Data science is a principle of machine learning that uses several tools and algorithms to find patterns from raw data. This has become quite a buzz in the tech world and almost every industry uses it to leverage its business, even sports. Surprised? Around 2.7 zettabytes of data are produced digitally which can be analyzed to make competitive strategies.

This is called sports analytics

You might not have heard about it because this field of education is not that popular yet. Most analysts who work in the sports industry either have a master’s in maths or statistics and chose sports analytics as a minor specialization. But the awareness has started to spread.

Currently, there are specialized undergraduate-level degrees in data science that can help you learn insightful skills like monitoring, managing, representing, evaluating, and analyzing data in a way it will benefit a team or a club.

Role Of Data Science And Predictive Analysis

Data science is used to make decisions and predictions along with predictive casual analytics and machine learning. In simplified terms, sports analytics is nothing but using the data related to a game or sport to come up with predictive machine learning models. This data can range from individual performance of the players, weather conditions on a matchday to recent records of the team’s performance, etc. With this data, the main objective would be to improve the overall performance of the team and increase their probability of winning.

In the sports industry, predictive analysis is done to evaluate the insights and inform the team of the necessary steps they should take on the game day. Websites like ESPN, Cricbuzz, etc. use data science to predict the performance of players and teams in different league matches. These machine learning models are prepared by analyzing the base and the history of the team, how the players might perform against the rival team, weather conditions, and many other small considerations. Predictive analysis uses three primary elements:

1. Player Analysis

Predictive analysis, firstly, evaluates individual player performance. This allows players to know what their best form is, what workouts and practices they need to follow to maintain their best form depending on the previous game.

2. Team Analysis

Unlike player analysis, team analysis means evaluating the performance of the entire team as a whole. This is done to create a base for machine learning models, deep neural networks, and many more such models that can contribute to the team’s win.

3. Fans Management Analysis

While this has not contributed to winning, fan data is gathered from different social media handles like Twitter and Instagram to find patterns using many clustered algorithms. This is one to attract more fans to sell the team merch.

Big Data In Sports

Big Data brought a significant difference to the world of sports.

• It helped to personalize the broadcasting of the game/match.
• It enhanced the training results with better workout regimes and game statistics
• It helps team managers/recruiters make data-backed decisions
• It offers advanced athlete recovery tracking which results in an increased probability of wins.

Future Of Sports Analytics

The true potential of data analytics in sports will only come out when technicalities are understood by the industry. While it’s not rocket science, it requires an undergraduate degree in data science to excel in getting the best results of team performance and victory. Big football leagues are already witnessing the positive difference artificial intelligence and machine learning are making. Teams can apply the power of data science and AI to improve winning chances if done right.

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It has been a widely acknowledged fact that big data has become a big game changer in most of the modern industries over the last few years. As big data continues to permeate our day-to-day lives the number of different industries that are adopting big data continues to increase. It is well said that when new technologies become cheaper and easier to use, they have the potential to transform industries. That is exactly what is happening with big data right now. Here are 10 Industries redefined the most by big data analytics-

Sports

Most elite sports have now embraced data analytics. In Premier League football games, cameras installed around the stadiums track the movement of every player with the help of pattern recognition software generating over 25 data points per player every second. What more? NFL players have installed sensors on their shoulder pads to gather intelligent insights on their performance using data mining. It was analytics which helped British rowers row their way to the Olympic gold.

Hospitality

Hotel and the luxury industry have turned to advanced analytics solutions to understand the secret behind customer satisfaction initiatives. Yield management in the hotel industry is one common use of analytics which is an important means to tackle the recurring peaks in demand throughout the year in consideration with other factors which include weather and local events, that can influence the number and nationalities of guests checking in.

Government and Public Sector Services

Analytics, data science, and big data have helped a number of cities to pilot the smart cities initiative where data collection, analytics and the IoT combine to create joined-up public services and utilities spanning the entire city. For example, a sensor network has been rolled out across all 80 of the council’s neighborhood recycling centres to help streamline collection services, so wagons can prioritize the fullest recycling centres and skip those with almost nothing in them.

Energy

The costs of extracting oil and gas are rising, and the turbulent state of international politics adds to the difficulties of exploration and drilling for new reserves. The energy industry Royal Dutch Shell, for example, has been developing the “data-driven oilfield” in an attempt to bring down the cost of drilling for oil.

And on a smaller but no less important scale, data and the Internet of Things (IoT) is disrupting the way we use energy in our homes. The rise of “smart homes” includes technology like Google’s Nest thermostat, which helps make homes more comfortable and cut down on energy wastage.

Agriculture and Farming

The power of AI has embraced even traditional industries like Agriculture and Farming. Big data practices have been adopted by the US agricultural manufacturer John Deere which has launched several data-enabled services that have led farmers to benefit from the real-time monitoring of data collected from its thousands of users worldwide.

Education

Education sector generates massive data through courseware and learning methodologies. Important insights can identify better teaching strategies, highlight areas where students may not be learning efficiently, and transform how the education is delivered. Increasingly educational establishments have been putting data into use for everything from planning school bus routes to improving classroom cleanliness.

Banking and Securities

Securities Exchange Commission (SEC) has deployed big data to track and monitor the movements in the financial market. Big data and analytics with network analytics and natural language processors is used by the stock exchanges to catch illegal trade practices in the stock market.

Retail traders, Big banks, hedge funds and other so-called ‘big boys’ in the financial markets use big data for trade analytics used in high-frequency trading, pre-trade decision-support analytics, sentiment measurement, predictive analytics, etc.

This industry also heavily relies on big data for risk analytics including; anti-money laundering, demand enterprise risk management, “Know Your Customer”, and fraud mitigation.

Entertainment, Communications and the Media

The on-demand music service, Spotify uses Hadoop big data analytics to collate data from its millions of users across the world. This data is used and analyzed to give customized music recommendations to its individual users. Over the top media, services have relied big on big data to offer customized content offerings to its users. An important move in the growing competitive market.

 Retail and Wholesale Trade

Big data has in a big way impacted the traditional brick and mortar retailers and wholesalers to current day e-commerce traders. The retail and whole industry has gathered a lot of data over time which is derived from POS scanners, RFID, customer loyalty cards, store inventory, local demographics, etc. Big data is applicable to the retail and wholesale industry to mitigate fraud, offer customized products to the end user thereby improving the user experience.

Transportation

Big data analytics finds huge application to the transportation industry. Governments of different countries use big data to control the traffic, optimize route planning and intelligent transport systems and congestion management.

Moreover, the private sector uses big data in revenue management, technological enhancements, logistics and to gain a competitive advantage.

Big data is improving user experiences, and the massive adoption change has just begun.

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