The global football analysis software market is experiencing robust growth, driven by the increasing adoption of data-driven strategies by football clubs, associations, and organizations. The market size in 2025 is estimated at $619.8 million. While the exact CAGR isn't provided, considering the technological advancements in sports analytics and the rising demand for performance optimization, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 12-15%. This growth is fueled by several factors. The increasing accessibility of advanced analytics tools, coupled with a growing understanding of their value in enhancing player performance, tactical decision-making, and talent scouting, significantly contributes to this upward trend. The prevalence of cloud-based solutions offers scalability and cost-effectiveness, further boosting market expansion. Furthermore, the rising popularity of football globally, coupled with increased investment in sports technology, fuels the demand for sophisticated analysis tools. Different segments within the market, such as cloud-based versus on-premise solutions and applications tailored to football clubs versus broader football organizations, offer diverse revenue streams and cater to specific needs. Competition is fierce amongst established players like Nacsport, Hudl, and Dartfish, along with emerging companies pushing innovation in areas such as AI-powered video analysis and advanced statistical modeling. The competitive landscape is dynamic, with both established players and new entrants vying for market share. Geographical distribution shows significant demand in regions like North America and Europe, driven by the mature football infrastructure and high levels of investment in sports technology. However, growth is also anticipated in emerging markets like Asia Pacific and Middle East & Africa, as football's popularity and the adoption of advanced analytical techniques expand. The market's continued expansion hinges on the ongoing development of more sophisticated analytics capabilities, including advanced AI algorithms and integration with wearable sensor technology. Furthermore, factors like improved user interfaces and easier access to training and support will also influence future market growth, driving adoption among a wider range of users. This suggests a promising future for the football analysis software market, underpinned by the continuing convergence of technology and sports performance optimization.

Here are a few use cases for this project:

1. Real-time match analysis: The "Football" model can be used to provide real-time insights and statistics about the ongoing match, such as ball possession percentages, player movements, goal attempts, successful corner kicks, and identification of goalkeepers making crucial saves.

2. Automated highlight generation: By identifying critical events like goals, corners, and exceptional goalkeeper saves, the model can automatically create highlight reels of important moments in a football match, saving content creators and broadcasters significant editing effort.

3. Performance analytics for teams and coaching staff: The model can be used to analyze and quantify individual player performance and team dynamics during a match, providing valuable insights for coaching staff to optimize strategies, identify strengths and weaknesses, and enhance team performance.

4. Enhanced fan engagement: With its ability to identify various elements of a football match, the model can be used to develop interactive applications and augmented reality solutions that engage fans and provide them with additional information, such as player statistics, goal breakdowns, or immersive replays of key events.

5. Referee decision support: The model can be integrated into a decision support system for referees, assisting with offside calls or other contentious decisions by providing accurate information about the positions of the ball, players, and goalkeepers during critical moments.

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Sports Analytics Market Size 2025-2029

The sports analytics market size is forecast to increase by USD 8.4 billion, at a CAGR of 28.5% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing adoption of cloud-based deployment solutions. This shift towards cloud-based technologies enables organizations to store and process large volumes of data more efficiently, facilitating real-time insights and informed decision-making. Additionally, the integration of wearable devices in sports is another key trend, providing teams and athletes with real-time performance data and analytics, leading to enhanced training and improved player safety. However, the market faces challenges, including the limited potential for returns on investment. The high cost of implementing and maintaining advanced analytics systems, as well as the need for specialized skills and resources, can deter smaller organizations from entering the market.
Furthermore, ensuring data privacy and security remains a significant challenge, particularly in light of the sensitive nature of sports data. To capitalize on market opportunities and navigate challenges effectively, companies must focus on offering cost-effective solutions, providing robust data security, and investing in talent development to meet the growing demand for sports analytics expertise.

What will be the Size of the Sports Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in technology and the increasing value placed on data-driven insights across various sectors. Game analytics and real-time data processing are revolutionizing team performance, enabling coaches to make informed decisions during games. Wearable sensors and biometric data are transforming athlete performance analysis, providing valuable insights into player conditioning and injury prevention. Sports sponsorship and marketing are leveraging data to optimize campaigns and enhance fan engagement. Data security and privacy are becoming paramount, with the growing use of sensitive biometric data. Sports ethics and regulation are also gaining importance, ensuring the ethical use of data and compliance with industry standards.

Sports broadcasting is being enhanced through data visualization and video analysis, providing viewers with a more immersive experience. Machine learning models and predictive analytics are being used to improve player scouting and talent identification. Sports research and education are benefiting from the wealth of data available, leading to new discoveries and advancements in sports science. Sports technology is driving innovation in sports equipment, sports medicine, and sports training. Data integration and processing are becoming more sophisticated, enabling more accurate performance metrics and coaching strategies. Sports law and governance are adapting to the changing landscape, ensuring fair play and compliance with regulations.

The market is a dynamic and ever-evolving ecosystem, with continuous innovation and applications across various sectors. The integration of data into sports is transforming the way teams and organizations operate, providing valuable insights and competitive advantages.

How is this Sports Analytics Industry segmented?

The sports analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Football
  Cricket
  Hockey
  Tennis
  Others


Solution

  Player analysis
  Team performance analysis
  Health assessment
  Fan engagement analysis
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By Type Insights

The football segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the increasing popularity of sports and the subsequent demand for in-depth analysis. Football, as the most widely followed sport, drives a substantial portion of this demand. Sports facilities, from domestic leagues such as the Champions League, English Premier League, and Spanish La Liga to international tournaments like the World Cup and European Championship, attract massive viewership. To cater to this demand, various companies and data suppliers have emerged, offering solutions in areas such as team performance, sports infrastructure, biometric data, player scouting, sports psychology, player tracking, sports equipment, sports medicine, sports management, game analyt

Football Ai Analytics

## Overview

Football Ai Analytics is a dataset for object detection tasks - it contains Football Players annotations for 372 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The paper explores process mining and its usefulness for analyzing football event data. We work with professional event data provided by OPTA Sports from the European Championship in 2016. We analyze one game of a favorite team (England) against an underdog team (Iceland). The success of the underdog teams in the Euro 2016 was remarkable, and it is what made the event special. For this reason, it is interesting to compare the performance of a favorite and an underdog team by applying process mining. The goal is to show the options that these types of algorithms and visual analytics offer for the interpretation of event data in football and discuss how the gained insights can support decision makers not only in pre- and post-match analysis but also during live games as well. We show process mining techniques which can be used to gain team or individual player insights by considering the types of actions, the sequence of actions, and the order of player involvement in each sequence. Finally, we also demonstrate the detection of typical or unusual behavior by trace and sequence clustering.

Sports Data Analytics Service Market Outlook

The global sports data analytics service market size was valued at approximately $2.3 billion in 2023 and is projected to reach around $6.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.6% during the forecast period. This robust growth is primarily driven by increased investments in sports technology, the rising importance of data-driven decision-making in sports, and the growing adoption of advanced analytics to enhance player performance and fan engagement.

The surge in the adoption of sports data analytics is attributed to the increasing competitive nature of sports, where teams and individual athletes are leveraging data to gain a strategic edge. Data analytics provides insights into player performance, injury risks, and optimal training regimens, which can significantly impact the outcomes of games and overall team performance. This, coupled with the rising investments in sports technology, is propelling the market growth. Additionally, the growing popularity of fantasy sports and sports betting has further fueled the demand for real-time data analytics to make informed decisions.

Another significant growth factor is the rising focus on enhancing fan engagement and experience. Sports organizations are increasingly using data analytics to understand fan preferences, behavior, and sentiment. This information is crucial for tailoring marketing strategies, improving fan interactions, and ultimately increasing revenue from ticket sales, merchandise, and digital platforms. The integration of advanced technologies, such as artificial intelligence (AI) and machine learning (ML), is also enabling more sophisticated data analysis, driving further growth in the market.

Performance Analytics plays a crucial role in the sports data analytics service market, offering teams and athletes the ability to delve deeper into their performance metrics. By leveraging performance analytics, sports organizations can track and analyze various aspects of athletic performance, from speed and agility to endurance and skill execution. This data-driven approach not only aids in identifying areas for improvement but also helps in crafting tailored training programs that enhance overall performance. The integration of performance analytics into sports strategies allows for a more comprehensive understanding of both individual and team dynamics, ultimately leading to more informed decision-making and competitive advantages on the field.

Moreover, the healthcare and fitness tracking aspects of sports data analytics are gaining traction. With a growing emphasis on athlete health and well-being, sports teams are using data analytics to monitor players' physical conditions, predict injuries, and design personalized training programs. This proactive approach not only enhances performance but also extends players' careers and reduces healthcare costs. The increasing availability of wearable devices and IoT sensors is further supporting the collection and analysis of health-related data.

Regionally, North America is expected to dominate the sports data analytics service market, driven by the presence of major sports leagues, high technological adoption, and substantial investments in sports analytics. Europe is also anticipated to witness significant growth, supported by the increasing popularity of sports analytics among football clubs and other sports organizations. The Asia Pacific region is expected to emerge as a lucrative market due to the growing sports industry and rising investments in sports technology in countries like China, India, and Japan.

The rise of Fantasy Sports Service has significantly contributed to the growing demand for sports data analytics. Fantasy sports enthusiasts rely heavily on real-time data and analytics to make informed decisions about player selections and game strategies. This burgeoning interest in fantasy sports has prompted sports organizations and analytics firms to develop more sophisticated data solutions that cater to the unique needs of fantasy sports players. By providing detailed player statistics, performance forecasts, and injury updates, fantasy sports services enhance the user experience and engagement, driving further growth in the sports data analytics market. The intersection of fantasy sports and data analytics continues to open new avenues for innovation and fan in

Football Analysis Software Market Outlook

The global football analysis software market size was valued at USD 1.2 billion in 2023 and is projected to reach USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.2% during the forecast period. This significant growth is primarily driven by the increasing adoption of data analytics in sports for enhancing team performance, player efficiency, and strategic planning. The integration of advanced technologies such as machine learning (ML) and artificial intelligence (AI) into football analysis software is facilitating more precise and actionable insights, further fueling market expansion.

One of the key growth factors contributing to the expansion of the football analysis software market is the rising investment in sports technology by professional clubs and sports academies. These investments are aimed at utilizing sophisticated software solutions to analyze match data, evaluate player performance, and optimize team tactics. The growing popularity of football worldwide and the increasing competitiveness among clubs are compelling teams to adopt innovative technologies that can provide a competitive edge. Additionally, the proliferation of wearable technology and IoT devices is generating vast amounts of data, which can be analyzed using football analysis software to derive valuable insights.

Moreover, the increasing focus on player safety and injury prevention is driving the demand for football analysis software. By analyzing players' movements and physical conditions during training and matches, coaches and medical staff can identify the risk of injuries and implement preventive measures. The software can also assist in managing players' workload and providing personalized training programs, thereby enhancing overall player health and performance. This emphasis on player welfare and performance optimization is significantly contributing to the market's growth.

The growing trend of video analysis in sports is another crucial factor propelling the football analysis software market. Video analysis tools enable coaches to breakdown match footage, analyze key moments, and communicate strategies effectively to players. These tools are not only used for performance analysis but also for talent scouting and recruitment. By evaluating players' performances through video analytics, clubs can make informed decisions on player acquisitions and transfers. The integration of 3D simulation and augmented reality (AR) technologies in video analysis is further enhancing the capabilities of football analysis software.

In the realm of sports technology, Sports Graphics have emerged as a pivotal tool for enhancing the visual representation of data in football analysis software. These graphics are instrumental in transforming complex data sets into easily digestible visual formats, such as heat maps, player movement trails, and tactical diagrams. By employing Sports Graphics, coaches and analysts can effectively communicate strategies and insights to players, making it easier to understand and implement tactical adjustments. The integration of dynamic and interactive graphics into football analysis software is not only improving the clarity of data presentation but also enhancing the overall user experience. As the demand for visually engaging and informative content grows, Sports Graphics are set to play an increasingly important role in the evolution of football analysis tools.

Regionally, North America and Europe are leading the market due to the presence of advanced sports infrastructure and high adoption rates of sports technology. In North America, the United States has emerged as a significant market for football analysis software, driven by the increasing focus on soccer and the presence of major sports tech companies. Europe, with its rich football heritage and technologically advanced clubs, is also witnessing substantial growth. The Asia Pacific region is expected to grow at the highest CAGR during the forecast period, attributed to the rising popularity of football, increasing investments in sports infrastructure, and the growing adoption of technology in sports.

Component Analysis

The football analysis software market can be segmented by components into software and services. The software segment is further divided into various types o

Football Analytics

## Overview

Football Analytics is a dataset for object detection tasks - it contains PlayerFootballTracking annotations for 582 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Market Overview The global sports analytics market is projected to grow from $2.87 billion in 2023 to $49.68 billion by 2033, exhibiting a robust CAGR of 30.04%. This surge is attributed to the rising demand for data-driven insights to enhance player performance, optimize team strategies, and engage fans. The growing adoption of advanced technologies such as AI, machine learning, and data visualization tools is fueling market expansion. Additionally, increased investment by sports organizations and the proliferation of sports betting are driving market growth. Key Market Segments and Trends The market is segmented by sport, including football, cricket, hockey, basketball, American football, and other sports. Football holds a dominant share due to its global popularity and the availability of extensive data. Furthermore, the growing trend towards personalization and customization of sports content is driving demand for player-based and team-specific analytics. Emerging companies are playing a significant role in market innovation, introducing cutting-edge solutions and expanding the scope of sports analytics. Prominent players in the market include SAS Institute Inc., Catapult Group International Ltd., Trumedia Networks, IBM Corporation, Stats LLC, Opta Sports, Tableau Software Inc., Oracle Corporation, Sportsradar AG, and SAP SE. The global sports analytics market size was valued at USD 1,466.7 million in 2021 and is projected to grow from USD 1,763.4 million in 2022 to USD 5,649.1 million by 2029, exhibiting a CAGR of 17.9% during the forecast period. The market is driven by the increasing adoption of analytics in sports to improve performance, identify new talents, and enhance fan engagement. Recent developments include: October 2023, Texas A&M Athletics Sports Science announced that it has entered into an arrangement with Gemini Sports Analytics to offer the Aggies' staff Gemini’s AI software platform built-for sports that is projected to empower the Aggies to access prognostic analytics in addition to metrics to aid support student-athletes. The Gemini application authorizes stakeholders by offering predictive data analytics to the end users, cumulative interdisciplinary professionals' efficiency, and permitting high-level decision-makers to make game-changing choices faster., February 2023: Gemini Sports Analytics is an AI and Automated Machine learning tool, and SIS (Sports Info Solutions) announced a partnership to pre-integrate SIS data into the Gemini app. Along with the data integration, the two companies would leverage their complementary offerings and develop solutions for their current and future clients. Gemini's mission is to make it faster and easier for sports organizations across the globe to use predictive analytics in their decision-making processes around recruitment, player development, personnel, health and performance, and other management choices.. Key drivers for this market are: Rising Adoption of Big Data Analytics, AI and ML Technologies, Increase in Investments in the Newer Technologies. Potential restraints include: Lack of Awareness About the Benefits of Sports Analytics Solutions. Notable trends are: Football Sport is Expected to Hold Significant Market Share.

The global sports analytics software market is experiencing robust growth, projected to reach $1804.4 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 22.3% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the increasing adoption of data-driven decision-making in professional and amateur sports is driving demand for sophisticated analytics tools. Teams and organizations are leveraging these tools to enhance player performance, optimize training strategies, improve recruitment processes, and ultimately achieve better competitive outcomes. Secondly, advancements in technology, including the development of more powerful and affordable cloud-based solutions, are making sports analytics more accessible and cost-effective for a wider range of stakeholders. The availability of real-time data capture and analysis capabilities also significantly contributes to this growth. Furthermore, the rising popularity of various sports globally, particularly in emerging markets, expands the potential user base for these analytical solutions. Segmentation within the market reveals strong growth across multiple sports, including basketball, football (soccer), and tennis, with cloud-based solutions outpacing on-premises deployments due to their scalability and accessibility. Leading players like IBM, SAP, and Stats Perform are strategically positioning themselves to capitalize on this growth, through acquisitions, partnerships, and continuous product development. The market's growth trajectory is further supported by several emerging trends. The integration of artificial intelligence (AI) and machine learning (ML) algorithms into sports analytics platforms is enabling more predictive and insightful analysis, identifying previously unseen patterns and providing a competitive edge. The rise of wearable technology and sensor integration allows for the capture of richer and more granular data, fueling the analytical process. While factors such as the initial investment costs associated with implementation and the need for specialized expertise might act as temporary restraints, the overall market outlook remains overwhelmingly positive, with sustained growth projected throughout the forecast period driven by continuous technological innovation and the ever-increasing importance of data-driven decision-making in the sports industry. Geographical expansion, particularly within regions with strong sporting cultures and developing infrastructure, will further contribute to the market's overall success.

Sports Analytics Market is Segmented by Sport (Football, Cricket, Basketball, and More), Component (Software, Services), Deployment (On-Premise, Cloud), End User (Sports Teams/Clubs, Leagues and Federations, Individual Athletes, Sports Betting Operators, Others), Geography. The Market Forecasts are Provided in Terms of Value (USD).

The global football analysis software market is experiencing robust growth, projected to reach $351.9 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 8.4% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing professionalism within football at all levels—from youth academies to professional leagues—fuels a greater demand for data-driven insights to improve player performance, tactical strategies, and overall team effectiveness. Secondly, technological advancements in video analysis, AI-powered scouting tools, and wearable sensor technology are constantly enhancing the capabilities of these software solutions, making them more accurate, efficient, and accessible. Furthermore, the rising adoption of cloud-based solutions facilitates seamless data sharing and collaboration among coaching staffs, scouts, and analysts, irrespective of geographical location. Finally, the growing availability of affordable, user-friendly software options is expanding the market's reach beyond elite clubs to smaller organizations and even grassroots levels. The market segmentation reveals a strong preference for cloud-based solutions due to their scalability, accessibility, and cost-effectiveness. Application-wise, football clubs represent the largest segment, followed by football associations and organizations. Geographically, North America and Europe currently dominate the market, owing to the higher level of investment in technological advancements and a more established football infrastructure. However, Asia-Pacific is poised for significant growth in the coming years, driven by the rising popularity and investment in football within the region. Competitive forces are substantial, with numerous established players such as Nacsport, Hudl, and Dartfish alongside emerging competitors continuously innovating and vying for market share. The restraining factors include the high initial investment cost for some advanced software solutions and the need for specialized training to effectively utilize the advanced analytics capabilities. However, these challenges are being mitigated by the availability of more affordable options and comprehensive training programs.

The global sports analytics service software market is experiencing robust growth, driven by the increasing adoption of data-driven strategies by sports teams, leagues, and media organizations. The market's expansion is fueled by several key factors: the rising availability of data from various sources (wearable technology, video analysis, etc.), advancements in artificial intelligence and machine learning algorithms enabling sophisticated data analysis, and the growing need for competitive advantage in professional sports. The market is segmented by application (basketball, football, and others) and operating system (Android, iOS, Windows, and others). While precise market sizing is unavailable, based on industry reports and trends indicating strong CAGR, we can project a 2025 market value in the range of $2.5 billion, considering the significant investments in technology and data analysis within the sports industry. This projection anticipates steady growth over the coming years, particularly in regions like North America and Europe, where the adoption of advanced analytics is more mature. However, challenges remain, including the high cost of software and implementation, data privacy concerns, and the need for skilled professionals to interpret and utilize the complex data generated. The competitive landscape is fragmented, with several key players vying for market share. Established companies like Stats Perform, Genius Sports, and Sportradar Group are leveraging their extensive data networks and existing client relationships to maintain a strong position. Smaller, specialized companies offer niche solutions, fostering innovation within specific segments of the market. Future market growth will depend on continued technological advancements, the integration of novel data sources, and the ability of these companies to effectively address the data security and privacy concerns of clients. Expansion into emerging markets and the development of user-friendly interfaces that make advanced analytics accessible to a wider range of users will be crucial for driving further growth in the coming years. The increasing reliance on data-driven decision-making in amateur and youth sports is also expected to contribute significantly to market expansion in the long term.

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates much more events and it is very important and interesting to take into account the context in which those events were generated. This dataset should keep sports analytics enthusiasts awake for long hours as the number of questions that can be asked is huge.

Content

This dataset is a result of a very tiresome effort of webscraping and integrating different data sources. The central element is the text commentary. All the events were derived by reverse engineering the text commentary, using regex. Using this, I was able to derive 11 types of events, as well as the main player and secondary player involved in those events and many other statistics. In case I've missed extracting some useful information, you are gladly invited to do so and share your findings. The dataset provides a granular view of 9,074 games, totaling 941,009 events from the biggest 5 European football (soccer) leagues: England, Spain, Germany, Italy, France from 2011/2012 season to 2016/2017 season as of 25.01.2017. There are games that have been played during these seasons for which I could not collect detailed data. Overall, over 90% of the played games during these seasons have event data.

The dataset is organized in 3 files:

• events.csv contains event data about each game. Text commentary was scraped from: bbc.com, espn.com and onefootball.com
• ginf.csv - contains metadata and market odds about each game. odds were collected from oddsportal.com
• dictionary.txt contains a dictionary with the textual description of each categorical variable coded with integers

Past Research

I have used this data to:

• create predictive models for football games in order to bet on football outcomes.
• make visualizations about upcoming games
• build expected goals models and compare players

Inspiration

There are tons of interesting questions a sports enthusiast can answer with this dataset. For example:

• What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models
• When are teams more likely to score?
• Which teams are the best or sloppiest at holding the lead?
• Which teams or players make the best use of set pieces?
• In which leagues is the referee more likely to give a card?
• How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous?
• Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers)
• Which teams have a bias for attacking on a particular flank?

And many many more...

Manual annotation of the start and end time of passes in the first and second halves of five soccer matches in the Italian first division. The annotation has been made using the Pass Tagging Interface available at https://github.com/jonpappalord/PassNet.If you use these data, please cite the following paper:Sorano, D., Carrara, F., Cintia, P., Falchi, F., Pappalardo, L. (2020) Automatic Pass Annotation from Soccer VideoStreams Based on Object Detection and LSTM, In: Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. List of files:- Chievo Verona vs Juventus, season 2017/2018:-- First half: chievo_juve_1_Pass.csv -- Second half: chievo_juve_2_Pass.csv- Roma vs Juventus, season 2016/2017:-- First half: roma_juve_1_Pass.csv-- Second half: roma_juve_2_Pass.csv- Roma vs Lazio, season 2016/2017-- First half: roma_lazio_1_Pass.csv-- Second half: roma_lazio_2_Pass.csv- Roma vs Milan, season 2016/2017-- First half: roma_milan_1_Pass.csv-- Second half: roma_milan_2_Pass.csv- Sassuolo vs Inter, season 2016/2017-- First half: sassuolo_inter_1_Pass.csv-- Second half: sassuolo_inter_2_Pass.csv

Count-based metrics extracted from the 2016 UEFA Men’s European Football Championship and the 2017 UEFA Women’s Championship. Key metrics by player position and gender were extracted from match action logs and integrated as a single dataset. The resulting dataset of length $n = 4211$ contains 33 variables. The gender target variable is expressed as 1 for male players and 0 for female players. There are 2700 male instances and 1511 female instances. The dataset contains two categorical variables; match period is expressed as 1H for the first half, 2H for the second half, and E1, E2, and P for the two possible overtimes and the penalties respectively, player position in the team formation has the following options: Defender, Midfielder, Forward, Goalkeeper, Substitute Defender, Substitute Midfielder, Substitute Forward and Substitute Goalkeeper. Table \ref{tab:stats} shows the mean value and standard deviation per gender of each of the 30 numerical features of the dataset.

Analysis of ‘Football team’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shiv28/football-team on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context Football analytics

Content Detailed attributes for every club registered in the latest edition of UEFA database.

--- Original source retains full ownership of the source dataset ---

If you use these data cite the following paper: - Pappalardo et al., (2019) A public data set of spatio-temporal match events in soccer competitions, Nature Scientific Data 6:236, https://www.nature.com/articles/s41597-019-0247-7The coaches data set describes all coaches of the clubs and the national teams of the seven competitions we make available. It consists of the following fields:- wyId: the identifier of the coach, assigned by Wyscout.- shortName: the short name of the coach;- firstName: the first name of the coach;- middleName: the middle name (if any) of the coach;- lastName: the last name of the coach;- birthDate: the birth date of the coach, in the format "YYYY-MM-DD";- birthArea: geographic information about the coach's birth area;- passportArea: the geographic area associated with the referee's current passport;- currentTeamId: the identifier of the coach's team. The identifier refers to the field "wyId" in a team document.

Here are a few use cases for this project:

1. Player Performance Analysis: Use the "Football Player Tracker" to analyze individual player performances during football games. This could include tracking their movements, analyzing their tactical decisions, or assessing the overall efficiency of the team's formations and strategies.

2. Automated Sports Coverage: Employ this computer vision model for automated, real-time sports-broadcast coverage. It could provide detailed tracking information about players to sports commentators to enhance their analysis during live broadcasts.

3. Learning and Coaching: Coaches can use this model to educate players by visually demonstrating their movements and activities on the field. This could be incredibly beneficial for training sessions, providing a unique method to improve player's understanding of their role and performance.

4. Sports Betting: Sports betting companies could use this model to provide real-time data and analytics to their customers, enhancing their betting experience by supplying in-depth information about player performances and behaviors.

5. Game Strategy Development: Use the data gathered by this computer vision model to assist in the creation or tweaking of a team's game strategies. By understanding which player/classes are performing well in certain roles, the coaching staff can better plan their strategies for future games.

The sports analytics software market is experiencing robust growth, driven by the increasing adoption of data-driven strategies by professional and amateur sports teams and organizations. The market's expansion is fueled by several key factors, including the rising availability of high-quality video and sensor data, advancements in artificial intelligence (AI) and machine learning (ML) algorithms for performance analysis, and a growing emphasis on player development and injury prevention. The cloud-based segment dominates the market due to its scalability, accessibility, and cost-effectiveness, attracting a broad range of users from individual coaches to large sports organizations. While football and basketball currently hold significant market share in applications, the market is witnessing diversification into other sports like tennis, baseball, and golf, further fueling its growth trajectory. The competitive landscape is dynamic, with a mix of established players and emerging startups offering a wide range of solutions tailored to specific needs. This includes integrated video analysis platforms, wearable sensor technology, and sophisticated data visualization tools. Geographic expansion, particularly in emerging markets of Asia-Pacific and regions in Africa is expected to contribute significantly to future market growth. However, challenges remain, including high initial investment costs for advanced systems and the need for skilled personnel to interpret and utilize the generated data effectively. The market is expected to show consistent growth over the forecast period, driven by technological advancements and increased demand across various sports. The forecast period (2025-2033) projects substantial growth, underpinned by continuous technological innovations in video analysis, AI-powered insights, and wearable sensor integration. Specific features such as improved player tracking, detailed performance metrics, and personalized coaching tools will drive demand. This necessitates considerable investment in research and development by key players to maintain competitiveness. Furthermore, strategic partnerships and mergers and acquisitions are likely to reshape the competitive landscape, leading to consolidation and the emergence of more comprehensive solutions. While data security concerns and the complexity of integrating various data sources present potential constraints, the overall market outlook remains positive, driven by the increasing recognition of the value of data-driven decision-making in the sports industry. The market's success hinges on the continued development of user-friendly interfaces and accessible pricing models to cater to a broader range of users across different budgets and technological expertise.