The Impact of Managerial Change: EPL 2017-2018

I noticed this tweet earlier today

By coincidence, Ron Smith had sent me a link to the paper too. It is available online at this link.

The paper encouraged me to think about the data I have been collecting from the EPL.

I have been tracking the impact of the eight managerial changes in the EPL this season with a very basic ‘momentum’ metric. This indicates that, after week 28 of the season, Leicester has benefited most from a change (+3) whilst West Brom has struggled (-7).

My maps of routes to safety or relegation, in order of managerial changes, are:

Crystal Palace

Leicester

Everton

West Ham

West Brom

Swansea

Stoke

Watford

Simon Gleave added a fascinating dimension to this conversation in his consideration of how many points are required to remain in the EPL.

He shared this graphic (14 February 2018) of the % chance of relegation this season:

Photo Credit

BFCvFCUM_ENPLPD_041114 (Matthew Wilkinson, CC BY-ND 2.0)

 

Data science, decisions and strategy

Garry Gelade has written a post to celebrate a decade of his involvement in football analytics. The post is titled Analytics as a Decision Support System.

In the course of that decade, Garry accessed:

  • an OPTA F9 spreadsheet (2008) (5Mb for 380 games)
  • an OPTA log file (2011) (200Mb for a season)
  • TRACAB (2016) (40-50Gb for a season at 25hz)

He notes:

This is of course a somewhat simplified picture of the progression, and omits the sports-science data generated from tracking devices and wearables and so on, but I think it is fairly representative of the increase in volumes of performance data that clubs could potentially take advantage of.

He adds:

Although some clubs are enthusiastic about the potentialities of data to inform decision-making, I have a suspicion that others are still rather uncertain, and while they may nominally claim to be “doing analytics” the real impact on their decision-making is rather limited.

Garry discusses analytics as decision-support as a four-stage process:

  • Information
  • Intelligence
  • Insight
  • Impact

Garry observes of this process:

The only reason data science exists as a function at all is to help the manager or coach do his job. The role of the analyst or data scientist is to support the footballing operations of the club by providing insights relevant to decision-making.

Shortly after reading Garry’s post, I noticed Chris Anderson’s response:

By coincidence, I have been researching intelligence augmentation.

I think the intersection of Garry’s post and Chris’s insight sits well in the augmentation debate.

Melanie Cook (2017), for example, connects decision support and strategy in this slide:

Garry’s four steps in analytics can inform the long-term horizon of strategic thinking as an iterative process. I imagine that after a decade of involvement in football analytics, he is in a great position to offer inputs to those willing to consider longer performance cycles.

Photo Credits

Chelsea v Manchester City, 15 March 2009 (Crystian Cruz, CC BY-ND 2.0)

Chelsea v Stoke, 30 December 2017 (Ungry Young Man, CC BY 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)