Analytical systems have become a backbone of most businesses over the past decade. Most of the time, basic software needed for operating a business in major verticals is easily accessible, without costing the client half their fortune. Clients can simply purchase software off the shelf and customize it.
So, does that mean that companies that build such software from scratch may soon become obsolete?
With enhanced capabilities come enhanced requirements.
Plasma, with its advancements in AI/ML combined with its expertise in real-time analytics and dashboards, can now foray into innovative areas such as sports science.
What is Sports Science?
Sports science captures visual data, throughout the game, and event logs that describe events such as passes, shots, tackles, and so on at specific times. This data is then analyzed and used by a team’s coach to gain a competitive advantage in training and recruitment.
Until recently, observational data was used by team coaches for tactical analysis of opposing teams. This approach failed to take advantage of important contextual information. There is only so much that a human mind can analyze in real time without tools to review the data later. However, detailed game logs can now be obtained through next-generation tracking technologies. This data provides context to every single goal scored in a game, along with the overall result analysis. Analysts are increasingly storing the data in a central repository for analysis. They can:
- Analyze decision patterns of a player and provide their team with solutions to get around them
- Analyze decision patterns of their own players and adjust physiological training to incorporate different reactions in similar situations
- Analyze opposing team’s dynamics and interactions and adjust their own strategy accordingly
- Find loopholes in their own formation and adjust it accordingly
The Role of Big Data Analytics
All this heavy analysis leads to the opposite problem where the shear amount of data becomes an obstacle. Methodological guidelines as well as theoretical modelling of tactical decision-making in team sports is targeted, but seldom achieves the desired result. What this means is that sportsmen do not perform extensive mathematical calculations before each kick. Most of the game is played on instinct – the sheer variation in player response under similar conditions makes the standard deviation so large that predictive analysis results become useless. Even if we try to anticipate and simulate the game the game result based on the model provided by the team’s coach, the actual setting of the game and the subsequent result may be quite different.
To top off the problems with data complexity, there is the issue of handling data that is outside the range of current tracking capabilities. An aerial trajectory, for example. No tool yet exists that can handle such complex non straight-line passes (or more complex motions involving the spin of the ball).
Still, it is projected that with exponential growth of data points being collected, we will be able to pin down more and more random variables involved in deciding the result of the game. Soon, our systems should be able to predict a game’s result with a confidence level of ~ 70%.
As exciting as all this may sound, we urge the reader to question: what is the end goal here? If we can accurately predict the result of games, do we really want to proceed in that direction and take away the fun of the game? Those are questions that will need to be answered as we move forward with our advanced technologies.