Artificial Intelligence and Big Data have been around for decades. Over the recent years,  AI and Big data are having a more obvious impact towards world economic, social, and political spheres in many ways to the public. It can be seen as beneficial in many sectors but it comes with a great responsibility. Large sum of money has been invested in these fields, as many government and businesses do not only save huge amount on cost of operation and execution, it also became more efficient. Many leading tech companies around the world are competing or working alongside to develop cutting edge Artificial Intelligence system in understanding algorithms and analyzing data to provide decisive solution to problems as we strive towards the future. This research will be exploring how Artificial Intelligence could potentially revolutionize sports and take it to new heights. Similarly, to many industries out there of how AI and Big Data allows institutions to fully utilize findings and insights. In order to understand the research question, we will look at what is Big Data. Moving onto an explanation on artificial intelligence and its brief history context. Lastly, how the combination of Big Data and Artificial Intelligence could potentially benefit sports in few possible ways.

Big Data 

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We are constantly providing data on daily basis to corporations whenever we are using our phone, driving, watching TV, and even just by switching on lights when at home. Horrifying volume of data are being generated every seconds through many events and activities we do. These data will be collected to better understand of our behavior towards the things we do. In the sense of business context, private corporations could use the data collected to be more effective on their strategic planning in capturing the market demand or even as bargaining chip for their own gain. For instance, marketing corporations have been using data provided by social networking sites to “amassing enormous consumer databases and, concomitantly, helping companies avoid inefficient ads posting towards disinterested consumers” (Millington, 2014, p. 487). To some extent, people could debate this as exploitation of data where marketers are using data to efficiently “map consumer archetypes and/or targeting consumers in advertising” (Millington, 2014, p. 487). The rise of smartphones and the start of gold rush era for apps in 2009 have created many innovative apps which would then provide high and rich volume of data in understanding consumer behavior. In exchange, the price we pay would be the data provided by us to be exploited in return we get to enjoy the betterment of the self through the apps. Certain apps “are marketed as tools for achieving desirable health and fitness outcomes and, as such, for personal empowerment” (Millington, 2014, p. 486).

Table 1. below show apps providing its own different functionality, therefore data collected from it would be use for different purposes but under the same category fitness.

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(Millington, 2014, p. 483)

In respect to this field, not only apps from mobile devices could generate large masses of data. “The implications of ubiquitous and pervasive digital technologies for healthcare and public health are profound” (Lupton, 2014, p. 175). Many innovative portable wearable technological devices are introduced to people with internal sensors to monitor different aspects of “bodily functions and activities and geolocation details” (Lupton, 2014, p. 176). Users explore new ways of experiencing, monitoring, and measuring their progress anywhere anytime. These amount of data related to health informatics can potentially produce “new knowledges about illness and disease and contributing to preventive medicine and health promotion” (Lupton, 2014, p. 178). Once again, Gill Press believe “the more data you have the more insights and answers will rise automatically from the pool of ones and zeros” and “new perspective approach by businesses, non-profits, government agencies, and individuals using data for better decision-making purposes” (Elish & Boyd, 2018, p. 59)

Artificial Intelligence

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Jarvis from Iron Man, Samantha from Her. Can you imagine a computer system developing its own consciousness and instinct? Leading tech companies such as Google and IBM have developed their own AI, AlphaGo and Deep Blue bested some of the world top ranking players in their respective field (Go and Chess). Realization for “Technology-centric companies who uses large quantities of data to power advanced machine learning algorithms, realized the performance of the machine could sustain a large amount of sophisticated analysis and predictive work. It then changes it approach to using new rhetoric means to differentiate themselves rebranding as AI” (Elish & Boyd, 2018, p. 60).

AI was introduced first in 1950s where the idea take form during the Dartmouth Summer Research Project on Artificial Intelligence. It focuses on system sciences, where scientists uses different systems to understand the world around us (Elish & Boyd, 2018, p. 60). This idea has caught the attention of government agencies and investors, money started pouring in as the research continued to 1970s where the progression ended due to “funding for AI research began to dry up” (Elish & Boyd, 2018). Decades later, the findings laid out by the previous researchers on AI has provided prominent foundation for many AI related projects to date.

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“AI would soon surpass the confines of straightforward machines, and even recreate intuition, that most human and ineffable element of human intelligence” – Nielsen, 2016

(Elish & Boyd, 2018)


Many scientists are competing to invent an “AI to accurately predict a glimpse of the future, but it seems to be close to impossible” (Chen & Asch, 2017, p. 2507) or perhaps something with self-learning ability to analyze big chunk of data to accurately decide the best move to play. “Accurate prediction of events in complex environments requires experience, an understanding of how the world works” (Adami, 2015, p. 426) and successfully evaluate the outcome of one’s actions might affect others.

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With machine learning capabilities deeply embedded in AlphaGo and DeepBlue. It is still impossible for machine “to squeeze out information that is not present, although it can improve the accuracy of prediction over the use of conventional regression models by capturing complex, nonlinear relationships in the data” therefore “machine-learning approaches are powered by identification of strong, but theory-free associations in the data (Chen & Asch, 2017, pp. 2507-2508). However, with just analyzing data alone, its more than enough for AlphaGo to challenge top ranking Go players around the world. It could be very likely that in the future AI could be strategic planner in sports, where analyzing bunch of data collected from player’s performances including mental and physical health and whoever they are competing with. It could potentially become a battle of AI versus AI in strategic planning using human as players on the field. It is very unlikely that coaches would be replaced by AI, because one of the reasons be mental support is a big factor when it comes to motivating players. Therefore, AI lack the empathetic emotions to understand players. Though, famous publicized experiments featuring AI competing against human shows that “public perception that machine intelligence is better or more advanced than human intelligence” (Elish & Boyd, 2018, p. 63). The purpose of machine learning is to have machine learn from it’s mistake and prevent from repeating the same mistake through complicated systematic algorithm data driven analysis approach. Ke Jie is the top 1 ranking Go player in the world, after suffering a horrendous lost against AlphaGo. It changed Ke perspective towards Go, he starts to understand from machine point of view and thus went on a 22 winning streaks against the top rankings Go players around the world (Guest, 2017).

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Other than using AI competing with human, World Anti-Doping Agency (Wada) has a plan for using AI in preventing doping in the future. Wada plan to use the amount of data it has on players to its advantage in spotting any irregularities, data include athlete biological passports, tests for qualifying, and the results of the athletes over the years (Wingmore, 2018) . By spotting any anomalies in specific player, he or she will then have to go through an anti-doping test by Wada. I believe one challenges Wada would be facing is when searching for a solution in morphology of the AI in analyzing the movement of the athletes in various sports. For instance, a facial recognition system does not know how a face would look like or what constitute as a face. Rather it has data previously created by agents under tagged data to match incoming data for validation. Incoming data will be broken down to three steps, (1) data humans have associated with being a face, (2) an algorithm to detect face features based on reliability (3) matching the incoming data with past data. “The mechanism of validation is not rooted in teaching a computer the intrinsic meaning of what is a face” (Elish & Boyd, 2018, p. 70). Rather a systematic science approach.


It is widely known that in major North American sports league, data generated by players are used for “developing their own proprietary methods to look for any competitive advantage” (Grow & Grow, 2017, p. 1576). Such sensitive data are normally highly protected secured by respective corporations. These data include “on-field performance, physical health, and even psychological make-up” (Grow & Grow, 2017, p. 1569). Introducing AI to this industry would shift the in-game strategy planning to new heights while the job of the coach is to maintain well-being and support players mental and physical issues. One could argue that coaches with vast experience on the sports could potentially be more valuable asset than having an AI, but AI has an unbiased analogy in formulating strategies during crucial time. Coach could then rely on the decision outcome by the AI with extra time to formulate the best possible ultimate strategy where mental and physical health into consideration. Foucault; “As techniques of quantification ordered by the logics of (neo)liberal governance and capitalism, both technologies take shape from within long histories of operationalizing statistics for business profit, population control, and governance” (Elish & Boyd, 2018, p. 58). With time, I believe funds will start coming in once a sports team rely on the integration of AI to show promising results or achievements.

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In the future, I believe artificial intelligence will be integrated to more things in our life more than we could imagine. Although it is already happening to an extent such as Siri or Alexa, AI could potentially use information we provide on daily basis to understand our behavior and provide a solution for us. Thus, the data collected through Siri or Alexa can be an important aspect in for institutions to study our behavior. In this case, sports can very well be integrated with artificial intelligence in many aspects. Wada using AI to prevent doping, AI competing with human to grant insights into new approach towards a game, and using AI along-side as a tool to formulate strategies in major sports such as baseball. This could improve player’s performance and achieving satisfying results. Although, the solution I have envisioned seem to be eccentric. I hope my prediction would come true as it would bring sports to new heights.

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Adami, C. (2015). Robots with instincts. Nature Publishing Group, 426-427.

Chen, J. H., & Asch, M. S. (2017). Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations. The New England Journal of Medicine, 2507-2509.

Elish, M. C., & Boyd, D. (2018). Situating methods in the magic of Big Data and AI. Communication Monographs, 57-80.

Grow, L., & Grow, N. (2017). Protecting Big Data in the Big Leagues: Trade Secrets in Professional Sports. Washington and Lee Law Review, 74, 1567-1622.

Guest. (2017, November 24). The Potential Of Artificial Intelligence In The Future Of Sports. Retrieved from SportTechie:

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Macmillan Publishers Limited, 436-444.

Lupton, D. (2014). Health promotion in the digital era: a critical commentary. Health Promotion International, 174-183.

Millington, B. (2014). Smartphone Apps and the Mobile Privatization of Health and Fitness. Critical Studies in Media Communication, 479-493.

Wingmore, T. (2018, March 18). Wada to use artificial intelligence to catch doping cheats more efficiently. Retrieved from iNews:

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