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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

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.

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 referees data set describes all referees in the national and international competitions we make available. It consists of the following fields:- wyId: the identifier of the referee, assigned by Wyscout.- shortName: the short name of the referee;- firstName: the first name of the referee;- middleName: the middle name (if any) of the referee;- lastName: the last name of the referee;- birthDate: the birth date of the referee, in the format "YYYY-MM-DD";- birthArea: geographic information about the referee's birth area;- passportArea: the geographic area associated with the referee's current passport;

⚽ Football Dataset (2015–2025) This dataset contains structured match and player information from ten years of football (2015–2025), compiled to support data exploration, sports analytics, and machine learning applications in football-related projects.

🧾 Dataset Overview: This dataset includes 16 columns, covering match-level and performance-level data across various leagues and international competitions. All records have been arranged in a clean and human-readable format for ease of use in data analysis.

📌 Columns Included: Date – Match date

Competition – Name of the league or tournament

Season – Football season (e.g., 2019–20)

Home Team – Team playing at home

Away Team – Team playing away

Home Goals – Goals scored by the home team

Away Goals – Goals scored by the away team

Full Time Result – Outcome (H: Home Win, D: Draw, A: Away Win)

Stadium – Match venue

City – City where the match was held

Referee – Referee of the match

Home Possession (%) – Possession percentage of the home team

Away Possession (%) – Possession percentage of the away team

Home Shots on Target – Accurate shots by the home team

Away Shots on Target – Accurate shots by the away team

Match Attendance – Number of spectators

🎯 Ideal For: Analyzing team and player performance trends

Predictive modeling in football analytics

Creating visual dashboards for sports media

Studying home vs. away advantages

Forecasting attendance based on past patterns

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...

StatsBomb Open Data

StatsBomb are committed to sharing new data and research publicly to enhance understanding of the game of Football. We want to actively encourage new research and analysis at all levels. Therefore we have made certain leagues of StatsBomb Data freely available for public use for research projects and genuine interest in football analytics.

StatsBomb are hoping that by making data freely available, we will extend the wider football analytics community and attract new talent to the industry. We would like to collect some basic personal information about users of our data. By giving us your email address, it means we will let you know when we make more data, tutorials and research available. We will store the information in accordance with our Privacy Policy and the GDPR.

Whilst we are keen to share data and facilitate research, we also urge you to be responsible with the data. Please register your details on https://www.statsbomb.com/resource-centre and read our User Agreement carefully.

Terms & Conditions By using this repository, you are agreeing to the user agreement.

If you publish, share or distribute any research, analysis or insights based on this data, please state the data source as StatsBomb and use our logo, available in our Media Pack.

Getting Started The data is provided as JSON files exported from the StatsBomb Data API, in the following structure:

Competition and seasons stored in competitions.json. Matches for each competition and season, stored in matches. Each folder within is named for a competition ID, each file is named for a season ID within that competition. Events and lineups for each match, stored in events and lineups respectively. Each file is named for a match ID. StatsBomb 360 data for selected matches, stored in three-sixty. Each file is named for a match ID. Some documentation about the meaning of different events and the format of the JSON can be found in the doc directory.

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/s41597-019-0247-7This dataset describes all players of the teams playing in seven national and international soccer competitions (Italian, Spanish, French, German, English first divisions, World Cup 2018, European Cup 2016). Each competition consists of the following fields:- birthArea: geographic information about the player's birth area;- birthDate: the birth date of the player, in the format "YYYY-MM-DD";- currentNationalTeamId: the identifier of the national team where the players currently plays;- currentTeamId: the identifier of the team where the player plays for. The identifier refers to the field "wyId" in a team document;- firstName: the first name of the player;- lastName: the last name of the player;- foot: the preferred foot of the player;- height: the height of the player (in centimeters);- middleName: the middle name (if any) of the player;- passportArea: the geographic area associated with the player's current passport;- role: the main role of the player. It is a subdocument containing the role's name and two abbreviations of it;- shortName2: the short name of the player;- weight: the weight of the player (in kilograms);- wyId: the identifier of the player, assigned by Wyscout.

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Fantasy Sports Analytics – Statistics and Analysis 2022-2026

The fantasy sports market analytics statistics provide and accurate prediction of the whole market. This data enables vendors to make informed decisions. The fantasy sports market share is expected to increase by USD 6.11 billion from 2021 to 2026, and the market's growth momentum will accelerate at a CAGR of 6.51%.

The fantasy sports market statistics also provide information on several market vendors, including Blitz Studios Inc., Dream Sports, Fantasy Power 11, Fantrax, Flutter Entertainment Plc, Fox Corp., GamesKraft Technologies Pvt. Ltd., Head Digital Works Pvt. Ltd., Josh Clemm, LivePools Pvt. Ltd., MyTeam11 Fantasy Sports Pvt. Ltd., Paramount, Playerzpot Media Pvt Ltd, Roto Sports Inc., RotoBash apps Pvt Ltd, Sachar Gaming Private Limited, The Football Association Premier League Ltd., The Walt Disney Co., and Yahoo Inc among others.

Only a specific statistics will certainly address your current needs but if you wish to get a glimpse about the full report, here is the sample for fantasy sports market analytics report 2022-2026, it will help you strengthen your plans and strategies for better growth.

This detailed analytics helps the new and established market players to access their current strategies and substitute them according to the data. this report extensively covers fantasy sports market segmentation by type (fantasy soccer, fantasy baseball, fantasy basketball, fantasy football, and others) and geography (North America, Europe, APAC, South America, and Middle East and Africa).

One of the key factors driving the global fantasy sports market growth is the launch of various apps for fantasy sports. The launch of various apps for fantasy sports is notably driving the fantasy sports market growth, although factors such as increasing traction of mobile video games and traditional e-sports may impede market growth.

This detailed analytics helps the new and established market players to access their current strategies and substitute them according to the data. You can get more information on the key market drivers, fantasy sports market trends, and challenges as well.

Description:

This comprehensive dataset offers detailed information on approximately 17,000 FIFA football players, meticulously scraped from SoFIFA.com.

It encompasses a wide array of player-specific data points, including but not limited to player names, nationalities, clubs, player ratings, potential, positions, ages, and various skill attributes. This dataset is ideal for football enthusiasts, data analysts, and researchers seeking to conduct in-depth analysis, statistical studies, or machine learning projects related to football players' performance, characteristics, and career progressions.

Features:

• name: Name of the player.
• full_name: Full name of the player.
• birth_date: Date of birth of the player.
• age: Age of the player.
• height_cm: Player's height in centimeters.
• weight_kgs: Player's weight in kilograms.
• positions: Positions the player can play.
• nationality: Player's nationality.
• overall_rating: Overall rating of the player in FIFA.
• potential: Potential rating of the player in FIFA.
• value_euro: Market value of the player in euros.
• wage_euro: Weekly wage of the player in euros.
• preferred_foot: Player's preferred foot.
• international_reputation(1-5): International reputation rating from 1 to 5.
• weak_foot(1-5): Rating of the player's weaker foot from 1 to 5.
• skill_moves(1-5): Skill moves rating from 1 to 5.
• body_type: Player's body type.
• release_clause_euro: Release clause of the player in euros.
• national_team: National team of the player.
• national_rating: Rating in the national team.
• national_team_position: Position in the national team.
• national_jersey_number: Jersey number in the national team.
• crossing: Rating for crossing ability.
• finishing: Rating for finishing ability.
• heading_accuracy: Rating for heading accuracy.
• short_passing: Rating for short passing ability.
• volleys: Rating for volleys.
• dribbling: Rating for dribbling.
• curve: Rating for curve shots.
• freekick_accuracy: Rating for free kick accuracy.
• long_passing: Rating for long passing.
• ball_control: Rating for ball control.
• acceleration: Rating for acceleration.
• sprint_speed: Rating for sprint speed.
• agility: Rating for agility.
• reactions: Rating for reactions.
• balance: Rating for balance.
• shot_power: Rating for shot power.
• jumping: Rating for jumping.
• stamina: Rating for stamina.
• strength: Rating for strength.
• long_shots: Rating for long shots.
• aggression: Rating for aggression.
• interceptions: Rating for interceptions.
• positioning: Rating for positioning.
• vision: Rating for vision.
• penalties: Rating for penalties.
• composure: Rating for composure.
• marking: Rating for marking.
• standing_tackle: Rating for standing tackle.
• sliding_tackle: Rating for sliding tackle.

Use Case:

This dataset is ideal for data analysis, predictive modeling, and machine learning projects. It can be used for:

• Player performance analysis and comparison.
• Market value assessment and wage prediction.
• Team composition and strategy planning.
• Machine learning models to predict future player potential and career trajectories.

Note:

Please ensure to adhere to the terms of service of SoFIFA.com and relevant data protection laws when using this dataset. The dataset is intended for educational and research purposes only and should not be used for commercial gains without proper authorization.

This dataset describes all the matches made available. Each match is a document consisting of the following fields:- competitionId: the identifier of the competition to which the match belongs to. It is a integer and refers to the field "wyId" of the competition document;- date and dateutc: the former specifies date and time when the match starts in explicit format (e.g., May 20, 2018 at 8:45:00 PM GMT+2), the latter contains the same information but in the compact format YYYY-MM-DD hh:mm:ss; - duration: the duration of the match. It can be "Regular" (matches of regular duration of 90 minutes + stoppage time), "ExtraTime" (matches with supplementary times, as it may happen for matches in continental or international competitions), or "Penalities" (matches which end at penalty kicks, as it may happen for continental or international competitions);- gameweek: the week of the league, starting from the beginning of the league;- label: contains the name of the two clubs and the result of the match (e.g., "Lazio - Internazionale, 2 - 3");- roundID: indicates the match-day of the competition to which the match belongs to. During a competition for soccer clubs, each of the participating clubs plays against each of the other clubs twice, once at home and once away. The matches are organized in match-days: all the matches in match-day i are played before the matches in match-day i + 1, even tough some matches can be anticipated or postponed to facilitate players and clubs participating in Continental or Intercontinental competitions. During a competition for national teams, the "roundID" indicates the stage of the competition (eliminatory round, round of 16, quarter finals, semifinals, final);- seasonId: indicates the season of the match;- status: it can be "Played" (the match has officially finished), "Cancelled" (the match has been canceled for some reason), "Postponed" (the match has been postponed and no new date and time is available yet) or "Suspended" (the match has been suspended and no new date and time is available yet);- venue: the stadium where the match was held (e.g., "Stadio Olimpico");- winner: the identifier of the team which won the game, or 0 if the match ended with a draw;- wyId: the identifier of the match, assigned by Wyscout;- teamsData: it contains several subfields describing information about each team that is playing that match: such as lineup, bench composition, list of substitutions, coach and scores: - hasFormation: it has value 0 if no formation (lineups and benches) is present, and 1 otherwise; - score: the number of goals scored by the team during the match (not counting penalties); - scoreET: the number of goals scored by the team during the match, including the extra time (not counting penalties); - scoreHT: the number of goals scored by the team during the first half of the match; - scoreP: the total number of goals scored by the team after the penalties; - side: the team side in the match (it can be "home" or "away"); - teamId: the identifier of the team; - coachId: the identifier of the team's coach; - bench: the list of the team's players that started the match in the bench and some basic statistics about their performance during the match (goals, own goals, cards); - lineup: the list of the team's players in the starting lineup and some basic statistics about their performance during the match (goals, own goals, cards); - substitutions: the list of team's substitutions during the match, describing the players involved and the minute of the substitution.

https://i.imgur.com/PLS0HB3.gif" alt="Example Video from Deploy Tab">

Here are a few use cases for this project:

1. Sports Analytics: The Soccer Players computer vision model can be used to analyze player performance during games by tracking player and ball positions, individual player actions, and goal-scoring events, allowing coaches and trainers to make data-driven decisions for improving performance and strategies.

2. Automated Highlight Reels: The model can be used to automatically curate soccer match highlights by identifying crucial moments such as goals, outstanding player performances, and referee decisions. This can streamline the video editing process for broadcasting and streaming companies.

3. Virtual Assistant for Soccer Enthusiasts: The Soccer Players model can be integrated into a mobile application, allowing users to take pictures or upload images from soccer matches and receive instant information about the teams (USA, NED), player roles (goalie, outfield player, referee), and other relevant classes such as ball and goal locations, enhancing their understanding and engagement with the sport.

4. Real-Time Augmented Reality (AR) Applications: The model can be used to create AR experiences for soccer fans attending live matches, providing pop-up information about players (such as player stats, team affiliations, etc.) and game events (goals, referee decisions) when viewing the live match through an AR device or smartphone.

5. Training and Scouting Tools: Soccer scouts and trainers can use the Soccer Players model to evaluate potential recruits or assess the performance of their own players during practice sessions. By rapidly identifying key actions (goals, saves, tackles) and providing context for each play, the model can help scouts and trainers make informed decisions faster.

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.

This dataset examines the complexity of network structures in professional and collegiate women’s soccer teams using directed network analysis based on tri-axial acceleration data. The study involved one professional team and one university-level team, with data collected from matches during their respective seasons. Directed network analysis identified dyads and triads, representing cooperative interactions among players, while movement entropy quantified the influence of individual movements within the team. Network diversity, defined as the variability in activation probabilities of dyads and triads, was calculated to evaluate the tactical dynamics and cooperative behaviors of the teams. Data were collected using GNSS devices equipped with tri-axial accelerometers, ensuring precise measurement of movement intensity. The findings provide insights into the structural and functional differences in team coordination between professional and collegiate levels. The dataset is anonymized an..., Participants Prior to participant recruitment, we calculated the minimum required number of matches using G*Power 3.1.9.4 (Heinrich Heine Universität Düsseldorf, Germany). This study employs a two-way analysis of variance (ANOVA) to primarily examine the interaction effects between the period of the match (the first half and second half of the match) and three team groups (professional teams during the first half of the season, professional teams during the second half of the season, and collegiate teams). Thus, the calculation for the F-test with ANOVA was conducted a priori, given an effect size of 0.40, an α error probability of 0.05, a power of 0.80, and a numerator df of 2 with six groups. The effect size (0.40) for this analysis was set based on findings from a previous study that examined changes in team coordination states during matches and reported a large effect size (η² = 0.240 to 0.263) for differences influenced by the level of the opposing team. The total required sample ..., , # Accelerometer-based network analysis in female soccer: performance levels and injuries

https://doi.org/10.5061/dryad.sf7m0cgh6

Description of the data and file structure

Dataset Overview

This dataset investigates the complexity of network structures in professional and collegiate women’s soccer teams, focusing on cooperative interactions and tactical dynamics. Data were collected during matches using GNSS devices equipped with tri-axial accelerometers, providing precise measurements of player movements and interactions.

Data Content

The dataset includes:

1. Player Movement Data: Tri-axial acceleration data recorded at 10 Hz for all players during each match. Data capture spans from match entry to exit, with segmentation into 45-minute halves. Stoppage time is excluded, and substitutions are handled by segmenting data accordingly.
2. Network Analysis Results: Quantitative results of directed network analysis, includ...

Artificial Intelligence (AI) in Sports Analytics Market Outlook

According to our latest research, the global Artificial Intelligence (AI) in Sports Analytics market size reached USD 2.8 billion in 2024. The market is expected to grow at a robust CAGR of 28.6% during the forecast period, reaching approximately USD 25.3 billion by 2033. This remarkable growth is being fueled by the increasing adoption of AI-driven solutions for data-driven decision-making, enhanced player performance analysis, and the rising demand for personalized fan experiences across sports organizations worldwide.

One of the primary growth factors for the AI in Sports Analytics market is the exponential increase in data generated from various sporting activities, including player statistics, match footage, and biometric data. The ability of AI algorithms to process and analyze large volumes of diverse data in real time is revolutionizing how teams and coaches approach training, strategy formulation, and in-game decisions. Advanced machine learning models are enabling sports organizations to extract actionable insights that were previously unattainable, leading to improved player performance, reduced injury risks, and optimized team management. As sports become increasingly competitive, the reliance on AI-powered analytics tools is expected to intensify, further driving market expansion.

Another significant driver is the growing emphasis on fan engagement and media innovation. Sports organizations are leveraging AI to deliver personalized content, interactive experiences, and real-time statistics to fans through digital platforms and broadcast media. AI-powered systems can analyze viewer preferences, social media interactions, and historical data to tailor content and advertisements, enhancing fan loyalty and opening new revenue streams. The integration of AI in broadcasting also enables automated highlight generation, advanced commentary, and immersive viewing experiences, which are reshaping the sports entertainment landscape and contributing to the rapid adoption of AI-based analytics solutions.

The increasing collaboration between technology providers and sports entities is further accelerating the marketÂ’s growth trajectory. Partnerships between AI software developers, sports analytics firms, and professional sports teams are resulting in the development of customized solutions tailored to specific sports and organizational needs. Investments in research and development, coupled with the proliferation of cloud computing and IoT devices, are making AI-powered analytics more accessible and cost-effective. As a result, even mid-tier and amateur sports organizations are beginning to adopt these technologies, broadening the marketÂ’s addressable base and fueling sustained growth.

From a regional perspective, North America currently dominates the AI in Sports Analytics market, accounting for the largest share in 2024, thanks to the presence of leading sports franchises, advanced technological infrastructure, and high investment in sports technology. However, Europe and the Asia Pacific regions are rapidly emerging as key growth markets, driven by increasing sports commercialization, digital transformation initiatives, and the rising popularity of sports such as football, cricket, and basketball. The Middle East & Africa and Latin America are also witnessing growing adoption, albeit at a relatively slower pace, due to increasing investments in sports infrastructure and the proliferation of digital platforms.

Component Analysis

The Component segment of the AI in Sports Analytics market is bifurcated into Software and Services. Software solutions constitute the backbone of AI-driven analytics, encompassing platforms for data collection, processing, visualization, and predictive modeling. These platforms are being widely adopted by sports teams and associations for tasks such as performance tracking, tactical analysis, and injury prevention. The demand fo

Snapshot img

Artificial Intelligence (AI) In Sports Market Size 2024-2028

The artificial intelligence (ai) in sports market size is forecast to increase by USD 6.42 billion at a CAGR of 33.13% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing demand for data integration and visual analytics in the sports industry. The integration of AI technologies enables sports teams and organizations to gain valuable insights from vast amounts of data, leading to improved performance and fan engagement. Moreover, the adoption of cloud-based AI solutions is on the rise, offering cost-effective and scalable alternatives to traditional on-premises systems. However, the market faces a notable challenge: the lack of a skilled workforce capable of developing and implementing AI algorithms. This shortage of expertise poses a significant obstacle for teams and organizations seeking to capitalize on the potential of AI in sports.
To overcome this challenge, companies are exploring partnerships with AI companies and academic institutions, as well as investing in training and upskilling their existing workforce. Effective collaboration and strategic workforce development initiatives will be crucial for organizations looking to stay competitive in the rapidly evolving AI in Sports market.

What will be the Size of the Artificial Intelligence (AI) In Sports Market during the forecast period?

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How is this Artificial Intelligence (AI) In Sports Industry segmented?

The artificial intelligence in sport market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Football
  Cricket
  Tennis
  Basketball
  Other


Offering

  Solution
  Services
  Fan engagement & Experience enhancement
  Solution
  Services
  Fan engagement & Experience enhancement


Sport Type

  Team Sports
  Individual Sports
  E-Sport
  Team Sports
  Individual Sports
  E-Sport


Technology

  Machine Learning
  Computer Vision
  Natural Language Processing


Application

  Athlete Performance
  Fan Engagement
  Injury Prevention
  Game Strategy


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

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

In the dynamic world of sports, Artificial Intelligence (AI) is revolutionizing various aspects, from recruitment and scouting to fan engagement. The football segment dominates the global AI market due to the integration of technologies like IoT devices, GPS trackers, and computer vision algorithms for player and ball movement tracking. Machine learning platforms, text analytics, robotic process automation, image and video analysis, deep learning, and speech recognition are driving growth. AI-powered coaching, predictive modeling, and performance measurement offer personalized training plans, while natural language processing facilitates social media integration and fan experience enhancement. Sports betting, fantasy sports, and sponsorship management also benefit from AI's predictive capabilities.

Smart stadiums, skill development, and sports governance are further areas of application. Computer vision, deep learning, and data-driven decision making optimize game strategy, performance, and fan engagement. Wearable technology, player health monitoring, and injury prediction are essential for athlete performance analysis and sports medicine. AI's role extends to referee assistance, fraud detection, ticket sales, and even game development with virtual athletes. The rise of AI in sports is a testament to the industry's embrace of technology, fostering a more immersive, harmonious, and data-driven sports ecosystem.

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The Football segment was valued at USD 514.10 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 31% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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In the dynamic world of sports, Artificial Intelligence (AI) is increasingly becoming a game-changer. Across North America, the use of AI is gaining momentum, driven by advanced economies such as the US and Canada. AI applications in sports span various sectors, from recruitment and scouting to player health and fan engagement. Management software in major sports events is a significant growth factor f

The global smart shin guard market is experiencing robust growth, driven by increasing participation in sports, technological advancements in protective gear, and a rising demand for performance enhancement tools among athletes. The market size in 2025 is estimated at $150 million, demonstrating significant potential for growth. Considering a projected Compound Annual Growth Rate (CAGR) of 15%, the market is poised to reach approximately $450 million by 2033. This growth is fueled by several key trends, including the integration of data analytics and wearable technology into shin guards, providing valuable insights into player performance and injury prevention. Furthermore, the increasing adoption of smart shin guards in professional sports leagues and the rising popularity of amateur athletic activities contribute significantly to market expansion. While challenges such as high initial investment costs and potential concerns about data privacy might act as restraints, the overall market outlook remains highly positive. The market segmentation reveals a significant demand across both men's and women's sports, with online sales channels gaining traction alongside traditional offline retail. Major players like Soccerment, Humanox, G-Form, GRYPHON Hockey, Pro Shin Guards, Nike, and Adidas are leading the innovation and market penetration. Geographically, North America and Europe currently hold the largest market shares, attributed to high sports participation rates and strong consumer spending. However, emerging markets in Asia Pacific, particularly China and India, offer significant growth opportunities due to rising disposable incomes and a burgeoning sports culture. The competitive landscape is dynamic, with existing players focusing on product differentiation through advanced features and strategic partnerships to expand their market reach. The market's future hinges on continued technological advancements, strategic collaborations between sports technology companies and athletic apparel brands, and increasing awareness among athletes about the benefits of smart shin guards. This report provides a detailed analysis of the rapidly expanding smart shin guards market, projected to reach $150 million by 2028. We delve into market segmentation, key players, emerging trends, and future growth potential, utilizing data-driven insights and industry expertise. This report is crucial for investors, manufacturers, and industry stakeholders seeking to navigate this dynamic market.

Football Leagues DataSet

1. Overview

2. The Csv file fields Description

3. Describing the structure of the complex formats in the CSV File fields

4. Example

5. Disclaimer

Overview

This dataset contains historical football data from Champions league tournament. (seasons: 2016/2017, 2017/2018, 2018/2019)

Csv file fields Description

Structure of the complex formats in the CSV File fields

Context

As many others I have asked myself if it is possible to use machine learning in order to create valid predictions for football (soccer) match outcomes. Hence I created a dataset consisting of historic match data for the German Bundesliga (1st and 2nd Division) as well as the English Premier League reaching back as far as 1993 up to 2016. Besides the mere information concerning goals scored and home/draw/away win the dataset also includes per site (team) data such as transfer value per team (pre-season), the squad strength, etc. Unfortunately I was only able to find sources for these advanced attributes going back to the 2005 season.
I have used this dataset with different machine learning algorithms including random forests, XGBoost as well as different recurrent neural network architectures (in order to potentially identify recurring patterns in winning streaks, etc.). I'd like to share the approaches I used as separate Kernels here as well. So far I did not manage to exceed an accuracy of 53% consistently on a validation set using 2016 season of Bundesliga 1 (no information rate = 49%).

Although I have done some visual exploration before implementing the different machine learning approaches using Tableau, I think a visual exploration kernel would be very beneficial.

Content

The data comes as an Sqlite file containing the following tables and fields:

Table: Matches

• Match_ID (int): unique ID per match
• Div (str): identifies the division the match was played in (D1 = Bundesliga, D2 = Bundesliga 2, E0 = English Premier League)
• Season (int): Season the match took place in (usually covering the period of August till May of the following year)
• Date (str): Date of the match
• HomeTeam (str): Name of the home team
• AwayTeam (str): Name of the away team
• FTHG (int) (Full Time Home Goals): Number of goals scored by the home team
• FTAG (int) (Full Time Away Goals): Number of goals scored by the away team
• FTR (str) (Full Time Result): 3-way result of the match (H = Home Win, D = Draw, A = Away Win)

Table: Teams

• Season (str): Football season for which the data is valid
• TeamName (str): Name of the team the data concerns
• KaderHome (str): Number of Players in the squad
• AvgAgeHome (str): Average age of players
• ForeignPlayersHome (str): Number of foreign players (non-German, non-English respectively) playing for the team
• OverallMarketValueHome (str): Overall market value of the team pre-season in EUR (based on data from transfermarkt.de)
• AvgMarketValueHome (str): Average market value (per player) of the team pre-season in EUR (based on data from transfermarkt.de)
• StadiumCapacity (str): Maximum stadium capacity of the team's home stadium

Table: Unique Teams

• TeamName (str): Name of a team
• Unique_Team_ID (int): Unique identifier for each team

Table: Teams_in_Matches

• Match_ID (int): Unique match ID
• Unique_Team_ID (int): Unique team ID (This table is used to easily retrieve each match a given team has played in)

Based on these tables I created a couple of views which I used as input for my machine learning models:

View: FlatView

Combination of all matches with the respective additional data from Teams table for both home and away team.

View: FlatView_Advanced

Same as Flatview but also includes Unique_Team_ID and Unique_Team in order to easily retrieve all matches played by a team in chronological order.

View: FlatView_Chrono_TeamOrder_Reduced

Similar to Flatview_Advanced, however missing the additional attributes from team in order to have a longer history including years 1993 - 2004. Especially interesting if one is only interested in analyzing winning/loosing streaks.

Acknowledgements

Thanks to football-data.co.uk and transfermarkt.de for providing the raw data used in this dataset.

Inspiration

Please feel free to use the humble dataset provided here for any purpose you want. To me it would be most interesting if others think that recurrent neural networks could in fact be of help (and even maybe outperform classical feature engineering) in identifying streaks of losses and wins. In the literature I mostly only found example of RNN application where the data were time series in a very narrow sense (e.g. temperature measurements over time) hence it would be interesting to get your input on this question.

Maybe someone also finds additional attributes per team or match which have substantial impact on match outcome. So far I have found the "Market Value" of a team to be by far the best predictor when two teams face each other, which makes sense as the market value usually tends to correlate closely with the strength of a team and it's propects at winning

Sample game of anonymised football run data. Showing all of the high speed runs made in that match with associated values at the start of the run, the mean value during the run and the accrued value (calculated as the difference). This is calculated for the in-possession team which may be both teams if possession changes over the course of the run, this is distinguished by the opposition value columns and the own-team value columns.

If you use these data, please remember to 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.Tensors extracted from soccer video broadcasts. Each file is a zip of folder and corresponds to a single half of a match. Each file in the folder (in .pickle format) corresponds to a frame of the video.This item contains the following files/matches:- roma_juve_1H_tensors.zip: tensors/frames of the first half of match Roma vs Juventus- roma_juve_2H_tensors.zip: tensors/frames of the second half of match Roma vs Juventus- roma_lazio_1H_tensors.zip: tensors/frames of the first half of match Roma vs Lazio- sassuolo_inter_1H_tensors.zip: tensors/frames of the first half of match Sassuolo vs Inter- sassuolo_inter_2H_tensors.zip: tensors/frames of the second half of match Sassuolo vs Inter