Data science is basically a blend of several tools, principles of machine learning, and algorithms in order to find patterns or trends from the raw data.
You must be wondering how data science is different from data analysis. Well, a data analyst only focuses on decoding the patterns or trends from past data repositories. While a data scientist will first perform an exploratory data analysis then seeks help from several machine learning principles and algorithms to evaluate the probability of the occurrence of a particular event in the future.
Data science is the future and is an integral part of almost all the industries, including sports.
It is merely because around 2.7 zettabytes of data are produced in our digital world and in order to analyze it to frame some useful competitive strategies, the need and importance of data science come into the picture.
Importance of Sports Analytics Degree
Sports analytics is not a very well established and popular undergraduate degree.
Most of the individuals who are working as an analyst in the sports industry have either pursued masters in maths or statistics and have selected sports analytics as a minor specialization.
But, the situations are changing and so are the educational degrees.
Now, we have specialized undergraduate degrees in data science that can help you inculcate exceptional skills like monitoring, managing, representing, evaluating, and analyzing data.
What Are Data Science And Predictive Analysis?
Data Science is used in making decisions and predictions along with using predictive causal analytics and also machine learning.
In contrast, sports analytics is nothing but using the data related to any of the games or sports in order to build predictive machine learning models.
The data related to sports primarily includes the individual performance of the players, weather conditions, and the recent/records of the team’s victory or defeats against all the other groups.
The primary objective of the sports analysis is to improve the overall performance of the team and ultimately enhancing the probability of winning.
The Predictive Analysis
Predictive analysis in the sports industry is primarily done in order to evaluate the insights and give an idea about what all preparations the team needs to do on the day of the actual game.
This proves beneficial and pays off in return as the improved team performance and enhancing the probability of the ultimate victory.
Integrating the predictive analytics with machine learning models and algorithms, you will be in a state to identify and evaluate the individual performance of a player at a particular position or gaming order, on the day of the match.
These machine learning models are prepared by taking the base and history of the players, how the players will be able to perform against the rival team, weather conditions on the day of the match in the home ground or at the foreign ground, and many other things.
There are three primary elements of the predictive analysis.
1) Player Analysis
As the name suggests, it evaluates the performance of the individual player and can also help the players in maintaining their fitness level depending on their past training sessions. Also, it has a benefit to access all the information related to the individual player on the same platform.
2) Team Analysis
It means analyzing and evaluating the statistics of the team as a whole, and It is essential to do so to build the great machine learning models like SVMs, deep neural networks, and many more that can directly contribute to the prediction of several winning combinations.
3) Fans Management Analysis
Fans data can be gathered from several social media handles like Twitter and Instagram in order to form groups and find patterns using several clustering algorithms. The team management must focus on the factors that attract the most fans, helping them to gain an extra fan base.
Uses of Big Data in Sports
There are several uses of big data that have brought a revolutionary change in the world of sports. Some of the primary uses of big data in sports are:
- Helps in personalizing the broadcasting of the entire game.
- Enhances the results from the training with the help of big data analytics.
- Assists in taking data-backed recruitment decisions of the player.
- Offers smart and advanced athlete recovery tracking.
Data Visualization via Dashboards
Data visualization is indeed a powerful tool in the data-focused modern world.
Merely using the raw data is never going to give you brief and hidden information related to the performance of the individual players or the team as a whole, considering the sports team.
By seeking the help of data analytics and representing the complex sets of data through graphs, or pie-charts, the team management will be able to utilize even the complex data sets with ease.
If done right, the administration will be able to take some wise decisions improving the ultimate chances of the victory.
As far as the industry of sports is concerned, the data visualization is used in displaying the important data via team manager dashboard, and the fans dashboard.
Future of Sports Data Analytics
In order to leverage the potential of the data analytics in the industry of sports to get the best results in terms of player performance and improved chance of ultimate victory, it is crucial to understand the technicalities.
It is indeed not rocket science, but it is also not that easy, hence, you must require an undergrad degree in data science to excel.
One of your favourite teams Manchester United and Real Madrid, are already leveraging the benefits of data analytics.
Data science is the future of sports analytics. Artificial intelligence and state of the art machine learning models has improved the capabilities of upgrading team performance. Nowadays, teams can apply the power of data science and AI to improve the winning chances of teams under any conditions. Hence, we can say that the scope of data science in the sports industry is very bright.