Abstract
The rise of Data Science and related fields of Big Data, Machine Learning, and Deep Learning has transformed the industrial landscape. The areas of sports and sports analytics are no exception. While to the layman, its influence may not be evident, but they have changed the way various sports are played up to different degrees. Hence, in recent times, sports institutions and clubs have given increased importance to such research that will ultimately help them have a competitive edge over rivals. The effects of these institutions incorporating these researches into their ways of competing have had impacts on and off the playing field. These effects aren’t only in terms of physiological enhancements of the athletes, but also socio-political and economic impacts as well. Out of the various sports implementing these techniques, we will focus on the effects mentioned above of Data Science on Football (“Soccer” in the USA). The following is a detailed review of the concepts as mentioned earlier.
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References
Parekh V, Shah D, Shah M (2020) Fatigue detection using artificial intelligence framework. Augment Hum Res. https://doi.org/10.1007/s41133-019-0023-4
Pandya R, Nadiadwala S, Shah R, Shah M (2020) Buildout of methodology for meticulous diagnosis of K-complex in EEG for aiding the detection of alzheimer’s by artificial intelligence. Augment Hum Res. https://doi.org/10.1007/s41133-019-0021-6
Kundalia K, Patel Y, Shah M (2020) Multi-label movie genre detection from a movie poster using knowledge transfer learning. Augment Hum Res. https://doi.org/10.1007/s41133-019-0029-y
Bondyopadhyay PK (1998) Moore’s law governs the silicon revolution. Proc IEEE 86:78–81. https://doi.org/10.1109/5.658761
Arnold U, Oberlander J, Schwarzbach B (2013) Advancements in cloud computing for logistics. Fed Conf Comput Sci Inf Syst FedCSIS 2013:1055–1062
Gandhi M, Kamdar J, Shah M (2020) Preprocessing of non-symmetrical images for edge detection. Augment Hum Res 5:1–10. https://doi.org/10.1007/s41133-019-0030-5
Patel D, Shah D, Shah M (2020) The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports. Ann Data Sci. https://doi.org/10.1007/s40745-019-00239-y
Ahir K, Govani K, Gajera R, Shah M (2020) Application on virtual reality for enhanced education learning, military training and sports. Augment Hum Res. https://doi.org/10.1007/s41133-019-0025-2
Jani K, Chaudhuri M, Patel H, Shah M (2020) Machine learning in films: an approach towards automation in film censoring. J Data, Inf Manag 2:55–64. https://doi.org/10.1007/s42488-019-00016-9
Bryant R, Katz R, Lazowska E (2008) Big-data computing: creating revolutionary breakthroughs in commerce, science, and society in computing research initiatives for the 21st century. Comput Res Assoc
Tambe P (2014) Big Data Investment, Skills, and Firm Value. Manage Sci 60:1452–1469. https://doi.org/10.1287/mnsc.2014.1899
Mcafee A, Brynjolfsson E (2012) Spotlight on big data big data: the management revolution. Harv Bus Rev 90:1–9
Li J, Shi Y (2001) An integer linear programming problem with multi-criteria and multi-constraint levels: a branch-and-partition algorithm. Int Trans Oper Res 8:497–509. https://doi.org/10.1111/1475-3995.00328
Shi Y, Tian Y, Kou G et al (2011) Optimization based data mining: theory and applications. Springer, London
Olsen D, Shi Y (2006) Introduction to business data mining. McGraw-Hill/Irwin, New York
Sukhadia A, Upadhyay K, Gundeti M et al (2020) Optimization of smart traffic governance system using artificial intelligence. Augment Hum Res. https://doi.org/10.1007/s41133-020-00035-x
Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artif Intell Agric 2:1–12. https://doi.org/10.1016/j.aiia.2019.05.004
Kakkad V, Patel M, Shah M (2019) Biometric authentication and image encryption for image security in cloud framework. Multiscale Multidiscip Model Exp Des 2:233–248. https://doi.org/10.1007/s41939-019-00049-y
Panchiwala S, Shah M (2020) A comprehensive study on critical security issues and challenges of the IoT world. J Data, Inf Manag 2:257–278. https://doi.org/10.1007/s42488-020-00030-2
Gupta A, Dengre V, Kheruwala HA, Shah M (2020) Comprehensive review of text-mining applications in finance. Financ Innov 6:1–25
Desai M, Shah M (2020) An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and convolutional neural network (CNN). Clin eHealth. https://doi.org/10.1016/j.ceh.2020.11.002
Thakkar H, Shah V, Yagnik H, Shah M (2020) Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis. Clin eHealth. https://doi.org/10.1016/j.ceh.2020.11.001
Ayankoya K, Calitz A, Greyling J (2014) Intrinsic relations between data science, big data, business analytics and datafication. ACM Int Conf Proceeding Ser 28-Septemb:192–198. https://doi.org/10.1145/2664591.2664619
Talaviya T, Shah D, Patel N et al (2020) Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif Intell Agric 4:58–73. https://doi.org/10.1016/j.aiia.2020.04.002
Shah K, Patel H, Sanghvi D, Shah M (2020) A Comparative analysis of logistic regression, random forest and KNN models for the text classification. Augment Hum Res. https://doi.org/10.1007/s41133-020-00032-0
Naik B, Mehta A, Shah M (2020) Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease. Vis Comput Ind Biomed Art 3:1–18. https://doi.org/10.1186/s42492-020-00062-w
Shah D, Dixit R, Shah A et al (2020) A comprehensive analysis regarding several breakthroughs based on computer intelligence targeting various syndromes. Augment Hum Res. https://doi.org/10.1007/s41133-020-00033-z
Drust B, Green M (2013) Science and football: evaluating the influence of science on performance. J Sports Sci 31:1377–1382. https://doi.org/10.1080/02640414.2013.828544
Lewis Michael (2004) Moneyball: The Art of winning an unfair game - Michael Lewis - Google Books
Fullerton HS (1912) The inside game: the science of baseball. Am Mag 70:2–13
Reep C, Benajmin B (1968) Skill and chance in association football. J Royal Stat Soc. Ser A (General) 131(4):581–585
Memmert D, Rein R (2018) Match analysis, big data and tactics: current trends in elite soccer. Dtsch Z Sportmed 69:65–72. https://doi.org/10.5960/dzsm.2018.322
Thabtah F, Zhang L, Abdelhamid N (2019) NBA game result prediction using feature analysis and machine learning. Ann Data Sci 6:103–116. https://doi.org/10.1007/s40745-018-00189-x
Hughes M, Franks I (2005) Analysis of passing sequences, shots and goals in soccer. J Sports Sci 23:509–514. https://doi.org/10.1080/02640410410001716779
Bojanova I (2014) IT enhances football at world cup 2014. IT Prof 16:12–17. https://doi.org/10.1109/MITP.2014.54
ZACH HELFAND (2015) Use of defensive shifts in baseball is spreading — because it works - Los Angeles Times. https://www.latimes.com/sports/la-sp-baseball-defensive-shifts-20150719-story.html. Accessed 3 Jan 2021
Alrababa’h A, Marble W, Mousa S, Siegel AA (2019) Can exposure to celebrities reduce prejudice? The effect of Mohamed Salah on islamophobic behaviors and attitudes. https://doi.org/10.31235/osf.io/eq8ca
Henderson JC, Foo K, Lim H, Yip S (2010) Sports events and tourism: the Singapore formula one grand prix. Int J Event Festiv Manag 1:60–73. https://doi.org/10.1108/17852951011029306
Constantinou AC, Fenton NE, Neil M (2012) Pi-football: a bayesian network model for forecasting association football match outcomes. Knowledge-Based Syst 36:322–339. https://doi.org/10.1016/j.knosys.2012.07.008
Epstein ES (1969) A scoring system for probability forecasts of ranked categories on JSTOR. J Appl Meteorol 8:985–987
Dixon MJ, Coles SG (1997) Modelling association football scores and inefficiencies in the football betting market. J R Stat Soc Ser C Appl Stat 46:265–280. https://doi.org/10.1111/1467-9876.00065
Moura FA, Martins LEB, Cunha SA (2014) Analysis of football game-related statistics using multivariate techniques. J Sports Sci 32:1881–1887. https://doi.org/10.1080/02640414.2013.853130
Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis, 99th edn. Wiley, Hoboken
Jones PD, James N, Mellalieu SD (2004) Possession as a performance indicator in soccer. Int J Perform Anal Sport 4:98–102. https://doi.org/10.1080/24748668.2004.11868295
Gama J, Passos P, Davids K et al (2014) Network analysis and intra-team activity in attacking phases of professional football. Int J Perform Anal Sport 14:692–708. https://doi.org/10.1080/24748668.2014.11868752
Hirotsu N, Wright M (2003) Determining the best strategy for changing the configuration of a football team. J Oper Res Soc 54:878–887. https://doi.org/10.1057/palgrave.jors.2601591
Hirotsu N, Wright M (2002) Using a markov process model of an association football match to determine the optimal timing of substitution and tactical decisions. J Oper Res Soc 53:88–96. https://doi.org/10.1057/palgrave/jors/2601254
Rotshtein AP, Posner M, Rakityanskaya AB (2005) Football predictions based on a fuzzy model with genetic and neural tuning. Cybern Syst Anal 41:619–630. https://doi.org/10.1007/s10559-005-0098-4
RotshteinKatel’Nikov APDI (1998) Identification of nonlinear objects by fuzzy knowledge bases. Cybern Syst Anal 34:676–683. https://doi.org/10.1007/BF02667040
Rotshtein AP, Shtovba SD (2001) Fuzzy multicriteria analysis of variants with the use of paired comparisons. J Comput Syst Sci Int 40:499–503
Tsakonas A, Dounias G, Shtovba S, Vivdyuk V (2002) Soft computing-based result prediction of football games. Ist Int Conf Inductive Model
Sæbø OD, Hvattum LM (2018) Modelling the financial contribution of soccer players to their clubs. J Sport Anal 5:23–34. https://doi.org/10.3233/jsa-170235
Hvattum LM (2013) Analyzing information efficiency in the betting market for association football league winners. J Predict Mark 7:55–70. https://doi.org/10.5750/jpm.v7i2.614
Gennaro Vince (2007) Diamond dollars: The economics of winning in baseball. In: Potomac Books Inc. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=18.%09Gennaro+2007.+Diamond+Dollars%3A+The+Economics+of+Winning.+Maple+Street+Press.+1-253&btnG=#d=gs_cit&u=%2Fscholar%3Fq%3Dinfo%3AvoGYPaWVTGQJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D0%26hl%3Den. Accessed 3 Jan 2021
Fairchild A, Pelechrinis K, Kokkodis M (2018) Spatial analysis of shots in MLS: a model for expected goals and fractal dimensionality. J Sport Anal 4:165–174. https://doi.org/10.3233/jsa-170207
Pollard R, Ensum J, Taylor S (2004) Estimating the probability of a shot resulting in a goal: the effects of distance, angle and space. Int J Soccer Sci 2:50–55
Anderson Chris (2010) Comparing the best soccer leagues in the world. In: Sport. Inc. 3.1(Fall). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=1.%09Anderson%2C+C.%2C+2010%2C+Comparing+the+best+soccer+leagues+in+the+world.+Sports%2C+Inc.+3.1%28Fall%29%2C+10-12&btnG=. Accessed 3 Jan 2021
Rein R, Memmert D (2016) Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. Springerplus. https://doi.org/10.1186/s40064-016-3108-2
Yiannakos A, Armatas V (2006) Evaluation of the goal scoring patterns in European Championship in Portugal 2004. Int J Perform Anal Sport 6:178–188. https://doi.org/10.1080/24748668.2006.11868366
Coutts AJ (2014) Evolution of football match analysis research. J Sports Sci 32:1829–1830. https://doi.org/10.1080/02640414.2014.985450
Bakker D, Müller A, Velupillai V et al (2009) Adding typology to lexicostatistics: a combined approach to language classification. Linguist Typol 13:169–181. https://doi.org/10.1515/LITY.2009.009
González-Víllora S, Serra-Olivares J, Pastor-Vicedo JC, da Costa IT (2015) Review of the tactical evaluation tools for youth players, assessing the tactics in team sports: football. Springerplus 4:1–17. https://doi.org/10.1186/s40064-015-1462-0
LI Ping (2005) Tendency of Offensive Tactics of Modern Football from the 11~(th) and 12~(th) European Football Championship--《Journal of Chengdu Physical Education Institute》2005年05期. J Chengdu Phys Educ Inst
Lu W-L, Ting J-A, Little JJ, Murphy KP (2013) Learning to track and identify players from broadcast sports videos. IEEE Trans Pattern Anal Mach Intell 35:1704–1716
Júlio G (2009) Trends of tactical performance analysis in team sports: bridging the gap between research, training and competition. Rev Port Ciências do Desporto 9:81–89
Carling C, Bloomfield J, Nelsen L, Reilly T (2008) The role of motion analysis in elite soccer work rate data. Sport Med 38:839–862
Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
Dutt-Mazumder A, Button C, Robins A, Bartlett R (2011) Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players? Sport Med 41:1003–1017. https://doi.org/10.2165/11593950-000000000-00000
Goecks J, Nekrutenko A, Taylor J et al (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. https://doi.org/10.1186/gb-2010-11-8-r86
Blankenberg D, Von Kuster G, Bouvier E et al (2014) Dissemination of scientific software with galaxy toolshed. Genome Biol 15:2–4. https://doi.org/10.1186/gb4161
Sharma M, Khera SN, Sharma PB (2019) Applicability of machine learning in the measurement of emotional intelligence. Ann Data Sci 6:179–187. https://doi.org/10.1007/s40745-018-00185-1
Xu Z, Shi Y (2015) Exploring big data analysis: fundamental scientific problems. Ann Data Sci 2:363–372. https://doi.org/10.1007/s40745-015-0063-7
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The authors are grateful to Indus University and School of Technology, Pandit Deendayal Petroleum University for permission to publish this research.
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Thakkar, P., Shah, M. An Assessment of Football Through the Lens of Data Science. Ann. Data. Sci. 8, 823–836 (2021). https://doi.org/10.1007/s40745-021-00323-2
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DOI: https://doi.org/10.1007/s40745-021-00323-2