It’s a great day in sports analytics! Today we are going to discuss network analytics and how it’s applied in sports.
Network analytics has been used by various fields for a very long time. It is used in process control networks which run across the manufacturing sector, utilities, health, and even in sports. The effectiveness of network analytics is so undisputed as it makes industrial networks quite safe, offers high productivity, and is much less expensive to operate.
Before we go ahead to talk about network analytics as a method of sport analytics, let’s see some design examples of this method and how it is used.
Analytics utilizes process control which are embedded in sensors and controllers that use simple commands to provide operations. For instance, the command could be: “if the pressure in the boiler is above X bars, then shut down the boiler”. So basically, it follows an “if and then” operation. By expansion of this, one can now build a model for analytics computation, which will enable the provision of solutions to complex problems.
Network analytics allows the computer to compare data that are inputted against data sets that are pre-programmed in the operational models. This comparison helps the computer make functional decisions in a way that it improves the operational process.
Sport analysts use network analytics to build a model around sports events, which are now compared to certain information that are already pre-programmed in the computer with the aim of getting certain functional decisions. These decisions help the analysts and coaches optimize the team and understand the variation in performances of teams and the factors responsible for them.
Presently, coaches and sport analysts employ the use of machine learning, which is augmented with network analytics in other to get their intended information. This is the reason why their analytics models are frequently updated and work in real time because the conditions in the network are constantly changing. However, some analysts prefer to only apply network analytics without the involvement of machine learning.
Let’s see how network analytics is benefiting sport analysts. There are many ways this method of sport analytics has shown great advantages. However, we will only discuss the three most common uses. They are (1) optimization of network resources, (2) provisioning of new revenue streams, and (3) market acceleration.
Optimization of Network Resources
This part is particularly helpful to coaches because it helps them manage resources provided by the team or club owner. When coaches have gathered their resources, they are always looking for the best way to use them in other to get optimum output. By applying network analytics to sports, coaches are able to understand their network, weigh their resources and utilize them in the best ways that yield higher performances and lower cost.
Provisioning of New Revenue Streams
Beyond optimization of resources, teams are always in the habit of finding ways to generate new streams of income for the club. So they employ network analytics to meet their needs. Network analytics open up new income opportunities by providing mining insights, which help the managers identify new streams and then build data which are driven for the sole aim of business.
When coaches are in need of new resources to increase the performance of their teams, they make use of this technique, because network analytics helps them plan for growth easier and the resources needed can easily be estimated, so that as soon as the market is declared open, they already know what they need.
In conclusion, network analytics is very beneficial for coaches, analysts and owners as it provides optimal ways for upgrading their teams and revenue opportunities.
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