Big Data in Sports

Overview

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 application of big data in sports has changed the way teams, coaches and sports organizations make decisions, track performance of players and engage with fans. The technology allows for the collection and analysis of a wide range of data, which includes but is not limited to player performance statistics, team strategies, and even weather conditions. The in depth analysis, through the help of the data, can be used to improve player performance, prevent injuries, and optimize team strategies. 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 organizations 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 needed 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 includes data on speed, distance, and accuracy of a player in any given sport.
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: It 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.

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:

1. 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.
2. 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 favored 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.
3. 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. Of course, 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.
4. 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 seems to have some negative effects on players and teams, hence why they need to be addressed. It is important that going forward there are set of rules and regulations adopted within a sporting organisation to help support a players right with their personal data and that Big Data is used in a fair, transparent, and ethical manner.

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