Introduction to the module

Overview

This module introduces you to the role data analytics plays in the recruitment and identification of sporting talent at a professional level. The module will introduce you to the theory of and practical approaches to analyzing key performance indicators within elite sporting contexts including professional sports clubs and national sports training programmes. Starting from an introduction to the history and current practice of talent identification and recruitment analytics, you will familiarise yourself with the fundamental skills necessary for the discipline. You will then encounter a range of practical, real-world situations in sport where data analytics has been used to inform recruitment or talent identification.

What will we cover

The module introduces you to the history and evolution of data analytics in recruitment and talent identification. You will become familiar with common terms and theoretical frameworks used in talent identification and recruitment. We will discuss the importance of key performance indicators and their use.

As the module progresses, you will explore more in depth use of data for talent identification purposes. We will discuss different talent identification models, examine the strengths and limitations of using these models and look at ethical aspects. Next, we will compare the recruitment analytical approaches used in different sports and hear from professionals working within the field of talent identification and recruitment.

Throughout the module we will be using real life case studies, showcasing current and past practices of data driven talent identification and recruitment. The module will include group discussions, challenges, and Problem-Based Learning (PBL) activities to enhance your understanding and foster practical application of theoretical concepts. You will gain experience in using R to calculate key performance indicators and compare athletes based on these.

Staff information

The module will be led and delivered by Dr Xanne Janssen (xanne.janssen@strath.ac.uk), who is based in Graham Hills 533.

Learning objectives

  • You will be able to evaluate the role data analytics play in talent identification and recruitment

  • You will be able to formulate the steps required to identify the relevant key performance indicators

  • You will be able to manipulate and organise data and identify key talent based on the key performance indicators

  • You will be able to critique for and against the use of specific key performance indicators for talent identification and recruitment purposes

  • You will be able to create a basic analytical program for recruitment analytics in R

Module Structure

Overview

The module will start with a 1-week introduction to recruitment analytics and talent identification in sport. After this the module is divided into four 2-week blocks in which specific topics are covered and case studies presented. You will discover the theoretical background of the topic, hear about and work on ‘real world’ case studies and gain experience in using R. The module will finish with a recap week.

Weekly syllabus

The module is delivered over 11 weeks. Each week contains two hours of face-to-face contact time and additional self-study tasks. During the self-study tasks you will sometimes be required to work independently, or work as part of a group.

Week One - An Introduction to talent identification and recruitment analytics

  • Introduction to the module

  • Introduction to talent identification and recruitment analytics

  • Practical using R and loading data sets

  • Group work strategy development

Week Two - Key performance indicators

  • Assessment 1 brief

  • Key performance indicators in sport

  • Group work case study introduction

  • Group work on case study

  • Practical cleaning data in R

Week Three - Key performance indicators

  • Group work case study presentation and reflection

  • Practical using FBRef data in R

  • Practical using StatsBomb data in R

Week Three part 2 - Talent identification in sport

  • Talent identification in sport

  • Group work case study introduction

  • Group work on case study

Week Four - No B1701 class

  • Intensive block for B1702

Week Five - Talent identification in sport

  • Group work case study presentation and reflection

  • Assessment 1 Q&A

  • Practical talent identification analysis in R

Week Six - Assessment 1

  • Assessment presentations

Week Seven - Creating reports

  • Assessment 2 brief

  • Creating R Markdown reports

  • Practical Creating R Markdown reports

Week Eight- Guest lecture Ross Goodwin

  • Guest lecture

Week Nine - Using sports data for recruitment

  • Group work case study introduction

  • Group work on case study

  • Practical Recruitment analytics in R

Week Ten- Using sports data for recruitment

  • Group work case study presentation and reflection

  • R Q&A

  • Assessment preparation

Week Eleven - Module Recap

  • Review/ consolidation

  • Assessment Q&A

Module Assessment

Assessment one

The first assessment for the module is due during Module Week 13 (academic week 6) . This assessment is worth 25% of your overall grade for the module.

For this assessment you will provide a 10-minute presentation in which you discuss the development and role recruitment analytics play in the identification of talent and key players. You will highlight commonly used key performance indicators for a sport of your choice and explain how they are measured and/or monitored.

An assessment briefing will be held during week 2.

Assessment two

The second assessment for the module is due at the end of week 19 (academic exam period). This assessment is worth 75% of your overall grade for the module.

For this assessment, you will be asked to submit a 2,500 word project report. You will be given a data set with some key questions which you need to address. Your report should discuss the case study, your methodological approach, results and the strength and weaknesses of the methods used.

You are expected to show evidence of reading in the academic literature, as well as drawing evidence from the practical case-studies included within the teaching programme.

A briefing for this assessment will be held during Module Week 7.

Reading List

1 M. D. Hughes and R. M. Bartlett, “The use of performance indicators in performance analysis,” Journal of Sports Sciences, vol. 20, no. 10, pp. 739–754, Jan. 2002, doi: 10.1080/026404102320675602

2 P. Leo, D. Simon, M. Hovorka, J. Lawley, and I. Mujika, “Elite versus non-elite cyclist – Stepping up to the international/elite ranks from U23 cycling,” Journal of Sports Sciences, vol. 40, no. 16, pp. 1874–1884, Aug. 2022, doi: 10.1080/02640414.2022.2117394

3 M. Lewis, Moneyball: The Art of Winning an Unfair Game. W. W. Norton & Company, 2004.

4 T. Davenport, “Analytics in Sports: The New Science of Winning.” 2014. Accessed: Jul. 13, 2023. [Online]. Available: https://www.readkong.com/page/analytics-in-sports-the-new-science-of-winning-6739419

5 M. Hughes, T. M. Hughes, and H. Behan, “The evolution of computerised notational analysis through the example of racket sports,” The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, vol. 10, no. 3, pp. 1–39, Dec. 2008, doi: 10.21797/KSME.2008.10.3.001

6 E. Morgulev, O. H. Azar, and R. Lidor, “Sports analytics and the big-data era,” International Journal of Data Science and Analytics, vol. 5, no. 4, pp. 213–222, Jun. 2018, doi: 10.1007/s41060-017-0093-7

7 K. Davids and J. Baker, “Genes, Environment and Sport Performance,” Sports Medicine, vol. 37, no. 11, pp. 961–980, Nov. 2007, doi: 10.2165/00007256-200737110-00004

8 K. E. Phillips and W. G. Hopkins, “Determinants of Cycling Performance: A Review of the Dimensions and Features Regulating Performance in Elite Cycling Competitions,” Sports Medicine - Open, vol. 6, no. 1, pp. 1–18, Dec. 2020, doi: 10.1186/s40798-020-00252-z

9 C. Andrews, “Data-driven transfers are football’s new normal,” Engineering & Technology, vol. 16, no. 8, pp. 1–7, Sep. 2021, doi: 10.1049/et.2021.0810

10 T. G. Cech, T. J. Spaulding, and J. A. Cazier, “Data competence maturity: Developing data-driven decision making,” Journal of Research in Innovative Teaching & Learning, vol. 11, no. 2, pp. 139–158, Jan. 2018, doi: 10.1108/JRIT-03-2018-0007

11 M. Hughes, T. Caudrelier, N. James, I. Donnelly, A. Kirkbride, and C. Duschesne, “Moneyball and soccer - an analysis of the key performance indicators of elite male soccer players by position,” Journal of Human Sport and Exercise, vol. 7, no. 2, pp. 402–412, 2012, doi: 10.4100/jhse.2012.72.06

12 M. Hill, S. Scott, R. M. Malina, D. McGee, and S. P. Cumming, “Relative age and maturation selection biases in academy football,” Journal of Sports Sciences, vol. 38, no. 11–12, pp. 1359–1367, Jun. 2020, doi: 10.1080/02640414.2019.1649524

13 J. Kim, “Perspectives on the Sports Analytics Revolution: An Introduction to the Special Issue,” Journal of Applied Sport Management, 2022, doi: 10.7290/jasm14eslv

14 K. Currell and A. E. Jeukendrup, “Validity, Reliability and Sensitivity of Measures of Sporting Performance,” Sports Medicine, vol. 38, no. 4, pp. 297–316, Apr. 2008, doi: 10.2165/00007256-200838040-00003

15 J. Baker, S. Cobley, J. Schorer, and N. Wattie, Routledge handbook of talent identification and development in sport, 1st ed. New York: Routledge, 2017.

16 I. Mujika and S. Padilla, “Physiological and performance characteristics of male professional road cyclists,” Sports Medicine, vol. 31, pp. 479–487, 2001, doi: 10.2165/00007256-200131070-00003

Additional Comments

This module makes use of R. It is recommend you install R and R Studio on your personal computer. R and R studio are available from: https://posit.co/download/rstudio-desktop/.

Libraries used in this module are:

  • tidyverse
  • worldfootballR
  • StatsBomb
  • corrplot
  • fmsb
  • summarytools
  • moments
  • caret
  • rpart
  • rpart.plot
  • flextable
  • rmarkdown
  • openxlsx