Six European Football Leagues Going into Christmas 2017

I have been following scoring patterns in six European football leagues (EPL, Ligue 1, Bundesliga, Serie A, Eredivisie and Primera) in the 2017-2018 season.

I have a particular interest in the outcome of scoring first and not losing in games in these leagues.

Prior to midweek games on 13 December 2017, the range of my data (n=875 games) thus far is:

In Ligue 1, the % of games in which the team that has scored first and not lost has ranged between 86% and 89%. In Serie A, the range is 72% to 80%. The other four leagues fit between these two leagues.

My BoxplotR visualisation of nine observations for these leagues is:

The box plot statistics are:

The EPL is about to enter an intense fixture period and I will be interested to observe any changes in pattern.

A separate project is to examine the games in which the team that scores first has lost (n=96 of the 875 games played). The Eredivisie has the largest number of these games (n=23 out of 134 games) and the Primera the smallest number (n=12 out of 150 games).

Photo Credit

Marco Verratti (PSG Officiel, Twitter)

Fireworks (AjaxDaily, Twitter)

Social practices and intersubjective acceptance

A photograph of notes made by Mara Averick and shared by her on Twitter.

Last week, I was introduced to Matthew Rampley’s exploration of visual culture. In his discussion of architecture, Matthew observed:

Architecture needs to be thought of less as a set of special material products and rather more as range of social and professional practices that sometimes, but by no means always, lead to building. (2005) (My emphasis.)

His mention of practices caught my attention, particularly as I was thinking about how we use space and place in convivial ways.

Two other papers this week have focused my attention on social practices and intersubjectivity.

The first is written by Daniel Dominguez (2017) and discusses web skill acquisition in open learning environments in the context of learner autonomy. Daniel considers “the heuristics and linking the practices of individuals using the web and the skills they develop from these practices” (2017:103). He observes “the new competencies the web offers for people to be active in constructing new pathways for social participation and, especially, learning”.

The second paper is written by Gary Schaal, Roxanna Kath and Sebastian Dumm (2016) on the topic of interpreting data visualisations. They present a hermeneutic methodology “for interpreting visualizations that aims at intersubjective acceptance”. Their paper is in German. My limited technical German led me to reflect on the points they made about the visualisation process:

  • Data sampling
  • Algorithmic analysis of the sampled data
  • Choice of visualisation for the algorithmic analysis
  • Hermeneutic interpretation of the chosen visualisation

Their own learning journey has been enriched by the work of Don Ihde, part of which has focused on science’s way of seeing that can be explored by visual hermeneutics.

Matthew, Daniel, Gary, Roxanna and Sebastian raise some very important issues for me as I continue my journey of open sharing in digital habitats. They remind me that as we share our work and induct students into digital connections, we can (and must) take a reflexive approach to what we are doing about our occupational social practices.

Photo Credit

Mara Averick’s notes (Twitter)

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)