We spend a lot of time in sport seeking optimal performance. We tend to be very optimistic about the processes that contribute to this optimisation outcome and are delighted when it occurs.
One of the aptitudes we require is the ability to differentiate the choices of interventions and treatments we share with our support team colleagues, coaches and performers.
Clare Thorp has written about one aspect of this differentiation, overcoming the fear of better options. She notes:
We have more choice than ever in our daily lives — but while choice is supposed to feel liberating, it can often feel exhausting instead.
Clare discusses, among other issues, decision-making styles used by ‘satisficers’ and ‘maximizers’ (Parker, de Bruin and Fischhoff, 2007). Satisficers choose options that are good enough, maximizers choose an option with the highest expected utility.
In a 2017 paper, Daniel Brannon and Brandon Solwisch focused “on how and why maximizers evaluate an individual product based on a salient characteristic—the number of features that it has”.
The proliferation of feature-rich resources for and in high performance sport raises some important issues for the decisions we make and the advice we give. Daniel and Brandon note:
- Maximizers evaluate products more favorably than satisficers when they have many features (“feature-rich”), but not when they have few features (“feature-poor”).
- Maximizers are more likely than satisficers to perceive feature-rich (compared to feature-poor) products as a means of signaling status to others.
- When maximizers no longer perceive feature-rich products as status signals, they do not evaluate them more favourably than satisficers.
They conclude with a discussion of status-signalling:
while past studies have found that maximizers experience post-decision regret because they look back at what could have been, it is also possible that they are disappointed when their purchase does not end up providing them with the positive social comparisons that they had originally hoped for …
It is sometimes very hard not to be part of an innovation momentum. Clare’s post and the literature are helpful stimuli to encourage us to think about how we personally come to make recommendations about innovation and adoption.
I think it is helpful to think about failure in this context too. Enter Sarah Milstein.
Earlier this year, Sarah wrote about How to Fail When You’re Used to Winning. She introduced her post with:
Innovation is a buzzword for our era. It evokes the promise of profiting tomorrow from today’s changes in technology. The word innovation implies a clean, crisp path. That’s a lie. In fact, innovation requires enormous amounts of failure — which then presents leadership challenges.
Sarah points out that “any team that must experiment constantly will fail a lot, and repeated failure almost always depresses people” (original emphases).
She adds:
when your team equates project failure with defeat, many will intuitively address the problem by narrowing the scope of new projects, in order to make them more likely to succeed.
She questions whether this approach is appropriate for entrepreneurial environments. I have always seen high performance sport as an entrepreneurial space and I found Sarah’s ideas resonating with the decision-making literature discussed earlier.
Sarah suggests the following framework for a team to reflect on direction:
- Develop a written vision and mission statement and refer to them often.
- Make failure an opportunity for learning rather than for blame.
- Ask colleagues to share the lessons they have learned from failure.
- Set a regular time when teams can raise a challenge they’re facing, and individuals can step up to offer relevant expertise or knowledge.
- Use a spreadsheet, database or repository to track notes, code, and other assets from failed projects that can be reused in future projects.
- Publicly celebrate incremental progress.
- Model the behaviours you want.
Sarah concludes:
Your path to succeeding at failure and maintaining morale will not be linear. You’ll stumble along the way and find yourself wanting to pretend you didn’t just trip. But stick with it. Teams that can maintain good spirits during hard times tend to win, and nothing feeds morale like success.
Edwin Thoen has something to share about dealing with failed projects too, particularly involving data:
The probability that you have worked on a data science project that failed, approaches one very quickly as the number of projects done grows.
He suggests:
- Make failing an option from the start.
- Plan realistically and include slack for messiness.
- Keep stakeholders in the loop.
- Write a final report.
As Dewi Koning indicates finding positives in failure amplifies shared learning.
Forced myself to write some positive notes on my "failed" data science project. I had so much trouble in the beginning with thinking of only 2, in the end I managed to write down 7 things we collectively learned from this project #successinfailure #datascience #rstats
— Dewi Koning (@DewiKoning) November 12, 2018
For much of my professional life I have been drawn to ‘good enough’ approaches. The more I have been involved in high performance sport, the more I have wanted to discuss fallibility in our pursuit of a dynamic performance optimisation. And to own failure as well as success.
I do believe that transparency about innovation decisions and their outcomes is immensely helpful as we all negotiate that very fine line between leading and bleeding edges.
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