Final Project

Data analytics on sports

Student Name Nezar Al Massad

Institution Name Dr. Mark O'Connell

Big Data and Data Warehouse

Introduction

Sports events produce huge amounts of data of all forms. Analysis of that data involves advanced data mining and machine learning approaches which facilitate a major impact on sports data analytics. This proposal focuses on the development of sports data analytics. It looks at the design, development, and analysis of methods used in sports analytics. The proposal will look into the business perspective and game strategy. The sports analytics proposal will also discuss the origin of data and how it gets into the database. It will discuss the type of database that is used for the storage and management of data. My Data Analytics Company will be known as ComStart DataBunch, a firm that focuses on data mining and machine learning techniques to support its development.

Description of ComStart DataBunch

ComStart DataBunch will focus on the management of available data on athletics. It will involve the coordination and management of data relating to over 120,000 runners participating in official races. The data will include pricing strategies relating to their training and their individual speed in the various stages of the races in which they participate. Also, the data will be about their overall performance. ComStart DataBunch will also ensure that it undertakes data mining techniques in the production of simple patterns that are beneficial to both professional and amateur athletes in athletics. The main focus for the development and operations of the company will be to avail more efficient Big Data powering computer capabilities to manage the information relating to the identified sportspersons (Erevelles, Fukawa, & Swayne, 2016).

The strategies already employed by sports companies have made it possible for spectators to acquire the needed information relating to what occurs in athletic sports. In the case of ComStart DataBunch will be able to market comparisons of the athletes to the market to promote the comprehension of what lies ahead when it comes to fantasy sports . Sports is an important segment when it comes to the development of society. Therefore, data mined from the sector will be instrumental in creating enthusiasm among spectators to continue watching the sport.

It will position the company to use Big Data and artificial intelligence to change the world of sports. Machine learning will serve as a framework through which professional teams and a diverse range of stakeholders can optimize not only marketing approaches, ticket sales, but also engagement of fans . It will also assist the athletics sector to draft selection, evaluate players, and make important decisions regarding the sector. The company will be able to develop a state-of-the-art solution to support different significant challenges in the sports analytics area. The situation will influence the exposure of unknown talents and also promote development in the sector.

Types of Databases to be used in the ComStart DataBunch

The type of database in relevance to the data regarding the athletics sports will be a Statistical form of a database . The database will be able to store both qualitative and quantitative data associated with sportspersons in the athletics world of sports. The main reason for the use of the database type is because of its capacity to store high accurate insight that ensures the maximization of performance. Such a database is able to drive participant and audience engagement. It may also provide sponsorship revenues for sports . Also, it will be able to manage sports automated performance evaluation. Audiences will have the capacity to acquire an accurate evaluation of the sports. The evaluation of team tactics depends on detailed data from a diverse range of sources.

Among them comprise of technical skills and the psychological performance of the involved stakeholders. It also initiates team formations among stakeholders in the representation of complex processes that are underlined in team tactical behavior. The main use of the database is to close the gap on the lack of adequate historical data. The database serves as a solution towards storing detailed game logs that are acquired via next-generation tracking technologies (Bhushan & Gupta, 2017). Also, it looks at psychological training data acquired via novel miniature sensor techniques.

The database is important to ensure dynamic and reliable research among the members of the society , who are considered sports enthusiasts. It eliminates the barriers created by large volumes of data, which has become an obstacle to methodological guidelines in ensuring tactical decision-making among teams in the athletic world of sports. Sportspersons have been having problems in terms of their medical applications. It is important for the database to focus on efficient and effective data governance . It should also ensure access to technologies. The database will record data, focusing on different issues associated with tactical evaluations in sports. The research will serve as a backbone towards the database in terms of acquiring and evaluating data for development.

Origin of Data for the ComStart DataBunch

The world of sports is dynamic and possesses diverse ranges of sports among sports enthusiasts, sportspersons, and management. Athletics has embraced statistics that has surpassed many sports in the international community. The first issue is that the sport allows data-collection into the participants’ locker rooms. Data is found everywhere. Every team in athletics, sports has data analysts on their staff who work with coaches and player evaluators. This is to ensure that the talents of the players are maximized and promote the identification of undervalued players . Analytics is a major source of information about the company (Paramita, 2018). ComStart DataBunch may be able to receive information from the sports sector to uncover new data relating to analyst talen t. Analytics is a major segment of virtually everything that is acquired in the sports by the company. It will be important to engage physically and virtually with major stakeholders in terms of acquiring data in the market.

Athletics is a complicated sport to evaluate. Unlike other sports , which comprise of discrete contests between sportspersons in the market. Athletics, by comparison, involves thousands of sports personnel who interact with each other in several ways. Historically, important data besides counting stats such as the number of winners have been difficult to record. However, the possibility of introducing a video system connected to the database ensures that data is obtained and recorded timely. The use of machine learning and cartography will be possible to evaluate the players better in terms of their performanc e. The new way of collecting data is instrumental in ensuring the development of changes in the sector. It will allow data, scientists to employ machine learning to evaluate ways to improve the athletics sports sector.

Getting data into the Database

Data is collected after the definition of the database belonging to ComStart DataBunch. The developers will have to write code that takes the data to the company’s user input . It will also establish data rows in the database table. This means that each time a user adds the information of an athlete, the application will be able to create a new database row . If the information is deleted, it means that the code also deletes the row. The development team in the company will also add some code before saving different data sets. This will ensure the validation of the inputs from the user (Bowser, Wiggins, Shanley, Preece, & Henderson, 2014).

The database may comprise of the value of Years which may be between 2000 and 2019. The statistical database will serve as a relational framework to support the needed interests of data collected. The data will be stored in the form of descriptive and table forms. The tables will be in the form of spreadsheets. The stored data will be in a row in the form of a spreadsheet. There are several column types in the database that will ensure data is recorded automatically via codes written and developed by the members of the programming teams. It will be difficult for one to misinterpret the data because it will be specific.

Conclusion

In conclusion, it is important to note that the data collected by ComStart DataBunch will be significant in influencing the development of the athletics sector . The Database will be able to store information with diverse states. It will focus on personal information, performance, and training statistics. It will also serve as an opportunity to support effective development in the sector through robust decision-making initiatives. Lastly, the existence of data analytics will ensure that the company uses the athletics sector as a key source in the collection and managing of the data.

References

Bhushan, K., & Gupta, B. (2017). Security challenges in cloud computing: state-of-art. International Journal of Big Data Intelligence, 4(2), 81-107.

Bowser, A., Wiggins, A., Shanley, L., Preece, J., & Henderson, S. (2014). Sharing data while protecting privacy in citizen science. interactions, 21(1), 70-73. Retrieved from www.researchgate.net/publication/261959198_Sharing_data_while_protecting_privacy_in_citizen_science

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904.

Paramita, G. (2018, September 2018). Data Governance in the Cloud. Dataversity. Retrieved from www.dataversity.net/data-governance-cloud/

�Runners!!

�Your customer = “the market” ??

�Ahhh fantasy sports !!!

�We are still talking about the sport of running here, aren’t we?

�Draft of runners??

�Hmmm what is a statistical form of database? A data warehouse??

�Ok I’m getting confused. Are we talking about runners here or about all kinds of athletes?

�Ahhhhh that’s your customer!!!! Or at least a key customer

�Totally confused. Runners? Teams? What?

�What society? Gamers?? Fantasy gamers?

�Medical?

�Data governance of your database is basically YOUR problem

�Hmmm ok this is another area of service to customers. Now your customer is Team Management

�…and recruitment

�Hmmm you are trying to make a distinction between what “athletics” is and what “sports” is…and the reader (me) thinks they are the same thing.

�what

�interesting. Video cameras where? At every sporting event?? You mean in addition to all the cameras that are already at each event? Special cameras that are attached somehow to your database???

�what

�transactional

�ok

�Hmm another “customer”