A Head of Football Analytics

Earlier this year, the canoe slalom program in Great Britain advertised for a performance data analyst.

I though this marked a fascinating change in the sport and underscored for me the opportunities that are now appearing in sport that signal a fundamental shift in how learning journeys are being experienced in industry and in education systems.

This week, Leicester City are adding to this momentum with the advertisement of a Head of Football Analytics opportunity. I hoped the club would extend its expertise in an area they energised with their Tactical Insights Day in 2016.

The Role

  • Produce unique and insightful performance metrics and analysis, using data modelling.
  • Ensure that existing and new databases are maintained and updated promptly.
  • Collaborate with appropriate members of staff at the club, and develop strategies to raise the overall levels of data literacy, analysis and visualisation.
  • Develop the integrated, club-wide approach to providing data driven insights for performance evaluation, player recruitment, sports science and medical aspects of the club.
  • Pro-active in the organisation and implementation of data analysis-based CPD learning activities within the club.

The person Leicester is seeking:

  • Significant experience of working as part of a professional sports organisation, or other sports-related industries.
  • Experience of managing large datasets, and producing high-quality data insights and visualisations for end-users.
  • Experience from other areas may still be considered, based on the relevance to this role.
    Masters or PhD in a numerate subject, this may include Statistics, Economics, Applied Mathematics, Engineering, Computer Science or related subjects.
  • Advanced coding ability (R, Python, XML/XSLT manipulations).
  • Demonstrable working knowledge of databases, SQL and database design.
  • Knowledge of using API’s to manage data sets, and experience using JSON scripts.
  • Familiarity with raw data files such as Opta F24 & TracAb.
  • Good time management & organisational skills, and ability to adhere to deadlines.
  • Excellent written and communication skills in English, with the ability to present results clearly (verbally & visually), and to develop close working relationships with existing staff members with varied levels of data analysis experience.
  • Demonstrable knowledge of football, and of how data analytics are currently being used to impact decision making processes in professional sport.

I noted Ted Knutson’s tweet about this opportunity.

Photo Credit

Leicester City ready for kick off (Ronnie Macdonald, CC BY 2.0)

#EL30 Graphing

Week 3 of Stephen Downes’ E-Learning 3.0 course is looking at Graphs.

Stephen recommended some resources for this topic. These included:

Vaidehi Joshi’s (2017) gentle introduction to graph theory. In her discussion of graphs, Vaidehi observes “in mathematics, graphs are a way to formally represent a network, which is basically just a collection of objects that are all interconnected”.  She distinguishes between directed graphs and undirected graphs and explains the ways edges connect nodes in these kind of graphs. An example of the former is Twitter (each edge created represents a one-way relationship), and of the latter Facebook (its edges are unordered pairs).

Vaidehi suggests a number of resources to provide details about graphs, one of them is Jonathan Cohen’s slide show Graph Traversal. He defines a graph as a “general structure for representing positions with an arbitrary connectivity structure” that has a collection of vertices (nodes) and edges (arcs). An edge connects two vertices and makes them adjacent.

A second resource shared by Stephen is Fjodor Van Veen’s (2016) Neural Network Zoo. In his post Fjodor shares a “mostly complete chart of Neural Networks’ and includes a detailed list of references to support his visualisation of the networks.

A third resource continues the visualisation theme. Vishakha Jha (2017) uses this diagram to inform the discussion of machine learning:

A fourth resource recommendation is Graph Data Structure and Algorithms (2017). This article aggregates a large number of links to graph topics. It includes this explanation:

One of the E-Learning 3.0 course members, Aras Bozkurt, exemplified this theme in this tweet and in doing so underscored the skills available within self-organising networks :

It was a great way to end and start conversations about graphs.

Photo Credits

Title image is from Gonçalves B, Coutinho D, Santos S, Lago-Penas C, Jiménez S, Sampaio J (2017) Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football. PLoS ONE 12(1): e0171156. https://doi.org/10.1371/journal.pone.0171156

Other images are frame grabs for the resources cited in this post.

A Coaching Mind

Lewis Lapham starts his post about the mind’s ability to reinterpret the past with a quote from Jeffrey Eugenides: “Biology gives you a brain, life turns it into a mind”.

Lewis’s post and Michio Kaku’s (2014) Future of the Mind prompted my thoughts about a coaching mind and consciousness.

I sense that these philosophical issues will become more important as coaches deal with performance data flows in their daily working environments. For example, Lewis’s observes:

The scientific-industrial complex focuses its efforts on the creation of artificial intelligence—computer software equipped with functions of human cognition giving birth to machines capable of visual perception, speech and pattern recognition, decision making and data management.

He adds:

Mind is consciousness, and although a fundamental fact of human existence, consciousness is subjective experience as opposed to objective reality and therefore outdistances not only the light of the sun and the moon but also the reach of the scientific method.

Michio notes “human consciousness … creates a model of the world and then simulates it in time, by evaluating the past to simulate the future”.

In my concept of a coaching mind, I have a sense of coaches’ experiencing different kinds of consciousness. Alain Morin (2006) identifies four kinds of consciousness that help me reflect on coaching as an emerging experience and learning journey:

  • Unconscious: being non-responsive to self and environment
  • Conscious: focusing attention on the environment; processing incoming external stimuli
  • Self-awareness: focusing attention on self; processing private and public self-information
  • Meta-self-awareness: being aware that one is self-aware

Alain concludes his paper with a recognition that there are other conversations to be had:

A great deal of effort still needs to be deployed in order to examine and compare additional consciousness-related concepts such as “meta-cognition”, “higher-order thought,” “autonoetic,” “visceral,” “first-order consciousness,” and “immediate self-awareness.”

Lewis, Michio, and Alain have helped me reflect on how coaches might flourish in an occupational culture that will extend the reach and application of artificial intelligence.

I am hopeful that the move towards and within meta-self-awareness might help us discuss how we, as mindful people, create our own intelligence augmentation and achieve a symbiosis with the tools that we integrate mindfully in our praxis.

Photo Credit

In the mind’s eye (Robert Couse-Baker, CC BY 2.0)

Feedback Loop (Robert Couse-Baker, CC BY 2.0)