Continuing learning and innocent climbing

I am staggered by the expertise shared openly.

Each day, my inbox delivers treasures that are growing in scale.

Today, thanks to Mara Averick (link), I discovered Danielle Navarro’s personal essay on Bayes factors (link).

Danielle’s post has given me a holiday reading list that will help me redefine my naive Bayes views and thinking.

As I was contemplating the references her post unlocked, I came across these images that I have taken to be the innocent climb of continuing learning and the joy of finding new inclines (aka steep learning curves):

(Source of this idea was from R-Ladies Sydney (link) via Real Python (link))

Performance Analytics and Pedagogy

Some recent posts have encouraged me to think about pedagogy for a new age of performance analytics in sport.

It started with Mine Cetinkaya-Rundel‘s speakerdeck Let them eat cake (first)! (link). Slide 16:

Slide 61 really pushed me to think about how we might share with a different kind of pedagogy:

… and brought back memories of Jo Ito‘s observation “education is what people do to you, learning is what you do to yourself”.

Next up was Karen Gold’s Transforming the First Ten Minutes of Class (link). In her post, she notes:

After attending Penny Kittle’s workshop on 180 Days: Two Teachers and the Quest to Engage and Empower Adolescents last summer, I made the decision to shift my teaching-. Like most teachers, I’ve done a lot of professional development. I’d come away refreshed and excited to try something new, but too often, it was challenging to incorporate a big, new idea into the fast-paced routine of school. Penny’s workshop was different. Something resonated with me that summer morning, and I thought, “I can do this. I WILL do this.”

Karen’s story shares her experiences of encouraging children to read at the start of a lesson. Day 1:

Instead of going over a syllabus or introducing course expectations, the librarians and I gave brief book talks, sharing novels we had read or that we knew were well-received by young adults.

This sounded like Mine’s cake to me. As did Solomon Kingsworth’s discussion of reading comprehension (link), he proposed:

If reading comprehension relies on background knowledge and mental models of the world, then the purpose of our lessons should be to leave the child with more knowledge and mental models.

Solomon talks about the pedagogy that shares the treasure that lies within each book.

This pushed me to think how we share treasure in our domain and epistemic culture in a new information age. And how, as The Economist suggested recently, our first step is “to understand that it is not data that are valuable. It is you” (link).

Three examples from sport appeared as I was pondering these issues:

Laura Seth shared news of a webinar hosted by the FA in January to discuss Performance Analysis & Effective Observations (link).

Mladen Jovanovic published Predicting non-contact hamstring injuries by using training load data and machine learning models (link).

Sam Robertson tweeted a list “of the type of sports science/analytics research I think we need to see more of in 2019”:

  • Optimising the structure, efficiency and communication practices of practitioner teams
  • More club, institution, university and manufacturer collaboration to address ‘whole of sport’ problems
  • Longitudinal skill/learning interventions in team sport settings.
  • New and better methods for coaches to improve communication, rapport & trust with athletes
  • Analysis of raw tracking data.
  • Interdisciplinary collaboration,  psychophysics (utility of visuals in reporting and learning), cognitive science.
  • Field application of work undertaken in other disciplines (deep learning & unstructured data), automation and semi-automation of many manual processes currently faced by sports practitioners, and human and machine integration.

Laura, Mladen and Sam are actively engaged in service delivery in high performance sport. As I read their posts I was thinking about how a pedagogy of praxis might engage the next generation of performance analytics.

I am thinking that my pedagogy will move even more strongly to an unmeeting approach with lots of mention of cake.

Photo Credit

Person holding black fruit near cake  (Alex Loup on Unsplash)

Thinking about options and failure

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.

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.

Photo Credit

Milky Way Galaxy seen from mountain range (Stephen Coetsee on Unsplash)