1. Adams, K., Kiefer, A., Panchuk, D., Hunter, A., MacPherson, R., & Spratford, W. (2020). From the field of play to the laboratory: recreating the demands of competition with augmented reality simulated sport. Journal of Sports Sciences, 38(5), 486-493.
2. Anderson, F., Grossman, T., Matejka, J., & Fitzmaurice, G. (2013, October). YouMove: enhancing movement training with an augmented reality mirror. In Proceedings of the 26th annual ACM symposium on User interface software and technology (pp. 311-320).
3. Azuma, R. T. (1997). A survey of augmented reality. Presence: teleoperators & virtual environments, 6(4), 355-385.
4. Basole, R. C., & Saupe, D. (2016). Sports data visualization [Guest editors' introduction]. IEEE Computer Graphics and Applications, 36(5), 24-26.
5. Benson, L. C., Clermont, C. A., Bošnjak, E., & Ferber, R. (2018). The use of wearable devices for walking and running gait analysis outside of the lab: A systematic review. Gait & posture, 63, 124-138.
6. Berndt, E. R., & Rappaport, N. J. (2001). Price and quality of desktop and mobile personal computers: A quarter-century historical overview. American Economic Review, 91(2), 268-273.
7. Berryman, D. R. (2012). Augmented reality: a review. Medical reference services quarterly, 31(2), 212-218.
8. Bertamini, M., & Makin, A. D. (2014). Brain activity in response to visual symmetry. Symmetry, 6(4), 975-996.
9. Bideau, B., Kulpa, R., Vignais, N., Brault, S., Multon, F., & Craig, C. (2009). Using virtual reality to analyze sports performance. IEEE Computer Graphics and Applications, 30(2), 14-21.
10. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
11. Casals, M., & Finch, C. F. (2017). Sports Biostatistician: a critical member of all sports science and medicine teams for injury prevention. Injury prevention, 23(6), 423-427.
12. Marchetti, B., & Casonato, C. (2021). Prime osservazioni sulla proposta di Regolamento dell'Unione europea in materia di intelligenza artificiale. BioLaw journal, 2021(3), 415-437.
13. Chen, Y., & Perez, Y. (2018). Business model design: lessons learned from Tesla Motors. Towards a sustainable economy: Paradoxes and trends in energy and transportation, 53-69.
14. Chen, Z., Ye, S., Chu, X., Xia, H., Zhang, H., Qu, H., & Wu, Y. (2021). Augmenting sports videos with viscommentator. IEEE Transactions on Visualization and Computer Graphics, 28(1), 824-834.
15. Claudino, J. G., Capanema, D. D. O., de Souza, T. V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports medicine-open, 5, 1-12.
16. Clephas, C., Foster, M., Stergiou, P., & Katz, L. (2020). Performance analysis of the flip turn in swimming: The relationship between pressures and performance times.
17. Cruz-Neira, C., Sandin, D. J., DeFanti, T. A., Kenyon, R. V., & Hart, J. C. (1992). The CAVE: Audio visual experience automatic virtual environment. Communications of the ACM, 35(6), 64-73.
18. Cust, E. E., Sweeting, A. J., Ball, K., & Robertson, S. (2019). Machine and deep learning for sport-specific movement recognition: A systematic review of model development and performance. Journal of sports sciences, 37(5), 568-600.
19. Dargan, S., Bansal, S., Kumar, M., Mittal, A., & Kumar, K. (2023). Augmented reality: A comprehensive review. Archives of Computational Methods in Engineering, 30(2), 1057-1080.
20. Di Salvo, V., Gregson, W., Atkinson, G., Tordoff, P., & Drust, B. (2009). Analysis of high intensity activity in Premier League soccer. International journal of sports medicine, 30(03), 205-212.
21. Du, M., & Yuan, X. (2021). A survey of competitive sports data visualization and visual analysis. Journal of Visualization, 24, 47-67.
22. Düking, P., Holmberg, H. C., & Sperlich, B. (2018). The potential usefulness of virtual reality systems for athletes: a short SWOT analysis. Frontiers in Physiology, 9, 128.
23. Faure, C., Limballe, A., Bideau, B., & Kulpa, R. (2020). Virtual reality to assess and train team ball sports performance: A scoping review. Journal of sports Sciences, 38(2), 192-205.
24. Fortes, L. S., Almeida, S. S., Praça, G. M., Nascimento-Júnior, J. R., Lima-Junior, D., Barbosa, B. T., & Ferreira, M. E. (2021). Virtual reality promotes greater improvements than video-stimulation screen on perceptual-cognitive skills in young soccer athletes. Human Movement Science, 79, 102856.
25. Fry, M. J., & Ohlmann, J. W. (2012). Introduction to the special issue on analytics in sports, part II: Sports scheduling applications. Interfaces, 42(3), 229-231.
26. Glowniak, J. (1997). The Internet as an information source for geriatricians. Drugs & aging, 10, 169-173.
27. Goebert, C. (2020). Augmented reality in sport marketing: Uses and directions. Sports Innovation Journal, 1, 134-151.
28. Goodfellow, Y.; Bengio, Y.; Courville, A. Deep Learning; The MIT Press: Cambridge, MA, USA, 2018.
29. Grainger, M., Weisberg, A., Stergiou, P., & Katz, L. (2020). Comparison of two methods in the estimation of vertical jump height.
30. Greenhough, B., Barrett, S., Towlson, C., & Abt, G. (2021). Perceptions of professional soccer coaches, support staff and players toward virtual reality and the factors that modify their intention to use it. PloS one, 16(12), e0261378.
31. Gutierrez, N. (2023). The ballad of morton heilig: on VR's mythic past. JCMS: Journal of Cinema and Media Studies, 62(3), 86-106.
32. Heer, J., & Shneiderman, B. (2012). Interactive dynamics for visual analysis: A taxonomy of tools that support the fluent and flexible use of visualizations. Queue, 10(2), 30-55.
33. Hendricks, S., Till, K., Den Hollander, S., Savage, T. N., Roberts, S. P., Tierney, G., ... & Jones, B. (2020). Consensus on a video analysis framework of descriptors and definitions by the Rugby Union Video Analysis Consensus group. British journal of sports medicine, 54(10), 566-572.
34. Hopkins, W. G. (1991). Quantification of training in competitive sports: methods and applications. Sports medicine, 12, 161-183.
35. Horton, J. F., Stergiou, P. R. O., Fung, T. S., & Katz, L. (2017). Comparison of Polar M600 optical heart rate and ECG heart rate during exercise. Medicine & Science in Sports & Exercise, 49(12), 2600-2607.
36. Hughes, M., & Franks, I. M. (2004). Notational analysis—a review of the literature. Notational analysis of sport, 71-116.
37. Isichei, B. C., Leung, C. K., Nguyen, L. T., Morrow, L. B., Ngo, A. T., Pham, T. D., & Cuzzocrea, A. (2022, March). Sports data management, mining, and visualization. In International Conference on Advanced Information Networking and Applications (pp. 141-153). Cham: Springer International Publishing.
38. Janiesch, K. (2021). C., Zschech, P., & Heinrich,“. Machine learning and deep learning,” Electron. Mark, 31(3), 685-695.
39. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
40. Cossich, V. R., Carlgren, D., Holash, R. J., & Katz, L. (2023). Technological breakthroughs in sport: Current practice and future potential of artificial intelligence, virtual reality, augmented reality, and modern data visualization in performance analysis. Applied Sciences, 13(23), 12965.
41. Kempe, M., Grunz, A., & Memmert, D. (2015). Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks. European journal of sport science, 15(4), 249-255.
42. Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.
43. Ko, A. J., Abraham, R., Beckwith, L., Blackwell, A., Burnett, M., Erwig, M., ... & Wiedenbeck, S. (2011). The state of the art in end-user software engineering. ACM Computing Surveys (CSUR), 43(3), 1-44.
44. Krizkova, S., Tomaskova, H., & Tirkolaee, E. B. (2021). Sport performance analysis with a focus on racket sports: A review. Applied Sciences, 11(19), 9212.
45. Lage, M., Ono, J. P., Cervone, D., Chiang, J., Dietrich, C., & Silva, C. T. (2016). Statcast dashboard: Exploration of spatiotemporal baseball data. IEEE computer graphics and applications, 36(5), 28-37.
46. Lames, M., & McGarry, T. (2007). On the search for reliable performance indicators in game sports. International Journal of Performance Analysis in Sport, 7(1), 62-79.
47. Lanier, J. (1992). Virtual reality: The promise of the future. Interactive Learning International, 8(4), 275-279.
48. Le Noury, P., Buszard, T., Reid, M., & Farrow, D. (2021). Examining the representativeness of a virtual reality environment for simulation of tennis performance. Journal of Sports Sciences, 39(4), 412-420.
49. Le Noury, P., Polman, R., Maloney, M., & Gorman, A. (2022). A narrative review of the current state of extended reality technology and how it can be utilised in sport. Sports Medicine, 52(7), 1473-1489.
50. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
51. Li, L. (2023). Summary of the Research Status of Artificial Intelligence in sports performance analysis of athletes. Open Access Library Journal, 10(8), 1-7.
52. Benites Zapana, P. R. (2024). Programa de habilidades sociales para la mejora de la adaptación frente a conductas de riesgo de los estudiantes de IESTP Manuel Núñez Butrón de Juliaca, año 2019.
53. Liebermann, D. G., Katz, L., Hughes, M. D., Bartlett, R. M., McClements, J., & Franks, I. M. (2002). Advances in the application of information technology to sport performance. Journal of sports sciences, 20(10), 755-769.
54. Liu, A.; Mahapatra, R.P.; Mayuri, A.V.R. Hybrid Design for Sports Data Visualization Using AI and Big Data Analytics. Complex Intell. Syst. 2023, 9, 2969–2980
55. López-Valenciano, A., Ayala, F., Puerta, J. M., Croix, M. D. S., Vera-García, F., Hernández-Sánchez, S., ... & Myer, G. (2018). A preventive model for muscle injuries: a novel approach based on learning algorithms. Medicine and science in sports and exercise, 50(5), 915.
56. Mackenzie, R., & Cushion, C. (2013). Performance analysis in football: A critical review and implications for future research. Journal of sports sciences, 31(6), 639-676.
57. Makki, S. A. M., Pissinou, N., & Daroux, P. (2003). Mobile and wireless Internet access. Computer Communications, 26(7), 734-746.
58. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133.
59. Michalski, S. C., Szpak, A., & Loetscher, T. (2019). Using virtual environments to improve real-world motor skills in sports: a systematic review. Frontiers in psychology, 10, 2159.
60. Milgram, P., Takemura, H., Utsumi, A., & Kishino, F. (1995, December). Augmented reality: A class of displays on the reality-virtuality continuum. In Telemanipulator and telepresence technologies (Vol. 2351, pp. 282-292). Spie.
61. Morgulev, E., Azar, O. H., & Lidor, R. (2018). Sports analytics and the big-data era. International Journal of Data Science and Analytics, 5, 213-222.
62. Murtagh, C. F., Naughton, R. J., McRobert, A. P., O’Boyle, A., Morgans, R., Drust, B., & Erskine, R. M. (2019). A coding system to quantify powerful actions in soccer match play: a pilot study. Research quarterly for exercise and sport, 90(2), 234-243.
63. Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., & Forghani, R. (2020). Brief history of artificial intelligence. Neuroimaging Clinics of North America, 30(4), 393-399.
64. Naik, B. T., Hashmi, M. F., & Bokde, N. D. (2022). A comprehensive review of computer vision in sports: Open issues, future trends and research directions. Applied Sciences, 12(9), 4429.
65. Neumann, D. L., Moffitt, R. L., Thomas, P. R., Loveday, K., Watling, D. P., Lombard, C. L., ... & Tremeer, M. A. (2018). A systematic review of the application of interactive virtual reality to sport. Virtual Reality, 22, 183-198.
66. Nicholls, S. B., James, N., Wells, J., & Parmar, N. (2022). Performance analysis practice within Olympic and Paralympic sports: A comparison of coach and analyst experiences. International journal of performance analysis in sport, 22(3), 343-351.
67. Nimphius, S., & Jordan, M. J. (2020). Show me the data, Jerry! Data visualization and transparency. International Journal of Sports Physiology and Performance, 15(10), 1353-1355.
68. Novatchkov, H., & Baca, A. (2013). Artificial intelligence in sports on the example of weight training. Journal of sports science & medicine, 12(1), 27.
69. Orlando, A. (2022, July). AI for Sport in the EU Legal Framework. In 2022 IEEE International Workshop on Sport, Technology and Research (STAR) (pp. 100-105). IEEE.
70. Oulasvirta, A., Dayama, N. R., Shiripour, M., John, M., & Karrenbauer, A. (2020). Combinatorial optimization of graphical user interface designs. Proceedings of the IEEE, 108(3), 434-464.
71. Pagé, C., Bernier, P. M., & Trempe, M. (2019). Using video simulations and virtual reality to improve decision-making skills in basketball. Journal of sports sciences, 37(21), 2403-2410.
72. Pai, P. F., ChangLiao, L. H., & Lin, K. P. (2017). Analyzing basketball games by a support vector machines with decision tree model. Neural Computing and Applications, 28, 4159-4167.
73. Panchuk, D., Klusemann, M. J., & Hadlow, S. M. (2018). Exploring the effectiveness of immersive video for training decision-making capability in elite, youth basketball players. Frontiers in psychology, 9, 2315.
74. Park, H. J., & Zhang, Y. (2022). Technology readiness and technology paradox of unmanned convenience store users. Journal of Retailing and Consumer Services, 65, 102523.
75. Passfield, L., & Hopker, J. G. (2017). A mine of information: can sports analytics provide wisdom from your data?. International journal of sports physiology and performance, 12(7), 851-855.
76. Pastel, S., Marlok, J., Bandow, N., & Witte, K. (2023). Application of eye-tracking systems integrated into immersive virtual reality and possible transfer to the sports sector-A systematic review. Multimedia Tools and Applications, 82(3), 4181-4208.
77. Perin, C., Vuillemot, R., Stolper, C. D., Stasko, J. T., Wood, J., & Carpendale, S. (2018, June). State of the art of sports data visualization. In Computer Graphics Forum (Vol. 37, No. 3, pp. 663-686).
78. Pino-Ortega, J., Rojas-Valverde, D., Gómez-Carmona, C. D., & Rico-González, M. (2021). Training design, performance analysis, and talent identification—A systematic review about the most relevant variables through the principal component analysis in Soccer, Basketball, and Rugby. International Journal of Environmental Research and Public Health, 18(5), 2642.
79. Pokharel, S., & Zhu, Y. (2018). Analysis and visualization of sports performance anxiety in tennis matches. In Advances in Visual Computing: 13th International Symposium, ISVC 2018, Las Vegas, NV, USA, November 19–21, 2018, Proceedings 13 (pp. 407-419). Springer International Publishing.
80. 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.
81. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".
82. Przednowek, K., Krzeszowski, T., Przednowek, K. H., & Lenik, P. (2018). A system for analysing the basketball free throw trajectory based on particle swarm optimization. Applied Sciences, 8(11), 2090.
83. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
84. Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., & Wisløff, U. (2009). Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. Journal of science and medicine in sport, 12(1), 227-233.
85. Rauschnabel, P. A., Felix, R., Hinsch, C., Shahab, H., & Alt, F. (2022). What is XR? Towards a framework for augmented and virtual reality. Computers in human behavior, 133, 107289.
86. Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus, 5, 1-13.
87. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
88. Ross, S. J., Hill, J. L., Chen, M. Y., Joseph, A. D., Culler, D. E., & Brewer, E. A. (2002). A composable framework for secure multi-modal access to Internet services from post-PC devices. Mobile Networks and Applications, 7, 389-406.
89. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson.
90. Sands, W. A., Kavanaugh, A. A., Murray, S. R., McNeal, J. R., & Jemni, M. (2017). Modern techniques and technologies applied to training and performance monitoring. International journal of sports physiology and performance, 12(s2), S2-63.
91. Sawan, N., Eltweri, A., De Lucia, C., Pio Leonardo Cavaliere, L., Faccia, A., & Roxana Moşteanu, N. (2020, December). Mixed and augmented reality applications in the sport industry. In Proceedings of the 2020 2nd International Conference on E-Business and E-commerce Engineering (pp. 55-59).
92. Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019). Wearable sensors for monitoring the internal and external workload of the athlete. NPJ digital medicine, 2(1), 71.
93. Sorrentino, R. M., Levy, R., Katz, L., & Peng, X. (2005). Virtual visualization: Preparation for the olympic games long-track speed skating. International Journal of Computer Science in Sport, 4, 40.
94. Spence, I. (2006, August). William Playfair and the psychology of graphs. In Proceedings of the American Statistical Association, Section on Statistical Graphics (pp. 2426-2436).
95. Strauss, A., Sparks, M., & Pienaar, C. (2019). The use of GPS analysis to quantify the internal and external match demands of semi-elite level female soccer players during a tournament. Journal of sports science & medicine, 18(1), 73.
96. Tanaka, K. (2017, March). 3D action reconstruction using virtual player to assist karate training. In 2017 IEEE Virtual Reality (VR) (pp. 395-396). IEEE.
97. Tanaka, K., Parker, J. R., Baradoy, G., Sheehan, D., Holash, J. R., & Katz, L. (2012). A comparison of exergaming interfaces for use in rehabilitation programs and research. Loading..., 6(9).
98. Tani, T., Huang, H. H., & Kawagoe, K. (2014). Sports play visualization system using trajectory mining method. Procedia Technology, 18, 100-103.
99. Thatcher, B., Ivanov, G., Szerovay, M., & Mills, G. (2020). Virtual reality technology in football coaching: barriers and opportunities. International Sport Coaching Journal, 8(2), 234-243.
100. Thornton, H. R., Delaney, J. A., Duthie, G. M., & Dascombe, B. J. (2019). Developing athlete monitoring systems in team sports: data analysis and visualization. International journal of sports physiology and performance, 14(6), 698-705.
101. Turing, A. M. (1980). Computing Machinery and Intelligence. Creative Computing, 6(1), 44-53.
102. Vinué, G. (2020). A web application for interactive visualization of European basketball data. big data, 8(1), 70-86.
103. Wang, T., & Li, T. (2022). Deep Learning‐Based Football Player Detection in Videos. Computational Intelligence and Neuroscience, 2022(1), 3540642.
104. Watanabe, N. M., Shapiro, S., & Drayer, J. (2021). Big data and analytics in sport management. Journal of Sport Management, 35(3), 197-202.
105. Wood, G., Wright, D. J., Harris, D., Pal, A., Franklin, Z. C., & Vine, S. J. (2021). Testing the construct validity of a soccer-specific virtual reality simulator using novice, academy, and professional soccer players. Virtual Reality, 25, 43-51.
106. Wright, C., Atkins, S., & Jones, B. (2012). An analysis of elite coaches’ engagement with performance analysis services (match, notational analysis and technique analysis). International Journal of Performance Analysis in Sport, 12(2), 436-451.
107. Yang, H., & Luo, C. (2022). [Retracted] Accuracy Analysis of Sports Performance Prediction Based on BP Neural Network Intelligent Algorithm. Security and Communication Networks, 2022(1), 4198920.
108. Plakias, S., Moustakidis, S., Kokkotis, C., Papalexi, M., Tsatalas, T., Giakas, G., & Tsaopoulos, D. (2023). Identifying soccer players’ playing styles: a systematic review. Journal of Functional Morphology and Kinesiology, 8(3), 104.