Home » Big Data in Sports: How it Transforms the Sector, Analytics Tools, etc

Big Data in Sports: How it Transforms the Sector, Analytics Tools, etc

Veronica March 19, 2023

BigDataScalability.com – The digitization in various industries, including the sports industry, has generated a massive amount of information or big data. Using that data has proved to be beneficial for the sports industry. Big data in sports comes from various sources, such as coaches, athletes, and internet activities.

Sports organizations will collect and analyze the datasets to get some insights. They will help the organizations make better decisions. Consequently, they will get some benefits from big data analysis. However, this process requires them to have adequate systems and data literacy skills.

Big Data in Sports Definition


Sports big data is an enormous volume of sports digital data. Its capabilities are far superior to traditional data. However, due to the sports’ big data characteristics, sports organizations must have adequate systems to collect, store, and analyze it.

What are the characteristics of big data in sports?

  • Volume: Every day, the sports industry generates exabytes to petabytes of digital information. The sources of this vast amount of data are sports events, schools, sports clubs, communities, etc.
  • Velocity: Velocity is the growth rate of sports data. Sports organizations must have systems that can keep up with the velocity of big data. Therefore, the enormous data can stream into the systems smoothly.
  • Variety: Sports big data varies greatly. Examples of big data in sports are physical exercise behaviors, physical fitness, competition results, and personal information of the sports players.
  • Value: Different sports datasets offers different value. Some datasets have evaluation values that allow analysts to evaluate the players’ or coaches’ performance. Meanwhile, others have prediction values that enable analysts to predict the performance of sports players or clubs.
  • Veracity: It concerns the accuracy of sports big data. A dataset is accurate if it has excellent quality and comes from trusted sources. Sports organizations that use highly accurate data will be able to have valid results of data analysis.

How Big Data Analytics is Transforming Sports


Big data analytics in sports has brought some positive changes to the sector. Below are some transformations in the sports sector brought by big data.

1. Wearables and Athlete/Player Monitoring During a Competition

Thanks to big data in sports, coaches can easily monitor their athletes or players during competition.  Wearables worn by athletes/players allow coaches to monitor their performance.

The devices record their heart rates, tiredness levels, temperature, hydration levels, etc. Based on the datasets, coaches can identify the players’ aspects that cause them to lose or win the game.

2. Personalized Live Game Broadcasting

Big data in sports and its analytics tools allow sports organizations or sports event broadcasters to enhance fans’ experience in watching the live games of their favorite teams or players. Big data analytics help them understand what fans want when they are watching a sports game.

As a result, they can personalize live game broadcasting based on the fans’ preferences and still be able to maintain sponsor participation. Therefore, both parties can get the best of the game.

3. Intelligent Athlete/Player Recovery Tracking

Athletes’ injuries will affect the performance of the whole team. However, sports organizations can efficiently and effectively track athletes’ recovery progress. Wearables will record their fitness level and biometrics.

Moreover, a specific tool will help injured athletes eat nutritious meals, get enough sleep, and perform adequate training during their recovery process. From the two tools, doctors can do something immediately if athletes seem to have slower recovery progress than expected.

4. Insight-Driven Recruitment Decisions

Recruiting new athletes or players is a complicated process. Sports organizations have a higher risk of recruiting the wrong athletes if they do not use big data.

Thankfully, big data sports offers comprehensive datasets regarding athletes. As a result, sports organizations can choose the most talented or promising athletes to hire.

5. Better Sporting Strategies

Sports big data analytics allow sports clubs or organizations to evaluate the athletes’ or team’s performance. It also gives them an accurate recommendation on sporting strategies. Consequently, athletes or teams will have a better sporting strategy for the next game.

6. Better On-Field Performance

Big data provides the sports industry with training data for professional athletes. Coaches can adopt this training strategy to improve their young athletes’ on-field performance.

The Applications of Big Data in Sports


The adoption of big data in sports will give sports organizations and athletes many benefits. Sports organizations can choose to apply one of the applications below in big data analytics.

1. Evaluation

Evaluating the performance of athletes or sports teams is an essential key to improving their performance and strategies in the future. Sports organizations need to collect historical datasets related to the athletes or sports teams for this big data application.

After collecting the datasets, they must clean them from noises and analyze them using the analytics tools. Different tools will provide different models of data-driven evaluation. Some common evaluation models are player ranking and team ranking.

Sports organizations must not follow their previous training strategies and playing strategies if they are not happy with the result of this big data analysis. Consequently, they can prevent themselves from having bad sports performance in the future.

Instead, sports organizations can copy the strategy of athletes or teams that are in higher positions. Doing it may help them to finish in a higher position at the end of the competition.

2. Prediction

Another essential application of big data in sports is predictive analytics. The first few steps of this big data application are almost similar to the previous one. However, the value of data used in this application is different.

Prediction analysis requires datasets with prediction values. The prediction gained from big data analytics offers several advantages for stockholders.

The prediction helps coaches find an extremely talented athlete. In addition, the prediction assists athletes and coaches in developing an effective training plan. Moreover, it also helps them to have a better understanding of their opponents and find ways to beat them.

The Analysis Methods of Big Data in Sports


When it comes to the adoption of big data in sports, sports organizations must not only have accurate and high-quality data. They must also employ the best analysis methods in analyzing the data. Below are some analysis methods in big data analytics in sports.

1. Sports Social Network Analysis

The sports social network analysis tells the connecting patterns in team sports. Sports organizations have used the analysis method since 2016, particularly during the European Football Championship.

This analysis requires the use of some technologies, such as machine learning and predictive analysis. In addition, the type of data analyzed in this method is mostly non-numerical data, such as social media posts.

2. Statistical Analysis

Several industries have applied statistical analysis in their big data analysis process. Sports organizations commonly use this analysis method to interpret the statistical features of sports datasets, such as minimum and maximum value, mean, and variance.

In performing this analysis method, coaches can explore athletes’ movement patterns. Using this analysis will allow them to develop effective training plans.

3. Player Analysis

In this analysis method, players track their datasets and analyze them on their own. The datasets must be about their performance, training, diet, and biometrics.

An unsatisfying performance may indicate a lack of training, a bad diet, or bad health conditions. Therefore, players can modify their training and diet to improve their performance.

4. Team Analysis

Players must adopt team analysis when they play as a team. This analysis method allows them to analyze their performance as a team.

Coaches may need to perform this analysis too. It will help them find the best lineup for the team. Therefore, coaches can create a winning team accurately.

5. Fan Analysis

Fan analysis is beneficial for sports event management. It allows them to identify what fans want. Therefore, they can offer what they want during a sports event.

This analysis also helps them create a more effective marketing strategy. Consequently, the management can gain more profit from the events at reduced costs.

Big Data Analytics Tools in Sports


Big data analytics tools come in many choices. Therefore, sports organizations can choose the most adequate tools based on their big data analytics needs.

1. Cloud-Based Data Collection Platform

Coaches, players, and sports organizations can use a cloud-based platform to collect sports data. Different cloud-based data collection platforms offer different services and features.

However, most of them will help you collect match performance data. Moreover, the platforms also collect players’ fitness data that are essential in improving their performance.

2. Sports Management Analytics

Sports management analytics is another cloud-based platform. This analytics tool is perfect for coaches who create enhanced routines for training, simplify operations, engage fans, and many more.

3. Game Scoring Applications

Game scoring applications are more suitable for small-league coaches. With these applications, they can collect data related to game scores and players’ statistics. Game scoring applications allow coaches and parents to keep track of the players’ performance.

4. Team Management Tool

The team management tool is another great tool for small leagues or clubs. This tool has varied useful tools that allow coaches to observe players, sync the team’s calendar, gather payments, and send invoices.

Big Data in Sports Betting


Many sports fans love to place a bet on their team. Many of them win the bet successfully, but others are not. Those who win, generally analyze the team’s last performance before they placed the bets.

Some of them even use data science or big data analytics to analyze the performance of their favorite team and get a prediction of the team’s performance. Consequently, they can place sports betting that has a higher chance of winning.

Big Data in Sports: Challenges


Big data application in sports does not always go smoothly. Sports organizations may need to face some challenges in this application.

Inadequate Systems

Small sports organizations do not have adequate systems to handle the vast amount of sports data that grows at a fast rate. Consequently, they cannot stream digital information smoothly into their systems.

In addition, the inadequate systems may not be able to work with the analytics tools efficiently. As a result, sports organizations cannot gain big data analysis as quickly as they want. The inadequate system is a big obstacle for big data analytics that needs advanced tools and systems.

Data Privacy and Security

Many platforms for big data in sports are cloud-based. Therefore, it is not 100% safe to save the data on these platforms. The risk of leaked data is quite high unless sports organizations invest a large amount of money to build a secured and private platform.


In long term, big data can help sports organizations improve their performance at lower costs. However, big data adoption requires a large investment at the front. Unfortunately, this investment is not affordable for many sports organizations.

The sports sector generates an enormous amount of digital data every day. It is what people call big data in sports. Digital information is highly beneficial for athletes, fans, coaches, and teams. Sports big data analysis can improve athletes’ performance.

Moreover, it allows coaches to build the best lineup and create data-driven training plans. However, due to its volume, velocity, and variety, the sectors need specific systems and tools to use the data for sports organizations’ benefits.

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