The beauty of football is hidden in its complexity. To be able to perform on an elite level, excellent physical, technical, and tactical skills are required (Impellizzeri and Marcora 2009 Impellizzeri FM, Marcora SM. 2009. Test validation in sport physiology: lessons learned from clinimetrics. Int J Sports Physiol Perform. 4(2):269-277. [Google Scholar]). These skills need to be expressed in the context of the game to beat the opponent. Traditionally, these skills are assessed using isolated lab or field tests for the purposes of talent identification (Huijgen et al. 2014 Huijgen BC, Elferink-Gemser MT, Lemmink KA, Visscher C. 2014. Multidimensional performance characteristics in selected and deselected talented soccer players. Eur J Sport Sci. 14(1):2-10.[Taylor & Francis Online], , [Google Scholar]) or evaluation of training interventions (Brink et al. 2010 Brink MS, Nederhof E, Visscher C, Schmikli SL, Lemmink KAPM. 2010. Monitoring load, recovery, and performance in young elite soccer players. J Strength Cond Res. 24(3):597-603. [Google Scholar]). Whether outcomes of these isolated tests truly reflect match performance is under constant debate. With the development and use of sensor technology in matches, the question arises if match analysis itself can serve as an alternative for isolated tests. Historically, lab tests were developed to assess physical capacity of football players (Reilly 1995 Reilly T. 1995. Science and Soccer. London: Routledge. [Google Scholar]). These test protocols were often derived from endurance sport and executed on a bicycle ergometer or a treadmill. Incremental and continuous protocols were used to assess physical capacity. The main advantage of these lab tests is standardization. However, these tests lack sport-specificity and do not mimic the intermittent character of the game. Next, the type of activity is straight running, while decelerations, accelerations, and pivoting on turf are important characteristics of football. Finally, lab tests are often time-consuming and usually limited to one player per test. To tackle these disadvantages, field tests were developed, like Yo-Yo intermittent recovery test, Interval Shuttle Run Test, and 30-15 fitness test (Krustrup et al. 2003 Krustrup P, Mohr M, Amstrup T, Rysgaard T, Johansen J, Steensberg A, Pedersen PK, Bangsbo J. 2003. The yo-yo intermittent recovery test: physiological response, reliability, and validity. Med Sci Sports Exerc. 35(4):697-705. [Google Scholar]; Lemmink et al. 2004 Lemmink KAPM, Visscher C, Lambert MI, Lamberts RP. 2004. The interval shuttle run test for intermittent sport players: evaluation of reliability. J Strength Cond Res. 18(4):821-827. [Google Scholar] ; Buchheit et al. 2008 Buchheit M. 2008. The 30-15 intermittent fitness test: accuracy for individualizing interval training of young intermittent sport players. J Strength Cond Res. 22(2):365-374. [Google Scholar]). Concurrent validity was assessed with comparison to traditional lab tests and construct validity to discriminate elite from sub-elite players. This supported the use of field tests in an applied setting. Since the development of these tests 15 years ago, they have found their way from professional football to amateur level across the globe. Similar to the development of tests to assess physical capacities, isolated tests were developed for technical skills, such as the Loughborough passing test (Ali et al. 2007 Ali A, Williams C, Hulse M, Strudwick A, Reddin J, Howarth L, Eldred J, Hirst M, McGregor S. 2007. Reliability and validity of two tests of soccer skill. J Sports Sci. 25:1461-1470.[Taylor & Francis Online], , [Google Scholar]). Tactical skills were traditionally captured by observation, but tests for decision-making skills are now also integrated into computer and tablet software or virtual reality environments. These isolated lab and field tests in the physical, technical, and tactical domain try to simulate the match as closely as possible. However, with the development of sensor technology available during matches we could also turn this around. So the question is, can we use outcomes from match analysis to assess physical, technical, tactical skills? From matches, one could derive physical performance based on distance covered, number of sprints, acceleration and deceleration profiles, and directional changes. It is expected that over time, more and more relevant physical performance indicators will be developed to track both external and internal load during match-play. Inertial measurement units have the potential to determine the mechanical load of the lower extremities but also capture technical performance indicators like ball control, passing and kicking (Blair et al. 2018 Blair S, Duthie G, Robertson S, Hopkins W, Ball K. 2018. Concurrent validation of an inertial measurement system to quantify kicking biomechanics in four football codes. J Biomech. 17(73):24-32. [Google Scholar]). Lab-on-a-chip development in healthcare can find its way to the sport field and quantify internal load based on skin temperature and sweat loss. Finally, tactical performance indicators, like passing efficiency, in combination with spatiotemporal features, such as space control and putting pressure on the opponent, may lead to more advanced measures in this domain (Rein and Memmert 2016 Rein R, Memmert D. 2016. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus. 5:1410. [Google Scholar]). However, it is known that large match-to-match variation exists as a result of contextual factors like playing strategy and formation, strength of the opponent, score line, and environmental conditions (Kempton et al. 2015 Kempton T, Sullivan C, Bilsborough JC, Cordy J, Coutts AJ. 2015. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J Sci Med Sport. 18(1):109-113. [Google Scholar]; Carling et al. 2016 Carling C, Bradley P, McCall A, Dupont G. 2016. Match-to-match variability in high-speed running activity in a professional soccer team. J Sports Sci. 34(24):2215-2223.[Taylor & Francis Online], , [Google Scholar]). This variability tends to be highest for high intensity activities such as sprinting and lowest for global measures like total distance covered (Kempton et al. 2015 Kempton T, Sullivan C, Bilsborough JC, Cordy J, Coutts AJ. 2015. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J Sci Med Sport. 18(1):109-113. [Google Scholar]). To control for some of the contextual factors, one could think of standardized (small sided) games (Rowel et al. 2018 Rowel AE, Aughy RJ, Clubb J, Cormack SJ. 2018. A standardized small sided game can be used to monitor neuromuscular fatigue in professional A-league football players. Front Physiol. 7(9):1011. [Google Scholar]). It is known that these games are closely related to the actual match, but the strategy, playing formation, and set-up can now be standardized (Olthof et al. 2018 lthof SBH, Frencken WGP, Lemmink KAPM. 2018. Match-derived relative pitch area changes the physical and team tactical performance of elite soccer players in small-sided soccer games. J Sports Sci. 36(14):1557-1563. [Google Scholar]). Besides, advanced data science methods, like machine learning, can help to control for known external factors over a series of matches (Rein and Memmert 2016 Rein R, Memmert D. 2016. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus. 5:1410. [Google Scholar]). In sum, information from matches can help practitioners and scientists to better understand the game. Identifying talents and evaluation of training interventions based on match performance may have its limitations, but the challenge for football scientists is to resolve this with new sensor technology and advanced data analysis techniques. This is not limited to physical, technical, and tactical aspects of the game but could also include mental aspects. Recently, face recognition has been used to determine emotions in sports like tennis (Kovalchik and Reid 2018 Kovalchik S, Reid M. 2018. Going inside the inner game: predicting the emotions of professional tennis players from match broadcasts. MIT Sloan Sports Analytics Conference, Boston. [Google Scholar]). Furthermore, driver fatigue was detected based on eye state (Lin et al. 2015 Lin L, Huang C, Ni X, Wang J, Zhang H, Li X, Qian Z. 2015. Driver fatigue detection based on eye state. Technol Health Care. 2:S453-63. [Google Scholar]). These developments will likely find their way into football. If we acknowledge that all information is already in the game, we may better understand football in all its complexity.
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