Adding Value to Sports Data: Opportunities, Optimizations and Controversies (2023)

Twenty years after the "Moneyball" era of American baseball, data has become the new gold in every sport. Fans of any sport will avidly study game, competition and individual performance statistics. But who owns the data, and are players and athletes at a disadvantage when it comes to trading and using it?

This is an increasingly controversial issue and isLondon International Litigation Week (LIDW) 2023It will be explored by our panel of experts who will consider why sport is so focused on data to drive decision-making, and the benefits to athletes and athletes if data is used correctly.

On May 17, 2023, sports and martial arts teams will be suspendedpick me upILinklatersPanelists will join them, includingAnya Proops KC(leading privacy and data protection silk from 11 King's Bench Walk),Jordan Gardner(21 Group's M&A and investment advisor),Spencer Nolan(Former Head of Media Rights, M&A and Partnerships, Nielsen Sport) ijeremy steele(CEO Analytics FC) as this promises to be an active group discussing various points of view.

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If you're interested in learning more about sports stats and litigation, please reply to the group event atMay 17, 2023by clickingshe.

For now, though, you can find a trailer of what to expect below...

1. type of data

Sports can generate massive amounts of data, and disagreements are starting to emerge about who "owns" the data. There are two broad categories: (a) 'match data' which refers to the actual outcome of a game, such as the score at the end of the game; (b) 'performance data' which refers to statistics and details about how teams and individual players play , including everything from heart rate to video clips.

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The two categories can also become blurred, making it harder to describe who actually "owns" (or, more accurately, "controls") the data.

2. Is performance data personal data?

In the UK, the main sources of personal data protection are:General Data Protection Regulation('General Data Protection Regulation’) and the Data Protection Act 2018 (UK law commonly referred to as “UK GDPR”). Article 4 of the GDPR generally defines "personal data" to include any information relating to a natural person (a) who is or can be identified directly from the data concerned; (b) who can be indirectly identified from that information in combination with other information. This term is broad enough to cover many aspects of the data collected during a game.

Performance data is used for many purposes on and off the field, including (but not limited to):

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  • Studying Opponents: A recent example in football is the recent past. Real Madrid coach Ancelotti said in an interview after the gamespokeOn how his side were able to use statistics from Chelsea's recent games to notice that their goalkeeper had a habit of standing outside his line at kick-offs.
  • Gaming and betting: For example, performance data is used to calculate odds and provide information (including real-time information) to bettors.
  • Player Trading: This is a potential source of revenue if done right, and it's the process of buying and selling players. Data analysis can identify trends in player performance or physical attributes that prompt clubs to buy or sell.
  • Acquisition strategy: Investors can use this data to identify suitable clubs and franchises to buy, especially in multi-club/multi-sport ownership models. The analysis can identify teams with outstanding youth academies and clubs whose team profiles can fit into an investor's strategy. Analysis can also predict possible closing positions and related income.
3. player information

A small number of players suggested that they should have more power and control over their performance data.

Players have also claimed that their likeness rights have been used without their permission in sports video games, such as EA's long-running FIFA series.

This development led player interest groups such as FIFPRO (the World Professional Football Association) to decide to developCharter of Player Data RightsAgreement with FIFA. The charter lays out some of the rights it believes players should have over their data. This is to allow players to access, transfer and control the use of sensitive data.

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4. Data exclusivity and gambling disputes

A related question to the statement about who owns the data is who has the right to collect and distribute the data. The most lucrative of these is the "speed betting" data marketplace, which refers to live performance data used by bookmakers and bettors. This data can be collected electronically, but is usually gathered through scouts who watch games live. To protect these exclusive rights, sports organizations often include terms and conditions on venue tickets to prevent others from collecting this information.

Three cases emerged in this area:

  • International Intelligence Service/TRP: An important case on this topic is The Racing Partnership ('Transnational Radical Party') and sports information services ('Information system"), which is equivalent toAppeldom stole. TRP accused SIS of illegally obtaining and distributing Quick Betting data from another organization that sent scouts to games. This allegedly violated the exclusivity contract TRP had with the track owner. The appeals court found: (a) upholding SIS' conclusion that they did not breach confidentiality by receiving data from another group; and (b) against SIS, which was found liable for abusing illegal means. The dispute eventually ended in a settlement before any order from the Supreme Court.
  • Smart Sport/Sportradar: In football, Genius Sports and Sportradar have had a similar debate over Premier League, EFL and SPFL data. Genius Sports has an exclusive data rights contract in these leagues, having signed a contract with Football DataCo, the legal entity set up by the league to control data rights. Sportradar filed a complaint at the Competition Appeals Court alleging that the exclusivity agreement breached competition law. This led Genius Sports to file a countersuit, alleging that Sportradar sent scouts to games and illegally collected data in violation of Genius Sports' exclusive license. The two parties settled the claim late last year. The terms of the deal allow Genius Sports to retain exclusivity to the Quick Betting data for these leagues. However, Sportradar has been granted permission to provide a secondary source for this data, which will be sub-licensed by Genius Sports. In return, Sportradar will stop sending scouts to games.
  • IMG Arena/Stat Exec: IMG Arena has reportedly filed a lawsuit against Stats Perform in the High Court. IMG Arena has the exclusive right to offer fast betting information on the "European Leagues", a collection of football leagues in 19 countries. IMG Arena claimed that Stats Perform violated the terms of the contract by sending scouts to games for which it had an exclusive license. If the case goes to trial, it could shed more light on who has the right to collect sports data (and under what circumstances).

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Why are databases very valuable in the sports industry? ›

This includes the ability to retrieve previous data for comparison with new performance and the use of data to highlight issues for deliberation. Databases are also useful as multimedia repositories of sports information.

How can data improve performance in sport? ›

Other statistics may be collected to investigate an individual player's performance, such as successful dribbles, passes, and interceptions. Analysis of this data provides coaches and players with greater insight into the strengths and weaknesses of their game, which is useful for their development.

What are the disadvantages of data analytics in sports? ›

Disadvantages of the system

The data's predictions can create an imbalance in the team, and it can further be difficult to handle the situations. It does not always depend on the numbers. A player's mental or emotional state also matters when these transfers are made, which cannot be analyzed in systems.

What are the benefits of big data in sports? ›

b) Analyzing team performance: Big data analyzes the various factors of the players that give an insight to the players to analyze their own strengths and weaknesses as well as that of the competitors thereby improving the team's performance.

What is the biggest challenge facing the sports industry overall? ›

Major issues in the Sport Industry
  • Sports Doping and the adverse publicity it causes.
  • The management of risks, particularly injury or death, associated with sport participation.
  • Child protection e.g. sexual misconduct of coaches and officials involving children.
  • Hooliganism in some sports and some countries of the world.

Why is data and database important in sports and entertainment? ›

Data is an important part of the sports industry for players, coaches, management, sports medicine workers and fans. Not only can data analytics help teams win games, these statistics can also help improve player performance, prevent injuries and encourage fans to attend games.

How does data analysis improve performance? ›

Professionals can use data analytics to improve business performance by helping organizations liberate data, identify and understand patterns, and leverage findings in real-world business applications.

What are the 3 V's of big data? ›

Dubbed the three Vs; volume, velocity, and variety, these are key to understanding how we can measure big data and just how very different 'big data' is to old fashioned data. Find out more about the 3vs of Big Data at Big Data LDN, the UK's leading data conference & exhibition for your entire data team.

What is the biggest challenges in data analytics? ›

Competencies are the biggest barriers when it comes to generating meaningful insights using the big data. Lack of structured data engineering methodologies is the most technical barrier in deriving insights.

What are 4 reasons or challenges that can cause data analytics to fail? ›

  • Common Pitfalls.
  • Pitfall #1: Lack of a comprehensive plan.
  • Pitfall #2: Lack of quick outcomes.
  • Pitfall #3: Poor or infrequent presentations.
  • Pitfall #4: Lack of talent.
Nov 1, 2021

What are the potential issues with data analytics? ›

5 Challenges in Implementing Big Data Analytics Based on Our Client's Experience
  • Inability to define user requirements properly.
  • Carrying out system changes without considering the impact on data of other departments.
  • Lack of a unified corporate picture.
  • Collecting meaningful data to the agreed standard.
Jan 9, 2023

What are the pros and cons of big data? ›

If a company uses big data to its advantage, it can be a major boon for them and help them outperform its competitors. Advantages include improved decision making, reduced costs, increased productivity and enhanced customer service. Disadvantages include cybersecurity risks, talent gaps and compliance complications.

Why do sports need external data? ›

When coupled with good analysis, these online data sources can provide teams with a real-time understanding of fan sentiments, competing teams' strategies and the impacts that players can have on viewership.

What are 3 issues in sports today? ›

Contemporary Issues
  • Doping.
  • Gender Issues.
  • Legal.
  • Politics.
  • Sociology.

What are three major issues in sport? ›

Issues in Sport
  • Developmental athletes over-compete and under-train.
  • Adult training and competition programs are imposed on developing athletes.
  • Training and competition formats designed for male athletes are imposed on females.
  • Preparation is geared to winning in the short-term, not long-term development.

What are four trends that impact the future of sport marketing? ›

Summary on sports marketing trends

Artificial intelligence, eSports, diversity and sustainability are important trends in the sports industry - and they also demand a fundamental change in sports marketing.

What are the 2 major areas of data in sports? ›

There are two key aspects of sports analytics—on-field and off-field analytics.

Why is data and data management important? ›

Data management helps minimize potential errors by establishing processes and policies for usage and building trust in the data being used to make decisions across your organization. With reliable, up-to-date data, companies can respond more efficiently to market changes and customer needs.

What is the importance of data and database? ›

Databases support good data access because: Large volumes of data can be stored in one place. Multiple users can read and modify the data at the same time. Databases are searchable and sortable, so the data you need can be found quick and easily.

What are 3 benefits of data analysis? ›

Data analytics helps businesses get real-time insights about sales, marketing, finance, product development, and more. It allows teams within businesses to collaborate and achieve better results. It is useful for businesses to analyse past business performance and optimize future business processes.

What are the five importance of data? ›

Those five areas are (in no particular order of importance); 1) decision-making, 2) problem solving, 3) understanding, 4) improving processes, and 5) understanding customers.

What is the most important key benefit of data analysis? ›

Data analytics techniques enable a business to take raw data and uncover patterns to extract valuable insights. As a result, data analysis helps companies make informed decisions, create a more effective marketing strategy, improve customer experience, streamline operations, among many other things.

What are the 7 V's of big data? ›

After addressing volume, velocity, variety, variability, veracity, and visualization — which takes a lot of time, effort, and resources —, you want to be sure your organization is getting value from the data.

What is value in big data? ›

The last V in the 5 V's of big data is value. This refers to the value that big data can provide, and it relates directly to what organizations can do with that collected data.

What makes a good data model? ›

The writer goes on to define the four criteria of a good data model: “ (1) Data in a good model can be easily consumed. (2) Large data changes in a good model are scalable. (3) A good model provides predictable performance. (4)A good model can adapt to changes in requirements, but not at the expense of 1-3.”

What is the most complex part of data analysis? ›

Prescriptive analytics is, without doubt, the most complex type of analysis, involving algorithms, machine learning, statistical methods, and computational modeling procedures. Essentially, a prescriptive model considers all the possible decision patterns or pathways a company might take, and their likely outcomes.

What are the 6 problem types in data analysis? ›

There are six common problem types in data analysis. These can be identified as making predictions, categorising things, identifying themes, finding patterns, spotting something unusual and discovering connections (Ximena et al.).

What are the 3 technical challenges to handling big data? ›

But, there are some challenges of Big Data encountered by companies. These include data quality, storage, lack of data science professionals, validating data, and accumulating data from different sources.

Will big data lost its popularity? ›

Big Data's popularity is at its peak and it has shown no signs of slowing down yet. According to Forbes – “The Hadoop market will reach almost $99B by 2022 at CAGR of around 42%.” According to Peer Research: “More than 77% of organizations consider Big Data as their top priority.”

What is the most challenging part in data collection? ›

Some of the challenges often faced when collecting data include the following:
  • Data quality issues. Raw data typically includes errors, inconsistencies and other issues. ...
  • Finding relevant data. ...
  • Deciding what data to collect. ...
  • Dealing with big data. ...
  • Low response and other research issues.

Why data analytics fails? ›

Wrong questions get asked

This is by far the most common cause for data project failure and the reason is simple: organisational leaders aren't data experts. They do not understand the highly technical language of analytics, they often don't know what data they need to answer their business questions.

How do you solve data analytics problems? ›

  1. What types of questions can data science answer? “Data science and statistics are not magic. ...
  2. Step 1: Define the problem. First, it's necessary to accurately define the data problem that is to be solved. ...
  3. Step 2: Decide on an approach. ...
  4. Step 3: Collect data. ...
  5. Step 4: Analyze data. ...
  6. Step 5: Interpret results. ...
  7. Conclusion.

What are the four types of data analytical method? ›

Four main types of data analytics
  • Predictive data analytics. Predictive analytics may be the most commonly used category of data analytics. ...
  • Prescriptive data analytics. ...
  • Diagnostic data analytics. ...
  • Descriptive data analytics.

What are data collection strategies? ›

Types of quantitative and qualitative data collection methods include surveys and questionnaires, focus groups, interviews, and observations and progress tracking.

What are the disadvantages of predictive analytics? ›

Drawbacks and Criticism of Predictive Analytics

Even if a company has sufficient data, critics argue that computers and algorithms fail to consider variables—from changing weather to moods to relationships—that might influence customer-purchasing patterns when anticipating human behavior.

What are the four C's of big data? ›

Specifically, we found that the connection between big data and big process revolved around the 'Four Cs'.” Those four Cs are customers, chaos, context, and cloud.

What are the 4 A's of big data? ›

Big Data analysis currently splits into four steps: Acquisition or Access, Assembly or Organization, Analyze and Action or Decision. Thus, these steps are mentioned as the “4 A's”.

What is the drawback of big data? ›

Drawbacks or disadvantages of Big Data

Big data analysis violates principles of privacy. ➨It can be used for manipulation of customer records. ➨It may increase social stratification. ➨Big data analysis is not useful in short run.

What types of data can be collected in sports? ›

This can include any information acquired as the game is playing out. Event data could relate to player actions, such as: The number of shots, passes, crosses, challenges, corners, forwards, offsides, or deflections, etc.

What can you do with sports data? ›

As a sports data analyst, your job is to collect and monitor the statistics for an athlete, game, or team. As part of this analysis, you may use an algorithm to help predict future performance, help apply the information to business decisions, or provide stats and feedback for announcers to mention during a game.

Why a database is very useful? ›

Databases support good data access because: Large volumes of data can be stored in one place. Multiple users can read and modify the data at the same time. Databases are searchable and sortable, so the data you need can be found quick and easily.

Why are databases so important? ›

Why are Databases Important? A database system stores essential data about a business: the data, when analyzed, becomes valuable information about a company and helps in the decision-making process.

What are the major benefits of database systems? ›

A database management system helps improve organizational security, integration, compliance, and performance.
  • Improved data sharing and data security. ...
  • Effective data integration. ...
  • Consistent, reliable data. ...
  • Data that complies with privacy regulations. ...
  • Increased productivity. ...
  • Better decision-making.
Feb 17, 2022

What is the main advantage of using a database? ›

Advantages of the Database Management System

The data is stored in a neater way and hence, more data can be stored. A DBMS is a highly secure platform so confidential and high-risk data can also be stored and accessed, securely. DBMS makes handling of data very simple.

What are the 5 purposes of database systems? ›

The functions of a DBMS include concurrency, security, backup and recovery, integrity and data descriptions.

What are the advantages and disadvantages of database? ›

Comparison Table for Advantages And Disadvantages Of Database
Additional information can be derived from same dataMultiuser DBMS can be more expensive
Database improves securityPerformance can be poor sometimes
It is cost-efficientDamage to database affects virtually all applications programs
2 more rows
Feb 15, 2022

What is the most important thing in database? ›

What Are the Most Important Elements of Databases?
  • Tables, records and fields must be connected. ...
  • The ability to support a broad variety of data so that it can be aggregated, analyzed, and reports generated.
Feb 7, 2016

What is the greatest value of the database to an organization? ›

Business needs databases to track all business transactions. Also, it will ensure the performance of the business more efficiently. It's actually a growth formula for your business.

What is the value of having a database? ›

Database benefits

analyse data in a variety of ways. promote a disciplined approach to data management. turn disparate information into a valuable resource. improve the quality and consistency of information.

What is the value of having a database versus not having one? ›

First, it allows for data sharing among employees and others who have access to the system. Second, it gives users the ability to generate more information from a given amount of data than would be possible without the integration.

What are the limitations of database system? ›

Limitations of Database Management System
  • More Costly. Creating and managing a database is quite costly. ...
  • High Complexity. ...
  • Database handling staff required. ...
  • Database Failure. ...
  • High Hardware Cost. ...
  • Huge Size. ...
  • Upgradation Costs. ...
  • Cost of Data Conversion.
Jun 19, 2020

What problems do database management systems solve? ›

DBMS provides the ability to control users and enforce policies for security and compliance management. This controlled user access increases the database security and makes the data less vulnerable to security breaches.

What is a primary key in a database? ›

A primary key is the column or columns that contain values that uniquely identify each row in a table. A database table must have a primary key for Optim to insert, update, restore, or delete data from a database table.


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