This dataset corresponds to actual data from the functioning of a sports facility and refers to all new users who signed up between June 1st 2014 and October 31st 2019. Demographic and service level agreement (SLA) data is collected by operators in the process of enrolling users in the activities they intend to practice. The data regarding the frequency of the sports facility and classes were obtained by the access control system where each user identifies himself with an RFID card to access the facilities on the days and times agreed in his SLA.

About Dataset – TL;DR

Comprehensive football (soccer) data lake from Transfermarkt, clean and structured for analysis and machine learning.

• 93,000+ players worldwide
• 2,200+ clubs across all major leagues
• 5.7M+ total records across 10 categories
• 902,000+ market valuations
• 1.9M+ player performance stats
• 1.2M+ player transfer histories
• 144,000+ injuries & 93,000+ national team appearances
• 1.3M+ teammate relationships

Everything in raw CSV format – perfect for EDA, ML, and advanced football analytics.

⚽ The Most Comprehensive Transfermarkt Football Dataset

A complete football data lake covering players, teams, transfers, performances, market values, injuries, and national team stats. Perfect for analysts, data scientists, researchers, and enthusiasts.

🗺 Entity-Relationship Overview

Here’s the high-level schema to help you understand the dataset structure:

https://i.imgur.com/WXLIx3L.png" alt="Transfermarkt Dataset ER Diagram">

📊 Key Coverage

• Players: 93,000+ professional players
• Teams: 2,200+ clubs, 7,700+ club relationships
• Data Volume: 5.7M+ total records
• Global Scope: Major leagues and competitions worldwide

🗂 Data Structure

Organized into 10 well-structured CSV categories:

Player Data (7 categories)

• Player Profiles
• Performances (matches, goals, assists, cards, minutes)
• Market Values (historical valuations)
• Transfer Histories
• Injury Records
• National Team Performances
• Teammate Networks

Team Data (3 categories)

• Team Details (club info)
• Competitions & Seasons
• Parent/Child Team Relations

🔗 What’s Inside?

• 902K+ market value records to track valuation trends
• 1.1M+ transfer histories with fees and movement
• 1.9M+ performance stats across seasons and competitions
• 144K+ injury records with days and matches missed
• 93K+ national team appearances
• 1.3M+ teammate relationships for chemistry analysis

💡 Why This Dataset?

Most football datasets are pre-processed and restrictive. This one is raw, rich, and flexible:

• Build custom KPIs and models
• Perform deep exploratory analysis (EDA)
• Train machine learning and prediction pipelines
• Combine with other football data sources

🚀 Example Use Cases

• Predictive Modeling – Player ratings, transfer value forecasts, injury risk
• Data Visualization & Dashboards – Club comparisons, performance analytics
• Scouting & Recruitment – Discover undervalued talent
• Network Analysis – Teammate relationships and synergy

🖥 Technical Details

• Format: CSV files, UTF-8 encoded
• Easy to Use: Ready for Python (pandas, numpy), R, SQL, BI tools
• Scalable: 5.7M+ rows for big-data analysis

💡 Working on a Cool Project?

I’m always excited to collaborate on innovative football data projects. If you’ve got an idea, let’s make it happen together!

📬 Contact Me

• GitHub: @salimt
• Issues: Feel free to use GitHub Issues if you’ve got dataset-specific questions.

⭐ Support & Visibility

If this dataset helps you:
- Upvote on Kaggle
- Star the GitHub repo
- Share with others in the football analytics community

Tags

football analytics soccer dataset transfermarkt sports analytics machine learning football research player statistics

🔥 Analyze football like never before. Your next AI or analytics project starts here.

🏆 Premier League Market Value Dataset (2025)

Welcome to my first real-world football dataset, scraped from Transfermarkt, containing detailed market value data for 499 Premier League players (2025).

📦 What This Dataset Provides

This dataset includes the following attributes for each player:

• 🧍‍♂️ Player Name
• 🧬 Age
• 🏟️ Club
• 🌍 Nationality
• 🧠 Position
• 💰 Current Market Value (in € millions)

Each field was carefully extracted and cleaned from public sources using custom Python scripts (available on GitHub below).

🔭 My Vision for This Dataset

This is just Phase 1. My goal is to:

• 📈 Add player form stats, contract data, and historical market values in future versions
• ⚽ Scrape other leagues (La Liga, Bundesliga, Serie A, etc.)
• 🤖 Build a machine learning model to predict future market values
• 📊 Enable research in sports analytics, scouting, and value forecasting

💡 Potential Use Cases

• Sports business & economics research
• Fantasy football value analysis
• ML model training (value prediction, clustering by position/value)
• Tableau / Power BI dashboards
• Scouting & recruitment simulations
• NLP + data fusion from other sources

📈 Update Frequency

• Dataset will be updated monthly
• Upcoming updates will include:
  • Player performance stats
  • Contract duration
  • Injury/transfer status
  • Form trend over time

Sports Data Monetization Market Outlook

According to our latest research, the global sports data monetization market size reached USD 3.72 billion in 2024, with robust growth driven by the surge in digital transformation across the sports industry. The market is projected to expand at a CAGR of 21.4% from 2025 to 2033, reaching an estimated USD 26.3 billion by 2033. This remarkable growth is primarily attributed to the increasing adoption of advanced analytics, the proliferation of digital platforms, and the rising demand for real-time sports data by broadcasters, betting companies, and fan engagement platforms worldwide.

One of the key growth factors propelling the sports data monetization market is the escalating integration of technology in sports. The deployment of IoT devices, wearables, and advanced video analytics has enabled the collection of granular data on players, teams, and events. This data, when processed and analyzed, provides actionable insights that are highly valuable for performance analytics, media broadcasting, and fantasy sports. The ability to monetize these insights through various channels, such as real-time statistics for broadcasters or predictive analytics for betting companies, has opened up substantial revenue streams. Moreover, the rise in demand for personalized content and immersive experiences has compelled sports organizations to invest in sophisticated data platforms, further accelerating market growth.

Another significant growth driver is the evolving landscape of fan engagement. As sports fans increasingly seek interactive and personalized experiences, organizations are leveraging data analytics to enhance fan interaction both online and offline. This includes the use of mobile applications, social media, and virtual reality platforms that utilize real-time data to deliver tailored content, live statistics, and predictive insights. The monetization of fan data, through targeted advertising and exclusive digital offerings, has become a lucrative avenue for sports entities, sponsors, and media companies. This shift towards data-driven fan engagement is fostering innovation and creating new business models in the sports ecosystem.

The expansion of sports betting and fantasy sports platforms is also fueling the growth of the sports data monetization market. With the legalization of sports betting in several countries and the global popularity of fantasy leagues, there is a soaring demand for accurate, real-time data feeds. Betting companies and fantasy sports operators rely heavily on comprehensive datasets to ensure fair play, enhance user experience, and optimize their offerings. This dependence on high-quality data has led to strategic partnerships between sports leagues, data providers, and technology vendors, creating a vibrant ecosystem for data monetization. The increasing willingness of fans and bettors to pay for premium data-driven services is expected to sustain this growth trajectory in the coming years.

Regionally, North America leads the market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The strong presence of major sports leagues, technological advancements, and high digital penetration are key factors contributing to North America's dominance. Europe is witnessing rapid growth due to the rising popularity of sports analytics and the expansion of betting regulations. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by the increasing adoption of digital technologies and the growing popularity of sports such as cricket, football, and eSports. The Middle East & Africa and Latin America are also showing promising potential, albeit at a comparatively nascent stage, as sports organizations in these regions begin to embrace data-driven strategies.

Component Analysis

The sports data monetization market, when analyzed by component, is segmented into software, services, and platforms. The software segment dominates the market, accounting for the largest revenue share in 2024. This is largely due to the proliferation of advanced analytics tools, data visualization software, and AI-driven platforms that enable sports organizations to process and monetize vast datasets. Software solutions are integral to extracting actionable insights from raw data, which can be leveraged for performance analytics, fan engagement, and commercial partnerships. The increasing demand for customizable and scalable software solutions is expected t

Utilize our Decathlon dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding the sports and outdoor equipment industry dynamics and trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements.

Popular use cases include pricing optimization, where organizations can define a pricing strategy and create dynamic pricing models by comparing similar products and categories among competitors. Additionally, the dataset helps in identifying gaps in product inventory, recognizing increased demand for certain sports and outdoor items, and spotting trends that are gaining popularity with consumers. Furthermore, it supports market strategy optimization by leveraging insights to analyze key sports and outdoor trends and customer preferences, enhancing overall business decision-making.

Comprehensive dataset containing 1 verified Sports complex businesses in Long An, Vietnam with complete contact information, ratings, reviews, and location data.

According to Cognitive Market Research, the global Sports Injury Predictionmarket size is USD 2158.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 7.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 863.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 647.46 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 496.39 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.0% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 107.91 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.4% from 2024 to 2031.
Middle East and Africa hada market share of around 2% of the global revenue and was estimated at a market size of USD 43.16 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2024 to 2031.
The Software emerges as the dominant offering in the Sports Injury Prediction Market due to its critical role in analyzing data and providing actionable insights for injury prevention.

Market Dynamics of Sports Injury Prediction

Key Drivers for Sports Injury Prediction

Advancements in Predictive Analytics Technology to Increase the Demand Globally

The Sports Injury Prediction Market is being driven by advancements in predictive analytics technology. These technologies are revolutionizing the way injuries are anticipated and prevented in athletes. By analyzing various data points such as athlete performance, training intensity, biomechanics, and injury history, predictive analytics can identify patterns and signals that indicate a higher risk of injury. This proactive approach allows coaches, trainers, and medical professionals to implement targeted interventions and training modifications to reduce the likelihood of injuries, thereby enhancing athlete performance and prolonging careers. As these technologies continue to evolve, they are expected to play a crucial role in improving athlete health and safety across various sports disciplines.

Growing Emphasis on Injury Prevention and Athlete Well-being to Propel Market Growth

Another key driver in the Sports Injury Prediction Market is the growing emphasis on injury prevention and athlete well-being. With the rise in awareness about the long-term consequences of sports-related injuries, there is a heightened focus on implementing strategies to minimize injury risk. This includes comprehensive injury prevention programs, personalized training regimens, and the use of wearable devices to monitor athlete health metrics. Additionally, there is an increasing demand for evidence-based approaches to injury prevention, leading to collaborations between sports organizations, medical professionals, and technology providers. This collective effort aims to create a safer environment for athletes and improve overall athletic performance, driving the growth of the Sports Injury Prediction Market.

Restraint Factor for the Sports Injury Prediction

Limited Accuracy and Reliability of Predictive Models to Limit the Sales

One significant restraint in the Sports Injury Prediction Market is the limited accuracy and reliability of predictive models. While advancements in technology have enabled the development of sophisticated algorithms and data analytics tools for injury prediction, these models often face challenges in accurately predicting complex sports injuries. Factors such as individual variability in athletes' physiology, training regimes, and playing conditions can significantly impact injury outcomes, making it difficult to create one-size-fits-all predictive models. Additionally, the dynamic nature of sports and the evolving understanding of injury mechanisms further complicate the development of highly accurate predictive models.

Impact of Covid-19 on the Sports Injury Prediction

The COVID-19 pandemic has significantly impacted the sports injury prediction market. With the suspension or cancellation of sporting events globally, there has been a decline in the demand for sports injury prediction technologies and services. The focus of healthcare resources and research has shifted towards com...

Comprehensive dataset containing 4 verified Sports club businesses in Long An, Vietnam with complete contact information, ratings, reviews, and location data.

Context

League of Legends is a popular global online game played by millions of players monthly. In the past few years, the League of Legends e-sports industry has shown phenomenal growth. Just recently in 2020, the World Championship finals drew 3.8 million peak viewers! While the e-sports industry still lags behind traditional sports in terms of popularity and viewership, it has shown exponential growth in certain regions with fast-growing economy, such as Vietnam and China, making it a prime target for sponsorship for foreign companies looking to spread brand awareness in these regions.

While the e-sports data industry is also showing gradual growth, there is not much available publicly in terms of published analysis of individual games. This may be due to the fact that the games are fast-changing compared to traditional sports--rules and game stats are frequently and arbitrarily changed by the developers. Nevertheless it is an interesting field for fun researches: hence the reason for many pet projects and graduate-level papers dedicated to this field.

All existing League of Legends games (minus custom games, including ones from competitions) are made available by Riot's API. However, having to request and parse the data for every single relevant game is quite annoying; this dataset intends to save that work for you. To make things (hopefully) easier, I parsed all JSON files returned by Riot API into CSV files, with each row corresponding to one game.

Components

This dataset consists of three parts: root games, root2tail, and tail games.

I found that quite often when trying to predict the outcome of a match prior to its play, the historical matches of a player prior to that game count as an important factor (Hall, 2017). For such purpose, root games contains 1087 games from which tail games branches out.

Tail games contains historical matches of each player for every game in root games. Root2tail maps root games's each player's account ID and that player's controlled champion ID to a list of matches that can be found in tail games.

To simplify the explanation, if you want to access historical matches of a player in root games file, 1. Get player's account ID and the game ID. 2. Load root2tail file. 3. Queue for matching row on account ID and game ID. 4. The corresponding row contains a list of game IDs that can be queued on tail_games files.

Note that root2tail documents most recent 5 matches, or a list of matches played within the past 5 weeks, prior to the game creation date of the corresponding "root game". It also only documents the most recent games by the player played with the same champion he/she played in the "root game". In cases where there is an empty list, it means the player has not played a single match with the same champion within the past 5 weeks.

Content

How was this data collected?

On 2020, December 5th, I fetched the list of current players in Challenger tier, then recursively gathered historical matches of those players to consist root games, so this is the data collection date.

What do the rows and columns of the csv data represent?

Root2tail is self-explanatory. As for the other files, each row represents a single game. The columns are quite confusing, however, as it is a flattened version of a JSON file with nested lists of dictionaries.

I tried to think of the simplest way to make the columns comprehensible, but looking at the original JSON file is most likely the simplest way to understand the structure. Use tools like https://jsonformatter.curiousconcept.com/ to inspect the dummy_league_match.json file.

A very simple explanation: participant.stats._ and participant.timeline._ contains pretty much all match-related statistics of a player during the game.

Also, note that the "accountId" fields use encrypted account IDs which are specific to my API key. If you want to do additional research using player account IDs, you should fetch the match file first and get your own list of player account IDs.

Acknowledgements

The following are great resources I got a lot of help from: 1. https://riot-watcher.readthedocs.io/en/latest/ 2. https://riot-api-libraries.readthedocs.io/en/latest/

These two actually explain everything you need to get started on your own project with Riot API.

The following are links to related projects that could maybe help you get ideas!

1. Kim, Seouk Jun, https://towardsdatascience.com/discussing-the-champion-specific-player-win-rate-factor-in-league-of-legends-match-prediction-3d83d7e50a94 (2020)
2. Huang, Thomas, Kim, David, and Leung, Gregory, https://thomasythuang.github.io/League-Predictor/ (2015)
3. Jiang, Jinhang, https://towardsdatascience.com/lol-match-prediction-using-early-laning-phase-data-machine-learning-4...

Abstract Outsourcing allows quick reactions to market fluctuations and allows firms to focus on their core business. This strategy is currently used in the Brazilian apparel industry. The paper analyzes the interaction between big customers (some multinationals firms) and Brazilian suppliers in the sports apparel industry to verify how these relationships can contribute to the capability development of suppliers. Multiple case studies were conducted. The findings indicated that collaborative, and long-term relationships are beneficial to businesses and customers significantly influence the development of the sewing factory suppliers with whom they work.

This data set provides data related to measuring consumer behavior in the context of sports marketing among football fans in the Indonesia Premier League. The survey was conducted online using a Google form with a Likert scale. Questions in the questionnaire include marketing variables represented by brand commitment (12 questions), brand trust (4 questions), brand satisfaction (8 questions), brand loyalty (3 questions), and brand attachment (4 questions). The survey was conducted in June–September 2022. A total of 258 football fans across Indonesia were selected using non-probability sampling techniques. Survey data is analyzed using structural equation modeling (SEM) using Smart PLS software to identify estimates of primary construction relationships in the data. The data can help football club managers and business operators in the field of football sports map and plan marketing strategies for organizational development and gain valuable economic benefits. There are three attachments: 1. Analysis of Smart PLS data (this data shows the results of data analysis in the Smart-PLS output format that is exported to Microsoft Excel) 2. Questionnaire: "Sports Marketing in Indonesia: Football Fans" (This data contains the distribution of questionnaire questions to respondents in Microsoft Excel.) 3. Data in Brief: Sports Marketing in Indonesia Soccer Fans_revision This data contains the results of the questionnaire's completion by respondents. Authors replace province-based clusters to facilitate data encoding and reading and avoid multiple interpretations of domicile location in homepage data. The research data was collected using an online survey questionnaire, using a likerts scale of 1-5 accessible through https://forms.gle/Ask9YzAnhKx6yy9. WhatsApp was used to distribute questionnaires to respondents because it is the 3rd largest WhatsApp user in the world [2] with the largest number of football fans reaching 69% [1], as well as considering the effectiveness of research coverage where the Indonesian region consists of diversity. The questions in the questionnaire use Indonesian to facilitate the understanding of respondents in filling out the questionnaire. The English questionnaire is provided as an additional file. The total sample in the study amounted to 258 respondents from various club fans who had their membership status verified by the club's fan leader chairman. Researchers designed survey instruments using research designs based on previous research [1]. Part A of the survey asks about the sociodemographic profile of respondents, including name (optional), gender, occupation, and place of residence. Meanwhile, part B contains questions to measure consumer behavior variables namely commitment, trust, satisfaction, loyalty, and attachment in the context of sports marketing. as shown in Table 1.

A comprehensive dataset containing crowdsourced rankings of nearly all ski resorts worldwide. The dataset includes detailed information on each resort, such as location, snowfall, number of lifts and slopes, total slope length, and vertical drop. The dataset is updated regularly as more votes are collected.

Here are a few use cases for this project:

1. Pool Ball Tracking: This model can be used to track the movement and positions of pool balls in a game. This could be valuable for creating an automatic scoring system or for providing players with analytics post-game to improve strategizing and skill building.

2. Online Gaming: Within virtual pool or billiards games, the model could facilitate the identification of balls on various table layouts, aiding in the creation of more realistic gaming experiences.

3. Production Line Quality Control: In industries where pool balls are manufactured, the model could be used for quality control, automatically detecting and classifying pool balls based on their color. This would ensure that the manufacturing process is accurate and efficient.

4. Pool Tutoring Applications: Pool training mobile apps might use this model to analyze the user's performance based on their shot selection and ball positioning strategy. Over time, this kind of application could provide personalized coaching recommendations to help users improve their pool-playing skills.

5. Sports Live Broadcasting: In the sports broadcasting industry, this computer vision model could be utilized to detect pool balls in real-time during matches. This can lead to automated statistics generation for live commentary and more enhanced viewer experiences.

This dataset is about books. It has 1 row and is filtered where the book publisher is Coachwise Business Solutions on behalf of Sport England. It features 7 columns including author, publication date, language, and book publisher.

Adidas fashion products dataset extracted by crawl feeds team using in-house tools. Last extracted on 24 sept 2022.

If you're looking to dive deep into the world of sports and fashion, exploring the products dataset from Adidas can be an eye-opening experience. This dataset offers a comprehensive view of the vast array of products Adidas has to offer, from cutting-edge athletic wear to the latest sneaker releases. Whether you're a data enthusiast, a sportswear retailer, or simply a fan of the brand, this dataset can provide valuable insights into trends, popular items, and even the evolution of Adidas' product line over time.

By analyzing the products dataset from Adidas, you can uncover patterns in consumer preferences and gain a better understanding of how the brand has adapted to changing market demands. For instance, you might discover which products are most popular in different regions, or how seasonal trends impact sales. This kind of data-driven approach is crucial for anyone looking to make informed decisions, whether in retail, marketing, or product development. Engaging with the Adidas dataset not only enhances your knowledge of the brand but also equips you with the tools to stay ahead in the competitive sportswear industry.

Dataset customization is available (ex: format changes, adding or removing fields).

National policy documents, as the starting point of the “document transmission chain”, are often directional and instructive in content. Provincial governments, as transmitters of these policies, possess more precise localized decentralized knowledge and are responsible for their specific implementation. During the downward transmission of policy documents, some provinces tend to directly replicate national policies, which results in provincial governments adopting certain policies, but with unsatisfactory outcomes. Accordingly, this study applies the Word Embedding–Word Mover's Distance method to calculate, for the first time, the variation coefficient of policy text reproduction for provincial governments’ sports industry policies. This method is a semantic distance–based text similarity technique that quantifies the semantic divergence between central and provincial policy texts. The normalized variation coefficient ranges from 0 to 1, where a higher value indicates a greater divergence from the central policy text and a higher degree of local reproduction. On this basis, the study further explores the relationship between the speed of policy adoption and the variation coefficient of policy text reproduction at the provincial level. The results reveal significant heterogeneity among the 31 provincial governments in their policy adoption behaviors, which can be categorized into four types according to the characteristics of adoption speed and variation coefficient: rapid adoption with low variation, rapid adoption with high variation, slow adoption with high variation, and slow adoption with low variation.

The Celebrity Net Worth Dataset offers an in-depth look at the estimated financial assets and wealth of global celebrities, extracted from CelebrityNetWorth.com by Crawl Feeds. This dataset provides the latest available financial data as of January 31, 2022, making it a valuable resource for analyzing the earnings, investments, and overall wealth of prominent figures in various industries such as entertainment, sports, music, and more.

Key Features:

• Celebrity Names: Includes a comprehensive list of celebrities from various industries.
• Net Worth Estimates: Estimated total net worth, including assets and investments, for each celebrity.
• Industry Classification: Categorizes celebrities into industries such as Movies, Sports, Music, and Media.
• Source of Wealth: Details on how celebrities accumulated their wealth (e.g., acting, music, endorsements, business ventures).
• Data Extraction Date: Last extracted on 31st January 2022, providing the most recent snapshot of celebrity finances.
• CSV Format: Structured dataset available in CSV format, allowing seamless integration with data analysis tools.

Applications:

• Wealth Analysis: Conduct detailed studies on celebrity wealth, growth trends, and earnings potential.
• Industry Comparisons: Compare net worth estimates across different industries and categories.
• Financial Forecasting: Use historical data to predict future net worth trends for celebrities.
• Investment Insights: Identify key areas where celebrities invest or accumulate wealth.
• Market Research: Gain insights into the financial power of celebrity endorsements and their influence on industries.

For access to more updated celebrity net worth datasets, reach out to the Crawl Feeds team for further assistance.

NBA Team Market Size updated 2022 - TV Market Size - Metro Population in according city/state - Team Revenue

Here are a few use cases for this project:

1. Safety Regulation Compliance: The "Helmet" model can be used to ensure safety compliance on construction sites, mines, or any industries where safety helmets are mandatory. The model can automatically detect the individuals not wearing helmets and alert the management for immediate action.

2. Traffic Rule Enforcement: Authorities can use this model to enforce helmet laws for motorcyclists and bicyclists. It can be integrated with traffic surveillance cameras to identify riders without helmets, thus helping reduce the number of traffic rule violations.

3. Safety Analysis in Sports: In sports like cycling, skateboarding, or skiing, the model can be used to analyze the proportion of participants wearing helmets during the event. It can help in recognizing areas where safety measures could be enhanced.

4. Worker Insurance Claims: Insurance companies can use the "Helmet" model to determine whether or not workers were wearing their helmets at the time of an onsite accident, which may influence the claims adjudication process.

5. Consumer Behavior Studies: Market researchers can use this model to study the consumer behavior regarding helmet usage. This can provide valuable insights to helmet manufacturers and marketers about potential demand, preferences, and areas for product improvement.

Long-Term-Debt Time Series for Daktronics Inc. Daktronics, Inc. designs, manufactures, and sells electronic scoreboards, programmable display systems, and large screen video displays for sporting, commercial, and transportation applications in the United States and internationally. It operates through Commercial, Live Events, High School Park and Recreation, Transportation, and International segments. The company offers video display and walls; scoreboards and timing systems; LED message displays and sings; intelligent transportation systems dynamic message signs; mass transit display; sound systems; and digital billboards and street furniture, and digit and price displays. It also provides indoor dynamic messaging systems; and software and controllers, which includes Venus, a control suite software to control the creation of messages and graphic sequences for uploading to displays. The company serves out-of-home companies, retailers, quick-serve restaurants, casinos, shopping centers, cruise ships, commercial building owners, petroleum retailers, governmental transportation departments, transportation industry contractors, airlines, and sports and commercial business facilities. It sells its products through direct sales and resellers. Daktronics, Inc. was incorporated in 1968 and is headquartered in Brookings, South Dakota.