Sports Analytics for Practice and Training- MyAssignmenthelp.com

Question: Discuss about theSports Analyticsfor Practice and Training. Answer: Introduction: Big data and advanced techniques of analytics are being adapted into the industry of sports. The sports teams are integrating the analytics technology for effective management of the game, development of players, methods of practice and training, decision making in the finances and marketing. The governing bodies and the leagues of sports are able to implement the Big Data analytics technology to optimize the scheduling, facilitate the issues related to the allocation of resources and analyze the legal environment within the respective organization (Alamar, 2013). At the same time, the businesses those are related and associated with sports sector like the providers of media make effective use of the Big Data analytics more frequently to observe the relevant markets right from the fantasy games to the sponsorships. Big data analytics provides boundless possibilities for which the sector of sports may be providing a firm base for the analytical technology of Big data which will be useful in various industries. Every major professional team of sports either has staffs that are analytically experienced and expert or have a department of analytics. The teams most of the time has to scan for scout notes from the gamer clipboard, have the PDFs get converted to Excel and then submit those files to the top developers of data (Caro, 2015). Hence, another group of young and talented mathematicians does the math of the scouts while the general managers assist in determining the players which they feel to be fit and appropriate to fit in the club. This is a part of the creation of the profile of a sportsman or a player in determining the appropriateness or suitability of a player for the drafting and signing as a free agent and being acquired in the trade. The analytics play an important role in the career of a player as well as in determining the present and future of any professional game or sports. The popularity of making a decision that is data driven in sports has influenced the fans which are countering most of the analytical content. There are whole websites that are dedicated to the analysis and research of statistics of sports and the manner they associate to the prediction of the performance. Revolution of Big Data The revolution of Big data in the sports sector is parallel to every other domain where Big data analytics is used. The incentive to implement the Big data analytics like the other sectors has to lead to the integration of abundant and growing the available data that is concurrent with the significant developments in the technology and power of the computing sector. The use of methods of statistics for the evaluation of players is as obsolete as the sports, a few numbers of disciplines generate, evoke and engage as much information and data as sports does. For example, the contests of sports result in huge volumes of statistics which have been the matter if quantitative analysis. However, the integration of highly sophisticated methods for analysis is being used recently and has broadened the opportunities for applications for the industry of sports. The Big data analytics is getting oriented towards the usages that are multifaceted that assures the provision of benefits to the makers of the decision throughout the complete sports organization. Much of these practices have huge potential for the portability of the industry of sports. The incentive to utilize the Bid data analytics has developed from the integration of significant improvements and abundance of data in the computing power (Caro, 2015). The objective of sports analytics is to assist the makers of the decision within the agencies and firms associated with sports in making better assessments. Different teams of different sports have objectives which are twofold and they integrate the success on the field of sports with the management of financial targets. Both of these goals are not exclusive mutually (Steinberg, 2015). Advances in Big data analytics result in creating meaningful methods for comprehension and prioritizing data that can be used in the improvement of the making of the decision with regards to both goals. The general managers and the development staff of the players who develop and procure talent along with assembling the rosters to make use of the analytical methods which are sophisticated (Cordes Olfman, 2016). The innovative and latest Big data analytical technology assist the coaches in preparing the athletes physically, devise the strategies of the game and regulate and manage the talent within the sports. The use of Big data analytics is not restricted to the decisions of the personnel and the management of games. The sports analytics has crossed over into the boardroom recently where the leagues and teams are implementing big data in order to make decisions regarding the pricing, distribution, allocation of resource and marketing. However, the implementation of big data analytics still differs broadly depending upon the game or sport. The behaviour of the consumers market for the products and financial outlays are included the potential subjects of the advanced evaluation of statistics (Davenport, 2014). Development of Sports Analytics People began to develop the empirical analysis of baseball in the late 1970s to generate the advanced statistical evaluations of the performance of the sports players. This approach was pioneered by Bill James which is called as the Sabermetrics. Baseball facilitates itself to the evaluation on the basis of quantitative empirics as it generates the performance of the date that is available and is ideal for the analysis (Dizikes, 2013). He also sought the ways by which development of statistics could be done in an objective way in the activities of the game specifically those which directly associate with the objective of the team to win the sports. The Current State of Analytics in the Industry of Sports: The clubs and organizations associated with the sports broadly differ in the enthusiasm and usage of the Big data analytics. The acceptance of the sports industry regarding the techniques of analytics is hardly general. The teams who have the disadvantages like the small size of the market have the highest probability to aggressively implement Big data analytics. However, no system of archetypal analytics exists for the clubs and organizations of professional sports. Investment in the technology and personnel of Big data analytics varies significantly throughout the industry of sports. Furthermore, the lack of approach or the approach itself regarding the integration of the Big data integration into the process of the organizations remains as varying as the industry of sports entirely (Forouhar, et al., 2013). Organizations are setting up the procedures of analytics procedures in which they observe the best copes of gaining effective returns on the investments and competitive advantage provided the industry needs to acquire knowledge. The organizations associated with sports need to understand the methods and procedures of Big data analytics along with the manner and procedure to integrate them into the strategic plans and methods. These organizations along with the other organizations belonging to other industries fall behind. The new and innovative technologies in the evaluation of the players and the management of games continue to grow (Gerrard, 2014). For instance, the methods of Big data analytics in the NBA are developing with the installations and setting u p of the many numbers of camera systems that capture the details of the games. The Big data analytics similar to the GPD systems have tracking methods which are player optically player specific are another latest development. These devices are capable of measuring the relative speed of the sportsperson, their movements, and placements on the court along with their interactions. The above improvements provide the sports team with the information that is well beyond what can be obtained from the conventional analysis of the statistics. The financial incentives play a crucial role in the implementation of analytics (Gerrard, 2016). The salary cap of NFL which dictates the limit of the payroll provides the teams of the NFL with specific barriers. The Patriots have been successful with the management system of the analytical cap which identifies the inefficiencies in the market of the player, which in this context is not given much value to the veteran agents who are free (Schrader, et al., 2016). On the respect of the business, the initiative of demand that is based on the pricing of the ticket has been a major development. The industries of other sectors such as the airlines industries have extended practiced models of dynamic pricing that is confronted by the similar constraints regarding the fixed supply and consumptions which are time-specific. Many teams of NBA and many teams of NHL are presently having dynamic pricing while some have their inventory of seats but conventionally the tickets available for the events of the sports had been priced before the resuming of the seasons which did not fluctuate with alterations in demand (Gerrard, 2016). Till date, most of the clubs were not interested in charging different prices for the games even during the variance in the demands occurs which could also lead to rejection. For example, the broker secondary ticket market sales data of Stub Hub's provided clubs with a transparent indication regarding the amount of revenue they were being left out with on the table by not permitting their prices to fluctuate with the changing conditions of the market. Furthermore, the evolution of better and more data along with the computational speed and power has made convenience dynamic pricing. Professional sports teams compete with each other in the labour market in the star players form which is short in supply. There have been arguments that the salary caps are vital in order to preserve the competitive balance. The caps in salary also assist in limiting the expenditure of the teams for the players. The thesis of the Money ball by Michael Lewis and what is the theory of Big data analytics in the sports refers to the small market teams of baseball can be successful by spending their capital; in a wise manner. The famous star players demand huge salaries because of the high level of celebrity status and skills. The players who have a high rating of on-base percentages and are overlooked by the teams of the major market can be recruited at many low salaries than the star players (Halvorsen, et al., 2013, February). All of these capping and salaries can be effectively managed by the integration of Big data analytics in the sports analytics. The implementation of the dynamic pricing is spreading into various areas very rapidly. The application range extends from the performing arts and, live entertainment to the toll roads in the highway and there has been a regular shift from the conventional strategies regarding the fixed prices. Most of the retailers are shifting from the conventional prices which are fixed and the strategies of discount to the dynamic pricing. From a research report from Marketplace, it has been revealed that the retailing through Internet is driving the transferring towards the direction of dynamic pricing in the details (Kasiri-Bidhendi, et al., 2015, September). The notions regarding the appropriate place to buy are imperative as the selling through web becomes growingly prominent. The industries which have the similar dynamics include the fixed on place and capacity, specific consumption based on time could obtain a great deal of knowledge from the instances and in the industry of sports. The instance includes examples from the restaurants, performing arts and movie theatres. Even, the Broadway and some other local organizations have their dynamic pricing which is carefully implemented for their performance events and shows. The most popular instance of the application of the big data analytics in sports is from the movie from Hollywood, Moneyball which is a true story of a coach of baseball who used to integrate a stellar team in spite of having a restricted budget. The most of the application of the Big data analytics is relevant in the sectors of strategy and training. It is now evident that the analytic of performance is the most vital tool currently for the sportsmen and players and their respective teams or the identification of the strengths and weaknesses to track their improvements in the game (Mahmood Takahashi, 2015, October). This is done by observing specific trends in the players' performance and in studying the performance of the opponents so as to devise a strategy that is effective to defeat them. In each game and sport, there is huge scope for the Big data analytics in deriving more sense out of the sports play data. The activity of gathering data is a barrier as most of its parts still depend on the humans who observe the game and give their interpretation regarding which data to be fed into the system. The Big data analytics excludes a lot of subjectivity in the data which in other ways would include a black-box for the analytics tools. Big data has the advancements in the processing of image and artificial intelligence which is assisting in reducing the gap which is getting addressed at a faster rate than the awareness of most of the people (Mahmood Takahashi, 2015, October). Using of Big Data for the Engagement of the Audience: Most of the sports fans, in the last decade, had witnessed a fad to collect the trading cards that had the data of the performances of their favourite sportsmen or players. The appetite for the numbers is increasing too fast as the audience is getting more tech savvy. This has also led to an entirely new industry of sports fantasies that makes use of the data of the real world sportsman or players in order to stimulate the tournaments that are virtual where the users can have their own team build and managed along with betting on their favourite players. Even the live broadcasts of the sports programs presently include more things than simply commendatory that keep the audience engaged (Miller, 2015). The owners of the teams make use of the analytics that are based on real time and animation in order to gauge the scenarios and to predict the performance of the important players and make use of the analytics of social media to measure the sentiments and involvement of the audience so as to create the correct content for the appropriate opportunity. Betting Scandals: TheDarker Truth Big data can help in identifying and restricting the occurrence of the misuse in the hands of the wrong people. Integrating Big data analytics can recognize and restrict the illegal betting that happens in the sports and games which are possible due to the incorporation of latest technology in the arena of sports. The availability of huge amount of analytics and data has made it convenient and easier for the managers of teams for optimization of their budget while recruiting the sportsperson and players (Mondello, 2014). In the same way, it has made it convenient for the authorities to capture the amateur bettors and restrict them in participating in the crimes like illegal betting by providing them with less access to different and more advanced tools and information that cannot be even used by the professional bettors. As a result, there has been the reduction in the market of online betting which was seen to be highly growing in the past years and was the cause for almost every sports scandal in the sports management companies. The use of Big data analytics in the monitoring, regulation, and analysis of the collected data from the specific procedures and processes is in its initial stage. For the professionals' sports in the world, the applications are still limited to practices only. However, the research on the Big data analytics and its implementation along with its possible implications on the sports industry is advancing these processes at a rapid rate (Mondello Kamke, 2014). The modification of such type of tracking, analysis, and monitoring could also be highly useful in other sectors. The production time that is lost due to the injuries at the workplace results in costs for both the employers and the workers in billions of dollars every year across the world. Almost a portion of these mishaps is caused due to the fatigue of the players or the workers as they are inappropriately spaced or placed in the area of work. Regulating the systems of Big data analytics could be developed for the implementation on a sector to sector basis. The optimization of the jobs and games that are related to the interactions physically could result in growth in the productivity along with minimizing the injuries taking place in the workplace to a great extent (Passfield Hopker, 2016). Moreover, the new and innovative applications of the technology of Big data analytics could be improved to enhance the productivity and efficiency of the workforce in the sports industry. One challenge in the workplace in the sports sector is that the employees might perceive them to be an enforcing force in the opposition to a way that is proactive in nature so as to protect them from any kind of injuries during the working day (Passfield Hopker, 2016). Conclusion: It is to be noted that the industry of sports is gaining momentum with the integration of Big data analytics and it largely depends on the way the sports industry is currently implementing the new and innovative technology in the industry of sports. Moreover, the current methodologies develop and advance, the new and innovative techniques and technologies which could continue to be growing. Most of the valuable aspects of the industry of sports are transferable to other sectors too. The professionals who are resourceful need to identify and recognize these scope and opportunities and take appropriate advantage of them. The data in the sports industry is unique in the aspect in which it exists and is readily available to the general mass, mostly regarding the performance of the athletes. Hence, the statistical analysis which is facilitated by the advance technology like Big data analytics has enhanced the networks of communication through the Internet which has expanded the ability to gather and disseminate the information related to sports. It has also expedited the improvements of the analytics regarding the sports and the organizations associated with sports. References Alamar, B. C. (2013).Sports analytics: A guide for coaches, managers, and other decision makers. Columbia University Press. Caro, C. A. (2015). A Retrospective Look at College Football in the Late BCS Era-A Case Study in Sports Analytics, Sports Management, and Sports Economics.Journal of Business Case Studies (Online),11(2), 71. Cordes, V., Olfman, L. (2016). Sports Analytics: Predicting Athletic Performance with a Genetic Algorithm. Davenport, T. H. (2014). What businesses can learn from sports analytics.MIT Sloan Management Review,55(4), 10. Dizikes, P. (2013). Sports analytics: a real game-changer.Massachusetts Institute of Technology. Forouhar, A. S., Kellogg, M. M., Ohiomoba, K., Akhmetgaliyev, E. (2013).U.S. Patent Application No. 14/398,942. Gerrard, B. (2014). Sports Analytics: A Guide for Coaches, Managers and Other Decision Makers. Gerrard, W. J. (2016). Sports analytics. Gowda, M., Dhekne, A., Shen, S., Choudhury, R. R., Yang, X., Lei, Y., ... Essanian, A. (2017). Bringing iot to sports analytics. NSDI. Halvorsen, P., Sgrov, S., Mortensen, A., Kristensen, D. K., Eichhorn, A., Stenhaug, M., ... Johansen, D. (2013, February). Bagadus: an integrated system for arena sports analytics: a soccer case study. InProceedings of the 4th ACM Multimedia Systems Conference(pp. 48-59). ACM. Kasiri-Bidhendi, S., Fookes, C., Morgan, S., Martin, D. T., Sridharan, S. (2015, September). Combat sports analytics: Boxing punch classification using overhead depthimagery. InImage Processing (ICIP), 2015 IEEE International Conference on(pp. 4545-4549). IEEE. Mahmood, K., Takahashi, H. (2015, October). Cloud based sports analytics using semantic web tools and technologies. InConsumer Electronics (GCCE), 2015 IEEE 4th Global Conference on(pp. 431-433). IEEE. Maxcy, J., Drayer, J. (2014). Sports Analytics: Advancing Decision Making through Technology and Data.Temple University. Miller, T. W. (2015).Sports analytics and data science: winning the game with methods and models. FT Press. Mondello, M. (2014). The MIT Sloan Sports Analytics Conference.International Journal of Sport Communication,7(3), 420-421. Mondello, M., Kamke, C. (2014). The Introduction and Application of Sports Analytics in Professional Sport Organizations.Journal of Applied Sport Management,6(2). Passfield, L., Hopker, J. G. (2016). A mine of information: can sports analytics provide wisdom from your data?.International journal of sports physiology and performance, 1-17. Rodenberg, R. M., Feustel, E. D. (2014). FORENSIC SPORTS ANALYTICS: DETECTING AND PREDICTING MATCH-FIXING IN TENNIS.Journal of prediction markets,8(1). Schrader, D., Gupta, A., Iyer, L., Schiller, S., Sharda, R. (2016). Sports Analytics Research Collaborations: Connecting Business Schools with Athletic Departments. Steinberg, L. (2015). Changing the game: The rise of sports analytics.Forbes.