Writing a report

Earlier this week, Avinash Kaushik wrote about Responses to Negative Data (link). Shortly after his post was published, I found a link to a Turing Institute blog post, written by Franz Kiraly, What is a data scientific report? (link).

Both posts have helped me to think about the why, what and how of sharing observations, analyses and insights.

Franz, the author of the Turing blog post suggest that a stylised data report is characterised by:

  1. Topic. Addresses a domain question or domain challenge in an application domain specific to a data set.
  2. Aim. Data-driven answers to some domain question.
  3. Audience. Decision-makers or domain experts interested in ‘evidence’ to inform decision-making.

Franz suggest five principles that inform good reporting:

  1. Correctness and veracity
  2. Clarity in writing
  3. Reproducibility and transparency
  4. Method and process
  5. Application and context

Whilst there are some issues I take with Avinash’s and Franz’s posts, I do think they both raise some fundamental issues for us as we contemplate sharing our data-informed stories. I am particularly interested in how the curiosity and openness Avinash describes meets Franz’s five principles.

As I was concluding this post, up popped a link to Samuel Flender’s post How to be less wrong (link). This will be an excellent companion to the two posts discussed here. It also gives me an opportunity to extend my interest in Bayesian perspectives.

Photo Credit

Photo by Sandis Helvigs on Unsplash

Optimistic about Optimisation

Two Medium posts this week (written by Thomas Oppong and David Weinberger) have returned me to think about optimisation as an organisational opportunity that goes beyond the rhetoric of ‘world best‘ and ‘world leading‘ aspirations.

Pursuit of progress

Thomas Oppong in his discussion of ‘good enough’ and the pursuit of progress quoted Seth Godin:

You’re not in the perfect business. Stop pretending that’s what the world wants from you.Truly perfect is becoming friendly with your imperfections on the way to doing something remarkable.

Thomas concludes his post with the observation “Pursuing progress allows you to celebrate each step that feels like an accomplishment”.

Machine Learning

David Weinberger‘s Medium post considered maximising the benefits of machine learning without sacrificing its intelligence. I was particularly interested in this suggestion:

Accept that we’re not always going to be able to understand our machine’s “thinking.” Instead, use our existing policy-making processes — regulators, legislators, judicial systems, irate citizens, squabbling politicians — to decide what we want these systems optimized for. Measure the results. Fix the systems when they don’t hit their marks. Celebrate and improve them when they do.

In his discussion of optimisation, David made three proposals:

1. Artificial Intelligence systems ought to be required to declare what they are optimized for.
2. The optimizations of systems that significantly affect the public ought to be decided not by the companies creating those systems but by bodies representing the public’s interests.
3. Optimizations always also need to support critical societal values, such as fairness.

David concludes:

The concept of optimization has built into it an understanding that perfection is not possible. Optimization is a “best effort.”

Optimisation

I have been thinking about how sport systems might flourish without the burden of winning edges or outcome measures in a highly competitive global sport network.

I do have an intrinsic connection with ‘good enough’ and ‘best effort’ approaches. They are invitational and permit us to fail as a learning opportunity.

My hope is that the acceptance of optimisation processes that offer better ways of performing in less effortful ways encourages and supports playfulness and joy. This I take to be a profoundly ethical undertaking that requires us to adhere to transparent fairness.

This kind of approach offers not an edge but a wide open space for the flourishing of our imagination and optimism.

Photo Credit

Climb (Efren, CC BY-SA 2.0)

Actionable insights: sport analytics

 

Introduction

A post by Mary Hamilton (2017) about her time at The Guardian has sent me off thinking about actionable insights in sport analytics.

In her article, Mary shares thirteen lessons from her time as executive editor for audience at The Guardian. Three of the thirteen had a particular resonance with me.

Insight 1 is ‘Data isn’t magic, it’s what you do with it that counts‘. She notes “We make better decisions when we’re better informed, and all data is is information”. She adds that developing an in-house data resource, Ophan, “It’s not just about putting numbers into the hands of editorial people — it’s explicitly about getting them to change the way they make decisions, and to make them better”.

Insight 11 is ‘Radical transparency helps people work with complexity‘. Mary observes “In a fast-moving environment where everything is constantly changing … you have no way of knowing what someone else might need to know in order to do their job well. The only way to deal with this is to be a conduit for information, and not bottle anything up or hide it unless it’s genuinely confidential”.

Insight 13 is ‘What you say matters far less than what you do‘. Mary’s take is “This should be obvious, but it probably isn’t. It doesn’t matter what you say you want, it’s what you do to make it happen that makes a difference in the world”.

Action

Mary’s thirteenth insight underscores the importance of action. In another context, Adam Cooper (2012) proposes:

Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data.

 

He adds “‘actionable‘ indicates that analytics is concerned with the potential for practical action rather than either theoretical description or mere reporting”.

Some of the key contributors to the sports analytics literature have focused on action.

In 2011, Ben Alamar and Vijay Mehrotra defined sport analytics as:

the management of structured historical data, the application of predictive analytic models that utilize that data, and the use of information systems to inform decision makers and enable them to help their organizations in gaining a competitive advantage on the field of play.

Three year later, Chris Anderson defined sports analytics as:

The discovery, communication, and implementation of actionable insights derived from structured information in order to improve the quality of decisions and performance in an organization.

In 2016, Bill Gerrard observed “Sports analytics is all about using data analysis to provide actionable insight for coaches and sporting directors”. He added “Analytics is analysis for purpose. It’s a servant function, there to help managers to make better informed decisions”. In his conceptualisation, “Analytics is decision-driven, domain-specific data analysis”.

Meta-Issues

In his essay on Concerning Human Understanding, John Locke asserts:

it is ambition enough to be employed as an under-labourer in clearing the ground a little, and removing some of the rubbish that lies in the way to knowledge

Bill’s comment about the servant function of analytics took me back to John Locke and under-labouring. I thought that any sport analyst would be keen to be such a labourer and contribute to “clearing the ground a little”.

One aspect of an analyst’s role is, I think, to reflect on the place granularity will play in the actionable insights we share. Another is to consider our creation of actionable insights from a user’s perspective (Kunal Jain, 2015).

But as Alexander Franks and his colleagues (2016) point out there are some important meta-issues at play here too. They consider “the metrics that provide the most unique, reliable, and useful information for decision-makers”. They employ three criteria to evaluate sport metrics:

  • Does the metric measure the same thing over time? (Stability)
  • Does the metric differentiate between players? (Discrimination)
  • Does the metric provide new information? (Independence)

Alexander and his colleagues note:

In general, any individual making a management, coaching, or gambling decision has potentially dozens of metrics at his/her disposal, but finding the right metrics to support a given decision can be daunting. We seek to ameliorate this problem by proposing a set of “meta-metrics” that describe which metrics provide the most unique and reliable information for decision-makers.

They add:

The core idea of our work is that quantifying sources of variability—and how these sources are related across metrics, players, and time—is essential for understanding how sports metrics can be used.

They conclude their discussion of meta-metrics by proposing a fourth meta-metric: relevance.

Relevance could simply be a qualitative description of the metric’s meaning or it could a quantitative summary of the causal or predictive relationship between the metric and an outcome of interest…

In Practice

Earlier this week, the Crusaders rugby union team, from New Zealand, advertised for a sport scientist. The job description provides a fascinating empirical focus for the discussion in this blog post.

The position description has these elements:

  • Reporting to the Crusaders’ Head Strength and Conditioning Coach, you will be responsible for overseeing and coordinating all aspects of the Crusaders’ performance monitoring systems including further enhancement of data collection, processing and reporting methods.
  • You will also be responsible for collating and reporting on all performance monitoring data to ensure optimal player loading for conditioning and recovery.
  • To be successful in this role, you will need to be appropriately qualified by training and/or experience, including a proven ability in research, data analysis and reporting including an outstanding level of understanding of performance monitoring and analysis tools ideally in a rugby environment.
  • You will also need to demonstrate extensive experience in the use of GPS Technology both hardware and software…

This kind of role is now becoming more and more frequent. It will be good to learn how the post holder adapts to this role and provides relevant, actionable insights that are domain specific whilst being mindful that in addition to structured data there are increasing opportunities for the analysis of unstructured data.

Photo Credits

Tree (Keith Lyons, CC BY 4.0)

Crusaders v Cheetahs (Geof Wilson, CC BY-NC-ND 2.0)