Impact of Big Data Analytics on Esports

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Impact of Big Data Analytics on Esports


Abstract
Big data analytics plays a fundamental role in esports in this current era of rapid globalization. The data has allowed the esports sector to develop a serious industry that has prospered into a global digital ecosystem. Competitor and performance analysis plays a significant role in the preparation of the esports competitions. The opponent’s gameplay analysis focuses on various aspects of the player, including their strengths, weaknesses, previous tournaments, and how this information can be used to gain an advantage in the gaming competition. Besides the players and teams who compete in esports, big data is also being monetized by the gambling and betting sports industry. The use of big data analysis portrays that the analysis is of high significance in the industry because it enables stakeholders to protect their high stakes in the market. According to the estimates provided by Business Insider, the esports industry was estimated to have accrued over USD 1 billion in revenue for the first time in history in the year 2019. The increase in revenue is attributed to several factors, including an increase in investment in the industry, enhanced media advertisement, and media coverage that is associated with the esports industry. In addition to the funding and investment increase in the market industry, the research also concluded that there is a significant relationship between big data and esports. Big data analysis has thus, provided the stakeholders in the Esports industry new ways of analyzing the rich data sets that are collected during the competitions to attain an in-depth understanding of how teams play and the kind of information that fans demand during their viewing experience

Introduction
Big data analytics plays a fundamental role in esports in this current era of rapid globalization. The application of big data is expected to significantly influence the future of esports. The esports industry has focused on the collection of large amounts of data. The data has allowed the esports sector to develop a serious industry that has prospered into a global digital ecosystem (Chikish and García 497). Big data has managed to attain economic relevance in the esports industry because it has increased the returns in the business. Through initiating active competition, the application of big data is only going to increase in the future management of esports. In the dynamics of digital industry competition, the collection, aggregation, and analytics of data result in high business scalability.
Competitor and performance analysis plays a significant role in the preparation of the esports competitions. The preparation phase involves players looking into the strategies and game plans of their opponents. The opponent’s gameplay analysis focuses on various aspects of the player, including their strengths, weaknesses, previous tournaments, and how this information can be used to gain an advantage in the gaming competition (Nelson 6). The advent of big data in esports has, therefore, allowed players and teams to leverage vital information, which has transformed sporting competitions. The information analysis is attributed to the application of artificial intelligence supported tools of analysis, which evaluate a large volume of data points that have been harvested from digitally recorded gaming competitions. The tools provide competitors with useful statistics, thus helping individuals to identify players’ features and enabling them to predict their next move in a specific situation during the competition. Hence, the information provided by big data analysis is valuable and significant in the future of esports.
Besides the players and teams who compete in esports, big data is also being monetized by the gambling and betting sports industry. Betting on esports is an emerging trend in the digital industry. Sports betting has grown rapidly in the past five years, as fast as the number of viewers. According to Walkowski and Kempińska, in 2019, the total wage generated by the digital sports betting industry was approximately USD 8 billion (94). The use of big data analysis portrays that the analysis is of high significance in the industry because it enables stakeholders to protect their high stakes in the market. Given the high stakes associated with big data analytics, stakeholders like players, coaches, and the betting industry at large are ready and willing to spend huge investments on data services. Every stakeholder, therefore, uses the vital information to achieve appropriate valuation and effective protection of their business agreements.
The success of esports can be attributed to the incorporation of big data analytics in the entertainment platform. Esports has managed to acquire a positive competitive advantage rivaling traditional sporting events like NBA, NFL, and MLB. As such, there is a need to evaluate the impact of big data analysis in esports and how it will affect the future of the esports competitions like League of Legends, Overwatch, Fortnite, and Dota 2.

Methodology
Several approaches are used to explore data science tools, ranging from the use of statistical analysis to machine learning techniques. The various methods of big data analysis employed in this research paper will enable the researcher to generate new keys for understanding the strategies and performance axis employed in esports. The data analysis methodology focuses on the information that players use in the sporting competition of League of Legends. The two primary objectives of the methodology are based on how to get the data and identifying the axis for present and future analysis. The approach of data collection will focus on both collecting the primary and secondary sources of data. Primary data will be collected from the League of Legends World Championships, which often marks the end of a year of competition. Secondary data, on the other hand, will be collected from interviews and previous research articles.
Out of the 13 regions that compete in the championship globally, only 24 teams qualified for the championships, consisting of 124 players. There are various reasons why the world championship is used as an efficient analysis topic. First, the competition provides reasonable volumes of data that can be used to conduct conclusive sports analysis. The competition also presents analysts with a great diversity of players and strategies used by players from different geographical regions across the world (Scholz 7). Also, the world championship is considered the most watched tournament in the scene. The competition, therefore, provides one with multiple resources and materials to be used in the conclusive analysis. The analysis will also incorporate additional data from regular seasons of the major leagues to enable the researcher to identify general aspects of the play styles and player profiles from a specific region.
Lastly, the approach or method of analysis is the final step in the methodology used in the analysis of big data. After identifying the type of data to be collected in the analysis, there is a need to determine how the data can be collected. Riot Games, the company that develops the competitions in esports, has played a significant role in providing detailed data about every game that is publicly played (Lucht 114). The organization uses its public API to collect and generate different values and indicators about the players and games played during the season. However, researchers must acknowledge that tournaments are hosted on different servers than the ones used by regular players. Therefore, there is a need to access data from another server and not from the public API. This can be achieved by getting the identifier of every game. Hence, the data can be freely downloaded, whereas the researcher can select the columns that are relevant in the current data analysis. Companies like Oracle Elixir provide users with generalized major league data. The data enables one to attain a higher granularity and retrieve more complete data that can be categorized to focus on the specific aspects of the game that we need for analysis. With the incorporation of future extensions, the platform can allow users to acquire regular ranked games data that can enable the researcher to get large volumes and scouting tools that have more general tendencies.

Results/ Conclusion
According to the estimates provided by Business Insider, the esports industry was estimated to have accrued over USD 1 billion in revenue for the first time in history in the year 2019 (Ahn et al.). According to Ahn et al., the increase in revenue is attributed to several factors, including an increase in investment in the industry, enhanced media advertisement, and media coverage that is associated with the esports industry. The revenue is an accumulation of the returns brought in by various gaming communities in the industry, including League of Legends, Overwatch, Fortnite, and Super Smash Bros. Reports published by Newzoo also evaluated the future growth of the industry and concluded that the esports market will be estimated at roughly USD 1.8 billion by 2022 (Hanz 20). Therefore, the accelerated growth of the esports realm confirms that there is the growing need for data scientists in the digital sports market.
In addition to the funding and investment increase in the market industry, the research also concluded that there is a significant relationship between big data and esports. Big data analysis is essential in helping players and teams understand how to optimize their gameplay. Like in traditional sporting activities, winning a game was attributed to understanding the different strategies that work and the techniques that do not work. The incorporation of large samples of data, therefore, allows individuals to discover different techniques, strategies, and gameplays that can optimize the results in competition and ensure the victory of the player or team. Games such as League of Legends have incorporated tools that allow analysts to re-watch the games and put the games into a database for further future analysis (Soderin 124). As such, after the end of every game, the data collected from the game is stored for analysis by breaking it down into various steps. Games such as League of Legends rely on big data. The data enables players to see how impactful their strategies were during the previous games. The data does not only show the results of the previous matches but provides detailed graphics of the gold earned, damage dealt, and top players in the competition. The data provided by the game developers is also complemented by some popular League of Legends websites known as Mobalytics.
Big data analysis has attributed to the growth of the esports industry that is portrayed by increased viewership of the esports competitions. The viewers and players together comprise the stakeholders who use big data analysis to predict the outcomes of the game. The players use the information provided to determine the best playing strategy that can allow them to attain a competitive advantage. The viewers, on the other hand, use the information to predict which teams will win the competition and therefore decide to either support the team or bet for the team (Block et al. 33). Big data analysis is, therefore, initiating significant changes in the esports industry by increasing the number of players, viewers, and profitability of the entire esports industry.

Discussion
Data analytics and hybrid cloud technologies have been used ever since by traditional sporting companies to improve their performance. The incorporation of data analytics in the esports industry is an emerging trend that is portrayed by professional esports teams, players, and broadcasters. Esports teams and players use big data sporting analytics to gain a competitive advantage against their competitors. Big data analysis enables players and teams to practice and develop practical strategies that can be incorporated during the game. The competition operators, on the other hand, use the information to enhance the viewing experience of the fans and achieve better regulation of the Esports industry. Big data analysis has therefore provided the stakeholders in the Esports industry new ways of analyzing the rich data sets that are collected during the competitions to attain an in-depth understanding of how teams play and the kind of information that fans demand during their viewing experience (Smerdov et al. 9). Big data analysis has thus provided the industry with more advanced forms of tracking and analyzing the esports gaming information on the cloud to be accessed by stakeholders all over the world. The platform provides players with a more sophisticated tool and data analysis cross-collaboration in the esports industry.
The results of the research study suggest that big data analysis in the esports industry has attributed to explosive growth and popularity of the industry. Winning sporting competitions does not primarily depend on the dedication, skills, or luck of the players. Strategy and analysis of the past gaming sports competitions performances play a fundamental role in determining the winner of the esports competitions. The secret of success in the esports sphere relies on data analysis, whereas the industry is overflowing with relevant information that is demanded by the stakeholders in the industry. Big data analysis has been achieved by the ability of the esports games to be recorded, re-watched, and stored into the database for future reference. In the traditional sporting industry, pro-players and teams were required to physically attend and watch their rivals play in tournaments. However, in the contemporary digital era, online data analysis has allowed players to create pre-game strategies, analyze matches, and develop effective training strategies to win the esports competitions.
Big data analysis also enhances the level of competition in esports gaming by allowing players and team members to correct their previous mistakes. Through big data analysis, coaches can record their training activities and both tournaments and matches to look back and identify some of the weaknesses that prevented them from achieving their goals. The analysis also provides the team with some of their strengths and how they can use the strength factors to their winning advantages.
In the future, one might likely see an increase in the number of API creations and developments. The aspects of statistics being recorded in the future esports competitions might also improve depending on the demand of the stakeholders in the industry. Sports broadcasters have acknowledged that technological advancement has increased access to data and information analytics in the sporting industry. The big data analytic tools get better, and stakeholders have easy access to the information stored in the computer cloud provided they have access to internet connectivity. In addition to helping players and teams to improve their gaming strategy, big data analytics are also used by media platforms in customizing the broadcast actions. For example, companies like YouTube and Twitter use big data analytics to recommend esports content based on the previously watched matches. The incorporation of big data analysis in esports has, therefore, revolutionized the aspects of competition in the cybersports industry.

Works Cited


Ahn, Joseph, et al. “The One Billion Dollar Myth: Methods for Sizing the Massively Undervalued Esports Revenue Landscape.” International Journal of Esports, vol. 1, no. 1, 2020.

Block, Florian, et al. “Narrative Bytes: Data-Driven Content Production in Esports.” Proceedings of the 2018 ACM International Conference on Interactive Experiences for TV and Online Video. Association for Computer Machinery, 2018, pp.29-41.

Chikish, Yulia, and Jaume García. “eSports: A New Era for the Sports Industry and a New Impulse for the Research in Sports (and) Economics.” Sports (and) Economics. FUNCAS (Spanish Savings Banks Foundation), 2019, pp.477-508.


Hanz, Alex. “Exploring the eSports Environment: A Marketing-Oriented Analysis of the Biggest Unnoticed Sponsoring Channel.” Theseus.fi, 2020, https://www.theseus.fi/bitstream/handle/10024/339803/Bachelor%20Thesis%20Alex%20Hanz.pdf?sequence=2. Accessed 28 Dec. 2020.
Lucht, Falko. “The Success of the Freemium Business Model. How Riot Games Flourishes with a Free to Play Game.” Manager, vol. 29, 2019, pp.114-124.

Nelson, Tyler. The Success of the Overwatch League: Is It Sustainable? Ball State University, 2020.
Scholz, Tobias M. Assembling Intercultural Teams in Esports–Implications from the League of Legends European Championship. University of Siegen, 2020.

Smerdov, Anton, et al. “AI-Enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors.” arXiv, 2020, https://arxiv.org/pdf/2012.03491.pdf. Accessed 28 Dec. 2020.

Söderin, Jonathan. “Feature Development for Esports Broadcasts with a Focus on the Intermission between Matches.” Diva-portal.org, 2017, https://www.diva-portal.org/smash/get/diva2:1109710/FULLTEXT01.pdf. Accessed 28 Dec. 2020.

Walkowski, Maciej, and Weronika Kempińska. “Characteristics of the Chinese Gaming and Esports Market. Applications for Polish Game Manufacturers.” Przegląd Politologiczny, vol. 3, 2020, pp.87-108.

Christopher

Author Since: January 20, 2021

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