Big Data In Sports

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. In terms of sports, Big Data uses large and complex data sets for the analysis and management of sports performance and operations. The use of Big Data technology allows for the collection and analysis of a wide range of data, such as, player performance statistics, team strategies, and even weather conditions. Through this data, athletes, coach, and sport organisations, can find ways to help improve player performance, for example shooting accuracy in Basketball, optimise team strategies, for example, how to line up during a soccer game, and improve fan engagement. Today, Big data is currently being used in a wide range of sports, including football, basketball, baseball, and soccer. Some of the businesses which are promoting the use of Big Data in sports are SAP, Oracle, and IBM, as well as sports organisations such as the NFL, NBA and FIFA.

Data Collection From An Athlete

Type Of Data Used in Sport Analytics

As we are aware, Big Data in sports consists of the collection and analysis of large and complex sets of data. The data collected by this technology can be broken down into two categories:

1. On-field data analytics:This area involves tracking key on-field data metrics to influence methodologies that may be used to improve in-game strategies, nutrition plans, and other vital areas that could ethically boost athletes' performance levels. On-field data analytics, can be broken down into different types of data:

1. Player Performance data: This data tracks speed, distance covered, and accuracy of a player in a a given sport during games or practise sessions.
2. Team Performance data: This includes data on team strategies, such as formations in soccer or football, or even when a team should be in attack or defence, in any given sport.
3. Environmental Data: This includes data on weather conditions, field conditions, and other environmental factors that can impact performance of a team or player. Sometimes, coaches plan their starting team based on the weather as some players are better suited for different weather conditions.

2. Off-field data analytics: This area involves monitoring important off-field data metrics such as ticket sales, merchandise sales, fan engagement, etc. This type of data analytics seeks to assist decision-makers in sporting teams make better decisions directed toward increased growth and profitability. The most important of this data is that which is related to the fans of a sport. This could be the number of tickets a team sold, the number of followers a team has on social media, and merchandise sales. All this data is used to see whether a team is growing in popularity or not. Popularity is often linked with how well a team is doing.

With the use of Big Data in Sports, companies can improve human resources practices and customer relationship management by using astute data analysis in sports. Teams and associations can make key decisions about their core products and services to help improve the experience for customers and maximise revenue.

Data Sources

In order for teams and sports organisations to carry out Big Data analysis, the data initially needs to be collected. Collection of this data comes from a variety of sources:

Sensor Data

Data Collection Using A Sensor

This includes data which is collected through wearable sensors to help track a player's movements and performance. In the sporting context, inertial sensors are made up of an accelerometer to measure force and acceleration, a gyroscope to give an indication of rotation, and a magnetometer to measure body orientation. This data can also be used to help prevent injuries, as sensors help warn of out of bound results, and help coaches decide training plans for players.

Video Footage Data

This data is all the footage which is collected by broadcasting channels, teams media, and video replays, and this data can be used to help improve player performance, make quick game decisions, and analyse player movements. Sports video analysis is the use of video to analyse the performance of players. It uses the latest technology to help players improve their skills and have an advantage over the opponent. Video footage also helps give interested parties better insight into performance as there is the ability to slow down camera speed to allow for better capture of a player's movements.

Social Media and Customer Data

This data is collected from social media platforms, such as Instagram, Twitter, Facebook, etc, which we see more and more teams within the sports world creating accounts in. With more people spending more time online, fans take to social media to engage in sports-related topics and content. Also, organisations are building creative ways to promote their sports teams and encourage sports conversation. From live-filming games to sharing memes and trash-talking, people no longer just watch sports. They actively engage with them. Social media data helps predict customer data, which is all about how fans interact with the clubs in terms of money by seeing whether fans would be willing to buy club merchandise or tickets to a game.

Publicly Available Data

This data is spread out all across the internet in different forms, such as databases, video footage,articles, and is accessible to anyone wanting to view it. This data can be used by different parties for their own benefits, such as gamblers may use it to make bets on a game or organisations may use it to decide who to give sponsorship titles to. Clubs may also use this information to help prepare their team for a game against another. NumberFire uses public data sources and predictive modelling algorithms to provide projections that are used by fantasy sports fans.

These data sources are used in a joint manner to help provide valuable analysis by providing insights into player performance, fan behaviour and also make game decisions to help a team have their best probability of a win while improving the sports industry.

Methods and Techniques:

In order for Big Data to be useful for teams, managers and organisations, different methods and techniques have to be used in order to help in the collection, storage and analysis of data. These methods and techniques include, but are not limited to, Data warehousing, Data mining, Data Visualisation, Predictive modelling, and cloud computing.

Data Warehousing

This method involves collection and storing of large amounts of data, required for Big Data analysis, in a central location, where the data can be easily accessed and analysed. Currently, the International Olympic Committee, Association for Summer Olympic Federation, and the Olympic Channel are working on a project referred to as the Sports Data Warehouse. The goal of this project is to create a unique repository of information where historical results, live results data, athlete biographies calendar data and other information are aggregated, verified and organised in a meaningful way. Primarily this will increase the ease of access to this information from and to Federations, Athletes, Organising Committees or other stakeholders of the Olympic Movement.

Data Mining

This is a process of extracting useful information and making meaningful insights from large datasets through Machine learning and algorithms. Using Data Mining in Sports can result in better team performance by matching players to certain situations, identifying individual player contribution, evaluating the tendencies of opposition, and exploiting any weaknesses. This can be useful for interested parties, such as managers and sports organisations, as they could use this information to help better team performance and boost fan growth.

Data Visualisation

This method involves the graphical representation, such as charts and graphs of data, in order for it to be easier understood by a more general audience and to better help in analysis. Data visualisation tools are providing appropriate performance analytics of potential players according to the team’s mentality and culture. The sports arena has received massive success by adopting this modern technique in the data-driven world. Recently, it is one of the modern winning strategies of popular coaches in the sports industry. The coach can predict and plan strategies through data visualisation but it is unpredictable to the opponent The data is stored in a centralised location which is usually on the cloud. Cloud computing is used to store the data to help ease access and provide faster analytics which in turn allows faster data visualisations to be created.

Predictive Modelling

This method involves using the available input of data to make future predictions by statistical methods, such as linear regression, or by looking for patterns within the data. Predictive modelling is mainly used in sports by managers and organisations to help predict an outcome based on historical data. This also applies to gamblers. Trends in sport data can be identified and manipulated for personal, competitive, or economic advantages. The motivation for doing so has led to many sport-specific developments such as statistical simulations and sport-independent techniques in machine learning. These models are mainly used to see win/loss probabilities and others. The above methods and techniques are used within the sports field to help improve team performance, player performance, and help coaches make better decisions. The methods help gain insights from the data and then provide the interested parties with an outcome to allow them to make better decisions.

Ethical Issues

The use of big data in sports does come alongside a number of ethical issues within the field of Information and Technology which is up for debate within players, teams and businesses who make use of Big Data. Some of these key ethical issues include:

Concerns On Data Privacy

The collection of an athlete’s information relates to privacy for the athlete and their extended families that may share many of the relevant physiological and genetic data that were collected. This raises concerns on the security and privacy of the data being collected. The athletes would like to limit who has access to their personal data and would also want to ensure that their data has been collected and stored in a way that they deem fit and secure.

Concerns On Bias and discrimination

Algorithms need to account for real-world noise to avoid bias. Algorithmic bias describes systematic and repeatable errors in a computer system that create "unfair" outcomes, such as "privileging" one category over another in ways different from the intended function of the algorithm. When we relate this to Big Data in sports, it raises concerns as to whether the algorithms being used are favoured to players of a singular race. It would be important to ensure that the algorithms are free from bias and provide analysis in a way that benefits all the athletes.

Concerns On Performance Manipulation

The opportunity to opt not so much for the best athlete as for the right athlete, therefore, the professional who needs to be deployed in a given context and time, can prove to be a decisive aspect. With Big Data being so good at providing details for all athletes and giving data which coaching staff would find helpful, a risk is imposed. This is the risk of some players being left out. Ofcourse, the coaching staff want the best for the team, and that comes with the best athletes and Big Data will provide the best feedback for the better athletes and leave out the beginners. There needs to be a system developed where we can help players grow together without leaving anyone behind.

Concerns On Fairness in competition

Data analytics could be seen as a clear competitive advantage, allowing those players and teams that can afford it to play better and smarter. This is similar to being rich, if you have the money you can afford much more stuff than the less fortunate. You see similar things in sports as not all sport teams earn the same, there is a varying amount of pay from the top teams to the ones who end at the bottom. So, the teams at the top are going to invest in Big Data analytics which would see them gain an even further advantage than the teams at the bottom. A solution needs to be introduced, such as a budget cap, for teams in a sport so that there is fairness within the sport else it could become boring to watch.

The ethical issues brought upon the use of Big Data in sports could have negative effects on players and teams, hence why they need to be addressed. It is important that sporting organisations lay out a set of rules and regulations to ensure that a players personal data is protected and that the use of Big Data is not abused and it is actually implemented in a fair, transparent, and ethical manner.

Examples Of Big Data In Sports

Big Data has already earned its name within the sports field meaning that several teams have already been using it in different sports. A couple of examples include:

In Soccer, English Premier League club Manchester City works with data analysis provider SAP to implement a variety of cloud-based solutions to simplify its worldwide operations, scale its business, increase productivity and enhance its fans' experience. SAP Challenger Insights helps Manchester City prepare for matches by providing data-driven insights surrounding an opponent’s tactics, such as their offensive and defensive formations.

In the National Football League, the Seattle Seahawks are using Microsoft’s hardware, the surface tablet, to help strategize their games. Microsoft's software — specifically, Microsoft Azure and Power BI — is also increasingly making an impact for top professional teams like the Seattle Seahawks, which use the company’s latest machine learning and predictive analytics technology to help predict and prevent injury.

In the National Basketball Association, the Miami Heat used big data to analyse fan behaviour and improve fan engagement. Data collected are used to help the Heat understand sports fans and formulate the best ways to cater to their needs. To illustrate, an understanding of a fan’s preference in gate entrance, assembling at a certain point after grabbing a hot dog, the sales of food and merchandise. Through this data collection, the Miami heat is able to predict sales of merchandise and food.

With the rise of interest in Big Data in Sports, the future seems to be bright for this growing field as well as the field of sports. The use of Big Data has created several exciting trends and developments, such as Machine learning, which has already been able to help improve player performance, improve and monitor fan interaction for teams, and allow sporting organisations to create better marketing strategies. The future of BIg Data holds the ability to change the sports industry and provide a wealth of benefits as long as it is used in an ethical manner.