Real-Time Versus Post-Game Data

The validity and reliability of devices is a crucial factor to guarantee high quality measures (Malone et al., 2017).

In this regard, the accuracy of EPTS (Bastida Castillo et al., 2018; Bastida-Castillo et al., 2019; Bastida-Castillo et al., 2019; Leser et al., 2014; Linke et al., 2018) and MEMS (Chambers et al., 2015; Cummins et al., 2013; Hausler et al., 2016) has been widely shown in the field of team sports performance. However, it seems that real-time data may differ from data downloaded after a session using EPTS (Aughey & Falloon, 2010; Weaving et al., 2017) and MEMS (Weaving et al., 2017). This has been compared using EPTS during Australian Football matches (Aughey

& Falloon, 2010) and with professional rugby league players (Weaving et al., 2017). Aughey and Falloon (2010) compared the signal (smallest meaningful difference [SMD]) to the noise (typical error [TE]) of real-time to post- session data, finding that whilst the signal exceeded the noise (SMD = 134.6 m; TE = 55.8 m) for total distance, this was reduced considerably during jogging (4.2 to 5.0 m·s⁻¹; SMD = 33.5 m; TE = 30.1 m) and running (5.0 to 6.9 m·s⁻¹; SMD = 31.9 m; TE = 31.3 m). In particular, the noise exceeded the signal during real-time collection for sprinting (SMD = 17.3 m; TE = 23.7 m). These findings showed that only total distance (SMD = 134.6 m; TE

= 55.8 m) demonstrated an acceptable signal:noise ratio, suggesting that real-time data possesses limited validity and applicability to practice. Similarly, Weaving et al. (2017) assessed this aspect using data derived from GPS.

Their study suggests that practitioners should be confident of making decisions in real-time regarding the accumulation of a player’s high-speed (5 to 7 m·s⁻¹) distance. However, they should acknowledge the small error between post-session and real-time when quantifying very-high-speed (> 7.1 m·s⁻¹) running exposure (Weaving et al., 2017). Therefore, at present, practitioners should not assume that the data they receive in real-time during the session is comparable to that which they use to inform the planning of training (e.g. post-session data).

Weaving et al. (2017) compared session data vs post-session data using MEMS with professional rugby league players. Using Catapult models (Minimax, Team Sport 2.0, Catapult Innovations) poorer agreement was found for very-high-speed running (i.e. > 7 m·s⁻¹) compared to lower speeds. This is of interest for any team sports such as soccer, rugby, basketball, handball, futsal or American football where quantifying collision and accelerative-based activity during training and competition is an important aspect. Thus, monitoring accelerometer derived measures in real time is an attractive capability for practitioners. Weaving et al. (2017) found that whilst post-session PlayerLoad™ (the vector sum of accelerations in the three orthogonal axes) data has been found to be both reliable and valid in real-time, moderate errors were found when compared to the data downloaded directly from the device. It is unknown why real-time PlayerLoad™ data demonstrated greater errors compared to speed-derived methods. It is possible that differences in how the real-time receiver receives data from the 100Hz three-axial accelerometer compared to 10Hz GPS can explain the greater errors associated with real-time PlayerLoad™.

Therefore, practitioners should ideally refrain from interpreting real-time PlayerLoad™ during training and competition but should estimate post-session data from real-time data (Weaving et al., 2017).

Lessons Learned and Concluding Remarks

Real-time feedback could become a competitive advantage. Due to the technological development and federation permission such as FIFA’s, radio-frequency based technology has become very popular. Today, the development of WBAN has allowed the transmission of data in real time from devices to the PC, tablet or mobile, where the data are shown. However, each manufacturer has different characteristics and not all of them allow real-time feedback.

Until now, there has been debate about the accuracy of real-time data versus data downloaded after a session.

Today, caution is necessary in the interchangeability of data downloaded in real time or post session, especially, when high intensity variables are analysed. So, manufacturers should develop the technology to make their data more precise in real time.

References

Alarifi, A., Al-Salman, A., Alsaleh, M., Alnafessah, A., Al-Hadhrami, S., Al-Ammar, M., & Al-Khalifa, H. (2016). Ultra wideband indoor positioning technologies: Analysis and recent advances. Sensors, 16(5), 707. doi:10.3390/s16050707

Andreassen, K., Johansen, D., Johansen, H., Baptista, I., Pettersen, S. A., Riegler, M., & Halvorsen, P. (2019). Real-time analysis of physical performance parameters in elite soccer. 2019 International Conference on Content-Based Multimedia Indexing (CBMI), 1–6. doi:10.1109/CBMI.2019.8877422

Arbia, B. (2018). Data communications algorithms for emerging wearable and urban sensing networks. Electronic, Information and Communication Technologies, 132.

Aughey, R. J., & Falloon, C. (2010). Real-time versus post-game GPS data in team sports. Journal of Science and Medicine in Sport, 13(3), 348–349. doi:10.1016/j.jsams.2009.01.006

Bartlett, R., Button, C., Robins, M., Dutt-Mazumder, A., & Kennedy, G. (2012). Analysing team coordination patterns from player movement trajectories in soccer: Methodological considerations. International Journal of Performance Analysis in Sport, 12(2), 398–424. doi:10.1080/24748668.2012.11868607

Bastida Castillo, A., Gómez Carmona, C. D., De la Cruz Sánchez, E., & Pino Ortega, J. (2018). Accuracy, intra- and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time–motion analyses in soccer.

European Journal of Sport Science, 18(4), 450–457. doi:10.1080/17461391.2018.1427796

Bastida-Castillo, A., Gómez-Carmona, C. D., De La Cruz Sánchez, E., & Pino-Ortega, J. (2019). Comparing accuracy between global positioning systems and ultra-wideband-based position tracking systems used for tactical analyses in soccer. European Journal of Sport Science, 19(9), 1157–1165. doi:10.1080/17461391.2019.1584248

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. doi:10.2165/00007256-200838100-00004 Castellano, J., Puente, A., Echeazarra, I., Usabiaga, O., & Casamichana, D. (2016a). Number of players and relative pitch area per

player: Comparing their influence on heart rate and physical demands in under-12 and under-13 football players. PLoS One, 11(1), e0127505. doi:10.1371/journal.pone.0127505

Castellano, J., Silva, P., Usabiaga, O., & Barreira, D. (2016b). The influence of scoring targets and outer-floaters on attacking and defending team dispersion, shape and creation of space during small-sided soccer games. Journal of Human Kinetics, 51(1), 153–163. doi:10.1515/hukin-2015-0178

Cavallari, Riccardo, Flavia Martelli, Ramona Rosini, Chiara Buratti, and Roberto Verdone. (2014). A survey on wireless body area networks: Technologies and design challenges. IEEE Communications Surveys & Tutorials, 16(3), 1635–1657.

Chambers, R., Gabbett, T. J., Cole, M. H., & Beard, A. (2015). The use of wearable microsensors to quantify sport-specific movements. Sports Medicine, 45(7), 1065–1081. doi:10.1007/s40279-015-0332-9

Cummins, C., Orr, R., O’Connor, H., & West, C. (2013). Global Positioning Systems (GPS) and microtechnology sensors in team sports: A systematic review. Sports Medicine, 43(10), 1025–1042. doi:10.1007/s40279-013-0069-2

Dogramaci, S. N., Watsford, M. L., & Murphy, A. J. (2011). The reliability and validity of subjective notational analysis in comparison to global positioning system tracking to assess athlete movement patterns. Journal of Strength and Conditioning Research, 25(3), 852–859. doi:10.1519/JSC.0b013e3181c69edd

FIFA. (2015). EPTS Electronic Performance and Tracking Systems. Retrieved from: https://football-technology.fifa.com/en/media- tiles/epts/

Glazier, P. S., Davids, K., & Bartlett, R. M. (2003). Dynamical Systems Theory: A Relevant Framework for Performance-Oriented Sports Biomechanics Research. N.p.: Sportscience. 9.

Hausler, J., Halaki, M., & Orr, R. (2016). Application of global positioning system and microsensor technology in competitive rugby league match-play: A systematic review and meta-analysis. Sports Medicine, 46(4), 559–588. doi:10.1007/s40279-015- 0440-6

Jovanov, E., Milenkovic, A., Otto, C., & de Groen, P. C. (2005). A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2(1), 6. doi:10.1186/1743-0003-2-6 Le, J. (2016). Critical Essay Assignment: The Implications of Technological Advancement in Soccer. 16. Denison University.

Leser, R., Schleindlhuber, A., Lyons, K., & Baca, A. (2014). Accuracy of an UWB-based position tracking system used for time- motion analyses in game sports. European Journal of Sport Science, 14(7), 635–642. doi:10.1080/17461391.2014.884167 Link, D., Weber, M., Linke, D., & Lames, M. (2019). Can positioning systems replace timing gates for measuring sprint time in ice

hockey? Frontiers in Physiology, 9, 1882. doi:10.3389/fphys.2018.01882

Linke, D., Link, D., & Lames, M. (2018). Validation of electronic performance and tracking systems EPTS under field conditions.

PLoS One, 13(7), e0199519. doi:10.1371/journal.pone.0199519

Luteberget, L. S., Spencer, M., & Gilgien, M. (2018). Validity of the catapult ClearSky T6 local positioning system for team sports specific drills, in indoor conditions. Frontiers in Physiology, 9, 115. doi:10.3389/fphys.2018.00115

Malone, J. J., Lovell, R., Varley, M. C., & Coutts, A. J. (2017). Unpacking the black box: Applications and considerations for using GPS devices in sport. International Journal of Sports Physiology and Performance, 12(Suppl 2), 18–26. doi:10.1123/ijspp.2016- 0236

Moreno-Perez, V., Malone, S., Sala-Perez, L., Lapuente-Sagarra, M., Campos-Vazquez, M.A. and Del Coso, J. (2019). Activity monitoring in professional soccer goalkeepers during training and match play. International Journal of Sports Physiology and Performance, 20(1), 19–30.

Owen, A. L., Newton, M., Shovlin, A., & Malone, S. (2020). The use of small-sided games as an aerobic fitness assessment supplement within elite level professional soccer. Journal of Human Kinetics, 71(1), 243–253. doi:10.2478/hukin-2019-0086 Pons, E., García-Calvo, T., Resta, R., Blanco, H., López del Campo, R., Díaz García, J., & Pulido, J. J. (2019). A comparison of a

GPS device and a multi-camera video technology during official soccer matches: Agreement between systems. PLoS One, 14(8), e0220729. doi:10.1371/journal.pone.0220729

Reche-Soto, P., Cardona, D., Díaz, A., Gómez-Carmona, C. D., & Pino-Ortega, J. (2019). Demandas tácticas de juegos reducidos en fútbol: Influencia de la tecnología utilizada. Revista Internacional de Medicina y Ciencias de La Actividad Física y Del Deporte, 19(76), 729. doi:10.15366/rimcafd2019.76.011

Rico-González, M., Los Arcos, A., Nakamura, F. Y., Moura, F. A., & Pino-Ortega, J. (2019). The use of technology and sampling frequency to measure variables of tactical positioning in team sports: A systematic review. Research in Sports Medicine, 28(2), 279–292. doi:10.1080/15438627.2019.1660879

Salayma, M., Al-Dubai, A., Romdhani, I., & Nasser, Y. (2017). Wireless Body Area Network (WBAN): A Survey on reliability, fault tolerance, and technologies coexistence. ACM Computing Surveys, 50(1), 1–38. doi:10.1145/3041956

Scott, M. T. U., Scott, T. J., & Kelly, V. G. (2015). The validity and reliability of global positioning systems in team sport: A brief review. Journal of Strength and Conditioning Research, 30(5), 1470–1490.

Serpiello, F. (2019). Compare GPS - upper price tier. Compare Sports Tech.

Weaving, D., Whitehead, S., Till, K., & Jones, B. (2017). Validity of real-time data generated by a wearable microtechnology device. Journal of Strength and Conditioning Research, 31(10), 2876–2879. doi:10.1519/JSC.0000000000002127

Young, D., Mourot, L., & Coratella, G. (2018). Match-play performance comparisons between elite and sub-elite hurling players.

Sport Sciences for Health, 14(1), 201–208. doi:10.1007/s11332-018-0441-6

Part III

Data Processing

Performance Variables

9 Kinematical Variables

Pedro Reche-Soto Ortega, José Pino- and Luca Paolo Ardigò

Introduction

Micro-electromechanical systems (MEMS) have continuously improved over the last two decades. Presently, they are included in consumer products commonly referred to as “wearables” and offer data from sensor systems such as the Global Navigation Satellite System (GNSS) (Castellano et al., 2011), accelerometers (Montgomery et al., 2010), and heart rate monitoring sensors (Achten & Jeukendrup, 2003). This allows sports scientists to automate time–motion analysis and therefore replace error-prone and subjective qualitative with quantitative data-gathering procedures in team and individual sports (Lutz et al., 2019).

The quantification of team sports, in comparison to single-activity tracking, remains more complex as it evolves around the concept of two parties trying to outperform one another and win a competitive game sharing mutual objectives (Lames et al., 2010). This is emphasized by the fact that during a competition there is constant activity in the form of actions and reactions to the movements of every participant. The constant adaptation frames the whole contest as a product of dynamic interaction processes (Lames et al., 2010). Hence, it is also necessary to track the off- the-ball movements of players to capture every dimension of these interaction processes (Licciardi et al., 2020). This highly dynamic nature of contests arises by chance, non-linear actions, such as short sprints and high- intensity runs, and other stress factors, especially game climax (Coutts &

Duffield, 2010; Lames et al., 2010).

The recent state of wearables has enabled scientists to obtain more

objective, sophisticated, and instant quantification in team sports (Cunniffe

et al., 2009). The collected game data allow the analysis of physical (Boyd,

2011) as well as individual and team-tactical behavior (Memmert et al.,

2017). However, due to its complexity, scientific analyses of tactical

performance are still underrepresented (Sampaio & Maçãs, 2012; Sarmento

et al., 2014). Furthermore, the research focus remains on the area of

individual physical performance (Chambers et al., 2015). Other researchers

have recently begun to assess the performance of an individual as part of a

collective formation as well as trying to quantify team performance.