analysis.” [14]
1.
•
Open systems, those in ‘the field’ are naturally unstable due to uncertainty,
friction, chance, time constraints and unfolding events.
2

3. The boundaries of an optimization process
“This chapter lists a number of barriers to selecting an optimal course of action and further
asserts that optimization should not be used as a gold standard for decision making.” [14]
4.
“The task is restricted to selection between options.
Optimization requires that we restrict the task to… ‘the moment of choice’.”
1.
•
5.
[14]
Choosing is a binary action, yes to one, no to all others. An either/or moment
made in an unstable environment with shifting values, standards and
expectations.
“The number of alternatives generated must be exhaustive.
Unless we have considered all reasonable options we cannot be sure that we
have selected the best.” [14]
1.
•
6.
This assumes that the person making the decision is omniscient, a standard that
cannot be attained outside of small-world, closed system descriptive models.
“The optimal choice can be selected without disproportional time and effort.
1.
If the optimal choice is barely superior to the closest alternatives… The costs of
obtaining it offset the advantages it provides.” [14]
•
Fredkin’s paradox “concerns the inverse correlation of the difference between two
options and the difficulty of deciding between them. ‘The more equally attractive
•
two alternatives seem, the harder it can be to choose between them -- no matter
that, to the same degree, the choice can only matter less’.” Wikipedia
Furthermore, opponents will violently oppose your efforts which decreases
cognitive resources and increases the time needed for deliberation.
3

4. The boundaries of an optimization process
“This chapter lists a number of barriers to selecting an optimal course of action and further
asserts that optimization should not be used as a gold standard for decision making.” [14]
7.
“The options must be thoroughly compared to each other.
This entails comparing each option to every other option across a full set of
dimensions.” [14]
1.
•
8.
Each option generated by the omniscient process in 5 must be compared
against every other one. While these comparisons are being made situations
and values change to accommodate new information, feedback from previous
actions and unfolding interactions. [4, 11, 19]
“The decision maker must use a compensatory strategy.
The comparison needs to take into account the degree of difference between
each option, not simply that a difference exists.” [14]
1.
•
“All you see is the turn, you don’t see the road ahead” from Stand and Deliver,
http://www.youtube.com/watch?v=CtYiJX_rxEY.
9.
“The probability estimates must be coherent and accurate.
Probability estimates for different outcomes must be reasonably accurate.”
1.
•
[14]
Optimization models can’t allow for a close enough for government work
answer or an emotional response even when it’s called for i.e. “creative
desperation.” [14] This boundary reinforces the need for rational, analytical
methodology.
4

5. The boundaries of an optimization process
“This chapter lists a number of barriers to selecting an optimal course of action and further
asserts that optimization should not be used as a gold standard for decision making.” [14]
10.
“The scenarios used to predict failure must be exhaustive and realistic.
If the sequels [of scenarios] are shallow, the analysis cannot be very good.”
1.
•
11.
[14]
Is it soccer, soccer like or soccer strange? Is friction included? The farther the
scenario is from the game, the less friction is employed, the weaker it will be i.e.
running laps or juggling a ball are weak scenarios, basic 4v4 is strong. [18, 30]
“The evaluation scenario must be exhaustive.
Once the scenario has been generated, the examination must be rigorous… all
possible outcomes must be considered. In field settings, this would be
unrealistic.” [14]
1.
•
This requires time and consideration of every possible variation.
“While there is some benefit in having a gold standard as a yardstick for assessing
adequacy of our deliberations, there are drawbacks as well. The review of
assumptions listed [above] makes it clear that very few decision problems will
permit optimization, outside of the limited context problems presented in laboratory
studies.” [14]
5

6. Looking between the bookends
Best answer, worst answer or something in between
“Why do researchers and practitioners cling so tenaciously to the gold standard of
optimization? One reason is that the concept of maximization rests on a faith in
mathematics. Once the mathematical formulation for expected utility theory was
obtained the agenda for researchers and practitioners seemed clear: to find ways
to translate decisions into the appropriate formalism… Because maximization is
based on mathematical proofs, these theorems act as a bedrock. Few areas in
behavioral science can boast such a solid basis for investigation.” [14]
“Once we abandon optimization as a goal, the issue of training takes on new
possibilities. Previously, training was seen as instruction in the process of
optimization. Instead of teaching [or teaching with] analytical strategies, we may be
more effective in building up the expertise needed to handle [a] resource-intensive
process.” [14]
This implies a move away from preparation, the effort of ‘getting ready’ to
readiness itself. Preparation never ends, readiness is what is brought onto the field
at the moment of need.
6

7. “What makes for better decision making?” [24]
“Is the ‘best’ option the ‘right’ one?”
[24]
“We can begin then by identifying the two basic ways of solving a problem: intuitive
and effortful.” [24]
Intuitive (or naturalistic)
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Naïve intuition: the traditional street education, school of hard knocks.
Characteristics; Informed by ‘common sense’ and experience in other contexts.
Possible shortcomings; Especially sensitive to psycho-social factors like group think.
Good for; Expert decision makers where time is short.
Educated intuition: Klein’s recognition-primed decision making. Learning expertise.
Characteristics; Informed by specific expert strategies – mental simulation, prototypical
models; expectancies; cues; singular evaluation approach.
Possible shortcomings; ‘Satisficing’ e.g. settling for good enough. Missing the bigger
picture.
Good for: Situations that don’t merit a time consuming effort but must be reflected on.
Effortful (or rational): Optimization.
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Characteristics; Informed by rational, logical thinking making use of formal problem solving
tools.
Possible shortcomings; Unnecessary effort; slow; resource intensive; open to overload.
Good for; Novice decision makers; all decision makers where they have time and the
decision is both simple and important enough to use this approach. [24]
7

8. “Problem types” [24]
Matching the strategy to the situation
“Effective problem solving often hinges on recognizing the type of problem that is
being faced. In general terms, there are four main problem types: simplistic,
deterministic, random and indeterminate (or ‘Wicked’):” [24]
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“Simplistic problems: where there is one and only one answer. For example,
who is the goalkeeper?
Deterministic problems: where the only answer is arrived at by the
application of a formula, algorithm or protocol. For example, the circumference
of a circle is found by applying a certain formula. In soccer, offside.
Random problems: where there is only one answer, but there are a number of
possible correct answers. For example, who will win the EPL this year?
Indeterminate problems: where the answer itself is complex, hard to identify
or changes in time. For example, what is ‘success’ in a particular season?
Answering such a question means taking into account a huge range of factors
including how others see the issue, how the issue has changed and how your
earlier decisions and actions have themselves affected the issue. To use Rittel
and Webber’s terminology, these are ‘wicked problems’.” Selecting players at a
tryout, especially when there’s very little difference between them, again
Fredkin’s Paradox. (In the real world a numbering system, (1 to 5) is used over
different categories (passing, dribbling) to justify a subjective evaluation (the
evaluators gut feeling).) “Numbers don’t lie Mrs. Jones. Your sons a 3, average.”
Modified from the original text [24] to soccer.
8

9. Types of problems and the role of facts and judgments [24]
Matching the strategy to the situation
“When faced with a problem it is useful to categorise and ask: ‘what kind of problem is this?’
Once identified, we can apply the most appropriate problem solving process. As the diagram
shows, in both simplistic and deterministic problems, facts and algorithms are applied;
judgment is not… you simply use the appropriate SOP. However, random problems entail both
facts and judgment. To return to the question of the EPL winner, although it is possible that
any of the teams will win, pertinent evidence from performance, and reports etc., enables us to
narrow the field down considerably. In dealing with indeterminate problems, ‘facts’ are likely to
be rejected or contested by the various stakeholders making their use of little value or, worse,
counter-productive, particularly when those ‘facts’ are culturally derived.” [24]
Standardized curriculums
based on optimizing models
teach facts and algorithms
towards a predetermined point.
Learning curriculums build
expertise by allowing
individual judgments to be
tested against reality; trial, error,
reflection and repeat.
Reading the game requires
judgment.
[24]
9

10. Learning expertise – using judgment
“Very narrow areas of expertise can be very productive.
Develop your own profile. Develop your own niche. “ – Leigh Steinberg
“After reviewing the the literature, I identified a number of ways that experts in
different fields learn.” Gary Klein [15]

They engage in deliberate practice,* so that each opportunity for practice has a goal and
evaluation criteria.
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They compile an extensive experience bank.
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Feedback includes friction, competition, cooperation, the self, emotional, rational, physical, moral
elements that are frequently exposed to uncertain changes. This creates events which carry
“emotional ballast” and set up a clear demarcation between process and product. [18]
They enrich their experiences by reviewing prior experiences to derive new insights and
lessons from mistakes.

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Experts have width and depth in the memories available for recall and recognition. These reified
‘deposits’ should be easy to combine quickly into different configurations. Adaptability involves the
process of rapidly destroying old memories and creating new ideas out of the parts. [2, 22]
They obtain feedback that is accurate, diagnostic, and reasonably timely.


Deliberate practice in soccer centers on scoring and preventing goals, not completing a
decontextualized teaching curriculum. Scoring or denying a goal, winning, is the desired end state
of the process. Without that, the feedback that’s filling the experience bank can’t be trusted.
After action reports and spontaneous intra-event communication. Players must be questioned, and
honestly self-question about the events that just took place. Mental models are shared and
debated which enriches individual points of view against the “wire brush” of other opinions. Moral
courage is also developed in this process. [27, 28]
* Slides 13, & 14 deal with deliberate practice.
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11. Heuristics, building blocks for expertise
Working rapidly in the space between best and worst
“My own research on recognitional decision making has resulted in a model that
essentially blends three of the heuristics described by Kahneman and Tversky…
availability, representativeness, and the simulation heuristic… Experienced
decision makers are able to categorize situations rapidly as typical of various
prototypes, using representativeness [familiarity] and availability heuristics
[importance], and are able to evaluate courses of action suggested by these
prototypes by conducting mental simulations, using simulation heuristic
[imagination, progressive deepening], without having to compare options.” [14]
“Because the key to effective decision making is to build up expertise, one
temptation is to develop training to teach people to think like experts. But in
most settings, this is time consuming and expensive. However, if we cannot
teach people to think like experts, perhaps we can teach them to learn like
experts.” [15] Use learning curriculums as opposed to teaching curriculums.
11

12. “Notes on the Psychology of Expertise”
“You must continue to gain expertise, but avoid thinking like an expert.” - Denis Waitley
“An expert is not necessarily more gifted than other individuals. Psychologists use the term
expert to refer to an individual who is significantly more experienced than others in performing
a particular task. However, the difference between experts and novices cannot be reduced
solely to experience (time invested in learning how to perform tasks). There are at least five
qualitative differences between experts and novice.”
1. Novices rely on formal rules and procedures to guide them. Experts rely to a greater degree on
their accumulated experience.
2. Novices are highly conscious of the task performance process. This is a distraction and creates
additional "load" on cognitive processing. As expertise grows, performance of the task becomes
automatic. This cognitive phenomenon is called "automaticity."
3. As expertise is acquired, the learner's cognitive processing system becomes more efficient at
processing new information. As a result, experts can see the whole picture. They are also more
aware of the specific circumstances in which they are working. They have good self-monitoring
skills. Experts can make even very complex, difficult tasks look easy.
4. The expert has a larger number of strategies, and more effective strategies, for performing the
task. This may be the most critical difference between the expert and the novice. Experts know
how to get out of trouble because they have multiple strategies for dealing with the unexpected.
5. Experts are more flexible than novices. They rely on intuition in ways that novices find difficult to
comprehend.” [1]
12

13. *Deliberate practice models are built on optimization
“10,000 Hours Plus or Minus 10,000 Hours.”
[7]
“Scientists who study skill performance attempt to account for “variance” between
people [elite vs. novice]. Variance is a statistical measure of how much individuals
deviate from the average. In a sample of two runners, if one athlete completes the
mile in four minutes and the other runs it in five minutes, then the average is four
and a half minutes and the variance is half a minute [These players are +/- 30
seconds from the average]. The question for scientists is: What accounts for that
variance, practice, genes, or something else?” [7]
“It is not enough to say that practice matters… “There isn’t a single geneticist or
physiologist who says hard work isn’t important. Nobody thinks Olympians are
jumping off the couch.” [7]
“Scientists must go beyond saying that practice matters and attempt the difficult task
of determining how much practice matters. By the strictest 10,000-hours thinking,
accumulated practice should explain most or all the variance in skill. But that never,
ever happens. From swimmers and triathletes to piano players, studies report that
the amount of variance accounted for by practice is generally between low and
moderate.” [7]
13

14. *Deliberate practice models are built on optimization
“10,000 Hours Plus or Minus 10,000 Hours.”
[7]
“Scientists have increasingly realized that the inherited components of complex traits, the
athleticism, is most often the result of dozens or even hundreds or thousands of interacting
genes, not to mention environmental factors.” [7]
Deliberate practice has become a gold standard for developing talent. But when something as
complex as talent is reduced to a simplistic formula, like the one found in popular culture, the
answer always falls short. The original, difficult question has been replaced unconsciously by
a lesser, easier one. The idea behind deliberate practice; 10,000 hours, hard work, expert
coaching and myelin production is an example of this. It’s a matter of attribute substitution,
[12] where the indeterminate reality has been replaced by the theoretically simplistic. What
had required judgment has been reduced to a system of algorithms, facts and rules.

“Attribute Substitution is a psychological process thought to underlie a number of
cognitive biases and perceptual illusions. It occurs when an individual has to make a
judgment (of a target attribute) that is computationally complex, and instead substitutes
a more easily calculated heuristic attribute. This substitution is thought of as taking place
in the automatic intuitive judgment system, rather than the more self-aware reflective
system. This explains why biases are unconscious and persist even when the subject is
made aware of them. It also explains why human judgments often fail to show
regression toward the mean. Hence, when someone answers a difficult question, they
may be answering a related but different question, without realizing that a substitution
has taken place.” Dr. Martin Poulter http://biasandbelief.wordpress.com/2009/06/01/attribute-substitution/
14

15. Summary
Dropping optimization as the “gold standard” poses both a problem and opportunity for
influencing decision making in soccer.
The problem is that optimization is a deeply entrenched cultural belief. It is difficult to
acknowledge, let alone change, largely unconscious thinking and results in social double
binds. We’ll look at this in detail in Part 12, Culture in decision making.
When optimization is replaced by something less, some of the opportunities are;
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People have to rely more on their own judgments, people face moral and mental tests.
Encourages trial-and-error; improvisation.
Demands negotiation for collaboration which;
Creates opportunities for learning adaptive leadership and expertise.
Allows people to tackle random and indeterminate problems. Make it better, not perfect.
Work at a faster tempo. Both teams can use the ‘first one to act wins’ rule.
Avoids non-human technologies i.e. hard line SOP’s like zero tolerance policies and coaches
mandates.
Encourages fast and frugal heuristics as primary decision strategies.
Allows ‘gut feelings’ to become the stopping point of decision making. Emotion has a say.
System 1 learns from and updates System 2 through the dialog between participants i.e.
intra-activity communication and After Action Reflection and debate. The value and
experience gained from participation isn’t lost because a single correct process has to be
followed.
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19. Thank you
“I’ll live or die by my own ideas.” Johan Cruyff
Presentation created October 2013 by Larry Paul, Peoria Arizona.
All references are available as stated.
All content is the responsibility of the author.
For questions or to inquire how to arrange a consultation or workshop on this
topic you can contact me at larry4v4@hotmail.com, subject line; decision/action
model.
For more information visit the bettersoccermorefun channel on YouTube,
http://www.youtube.com/user/bettersoccermorefun?feature=watch
or Street soccer, a guide to using small sided games at Udemy,
https://www.udemy.com/street-soccer-a-guide-to-using-small-sidedgames/?sl=E0IZeFxSVw%3D%3D
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