An Assessment Over The Impact Of Artificial Intelligence On Sports Activities And The Sports Industry

Öz

Purpose: To determine various methods of artificial intelligence (AI) that have been developed, often based on an past sporting events, to generate well-formulated training plans, game strategies automatically, and score feedbacks; To develop proposals for strategies, policies and programs applicable by Türkiye dynamics. Method: Literature review, internet search for tools and programs available in the market and descriptive and content analysis techniques are used. Results: AI is a comprehensive, piercing, and cutting-edge discipline that is constantly evolving as an important area of research embedded in all fields of science, sport management information systems (S-MIS), computing, and technology. Sports is an excellent field to apply AI design approaches where AI is used as a helpful tool in predicting and determining human endeavors. Numerous studies in the literature have described the impact of AI on players and potential future players, fans, media, player training, development, scouting, recruitment, and the entire sports community related to sports. AI methods for the strategic management of athletes and sports organizations have matured by automatically generating sports plans, results, scores, and predictions in individual sports disciplines. Conclusion: With a view to the future of the relationship between athletes, sports economics, rules, and athletes, this research helps identify existent and prospective most effective application areas of AI with the help of main findings and recommendations in the literature. Suggestions have been developed for strategies, policies and programs that can be supported and implemented in Turkish conditions.

Anahtar Kelimeler

• Artificial Neural Networks
• Deep Strategy
• Robot Detection Systems
• Intelligent Sports Management Information Systems (S-MIS).

Yapay Zekanin Spor Aktiviteleri ve Spor Endüstrisi Üzerindeki Etkisi Üzerine Bir Değerlendirme

Öz

maç: Genellikle geçmiş spor etkinliklerine dayalı olarak geliştirilmiş çeşitli yapay zeka (YZ) yöntemlerini belirleme, iyi formüle edilmiş antrenman planları, oyun stratejileri otomatik olarak oluşturma ve geri bildirimleri puanlama kabiliyeti noktasında Türkiye dinamiklerine uygun strateji, politika ve program önerileri geliştirmek. Yöntem: Literatür taraması, piyasada bulunan araç ve programlar için internet taraması, betimsel ve içerik analizi teknikleri kullanılmıştır. Bulgular: YZ, bilim, spor yönebim bilgi sistemleri (S-YBS), bilgi işlem ve teknolojinin tüm alanlarına gömülü önemli bir araştırma alanı olarak sürekli gelişen kapsamlı, delici ve son teknoloji bir disiplindir. Spor, YZ’nin insan çabalarını tahmin etmede ve belirlemede yardımcı bir araç olarak kullanıldığı makine öğrenimi tasarım yaklaşımlarını uygulamak için mükemmel bir alandır. Literatürdeki çok sayıda çalışma, YZ'nin mevcut oyuncular ve gelecekteki potansiyel oyuncular, taraftarlar, medya, oyuncu eğitimi, geliştirme, izcilik, işe alma ve sporla ilgili tüm spor topluluğu üzerindeki etkisini tanımlamıştır. Sporcuların ve spor organizasyonlarının stratejik yönetimi için YZ yöntemleri, bireysel spor disiplinlerinde otomatik olarak spor planları, sonuçları, skorları ve tahminleri üreterek olgunlaşabilmektedir. Sonuç: Sporcular, spor ekonomisi, kurallar ve sporcular arasındaki ilişkinin geleceğine yönelik bu araştırma, literatürdeki temel bulgular ve öneriler yardımıyla AI'nın mevcut ve gelecekteki en etkili uygulama alanlarının belirlenmesine yardımcı olmaktadır. Türkiye şartlarında desteklenebilecek ve uygulanabilecek strateji, politika ve programlar için öneriler geliştirilebilmiştir.

Anahtar Kelimeler

• Artificial Neural Networks
• Deep Strategy
• Robot Detection Systems
• Intelligent Sports Management Information Systems (S-MIS).

Kaynakça

1. Ali W., Hassoun M. (2019) Artificial Intelligence and Automated Journalism: Modern Challenges and New Opportunities, International Journal of Media, Journalism and Mass Communication (IJMJMC), Vol. 5, No. 1, p. 40-49, ISSN 2454-9479, http://dx.doi.org/10.20431/2454-9479.0501004
2. Anik, L. (2018) How Football Video Assistant Referee (VAR) Affects Belief in the Competence of Human Referees. Behavioral scientist. Bond.
3. Arastey G. M. (2019) Application of video technology in football refereeing - Yes, https://www.sportperformanceanalysis.com/article/application-of-video-technology-in-football-refeareeing-var
4. Arastey G. M. (2021) Artificial Intelligence (AI) in Sports, https://www.sportperformanceanalysis.com/article/artificial-intelligence-ai-in-sports
5. Araujo, D., Couseiro, M., Seifert, L., Sarmento, H., & Davids, K. (2021). Artificial Intelligence in Sports Performance Analysis (1st ed.). Routledge. https://doi.org/10.4324/9781003163589
6. Åström, K. J., & Murray, R. M. (2008). Feedback systems: An introduction for scientists and engineers. Princeton university press.
7. Bacic B. (2008) Development of Connective Systems for Adaptive Sports Coaching, Neural Information Processing - Letters and Reviews, vol. 12, Issue 1-3, January-March
8. Baka A., Kornfeind P. (2012) Stability analysis of movement pattern in biathlon shooting. Science of Human Movement 31(2), 295-302

9. Bandura, A. (1977). Social learning theory. Prentice-Hall.
10. Barron D., Graham B., Matthew R., and Caroline S. (2018) Neural Networks and Professional Football Recruitment, Affective Computing and Human-Computer Interaction, https://doi.org/10.1371/journal.pone.0205818
11. Bera, A., (2021) "Artificial Intelligence: Transforming the Live Sports Landscape", SMPTE Motion Imaging Journal, vol. 130, no. 3, p. 28-34, April, doi: 10.5594/JMI.2021.3060823.
12. Blazevic, D. (2019). Artificial intelligence in sports. International Journal of Performance Analysis in Sport, 19(3), 314-328. https://www.tandfonline.com/doi/full/10.1080/24748668.2019.1621333
13. Bunker R. P., Tabtah F. (2019) Machine Learning Framework for Sports Performance Prediction, Applied Computing and Computer Science, Vol 15, Issue 1, Page 27-33, ISSN 2210-8327, https://doi.org/10.1016 / j.aci.2017.09.005 .
14. Burrell, J. (2017). The ethics of AI in sport. British Journal of Sports Medicine, 51(17), 1162-1166. https://bjsm.bmj.com/content/51/17/1162
15. Campbell M. (2002) Deep Blue. Artificial intelligence 134, 57-59
16. Catapult Sports (2021). AI-based Athlete Performance Solutions. [Website]. Retrieved from https://catapultsports.com/:
17. Chang W. Y. (2019) Fuzzy Algorithm-Based Sports Video Imaging Research, Journal of Visual Communication and Image Representation, Vol 61, Pages 105-111, ISSN 1047-3203, https://doi.org/10.1016/j. jvir.2019.02.033 .
18. Clark, J., Bjerke, M., & Carballo, L. (2019). Wearable AI in Sports: Predicting and Preventing Injuries Through Data.
19. Claudino, JG, Capanema, DD, de Souza, TV et al (2019) Current approaches to using artificial intelligence to assess injury risk and predict performance in team sports: a systematic review. Sports Med - Open 5, 28 https://doi.org/10.1186/s40798-019-0202-3
20. Deltatre (2021). AI-based Sports Broadcasting Solutions. [Website]. Retrieved from https://www.deltatre.com/
21. Dvořák, J., Schumacher, Y. O., McCrory, P., & Davis, R. (2020). Medical artificial intelligence in sports: Are we ready for it? British Journal of Sports Medicine, 54(11), 637-639.
22. European Parliament (2018). Artificial intelligence: future of humanity and governance of AI. Retrieved from https://www.europarl.europa.eu/doceo/document/TA-8-2018-0042_EN.html
23. European Parliament (2018). Artificial intelligence: future of humanity and governance of AI. Retrieved from https://www.europarl.europa.eu/doceo/document/TA-8-2018-0042_EN.html
24. Flushing, NY (2021) IBM Announces New Artificial Intelligence and Cloud-Based Innovations and Ideas for US Open 2021, Keep Fans More Informed, Join During Tennis Grand Slam, PR Newswire, https://newsroom.ibm.com/2021-08- 27-IBM-New-Inovations-and-Analysis-using-AI-and-Cloud-Technologies-For-2021-US-Open,-allowing-fans-to-be-more-informed- During the Grand Slam-R Tennis Tournament
25. Gillentine, A. and Schultz, J. (2001). Fantasy Football League Marketing: Using Simulation to Develop Sports Marketing Concepts. Journal of Marketing Education , 23 (3), 178–186. https://doi.org/10.1177/0273475301233003
26. Grand View Research, Inc. (2021). Sports Analytics Market Size, Share & Trends Analysis Report By Component (Software, Services), By Application (Performance Analysis, Player Tracking, Fan Engagement), By Deployment Model, By End-use, And Segment Forecasts, 2021-2028. Retrieved from https://www.grandviewresearch.com/industry-analysis/sports-analytics-market
27. Interstices Solutions (2021). AI-based Sports Broadcasting Solutions. [Website]. Retrieved from https://intersticesolutions.com/
28. Işık A., (2021) Türk futboluna yerli ve millî yapay zekâ, TFF, https://www.tff.org /default.aspx?pageID=286&ftxtID=35471
29. Keshav Rathy, Aditya Cole V., Priyam Somani, Manu KS, (2020) Artificial Intelligence Football Game in Practice: A Global Perspective, International Refereed Journal of Social Sciences, DOI URL : http://dx.doi.org /10.18843/rwjasc/ v11i2/03
30. Lei Wu et al (2021) "Research on a mining strategy to improve the psychological experience of athletes in the age of artificial intelligence." 1 January 1-11
31. Li, B., & Xu, X. (2021). The use of artificial intelligence in basketball sports. Journal of Education, Health and Sports, 11(7), 54-67. https://doi.org/10.12775/JEHS.2021.11.07.005
32. MarketWatch. (2021, March 12). Sports AI Market Worth USD 2.97 Billion by 2026 - Exclusive Report by MarketsandMarkets. Retrieved from https://www.marketwatch.com/press-release/sports-ai-market-worth-usd-297-billion-by-2026-exclusive-report-by-marketsandmarkets-2021-03-12
33. Metrica Sports (2021). AI-based Sports Analytics Solutions. [Website]. Retrieved from https://metricasports.com/
34. Nadikattu, R. R. (2020) Implementing new ways of artificial intelligence in sport (14 May 2020). Journal of Xidian University, Volume 14, Issue 5, Page Number: 5983–5997, available at SSRN: https://ssrn.com/abstract=3620017 or http://dx.doi.org/10.2139/ssrn.3620017
35. Newborn M. (1997) Kasparov vs. Deep Blue: Computer chess is getting old. spring
36. Novachkov, H. and Baka, A. (2013). Artificial intelligence in sports in the example of strength training. Journal of Sports Science and Medicine, 12(1), 27-37.
37. Osas (2021) Türkiye’de Espora Yapay Zeka Desteği!, https://www.esporgazetesi.com /turkiyede-espora-yapay-zeka-destegi/
38. Patton, M. Scott, N. Walker, A. Ottenwess, P. Power, A. Cherukumudi, & P. Lucy (2021) Predicting NBA Talent from Large Amounts of AN College Basketball Tracking Data, 15th MIT Sloan Sports Analytics Conference https://global-uploads.webflow.com /5f1af76ed86d6771ad48324b/606e51f34f594bb80721be39_MattScott-NBAtalent-RPpaper.pdf
39. Poole D., McWorth A., Gobel R. (1998) Computational intelligence: a logical approach. New York: Oxford University Press
40. Probst, L., Rauschenbach, F., Schuldt, H., Seidenschwartz, P., and Rumo, M., (2018) Integrated Real-Time Data Stream Analysis and Thumbnail Based Video Search in Team Sports, IEEE International Conference on Big Data (Big Data), 2018, p. 548-555. doi: 10.1109/BigData.2018.8622592.
41. PwC (2019) Application of Artificial Intelligence to Sports Industry, https://www.pwc.com.au/industry/sports/artificial-intelligence-application-to-the-sports-industry.pdf
42. Raiffa, H. (1968). Decision analysis: Introductory lectures on choices under uncertainty. Addison-Wesley.
43. Russell, S. J., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach.
44. Smith, R. (2021). AI and its Transformative Role in Sports Journalism and Broadcasting.
45. Stefanos, D. (2021). Machine learning approaches to classification of dribbling pass actions using SportVU NBA player position data, electronic summaries and theses . Paper 3908. https://dc.etsu.edu/etd/3908
46. Stergiou, N., & Decker, L. M. (2011). Human movement variability, nonlinear dynamics, and pathology: is there a connection? Human movement science, 30(5), 869-888.
47. Sternberg, R. J., & Sternberg, K. (2016). Cognitive psychology. Nelson Education.
48. Ulukan G., (2021) Yapay zeka destekli yerli mobil fitness uygulaması: DeepSport, https://webrazzi.com/2021/12/30/yapay-zeka-destekli-yerli-mobil-fitness-uygulamasi-deepsport/
49. Wang J., (2012) Research on the Application of Virtual Reality Technology in Competitive Sports, Procedia Engineering, Vol 29, Page 3659-3662, ISSN 1877-7058, https://doi.org/10.1016/j.proeng.2012.01. 548 .
50. Wei Z., (2017) Artificial Intelligence Table Tennis Training System Research, 6th International Conference on Next Generation Communication Technologies (FGCT), 2017, pp. 1-4, doi: 10.1109/FGCT.2017.8103738.
51. Wilson, J. M., Hoffmann, D. A., & Kassens-Noor, E. (2020). Artificial intelligence and sports: A systematic review. Sport, Education and Society, 25(3), 237-256.
52. Wyscout (2021). Professional Football Analytics and Scouting Platform. [Website]. Retrieved from https://www.wyscout.com/
53. Yu L., Ling P. and Zhang H., (2010) Analysis of Decision Support System, Methods and Tactics in Networked Sports and Application to Beijing Olympic Games, Second WRI Global Intelligent Systems Congress, 2010, p. 170-174, doi: 10.1109/GCIS.2010.139.