Big data has unprecedentedly influenced the development of the sports industry. The closely relevant services, such as big data services, could facilitate athletes in everyday training and game strategies to a great extent, hence becoming an indispensable method for winning in competitions. Advanced big data technology has made changes within the realm of sports. The proliferation of sports data has created a different set of opportunities and challenges around the field of big sports data. Sports big data is a product of the development of the Internet and sports.

Big Data management in sport

Big sports data management is largely concerned with techniques, tools, and platforms dealing with the same. Large data sets, however, are not easy to manage. The main goals of big data in sports are extracting the potential values of big sports data and improving quality and availability of data in decision-making.

Data collection in sports

An especially important characteristic of sports big data is their diversity. While sources themselves are extremely broad, types of data also become sophisticated. The development of the Internet of Things, the internet, and sports industries has given a lot of enrichment to the data in sports. Since network data is diverse in content and complex in composition, with many means of utilization, it is very hard to gather big datasets about online sports. Big data in online sports is usually gathered through indexing robots.

In general, the data collection for the crawler involves the following six aspects: 

• website analysis,
• extracting links,
• link filtering,
• content extraction,
• URL queuing
• data search

Currently, large dataset management applications are highly required but still afflicted with deficiencies and security problems. Large sports dataset collection is one of the pre-requisites or key tasks in data processing. Moreover, secure collection of large sports datasets is an essential step to all kinds of data applications which can provide results of large data analysis. Since suspicious data sources enable data collection to investigate a variety of malicious attacks, a secure methodology for collecting large sports datasets is required for different data applications. In this line, effective frameworks have been proposed based on blockchain and reinforcement learning to overcome these issues.

Big data labeling in sports

After collecting adequate sports data, the subsequent stages in processing create labeling for individual examples. More often than not, data collection goes in tandem with data labeling. When crawling information from the internet for the construction of the knowledge base, information taken is assumed to be true and hence marked as true by default.

Data labels fall into three categories:

• Existing labels. These existing labels can be used to learn from them to predict other labels.
• Crowd driven. Recently, many crowdsourcing techniques can be used to help gamers make their labeling more effective.
• Poor labels. While it is desirable to generate correct labels all the time, this implementation process can be very expensive. Weak label is an alternative method that is used in many applications as labeled data.

Improvement of existing data

Technology in machine learning can be used to deal with noisy data and uncorrected labels. There are many publications on the improvement of data quality. Based on the quality rules, value relationships, and reference data, the cleaning system builds a probabilistic model that captures how data was generated. Other data cleaning tools have also been developed to transform raw data into a better form for further analysis. The other way of obtaining quality labels from data is by improving the existing labels. They studied how the quality of data is improved (or degraded) by repetitive labeling and focused on enhancing the quality of the training labels used for supervised induction.

Big Data analysis methods in sport

Big data analytics is a technique that allows one to quickly extract valuable insight from different kinds of data. Analytics techniques in big data are capable of leveraging different algorithms calculating big data statistically to draw important analytics data and meet real needs.

Below we present the methods of Big Data analysis in sport:

• Statistical analysis

Under the statistical theory, he proposed a statistical analysis technique belonging to the applied mathematics branch. Statistical analysis can provide conclusions for large datasets. The statistical analysis technique is used to process sports datasets in sports industry research. Some statistical characteristics of sports datasets, like mean, variance, entropy, and maximum/minimum value, can be used in analyzing an athlete’s movement pattern. This type of statistical analysis will enable coaches to present an effective training plan.

• Sports social network analysis

Research in sports social networks may highlight the relationship patterns of team sports. Social network analysis techniques are used to identify some variables connected with Twitter influence. In the positioning-based variables, a network approach identifies the contribution made by individual players in the outcome of teams’ behavior in a simulated match.

• Big data analytics services platform for sports

It is a big data analytics platform, storing and analyzing massive data to form a big data mining system.

Recently, to promote the development of big data analytics in the area of intelligent sports, more and more researchers pay attention to distributed intelligent sensing technology. On the basis of the sports big data platform, analyze the game relation between profit and consumption intention in the sports hall.

How sports analytics is changing the game

Data analytics has changed the game. It is central to helping team managers, coaches, and players ensure that they are prepared to win. Since preparation is the key to victory, professional teams take sports analytics seriously and collect as much data as possible to make sure that they are ahead of the competition.

Among the most important metrics that teams analyze before a match include :

• Opposing player stats, such as typical plays or setups and scoring types.

• Recent wins and losses, and how each player’s performance contributed to those games.

• The weather conditions on the day of the match and the experience of the players in these conditions.

• Game stats, including the number of games they need to win to make the playoffs or break previous records.

Professional sports teams are working hard to collect the right data to prepare for the games. There are many ways that players, teams and fans use statistics and data to improve their position.

Player analysis

Stats help in improving players’ performance by being kept and then reflected upon to see how one did in previous games. The created various types of stats in nutrition, training hours, and in-game scores, such as an athlete’s running speed, amount of weight lifted, and amount of protein consumed in a day.

By capturing data and then comparing that information with how a player felt physically on game day or how they performed, players can work on their training routines or diets to enhance their game. When all the players take the time to analyze their results and work to pinpoint flaws in their game, this analysis and subsequent changes help deter situations where organizations become the most disappointing teams in the league.

Team analysis

While every player will worry over his or her individual performance, it is also important to play together as one team to achieve victory. When the members of your team apply data analytics as a team, they will be able to review results together.

The coaches can try out the combinations of players to discover if you obtain improved stats with other lineups on the pitch. By applying data analytics, team managers will be able to develop machine learning techniques meant to uncover the winning combinations of players and strategies that are effective.

Fan analysis

Sport is a business, and the more engaged fans are, the more profit organisations make. Using online data analytics, sport management teams can discover how and when fans are likely to come forward to be part of an event or even buy merchandise.

Executives will look at social media, attendance, and even merchandise sales to better understand what consumers are expecting from the game. This allows them to define what’s important to the fans. They manage facilities, so they’re able to provide and keep fans happy and coming back for more.

They can now design marketing strategies and advertisements with the fans in mind. The data enables them to identify quickly fans likely to engage with their team so that they do not spend their money to advertise consumers who are not interested in their sport.

Gambling in sports

In the event that players can analyze team performance from the past with accurate stats, it’s so much easier to tell when and where a team will finally succeed on-field.

Sports fans are more likely to gamble, for now they don’t have to blindly choose which team or player will perform well. Statistics allows them to develop a data-driven prediction method; bettors feel more confident betting on specific teams or players.

Athlete safety and data analysis

Athlete injuries can destroy a season or team performance. It is frustrating to coaches and negatively affects a player’s career when star players drop out due to an avoidable injury. Even as some injuries are not avoidable, data analysis helps players and the medical staff to understand when and how often injuries occur.

It can be used to allow the players to identify flaws in their form, hence being more careful when playing. Sports medicine people can also use the information to review how they have managed injuries, thus maximizing their success rate. They will, therefore, change treatment plans for specific injuries or players hoping to speed up recovery.

Scouting

This would normally involve the potential players being watched while practicing or playing in games. However, data visualizations and analyses with statistics about past performances are also an integral part of the recognition process. Not every promising college player can have a professional scout visit them in person. Stats help them decide who to visit, whom to watch, and when.

Data is an important part of the sports industry for players, coaches, managers, sports medicine professionals, and fans. Not only can data analytics help teams win games, but also these stats can help in improving player performance and prevent injuries, and even encourage fans to come out and watch games.