Big Data Analytics for National Basketball

下面为大家整理一篇优秀的paper代写范文- Big Data Analytics for National Basketball，供大家参考学习，这篇论文讨论了美国篮球大数据分析。在大数据时代，与篮球赛事相关的信息技术有了进步。在分析和处理篮球项目中的大量数据时，需要采用多种有效的手段和方法来评价运动员的竞技状态和表现，或调整或制定新的战略计划。因此，如何从海量的赛事数据中提取和利用有价值的信息，成为篮球爱好者、篮球教练、篮球队队员以及媒体界最为关注的问题。

Introduction

In the era of Big Data, there is a progress in information technology related to basketball events. When analyzing or processing the massive data in basketball events, a number of effective means and methods need to be adopted in order to evaluate the athletes’ competitive states and performances or to adjust or formulate new strategic plans. Therefore, how to extract and make use of the valuable information from vast event data have become the most important concerns for basketball fans, basketball coaches, basketball team members as well as the media field. By presenting the evolved advantages brought by data analysis for basketball industry, the paper develops a discussion on the data processing technology status for basketball events,

Big Data Analytics Advantages in Basketball Events

Big Data refers to a large volume data which is challenging to be captured, managed or analyzed by general software tools. Big Data is calculated by Terabytes, which means not only the large capacity but the significance in discovering new knowledge and creating new value through Big Data integration and analysis. By integrating various disciplines and technologies, Big Data analytics has adopted a variety of data processing methods. Thus it can set up different groups to describe things based on the attributes and characteristics of objects and identify the inherent relationship of the research objects, making it possible to have a preview of the future trends.

As the basketball data is massive and generally unstructured, traditional database analysis and statistics technology no long satisfy people’s demand for data intake, thus Big Data has been applied in many basketball events especially in the United States. By using Big Data analytics, people are able to find meaningful, valuable and hidden data in basketball events from massive data sources, with new perspectives in new methods. The findings can play a significant role in having scientific guidance and support with provide references, which are of great importance for coaches to formulate appropriate training and competition strategies, for teams to establish particular team building systems (Li & Dan 2017).

Big Data Analytics Status in the United States

The National Basketball Association (NBA) has experienced a relatively long history in collecting basketball players’ information for several decades. Before the 1990s, however, handwriting and handset were used to calculate the general data. It was after the 1990s when there was a rapid development of computer technology that the NBA developed mature and abundance data analytics system and management (Jia 2017). By establishing the basketball database, the NBA then developed detailed data of attack means, attack areas, vacancy rates, hit rate and so on. After entering the twenty-first century, there has been a further development of Big Data analytics of the basketball events, adopted by the National Basketball Association. The data statistics with the application of Big Data analytics becomes the most popular way for NBA to win games, evaluate players and optimize offensive and defensive tactics. For instance, it can build up player evaluation index with high level data. It can also make a specific calculation of the means of players’ attack completion process, the performance of key points and the influence on present team members. Nowadays, visual tracking Big Data analytics technology has become a new development goal in basketball events. The exploration and research process of Big Data analytics will go through more time and stages with its application in basketball events.

Fundamental personal data is the basis of all the data for the NBA. Starting from 1946, it used to only record scores, and assists (Wang 2015). Then more data were included. With the limitation of computer technology, there were only recording paper by manual scripts. In the twenty-first century, the performances of each player can be recorded with the introduction of the TV system for NBA. With the visual age of personal data, the advantages or disadvantages of a basketball player, the type of offensive ones or defensive ones can be identified through a set of data. These are the initial application of collected data. Then, NBA has entered the advanced period of personal data for basketball players, the representative of which is Yao Ming (Wang 2015). With the expansion of the NBA, there is a growing demand for accurate analysis of the team and players, thus simple and general data collection in a traditional way is abandoned. Then, the NBA has been making constant efforts to adopt the most advanced data acquisition methods. The diversified analysis means cannot satisfy the demand of NBA, a quick and effective data analytics is needed. With the application of Big Data analytics and other technological devices, the performances of each basketball player can be recorded during the event, then the images can be divided into movements and converted into data for further analysis conducted by the coach teams.

The Changes Brought by Big Data Analytics

With the Big Data analytics, there are some changes for NBA, including the team structure and the evolution of building up teams, processing with the times and technology.

There are a number of 30 teams in the NBA, each of which is composed of core basketball players, preathletes, the substitute players, the rotation players as well as those players who are refused to enter the event list. Each team must be involved in the 82 games per season, like huge event for the NBA, “schedule is very important, to analyze the fairness and impartiality of schedule” (Chang 2016). Generally speaking, the core players are the core of offensive and defensive strategies, who can be the point guard, the shooting guard like Jordan, the small forward, the power forward, and the center forward. With the analysis results of Big Data, the NBA then has adopted a series of balancing measures, making it possible for the increasing appearance of excellent teams, with strong competiveness among different teams. This kind of competiveness has strengthened the demand for Big Data analytics as well. There is a good tactical combination of team players. This is the power source of the NBA’s continuous improvement in Big Data analytics and the emergence of new ideas. This kind of change is reflected through the new recognition of player’s role and the pursuit of the team balance on the whole.

The changes in the rules of the NBA basketball events have contributed to the improvement of team balance. The strict trading system has restricted the arbitrariness of a team’s transaction. Through the Big Data analytics results, the rationality of a team can be identified through the topology images. Thus the basketball teams’ strategies for building up teams has been evolved with technological advancements in Big Data analytics. Some teams aim to win the championship on major basketball events. Some teams aim to win the post season qualification in divisional basketball events. Some even consider sacrificing the annual performance to allocate additional fees in support of rebuilding the team. In general, the NBA teams are paying more and more attention to cultivate the suitable basketball players with good physical conditions and basketball event awareness.

Conclusion

There are predictions from scientists that the Big Data technology will make a significant difference to all aspects of human life in the near future. As a matter of fact, it has been examined that the Big Data analytics has presented its important value and irreplaceable position in sports events. The professional data management has changing the data collecting and processing concepts and methods for NBA. It can maximize the basketball players’ competitive abilities in the event spots and make the evaluations of the performance with visualized, comprehensive, accurate and objective methods. The irreversible change brought by Big Data analytics will be penetrated into more sports types, with the possibility in completely subverting the traditional athlete selection ways, training methods, readiness process, competition and management concepts. As a result of Big Data analytics, the sports scientific research workers are supposed to adapt to the change of the times, learn the practice experience and Big Data theory initiatively and make Big Data analytics for sports projects with the combination of specific sports types.

References

Chang, C. (2016). Based on analytic hierarchy process model of big data analysis of the NBA schedule impact on team ranking. Journal of Computational & Theoretical Nanoscience, 13(12), 10001-10005.

Jia, L. (2017). Professional basketball club training management based on the big data thinking method. Agro Food Industry Hi Tech, 28(1), 2012-2017.

Li, L., & Dan, W. (2017). Training management of professional basketball clubs based on the big data thinking method. Agro Food Industry Hi Tech, 28(1), 2076-2079.

Wang, F. (2015). Structural variation caused by the pattern of NBA data management. China Sport Science & Technology.

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