📂 About This Dataset This dataset combines detailed player performance statistics from WhoScored with team and player meta-data from Transfermarkt. It covers over 1,500 players from top European leagues and includes metrics such as:

Expected Goals (xG) & xG per 90

Tackles, Interceptions, Key Passes, Assists

Pass Accuracy, Crosses, Long Balls

Total Minutes Played & Formations

Player Age, Height, Positioning

🧩 Use Cases Player Rating Prediction

Team Formation Impact Analysis

Identifying Underrated Players via xG vs. Goals

Clustering Players by Style or Efficiency

Fantasy Football Recommendations

🏗️ Data Sources WhoScored.com: Player match stats, tactical analysis.

Transfermarkt.com: Player bio, team formations.

📊 Features Snapshot 32 Columns

Over 20 numerical performance metrics

Cleaned, ready-to-analyze format

Small number of missing values (mostly in passing stats)

Player Performance Digital Twin Market Outlook

Based on our latest research, the global Player Performance Digital Twin market size reached USD 1.14 billion in 2024, and is expected to grow at a robust CAGR of 28.7% from 2025 to 2033. By the end of the forecast period, the market is projected to achieve a value of USD 10.23 billion by 2033. The primary growth driver is the increasing adoption of advanced analytics and digitalization in sports, which is revolutionizing athlete training, injury prevention, and performance optimization across both professional and amateur levels.

The surge in demand for Player Performance Digital Twin solutions is primarily fueled by the sports industry's quest for data-driven decision-making and personalized athlete management. Digital twin technology enables real-time monitoring and simulation of an athlete’s physical and physiological state, offering actionable insights that were previously unattainable. As clubs and organizations strive to maximize the value of their investments in talent, the ability to predict injury risk, optimize training loads, and tailor rehabilitation programs has become a critical competitive advantage. Furthermore, the integration of IoT sensors, wearable devices, and AI-powered analytics into sports ecosystems is facilitating the seamless collection and analysis of vast datasets, further accelerating market growth.

Another significant growth factor is the heightened focus on athlete health and longevity. With the increasing commercial stakes in professional sports, the cost of player injuries has become a major concern for teams and sponsors alike. Digital twin solutions offer predictive capabilities that help in early detection of fatigue, biomechanical imbalances, and potential injury triggers, enabling proactive interventions. This not only enhances player safety but also ensures optimal performance throughout the season. The growing awareness of mental well-being, coupled with the ability of digital twins to monitor psychological stressors, is also contributing to the holistic approach in athlete management, further boosting the adoption of these technologies.

The expanding application of Player Performance Digital Twin technology beyond elite sports teams to academies, individual athletes, and grassroots programs is another catalyst for market expansion. As the cost of digital twin solutions decreases and cloud-based deployment models become more prevalent, accessibility is improving for smaller organizations and aspiring athletes. This democratization of advanced sports analytics is fostering a more competitive and data-savvy sports ecosystem globally. Moreover, the rise of e-sports and virtual competitions is opening new avenues for digital twin applications, further diversifying the market landscape.

Regionally, North America leads the market, accounting for the largest share in 2024, driven by high technology adoption rates, significant investments in sports science, and a mature professional sports infrastructure. Europe follows closely, propelled by the widespread integration of sports technology in football, rugby, and athletics. The Asia Pacific region is witnessing the fastest growth, fueled by increasing investments in sports development, the emergence of new leagues, and government initiatives to promote athlete performance and well-being. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as sports organizations in these regions embrace digital transformation to enhance competitiveness.

Component Analysis

The Player Performance Digital Twin market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of digital twin platforms, enabling the creation, visualization, and simulation of virtual athlete models. These platforms leverage advanced algorithms, machine learning, and AI to process complex datasets and generate actionable insights. Leading sof

This dataset provides an archive of Fantasy Premier League (FPL) player performance data for eight seasons, spanning from 2016-2024.

The data was originally collected from https://github.com/vaastav/Fantasy-Premier-League, a public repository for FPL data.

The dataset has been meticulously cleaned and processed to ensure accuracy and consistency. This may include handling missing values, correcting inconsistencies, and standardizing formats.

The dataset includes a wide range of player statistics captured on a gameweek-by-gameweek basis. This allows you to analyze trends, identify patterns, and gain valuable insights into player performance.

This dataset can be a powerful tool for FPL enthusiasts and data scientists alike. Here are some potential applications: - Trend Analysis: Identify historical trends in player performance across different seasons and positions. - Predictive Modeling: Develop machine learning models to predict player points, performance, and transfers. - Informed Team Selection: Make data-driven decisions to optimize your FPL team for each gameweek. - Comparative Analysis: Compare player statistics across seasons and positions to uncover hidden gems and potential breakout stars.

Using this dataset, you can gain a deeper understanding of FPL player performance and enhance your decision-making for the upcoming season.

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.

Player Performance Digital Twin Market Outlook

According to our latest research, the global Player Performance Digital Twin market size stood at USD 1.12 billion in 2024, reflecting the rapid integration of digital technologies in sports performance analytics. The market is experiencing robust momentum, with a CAGR of 28.7% projected for the forecast period of 2025 to 2033. By 2033, the market is expected to reach a value of USD 9.57 billion, driven by the increasing adoption of digital twin solutions for athlete training, injury prevention, and real-time performance optimization. The primary growth factor is the surging demand for data-driven decision-making in professional sports, which is reshaping how athletes, coaches, and teams approach performance enhancement and strategic planning.

The growth trajectory of the Player Performance Digital Twin market is largely propelled by the rising emphasis on personalized athlete management and the need for continuous improvement in sports performance. Digital twin technology allows for the creation of virtual replicas of athletes, enabling real-time monitoring and simulation of physical and physiological parameters. This capability empowers coaches and sports scientists to design customized training regimens, predict injury risks, and optimize recovery processes. The proliferation of wearable devices and IoT sensors has further accelerated the collection of granular data, which feeds into digital twin models for actionable insights. As a result, the integration of AI, machine learning, and advanced analytics with digital twin platforms is becoming a cornerstone in elite sports, fostering a culture of innovation and evidence-based decision-making.

Another significant growth driver is the increasing commercialization of sports and the escalating competition among teams and individual athletes. With lucrative sponsorship deals and broadcasting rights at stake, sports organizations are under immense pressure to maximize player performance and minimize downtime due to injuries. Digital twin solutions offer a competitive edge by enabling predictive analytics, scenario planning, and performance benchmarking. These capabilities are not limited to professional sports but are also being adopted by sports academies, youth development programs, and individual athletes aspiring to reach elite levels. The democratization of digital twin technology, facilitated by cloud-based deployment and scalable software platforms, is expanding the addressable market and lowering barriers to entry for smaller organizations.

The regional landscape of the Player Performance Digital Twin market is characterized by strong adoption in North America and Europe, where sports technology ecosystems are well-established and investment in innovation is high. North America, in particular, is home to major sports leagues and technology providers, fostering a fertile environment for the deployment of digital twin solutions. Europe follows closely, driven by the popularity of football, rugby, and other team sports that demand sophisticated performance analytics. Meanwhile, the Asia Pacific region is emerging as a high-growth market, buoyed by government initiatives to promote sports excellence and the rising popularity of international sporting events. Latin America and the Middle East & Africa are also witnessing steady adoption, although market maturity varies across countries. The global outlook remains highly optimistic, with ongoing advancements in AI, sensor technology, and cloud computing expected to unlock new opportunities for market expansion.

Component Analysis

The Player Performance Digital Twin market is segmented by component into software, hardware, and services, each playing a critical role in the value chain. Software forms the backbone of digital twin solutions, encompassing data analytics platforms, simulation engines, and visualization tools. These software applications are designed to ingest, process, and interpret large volumes of physiological and biomechanical data, enabling the creation of accurate virtual models of athletes. The software segment is witnessing rapid innovation, with vendors incorporating machine learning algorithms, predictive analytics, and real-time data synchronization to enhance model fidelity and usability. As a result, software solutions are becoming increasingly modular and customizable, catering to the specific needs of different sports and user groups.

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Player Tracking System Market Outlook

According to our latest research, the global player tracking system market size reached USD 7.3 billion in 2024. The market is poised for robust growth, exhibiting a CAGR of 18.2% from 2025 to 2033. By the end of the forecast period in 2033, the player tracking system market is projected to attain a value of USD 37.7 billion. This remarkable expansion is primarily fueled by the increasing integration of advanced analytics and wearable technology in sports, growing demand for real-time player performance data, and the rising adoption of data-driven decision-making by sports organizations worldwide.

One of the most significant growth factors driving the player tracking system market is the rapid technological advancements in sports analytics. The proliferation of high-definition cameras, sophisticated sensors, and artificial intelligence algorithms has revolutionized the way player performance is monitored and analyzed. Teams and coaches are leveraging these systems to gain granular insights into player movements, fatigue levels, and tactical efficiency. The integration of player tracking data with video analytics platforms allows for a comprehensive understanding of both individual and team performance. This technological evolution not only enhances player development and injury prevention but also gives teams a competitive edge, thereby accelerating the adoption of player tracking systems across various sports disciplines.

Another key driver for the growth of the player tracking system market is the increasing commercialization of sports and the proliferation of professional leagues globally. With the surge in sponsorship deals, broadcasting rights, and fan engagement initiatives, sports organizations are under immense pressure to maximize performance and entertainment value. Player tracking systems play a pivotal role in delivering engaging content for fans, enabling broadcasters to provide real-time statistics and immersive viewing experiences. Additionally, the demand for personalized training programs and the need to monitor athlete health and wellness are compelling sports teams and associations to invest heavily in advanced tracking solutions. The convergence of sports science and technology is thus fostering a fertile environment for the expansion of the player tracking system market.

The growing awareness of player safety and regulatory compliance is also propelling market growth. Governing bodies and sports associations are increasingly mandating the use of player tracking systems to ensure adherence to safety protocols and to monitor compliance with training regulations. These systems enable real-time monitoring of biometric data, helping to detect early signs of fatigue or injury risk. This proactive approach to player health management is gaining traction not only in elite professional sports but also at the grassroots and amateur levels. As a result, the player tracking system market is witnessing widespread adoption across a diverse range of end-users, further contributing to its sustained growth trajectory.

From a regional perspective, North America continues to dominate the player tracking system market, accounting for the largest share in 2024. The region's leadership is attributed to the presence of major sports leagues, early adoption of technology, and significant investments in sports infrastructure. Europe follows closely, driven by the popularity of football, rugby, and other team sports. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by the increasing professionalization of sports and rising investments in sports technology. Latin America and the Middle East & Africa are also witnessing steady growth, supported by expanding sports leagues and government initiatives to promote sports development. The regional dynamics underscore the global appeal and adoption of player tracking systems, with each geography presenting unique opportunities and challenges for market participants.

Component Analysis

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.

Dataset

This dataset was created by Sakthivel06

Contents

It contains the following files:

The global player tracking market is experiencing robust growth, fueled by the increasing adoption of advanced analytics in sports to enhance player performance and team strategy. With a Compound Annual Growth Rate (CAGR) of 24.90% from 2019 to 2024, the market demonstrates significant potential. The market's expansion is driven by several factors, including the rising popularity of sports globally, technological advancements leading to more sophisticated and affordable tracking systems, and the growing demand for data-driven insights among coaches and athletes. The integration of wearable sensors, AI-powered analytics platforms, and high-speed video analysis is transforming the way teams train and compete, leading to increased investment in player tracking solutions. Market segmentation reveals strong growth across both solution and service offerings, catering to individual and team sports alike. North America currently holds a significant market share, driven by high technological adoption and a well-established sports industry infrastructure. However, Asia-Pacific is anticipated to witness significant growth in the coming years, propelled by rising participation in sports and increasing disposable incomes. The market faces challenges such as high initial investment costs for technology and the need for robust data infrastructure to support analysis. Nevertheless, the overall market trajectory remains positive, with significant opportunities for growth across various regions and segments. The competitive landscape is characterized by a mix of established technology providers and specialized sports analytics companies. Key players are continuously innovating to offer comprehensive solutions encompassing hardware, software, and analytics services. This competitive dynamic drives innovation and helps to improve the affordability and accessibility of player tracking technologies. Furthermore, the increasing demand for integrated solutions that seamlessly combine player tracking with other performance analysis tools, such as video analysis and biometrics, is shaping the market's future. The market is also witnessing the emergence of new entrants, offering niche solutions and specialized services targeting specific sports or performance aspects. This competitive landscape ensures continuous improvement in the quality, functionality, and accessibility of player tracking technology, benefiting athletes, coaches, and teams worldwide. Future growth will likely be driven by further advancements in sensor technology, the development of more user-friendly analytics platforms, and the integration of player tracking data with other performance metrics for comprehensive insights. Recent developments include: June 2022 - Stats Perform announced a new series of advanced football metrics by introducing live Opta Vision data feeds for the 2022-23 football season. Opta Vision represents a new generation of deeper sports data. By combining the company's industry-leading Opta event data with tracking data to create a single, merged dataset, Opta Vision delivers richer performance insights to professional teams. The merged dataset also utilizes the company's qwinn artificial intelligence to generate enriched data outputs. The new data outputs and predictive metrics include insights related to dynamic changes in a team's shape during a match., March 2022 - Catapult and Champion Data made a multi-year deal to supply performance analysis solutions to the Australian Football League (AFL). The teams across the AFL, AFLW and AFL Pathways will use Catapult's vector devices to empower data-driven decisions to enhance player performance, quantify findings to help mitigate the risk of injuries, and inform return-to-play processes.. Key drivers for this market are: Technological Advancements in Wearable Sports Devices. Potential restraints include: Technological Advancements in Wearable Sports Devices. Notable trends are: Wearable Devices Offers Potential Growth.

The dataset contains data for each of the players who have interacted with the NBA during a specific period of time (last season) and collects all the accumulated statistics. In addition, it summarizes the performance of each player through the rest of the data by means of the player efficiency rating (PER) variable, a metric that takes into account all the data extracted from a player.

We will create a customized sports dataset tailored to your specific requirements. Data points may include player statistics, team rankings, game scores, player contracts, and other relevant metrics.

Utilize our sports datasets for a variety of applications to boost strategic planning and performance analysis. Analyzing these datasets can help organizations understand player performance and market trends within the sports industry, allowing for more precise team management and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing player performance analysis, refining team strategies, and optimizing fan engagement efforts.

Here are a few use cases for this project:

1. Sports Analytics: Use the "Basketball Players" model to automatically track players' movements, ball possession, and referee decisions during live games or post-game analysis. This data can be used by coaches, analysts, and teams to inform and improve strategies, tactics, and player performance.

2. Real-time Game Commentary: Integrate the model into sports broadcasting platforms, providing real-time updates and statistics to commentators, allowing them to focus on in-depth analysis and storytelling while the model handles identification and stat-tracking.

3. Automated Sports Highlights: Utilize the model to automatically create highlights from basketball games by identifying key moments, such as successful shots, blocks, and referee decisions. This can streamline post-production process for sports media outlets and social media channels.

4. Training and Skill Development: Leverage the "Basketball Players" model to create feedback tools for players, identifying areas of improvement in team dynamics and individual technique during practice sessions or games.

5. Fan Experience: Employ the model in smartphone apps or AR devices, providing fans with real-time information on their favorite teams and players during live games, enhancing their overall experience and engagement.

This dataset provides empirical data on the impact of wearing cricket protective gear on agility and sprint performance among competitive cricket players. The study was conducted using two standardized tests: the New Multi-Change of Direction Agility Test (NMAT) and the Bangsbo Sprint Test, with performance recorded both with and without cricket gear. The dataset includes measurements from 144 male cricket players, categorized into three age groups: Under-16 (U16), Under-18 (U18), and Under-23 (U23). Key attributes include demographic details (age, height, weight, BMI), test performance times, and dominant hand preference. This dataset can be used for sports analytics, machine learning-based performance prediction, and optimizing training methodologies for cricket players.

Keywords: Cricket performance, agility, sprint test, protective gear, NMAT, Bangsbo Sprint Test, machine learning in sports, athlete performance analysis

Dataset Information: Subjects: 72 male competitive cricket players Age Groups: U16, U18, U23 Tests Conducted: NMAT (agility), Bangsbo Sprint Test (sprint performance) Conditions: With and without protective cricket gear Variables Included: Age, height, weight, BMI, NMAT times, Bangsbo sprint times, dominant hand, and player division

Column Descriptions: Age Group: U16, U18, U23 categories

Height (cm): Player's height in centimeters

Weight (kg): Player's weight in kilograms

BMI: Body Mass Index calculated from height and weight

NMATwithout Cricket Gears in sec: Agility test time without gear

NMATwith Cricket Gears in sec: Agility test time with gear

Bangsbo test wihout Cricket Gears in sec: Sprint test time without gear

Bangsbo test With Cricket Gears in sec: Sprint test time with gear

Methodology: Study Design: Cross-sectional study Testing Area: Cricket training facility with controlled conditions Equipment Used: Standard cricket gear (pads, gloves, helmet) Electronic timing gates for precise measurements

Procedure: Players completed NMAT and Bangsbo Sprint Test under both conditions (with/without gear). Each test was performed after a warm-up, with sufficient recovery time between trials to minimize fatigue. Performance times were recorded and analyzed.

Potential Research Applications: Sports Performance Analysis: Evaluating how wearing cricket gear influences speed and agility. Injury Prevention & Biomechanics: Understanding the potential risk of injury due to restricted mobility. Sports Equipment Optimization: Informing the development of lighter, performance-friendly cricket gear. Machine Learning for Sports Analytics: Predicting performance outcomes using AI-driven models.

Sports Game Data Software Market Outlook

The global sports game data software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 3.8 billion by 2032, exhibiting a CAGR of 13.2% during the forecast period. This robust growth can be attributed to the increasing digitization of sports, the expanding adoption of data analytics in sports management, and the rising demand for enhanced fan engagement solutions.

One of the primary growth factors driving the sports game data software market is the increasing reliance on data analytics to optimize team performance and strategy. Sports organizations and teams are increasingly using complex algorithms and data analytics tools to assess player performance, develop game strategies, and enhance overall team efficiency. Integrating data analytics enables teams to make informed decisions, reducing the margin for error and contributing to better performance outcomes. This transition to data-driven sports management is significantly boosting the demand for sports game data software.

Furthermore, the rising popularity of sports globally and the consequent increase in sports viewership are contributing to the expanding market for sports game data software. With more people engaging with sports events, there is a growing need for advanced software solutions that can enhance fan engagement by providing real-time data and interactive experiences. This trend is particularly prominent in regions such as North America and Europe, where sports events attract massive viewership, necessitating sophisticated fan engagement platforms to maintain and expand audience interest.

The developments in artificial intelligence (AI) and machine learning (ML) technologies are also pivotal in propelling the sports game data software market forward. These technologies enable the creation of advanced data analytics tools that can process vast amounts of data quickly and accurately. The integration of AI and ML in sports analytics not only helps in predicting player performance and game outcomes but also in developing personalized fan experiences. With continuous advancements in these technologies, the sports game data software market is poised for significant growth.

Cricket Analysis Software has emerged as a vital tool in the realm of sports analytics, particularly for cricket teams seeking to enhance their performance. This software leverages advanced data analytics to provide insights into player performance, game strategies, and opposition analysis. By analyzing historical data and real-time match statistics, cricket teams can develop more effective game plans and make informed decisions on the field. The integration of Cricket Analysis Software into team management processes not only aids in optimizing performance but also in identifying areas for improvement, thus contributing to the overall growth of the sports game data software market.

Regionally, North America currently holds the largest share of the market, driven by the presence of major sports leagues and the high adoption rate of advanced technologies in sports management. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the rising popularity of various sports, increasing investments in sports infrastructure, and the growing adoption of digital solutions. This regional growth is further supported by government initiatives promoting sports as a means to enhance health and fitness among the population.

Component Analysis

The sports game data software market can be segmented by component into software and services. The software segment dominates the market due to the rising demand for advanced analytics tools that can process and interpret large volumes of data. These software solutions provide critical insights that help sports teams and organizations make data-driven decisions. The software segment includes a variety of applications such as performance analysis tools, strategy development platforms, and fan engagement solutions, which are all essential for modern sports management.

In contrast, the services segment is also experiencing substantial growth, driven by the increasing need for professional services that support the implementation and maintenance of sports game data software. These services include consulting, training, and support services, which are crucial for ensuring the optimal use of data analy

So, I was trying to predict the rating of players in the FIFA21 game which is going to be released in the coming weeks by using their individual performance in the previous season and the rating in the previous edition of the game FIFA20. But, I couldn't find a dataset for this. So I had no option other than to scrape data from the PL website itself.

Each row in the datasets represents each player's performance in that particular season. It starts with Name, Position, Appearances, and the statistics of his performance throughout the season. Some entries are null because those attributes don't correspond to the position in which the player actually plays, for instance, a Forward will not have Number of saves; it doesn't make sense.

To all those football freaks like me, Feel free to use this dataset

Let me know if there's an error

NBA Player Game Stats - 2024/2025 Season

This dataset provides comprehensive performance statistics for NBA players throughout the 2024/2025 season. It includes both advanced and traditional stats, making it ideal for player performance analysis, efficiency assessments, and exploring game patterns and trends. Data was collected from reliable sources, ensuring quality and consistency across each record.

Column Descriptions

• Player: Name of the player.
• Tm: Abbreviation of the player's team.
• Opp: Abbreviation of the opposing team.
• Res: Result of the game for the player's team.
• MP: Minutes played, represented as a float (e.g., 23.5 = 23 minutes and 30 seconds).
• FG: Field goals made.
• FGA: Field goal attempts.
• FG%: Field goal percentage.
• 3P: 3-point field goals made.
• 3PA: 3-point field goal attempts.
• 3P%: 3-point shooting percentage.
• FT: Free throws made.
• FTA: Free throw attempts.
• FT%: Free throw percentage.
• ORB: Offensive rebounds.
• DRB: Defensive rebounds.
• TRB: Total rebounds.
• AST: Assists.
• STL: Steals.
• BLK: Blocks.
• TOV: Turnovers.
• PF: Personal fouls.
• PTS: Total points scored.
• GmSc: Game Score, a metric summarizing player performance for the game.
• Data: Date of the game in YYYY-MM-DD format.

Usage Examples

This dataset is perfectly suited for: - Statistical analysis: Gain insights into player and team performance trends. - Machine learning projects: Build predictive models using detailed player stats. - Performance prediction: Forecast player outcomes or team results. - Player comparisons: Analyze players across various metrics and categories. - Efficiency analysis: Evaluate player and team efficiency, comparing statistics across games. - Game trend exploration: Investigate patterns within the season, identifying shifts in strategies and performance.

This dataset contains detailed ball-by-ball information from various cricket matches. It provides an in-depth view of match events, such as player performance, wickets, and scoring patterns, enabling analysis of team strategies, individual contributions, and overall match outcomes.

Dataset Features:

• Match ID: A unique identifier for each match.
• Date: The date on which the match was played.
• Venue: The stadium or location where the match took place.
• Bat First: The team that batted first in the match.
• Bat Second: The team that batted second in the match.
• Innings: The innings number (1 or 2) during the match.
• Over: The over in which the ball was bowled.
• Ball: The specific ball in the over.
• Batter: The player on strike facing the delivery.
• Non-Striker: The player at the non-striker's end.
• Bowler: The bowler delivers the ball.
• Batter Runs: The runs scored by the batter from a specific ball.
• Extra Runs: Additional runs awarded due to extras (integer value.).
• Runs From Ball: Total runs scored off the delivery, including extras.
• Ball Rebowled: Indicates whether the ball was re-bowled (Yes - 1/No - 0).
• Wicket: Indicates whether a wicket was taken (Yes - 1/No - 0).
• Method: Describes how the batter got out (e.g., bowled, caught, LBW).
• Player Out: The name of the player dismissed.
• Innings Runs: Total runs scored in the respective innings.
• Innings Wickets: Total wickets lost in the innings.
• Target Score: The score the batting team is chasing (if applicable).
• Runs to Get: Runs needed to win at that point in the match.
• Balls Remaining: Number of balls left in the innings.
• Winner: The team that won the match.
• Chased Successfully: Indicates whether the target was successfully chased (1 for Yes, 0 for No).

Usage:

This dataset is ideal for cricket analytics and machine learning tasks, including: - Analysing player and team performance trends. - Training predictive models for match outcomes. - Developing simulation tools for cricket strategy optimisation. - Identifying key moments and contributors in matches.

Coverage:

The dataset encompasses critical match and ball-level details, capturing the intricacies of cricket gameplay. It is suitable for exploring various analytical dimensions, such as player efficiency, bowling performance, and team tactics.

License:

CC0 (Public Domain)

Who can use it:

This dataset is designed for data scientists, sports analysts, machine learning practitioners, and cricket enthusiasts interested in leveraging data for sports analytics.

How to use it:

• Build predictive models for match outcomes and player performances.
• Analyse player contributions in different match contexts.
• Conduct exploratory data analysis on cricket match events.
• Simulate match scenarios to evaluate team strategies.

Descriptive statistics statistic (mean and standard deviation) and ANOVA analysis (p-value) of different parameters of turns analysed according to field position and respective Effect Size (ES) of differences observed.

The global player tracking system market is experiencing robust growth, driven by the increasing adoption of advanced analytics in professional and amateur sports. The market's expansion is fueled by several key factors: a rising demand for performance optimization among athletes and teams across various sports, technological advancements leading to more accurate and sophisticated tracking systems (like the integration of AI and machine learning), and a growing acceptance of data-driven decision-making in sports coaching and training. The market is segmented by application (individual vs. team sports) and by type of system (optical, wearable, and others), with wearable systems currently holding a significant market share due to their portability and ease of use. However, optical systems are expected to witness substantial growth in the coming years owing to their ability to provide a comprehensive view of the playing field and player movements. The substantial investment in sports infrastructure and technology, particularly in developed regions like North America and Europe, further propels market expansion. However, factors such as the high initial cost of implementing these systems and the need for specialized expertise to analyze the generated data pose certain restraints. Considering a conservative estimate of a 15% CAGR (Compound Annual Growth Rate) based on the current market dynamism and technology adoption trends, the market is poised for significant expansion, with substantial growth expected across all major regions. The competitive landscape features a blend of established technology providers and specialized sports analytics companies, leading to continuous innovation and the development of more user-friendly and cost-effective solutions. The North American market currently dominates due to the region's advanced sports infrastructure and high adoption rates. However, Asia-Pacific is anticipated to showcase the fastest growth in the coming years, fueled by rising participation in sports and a growing interest in data-driven performance enhancements across various leagues and training programs in countries like India and China. The European market is also expected to experience steady growth due to the established sports culture and ongoing technological advancements. Further segment-specific growth will be influenced by factors such as the specific sports' popularity, investment in youth development programs, and the broader adoption of data analytics within the athletic sector. Overall, the player tracking system market is predicted to witness strong and sustained growth throughout the forecast period (2025-2033), driven by the confluence of technological advancements, data-driven decision-making, and the ever-increasing focus on athletic performance optimization.

Descriptive statistics statistic (mean and standard deviation) and ANOVA analysis (p-value) of different HIR distances and work-rate parameters analysed according to field position and respective Effect Size (ES) of differences observed.