Big Data Analytics Can Be the Next Step for High Performance Sports

Why have Big Data solutions been disseminated to improve sports performance?
After its use in laboratories, businesses and research institutes, always in order to improve medical procedures and find the cure of diseases or to fix financial issues, the analysis of large volumes of data through super processors had its benefits also lent to the sports industry.

Examples are diverse and reach such modalities as tennis, volleyball, basketball, soccer, martial arts and baseball. Among the reasons that explain why, in recent years, the use of this technology has become so common in sports.

Real-time performance data collection (such as heart rate during a start-up, pulse height, muscle strength decrease over exercise time, scam level) allows you to understand much more quickly and accurately what the physical limits of each Athlete, as well as which points should be improved; This same monitoring of data during a training allows physical and technical coaches to visualize, within minutes of a play, what groundbreaking mistakes have been made, which positioning failures gave way to an opponent's counterattack, among other tactical diagnostics; The ability to unravel the adversary's playing characteristics is also an indisputable advantage in high-performance competitions. Careful analysis, for example, of the characteristic movements of a tennis player's feet before executing a forehand or of the avoidance vices of a boxer during his fights may reveal weaknesses of rivals imperceptible to the naked eye, ensuring a much more effective preparation.

Big Data Solutions in Sport

Soccer
After the success of the German team in 2014, several clubs also started to incorporate data mining in the preparation of their athletes. One of them is Bayern Munich, which has bet on Big Data solutions to understand perfectly the kinetic energy involved in the movements of its players during training in order to reduce injuries. Such a system can warn the physical trainer that a player has already suffered an injury the last time he had to do a certain exercise, for example, and this allows the adaptation of the training routine according to the limitations or needs of each athlete In Brazil, Grêmio is one of the clubs that rely on Analytics to optimize performance.

Volleyball
The Brazilian team of volleyball is one of the pioneers in the use of Big Data in the sports sphere. Inside the gyms of the Volleyball Development Center, a kind of "HQ" of national volleyball, in the city of Saquarema, several cameras were installed at strategic points of the blocks, allowing the visualization of practically every possible angle. In addition, discrete electronic equipment is attached to athletes' bodies, allowing constant monitoring of heart rate, evaluation of contractions and muscle strains in each pass or cut, checking of impulse levels, among other analyzes. The response time of the players in front of the opponent's moves and the collective movement on the court are also recorded.

MMA
Neither MMA has escaped the competitive advantage gained through automated solutions. The most important athletes in the segment, such as Anderson Silva, Rodrigo Minotauro and Jon Jones, have used specialists in Big Data Analytics to develop descriptive, diagnostic and predictive analyzes of their training and the performances of opponents.

Tennis

Tennis found descriptive analysis a different function from the usual: transforming the experience of watching a Grand Slam in a rich process of interaction and visualization of multiple angles of the dispute, in real time. During the tournament, a gigantic amount of data is captured, analyzed and shared with the millions of fans who are watching the game of the grandstand or on TV (through apps that can be accessed from any smartphone). The tool combines data extracted from no less than 39 million sources, generating information collected from the last 5 years of different Grand Slams. The product of all this technology is the availability of the complete history of performance of each player, his or her possible weapons against opponents and what he or she can use to neutralize them.

Yes, sports have a lot to do with analytics, and for other viewers, analytics can sound fun if you think in that way!

Information gotten from: Changing the Game  the Rise of Sports Analytics

Sports Analytics with SAS, Data Analytics in Sport

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Big Data and Sports Actually Have Aspects in Common

Fun fact about me: I’ve played competitive volleyball for over 10 years, 5 years in Brazil when growing up and 5 years in the USA when I was in college. As a NCAA collegiate athlete, you are able to get access to every athlete’s statistics on a game of the season, no matter the sport played. For volleyball, in particular, the stats used to analyze an athlete’s performance can vary a lot as it’s a very fast game that require a broad skill set for many different plays, such as digs, assists, kills and blocks. Also, the stats used would include hitting errors, blocking errors, and hitting percentage. All of these statistics would always define a player’s performance and it would be a decision maker when a coach had to put a starter team together. At the end of the season, the players would be ranked by their record in scores and each different volleyball skill.This ranking happens on professional volleyball as well. It helps ranking each player by their professional records, which plays a big part in the hiring process of athletes for clubs all over the world. By using those statistics, not only volleyball clubs, but clubs of other sports, from all over the world make hiring decisions of players for the upcoming seasons. Also, the score a player gets by his or her performance, plus professional accomplishments, helps determine the salary range compatible for the type of player.

Therefore, big data analytics also has to do with sports, which help run a gigantic business in the world that involves a lot of money and international transactions.

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All About Using Data in the Right Way

By analyzing data’s operational information, it is possible to direct the marketing campaigns to encourage the consumption of different products by customers who demonstrate the potential for such purchase and improve the quality of the products themselves. Data analysis allows the identification of failures to optimize the internal processes where necessary, also reducing the cost with maintenance. The competitive advantage provided by the data has always existed, but it only took time to people realize it could be converted into profit. When Big Data is involved in planning (growing volumes of internal information and new sources such as mobile, cloud and social media) business competitiveness is taken to a whole new level. A company’s data is a valuable resource and it needs to be used in the company's favor. Companies should look at their data sources and explore them to improve relationships, build loyalty, retain customers and integrate roles within a company. Large companies collect large amounts of information about everything and this quantity becomes so abundant that it often does not receive proper attention. Emeralds are everywhere, but only those who are aware of the data will find out what is behind the curtain. 

What about you? Have you thought about investing in some data management in your business?

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Data Monetization: How to make it work?

Raising revenue is always a challenge for many companies. In e-commerce, for example, this is possible through three basic paths: increase in audience, increase in product variety and growth in conversion rate. In the current economic climate, it is easy to worry about the low profit margin and the hard-to-reach loyalty. And, in this concern, one can be beaten by the company's greatest treasure: the data. Realizing this movement and shifting the focus to take advantage of the data is to turn them into money. When well used, data can be the solution to generate revenue and save money.  Moreover, the traditional campaigns are dead. Modern marketing is a data exchange.

What you may not know is that these same data can be used to empower the team and be used as opportunities that activate the customer portfolio, showing that the company understands the customers’ behaviors and what they want. This way, customized campaigns and promotions can be developed to target different type of buyers. Data is also full of valuable information that can help companies track spending by allocating their resources and time to get the most return possible. Companies are willing to invest in data and everything that involves them; they will certainly be interested in extracting the most of them to boost their financial return.

Here are some steps to boost a company’s profit by using data analytics on an efficient way

1st step: Delivering value

First, determine what kind of data will have the greatest impact on your bottom line, using those that have the greatest potential to increase revenue / profits or reduce costs. Often this process will require preliminary research and analysis to discover patterns in customer behaviors and buying patterns. In addition to delivering fast returns, this approach will also produce tangible results that will support and justify the expansion of the strategy to other sectors and projects.

Step 2: Finding the origin of the data

Now you need to identify the data itself and where it can be found for collection. Some types of data may be more difficult to obtain than the information accessed at a company's CRM, but aligning existing data from the SAC, branding application, and search history will substantially increase the monetization potential.

Step 3: Turning data into money

Inconsistencies and redundancies can compromise the quality of data, which will often come from multiple sources. A company can only get maximum financial return from the data once it ensures the integrity of its information assets. If the goal is to monetize data by crossing patterns of customer behavior with a more targeted offering, the company will need to look for information in CRM, point of sale, and online consumer interaction with the brand. This will happen with the support of technology and a strategic partner for the venture.

Step 4: Integrating roles at a company

Data monetization opportunities exist at various levels of the business, so it is imperative that everyone involved actively participate in collecting information, increasing revenue, and reducing costs intelligently. Executive management, for example, could analyze high-level data to track critical performance metrics related to profitability, while analysts look for data on individual operating processes to identify areas that need improvement while maximizing productivity. The operation could capture and use information to improve customer interaction, bringing the experience to new levels of satisfaction, loyalty, and retention. Allowing the operational team to have access to data tends to have a positive impact on overall performance, as collaboration between firms has even more potential for monetization, making the value of shared information with partners and customers grow exponentially. Companies that follow such hierarchy tend not to be as successful, but once working as a team, goals can be reached with a faster turnaround.

Interesting, no? Who would imagine that one day a bunch of numbers like that would make such a difference on a company’s performance. Please feel free to comment and share this post!

Information gotten from: IT Business Edge, How To Monetize Data in Five Steps 

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Intro

Hello! My name is Amanda and I’m a Digital Marketing student. I’ll be writing about marketing and data analytics on the next weeks with an easy to understand language, as I’m just a beginner in this area.

Data is officially the currency that is trending nowadays in the business world. Companies seek get a better understanding of the data they own or trade data with other companies in order to improve their business. Owning data does not mean much if a company does not know how to handle it correctly towards its success. Therefore, the data analytics field has grown so much recently, and it has been requiring proper professionals to work in the area that are able to understand and manage data sets for different types of industries. The most valuable skill is to combine the understanding of a business goal, analyzing the problem of the business and being able to apply the knowledge of data to solve a problem at a company. Data analysts do not just analyze the data provided, but they apply the results for many different aspects of a business: increase business, analyze customer satisfaction, target prospect customers by marketing strategies, improve ROI of campaigns and know how the company is performing. These are a few of responsibilities that a data analyst has in a company that help take major decisions in businesses. Data is a very fresh topic and it already can change many business paths just by being managed in the correct or not way.

Stay tuned for the upcoming posts I’ll be talking about data analytics here.

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