The global sports market is growing speedily than ever at a CAGR(compound annual growth rate) of 13.5%. The industry is already showing an immense boom and is expected to rise from $388.28 billion in 2020 to $440.77 billion in 2021. It is not just audience zeal for sports that has resulted in this hype, but also the involvement of Big Data in the sports industry.
While Data Science and sports may seem totally unrelated to you, data plays a crucial role in today’s sports industry. Players, coaches, and team managers depend upon analytics to make decisions and develop strategies for the game. In terms of industry competitiveness, data plays a vital role in tracking the performance of a specific player or whole team. It also helps in improving the quality of the game, player safety, and fan experience.
Application of Big Data in the Sports Industry
Big data has proven to be a game-changer for the sports industry. Professional teams are now more focused on gathering data as it gives them a competitive edge in the field and increases their chances of victory.
However, a game can be affected by bewildering and uncontrollable variables such as the weather, the sportsman’s unique physiology, the choices of players for a match, and the decisions of referees and judges (sometimes based on political motives). Fortunately, analytics techniques come to the rescue by revealing predictive insights to make optimal decisions in the sports industry.
Here are some ways in which big data is used in the sports industry:
To Increase the player’s efficiency
In order to improve the performance, many players seek their data to analyze previous results. Sports analytics can provide good insights into players’ nutrition, training, and performance, like how many kilometres a player runs on average, how much protein they consume, etc. By pairing these insights with their field performance, players can alter their training routine. It also helps them to get better at the game.
For Team Analysis
Analyzing individual players also helps management make assumptions about how the team will work together. Coaches can compare the statistics to decide which combinations of the players are most likely to win the game. They can determine the strategies by creating advanced machine learning models like deep neural networks, SVMs, and more.
To Analyze the Fanbase
The reason sports is one of the most popular businesses is because of its immense fanbase. Data Analytics helps sports management teams identify when and how fans may attend a particular game or buy merchandise. By examining social media, participation, and merchandise sales, management gets a sense of audiences’ expectations from the game. Moreover, they can use these insights for launching promotional campaigns by recognizing their target audience.
Ensuring the player’s health and safety
A single injury can majorly harm a player’s career. In addition, injuries on the field lead to a decline in the performance of the whole team. While some damages are inevitable, data analytics enables the medical team and players to anticipate when and how injuries may occur and how to avoid them.
A recent case study showed that players training with senior teams, such as in the Premier League, wear a fitted shirt with GPS, accelerometer, gyroscope, and digital compass. Other organizations may use different devices like a fitness band. As they exercise, sensors monitor their heart rate, speed, and distance. By analyzing this data, coaches can surmise if the player is overdoing himself. It helps them regulate the training accordingly to avoid exertion during the game.
Professional sports organizations use analytics to find and recruit new players to the team. While many coaches can simply draw conclusions based on the player’s analytics, some test their skills on the ground to collect real-time data. The Sports Analyst can further develop correlations of the prospect’s data with that of the team and the game. It results in picking the right player for the right team.
Data made it easier for the sports administration to manage the game accurately with the available skills and resources. Not only does it help in improving team performances but also in regulating a fan’s behaviour. Genuine insights about the match can help in making it more interesting for both players and the audience. In addition, this new era of data in sports has also opened up new opportunities for data enthusiasts in the field of sports analytics.
Scope of Sports Analytics
Typically, Sports Analytics is the process of using mathematical and statistical principles in sports and other peripheral activities. While there are other industry-specific factors and priorities, most sports analysts use the methods of Data Analytics. A report by Grand View Research Inc suggested that the global sports analytics market size is progressing at a CAGR of 31.2% and will touch $ 4.6 billion by 2025.
If you are intrigued by the role of big data in the sports industry or interested in advancing your career in Sports Analytics then you should consider enrolling for a course in Data Analytics.
At SkilloVilla, we have prepared fully advanced career tracks and individual courses on Data Analytics with the help of top industry masters to nurture your career in the world of Data Analytics. Besides, you learn from the industry masters via LIVE interactive classes and concept videos.
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