Chapter 18: Further Reading

Foundational References

  1. Carling, C., Bloomfield, J., Nelsen, L., & Reilly, T. (2008). "The role of motion analysis in elite soccer: Contemporary performance measurement techniques and work rate data." Sports Medicine, 38(10), 839--862. A comprehensive review of motion analysis techniques and their application to physical performance measurement in soccer. Provides historical context for the evolution of tracking technology.

  2. Castellano, J., Alvarez-Pastor, D., & Bradley, P. S. (2014). "Evaluation of research using computerised tracking systems (Amisco and Prozone) to analyse physical performance in elite soccer: A systematic review." Sports Medicine, 44(5), 701--712. Systematic review of the two dominant optical tracking systems of the era, evaluating their accuracy, reliability, and the physical performance metrics derived from their data.

  3. Linke, D., Link, D., & Lames, M. (2018). "Validation of electronic performance and tracking systems EPTS under field conditions." PLOS ONE, 13(7), e0199519. Rigorous validation study comparing the accuracy of multiple tracking technologies under realistic match conditions.

Physical Performance and Speed Analysis

  1. Bradley, P. S., Sheldon, W., Wooster, B., Olsen, P., Boanas, P., & Krustrup, P. (2009). "High-intensity running in English FA Premier League soccer matches." Journal of Sports Sciences, 27(2), 159--168. Landmark study establishing positional profiles of high-speed running in the Premier League, demonstrating significant differences by playing position.

  2. Osgnach, C., Poser, S., Bernardini, R., Rinaldo, R., & di Prampero, P. E. (2010). "Energy cost and metabolic power in elite soccer: A new match analysis approach." Medicine & Science in Sports & Exercise, 42(1), 170--178. Introduces the metabolic power model for estimating energy expenditure from tracking data, incorporating both speed and acceleration.

  3. di Prampero, P. E., Fusi, S., Sepulcri, L., Morin, J. B., Belli, A., & Antonutto, G. (2005). "Sprint running: A new energetic approach." Journal of Experimental Biology, 208(14), 2809--2816. The theoretical foundation for the equivalent slope model used in metabolic power calculations.

  4. Abt, G., & Lovell, R. (2009). "The use of individualized speed and intensity thresholds for determining the distance run at high-intensity in professional soccer." Journal of Sports Sciences, 27(9), 893--898. Argues for individualized rather than fixed speed thresholds, demonstrating that fixed thresholds misclassify effort intensity for players with different physical capacities.

Collective Movement and Team Tactics

  1. Bourbousson, J., Seve, C., & McGarry, T. (2010). "Space-time coordination dynamics in basketball: Part 2. The interaction between the two teams." Journal of Sports Sciences, 28(3), 349--358. Introduces the stretch index and correlation-based synchronization metrics, originally in basketball but widely adopted in soccer analytics.

  2. Frencken, W., Lemmink, K., Delleman, N., & Visscher, C. (2011). "Oscillations of centroid position and surface area of soccer teams in small-sided games." European Journal of Sport Science, 11(4), 215--223. Examines the dynamics of team centroid and surface area in small-sided games, establishing methods applicable to full-match analysis.

  3. Fonseca, S., Milho, J., Travassos, B., & Araujo, D. (2012). "Spatial dynamics of team sports exposed by Voronoi diagrams." Human Movement Science, 31(6), 1652--1659. Applies Voronoi tessellation to team sports, demonstrating its utility for analyzing spatial dominance and tactical structure.

Pitch Control and Expected Possession Value

  1. Fernandez, J., & Bornn, L. (2018). "Wide open spaces: A statistical technique for measuring space generation in professional soccer." MIT Sloan Sports Analytics Conference. Introduces a motion-based pitch control model that accounts for player velocity and direction, enabling quantification of space creation and dominance.

  2. Spearman, W. (2018). "Beyond expected goals." MIT Sloan Sports Analytics Conference. Presents an expected possession value framework that combines tracking and event data to estimate the probability of scoring from any game state.

  3. Fernandez, J., Bornn, L., & Cervone, D. (2021). "A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions." Machine Learning, 110, 1389--1427. Extends the pitch control model into a full EPV framework, providing a continuous evaluation of possession value using tracking data.

Fatigue and Workload Management

  1. Mohr, M., Krustrup, P., & Bangsbo, J. (2003). "Match performance of high-standard soccer players with special reference to development of fatigue." Journal of Sports Sciences, 21(7), 519--528. Classic study documenting the temporal pattern of fatigue in elite soccer, establishing the characteristic decline in high-intensity running in the second half.

  2. Gabbett, T. J. (2016). "The training-injury prevention paradox: Should athletes be training smarter and harder?" British Journal of Sports Medicine, 50(5), 273--280. Introduces the Acute-to-Chronic Workload Ratio framework and the concept of the "sweet spot" for injury prevention.

  3. Hulin, B. T., Gabbett, T. J., Blanch, P., Chapman, P., Bailey, D., & Orchard, J. W. (2014). "Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers." British Journal of Sports Medicine, 48(8), 708--712. Foundational study demonstrating the relationship between workload spikes and injury risk, underpinning the ACWR methodology.

Pressing and Defensive Analysis

  1. Andrienko, G., Andrienko, N., Budziak, G., Dykes, J., Fuchs, G., Von Landesberger, T., & Weber, H. (2017). "Visual analysis of pressure in football." Data Mining and Knowledge Discovery, 31(6), 1793--1839. Comprehensive visual analytics approach to quantifying and visualizing pressing behavior using tracking data.

  2. Robberechts, P., Van Haaren, J., & Davis, J. (2019). "Who will win it? An in-game win probability model for football." arXiv preprint arXiv:1906.05029. Demonstrates the integration of tracking-derived features into predictive models for match outcomes.

Data Visualization and Tools

  1. Rein, R., & Memmert, D. (2016). "Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science." SpringerPlus, 5(1), 1--13. Discusses the challenges and opportunities of big data in soccer, including tracking data storage, processing, and analytical methodologies.

  2. Memmert, D., & Rein, R. (2018). "Match analysis, big data and tactics: Current trends in elite soccer." Deutsche Zeitschrift fur Sportmedizin, 69(3), 65--72. Reviews the state of tactical analysis using tracking data, including formation detection, space control, and pressing quantification.

Open Data and Reproducible Research

  1. Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., & Giannotti, F. (2019). "A public data set of spatio-temporal match events in soccer competitions." Scientific Data, 6, 236. Describes the Wyscout public dataset, which includes event data for multiple seasons and competitions. While not tracking data, it provides a foundation for integration exercises.

  2. Kloppy documentation. https://kloppy.pysport.org/ Open-source Python library for loading and standardizing tracking data from multiple providers (TRACAB, Second Spectrum, Metrica Sports, etc.). Essential for practical work with tracking data.

  3. Metrica Sports open tracking data. https://github.com/metrica-sports/sample-data Publicly available sample tracking datasets for educational and research purposes. Includes synchronized event and tracking data for two complete matches.

Textbooks and Monographs

  1. Sumpter, D. (2016). Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing. Accessible introduction to mathematical modeling in soccer, including sections on movement patterns and spatial analysis.

  2. Memmert, D., & Raabe, D. (2018). Data Analytics in Football. Routledge. Comprehensive textbook covering position data analysis, including chapters on tracking technology, physical performance, and tactical analysis.

  3. Brechot, M., & Flepp, R. (2020). "Dealing with randomness in match outcomes: How to rethink performance evaluation in European club football using expected goals." Journal of Sports Economics, 21(4), 335--362. Demonstrates the integration of spatial and contextual data into performance evaluation models.