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.

This dataset gives a game-by-game attendance to every NCAA FBS game from 2001 to today. Big thanks to the SportsDataVerse whose cfbfastR package was used to get a majority of this data. NCAA Statistics was used to get current year attendance data.

Major US Sports Venues Usage and Affiliations

Team, League, Conference and Population Usage Records

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset provides detailed information on major sport venues, along with their usage and affiliations. It includes data related to the National Association for Stock Car Auto Racing, Indy Racing League, Major League Soccer, Major League Baseball, National Basketball Association, Women's National Basketball Association, National Hockey League, National Football League, PGA Tour, NCAA Division 1 FBS Football, NCAA Division 1 Basketball and thoroughbred horse racing.* This dataset contains columns such as USE (which describes the type of use for the venue), TEAM (the team associated with the venue), LEAGUE (the league associated with the venue) , CONFERENCE (the conference associated with the venue), DIVISION (the division associated with the venue), INST_AFFIL(the institution affiliation associatedwith the venue), TRACK_TYPE(type of track at a specific point in time or over its complete life-cycle) as well as LENGTH_MILEGE ('length of track in milege') ROOF_TYPE(The type of roof covering used at a specific point in time or over its complete life-cycle) and plenty other variables. With this astounding range and quantity of data points -- spanning countries across different continents and leagues -- explore patterns in sports games you never even thought were possible!

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How to use the dataset

The MajorUS Sports Venues Usage and Affiliations dataset includes data on major sports venues from leagues including National Association for Stock Car Auto Racing (NASCAR), Indy Racing League (IRL), Major League Soccer (MLS), Major League Baseball (MLB), National Basketball Association (NBA), Women's National Basketball Association (WNBA), National Hockey League (NHL), National Football League(NFL), PGA Tour, NCAA Division 1 FBS Football, NCAA Division 1 Basketball, and thoroughbred horse racing. The columns provided include USE_, USE_POP, TEAM, LEAGUE,CONFERENCE,DIVISION ,INST_AFFIL,TRACK_TYPE. LENGTH_MI,ROOF_TYPESTADIUM_SH,`ADDDATAE , USEWEBSITE',and'COMMENTS'.

The `USE~ column specifies the type of usage of each venue at which point can be college athletics or professional athletics. The corresponding column to this is the ‘USE~POP’ which informs you about how many people are using each venue for a particular sport at a given time. For example if there were 6 NHL games being played that day then USE~ would say “professional Athletics” while USE~POP would state “NNN” reflecting there were NNN people spectating those events collectively: The next column is TEAM which represents what team sponsors or manages each venue or what teams will be playing in them.

 Following on from TEAM is LEAGUE; here you can find out what league each team represents such as MLB, NBA etc… The next three columns CONFERENCE/DIVISION/INST ~ AFFIL provide more specific details as they blur into collegiate level as well where CONFERENCE indicates which conference they belong within their respective division: while INST ~ AFFIL states its affiliated school body e.g.: Southeastern Conference > University of Arkansas Razorbacks . Rounding up our overview these last three columns TRACK ~ TYPE/LENGTH

Research Ideas

• Analyzing the affiliations and usage of different sports venues to determine which teams or leagues have the most presence across a certain geographic area.
• Comparing different stadiums within a given conference in terms of their roof type, track length, and stadium shape for optimal design features for new construction projects.
• Placing sponsorships or advertisements within each sporting arena based on audience size, league popularity, and team affiliation within a given conference or division

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contribut...

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.

From a company for sports statistics, we obtained data and profile photos from all soccer players (N = 2053) playing in the first male divisions of England, Germany, France and Spain in the 2012-2013 season and all referees (N = 3147) that these players played under in their professional career. We created a dataset of player\'96referee dyads including the number of matches players and referees encountered each other and our dependent variable, the number of red cards given to a player by a particular referee throughout all matches the two encountered each other.

Multiple independent analysts are recruited to investigate the same hypothesis or hypotheses on the same data set in whatever manner they see as best. The independent analysis strategies produce two datasets of interest: (1) the variation in analysis strategies, and (2) the variation in estimated effects.

These two can be partially independent. Different analysis strategies may converge to a very similar estimated effect - indicating robustness despite variation in analysis strategies. Alternatively, the estimated effect may be highly contingent on analysis strategy. In the latter case, there are at least two methods of resolution: (1) consider the central tendency of the estimated effects to be the most accurate, or (2) critically evaluate the analysis strategies to determine whether one or more should be elevated as the preferred analysis.

for more information, here is official link https://osf.io/47tnc/

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.

About this Dataset

The provided dataset contains a comprehensive set of data about football players from the top 42 European first leagues. The dataset encompasses various statistics and information related to these players, providing valuable insights into their performance, skills, and backgrounds. The data covers a wide range of categories, including player details, club information, performance metrics, awards and achievements, transfer history, youth career, social media presence, and much more.

The dataset includes the following key information for each player:

Player Information: Full name, age, date of birth, place of birth, position, sub-position, nationality, height, and outfitter.

Club Details: Club name, league country, league, market value, contract expiry date, and transfermarkt URL.

Performance Metrics: Appearances, goals, assists, yellow cards, red cards, starting eleven appearances, minutes played, and goal participation.

Player Performance Ratings: Seasonal performance rating and overall performance rating.

Awards and Achievements: Accolades, team achievements, youth trophies, and continental trophies.

Transfer History: Transfer fees, transfer dates, left clubs, and joined clubs.

Social Media Presence: Facebook, Twitter, and Instagram links along with followers, following, likes, and other related metrics.

Domestic and Continental Competitions: Appearances, goals, assists, yellow cards, red cards, minutes played, goal participation, clean sheets, and conceded goals in domestic league competitions, UEFA Champions League, UEFA League, and UEFA Conference League.

Domestic Cup Performances: Appearances, goals, assists, yellow cards, red cards, starting eleven appearances, minutes played, and goal participation in domestic cup competitions.

Player Attributes and Skills: Scoring frequency, accurate passes, successful dribbles, tackles, interceptions, shots on target, ground and aerial duels won, accurate long balls, clearances, dispossessed, possession lost and won, touches, fouls, saves, punches, high claims, crosses not claimed, and much more.

The dataset also provides injury-related information such as missed matches and days injured, allowing analysis of a player's injury history.

This comprehensive dataset serves as a valuable resource for football analysts, clubs, researchers, and enthusiasts to gain in-depth insights into the performance and profiles of football players from the top 42 European first leagues.

Category

Sports

Keywords

soccer,football,sport,transfer

Row Count

15633

Price

$2000.00

Here are a few use cases for this project:

1. Sports Analysis and Broadcasting: The soccer computer vision model can be used to analyze real-time events during a football match, like player movements, positions, and strategies. It can also auto-detect critical game moments such as goals or fouls. It's beneficial for broadcast companies, sports analysis teams, and coaching staff.

2. Player Performance Tracking: Football teams or individual players can use it to analyze, track, and improve their performance. It can monitor and evaluate players' movements, scores, strategies, and compare with others.

3. Officiating and Rule Enforcement: The model can be used as a technological aid for referees to make decisions related to fouls, offsides, penalty kicks, and more. It helps provide unbiased judgments based on actual event data.

4. Virtual Reality Football Games: This model can be used in creating virtual reality games by identifying and distinguishing the different characters in a soccer match like players, goalkeepers, and referees, making the gaming experience more realistic.

5. Automated Content Generation: Media companies can leverage this model for automated generation of sports news content. The model can be used to extract critical event data such as who scored, which player made the most significant moves, or who the referee was, etc. This can be quickly transformed into news articles or sports updates.

Here are a few use cases for this project:

1. Real-Time Game Analysis: The NBA-Player-Detector can be used by coaches or analysts to track player movements, interactions between players, and ball possession in real-time. This could provide valuable insights for decision-making during games and fine-tuning of strategies.

2. Enhanced Sports Broadcasting: Broadcast companies can use the model to automatically detect and highlight players on the screen during a live broadcast. It can help viewers follow the game more closely, especially in identifying less known players, and enhance the overall viewing experience.

3. Player Training and Evaluation: The NBA Player Detector can be used to analyze the performance of individual players during training sessions or competitive games. It could help trainers identify areas where a player could use improvement, such as shooting or passing skills.

4. Sports Betting and Predictions: Bettors or prediction companies can use real-time or historical data from the model to predict player or team performance. Such insights may influence betting odds or decision-making in fantasy sports.

5. Fan Engagement and Interaction: Sports apps can integrate the computer vision model for interactive features, such as allowing fans to click on a player during a live game stream to view their statistics or history. This could significantly enhance fan engagement and satisfaction.

Object Detection In Sports

## Overview

Object Detection In Sports is a dataset for object detection tasks - it contains Football Players annotations for 429 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).

This dataset is made possible with Collaboration of **[@Batros Jamali]

This dataset spotlights the world’s most elite basketball players — capturing their earnings, endorsements, team affiliations, and nationalities. Ideal for sports analysts, business researchers, and fans alike, it provides insights into what makes a basketball player not just successful, but legendary.

Note the income is in a Million USD dollars, For example 12.5 means 12.5 million USD dollars

Here are a few use cases for this project:

1. Player Performance Analysis: The "basketball" model can be utilized in sports analysis to evaluate player performance by identifying player movements, ball handling, shooting angles, shot success rate considering the rim, etc.

2. Augmented Reality Games: It could be used in the development of augmented reality (AR) sports games where real-world gestures and actions are mimicked in the game setting. The model can identify the person, ball, and rim to integrate these elements in the gameplay.

3. Sports Broadcasting Enhancement: The model can enhance the viewing experience by providing advanced tracking statistics in live broadcasts or highlights, such as identifying key moments where the person, ball, and rim interacted in significant ways.

4. Training and Coaching: It can be used to analyze training exercises and provide feedback. It can identify incorrect techniques or recommend improvements based on the data it gathers about the person's interaction with the ball and the rim.

5. Surveillance and Security in Sports Facilities: When installed in sports facilities, the model can help in identifying if the property is being used for its intended purpose. For example, if only people and the basketball are present but no interaction with the rim, it could suggest irregular activities.

Key Performance Indicators from Active People Survey (APS). Data on volunteering, club membership, tuition, organised sport, competition, satisfaction with local sports provision, for local authorities, based on Active People Survey. KPI 1 Participation is defined as taking part on at least 3 days a week in moderate intensity sport and active recreation (at least 12 days in the last 4 weeks) for at least 30 minutes continuously in any one session. Participation includes recreational walking and cycling. KPI 2 Volunteering is defined as ‘Volunteering to support sport for at least one hour a week’. KPI 3 Club membership is defined as ‘being a member of a club particularly so that you can participate in sport or recreational activity in the last 4 weeks’. KPI 4 Receiving tuition is defined as ‘having received tuition from an instructor or coach to improve your performance in any sport or recreational activity in the last 12 months’. KPI 5 Organised Competition is defined as ‘having taken part in any organised competition in any sport or recreational activity in the last 12 months’. KPI 6 Satisfaction is the percentage of adults who are very or fairly satisfied with sports provision in their local area. Organised sport is defined as the percentage of adults who have done at least one of the following: received tuition in the last 12 months, taken part in organised competition in the last 12 months or been a member of a club to play sport. A statistically significant change is indicated by 'increase' or 'decrease' and this means that we are 95% certain that there has been a real change (increase or decrease). For more information on measuring statistically significant change within Active People, see the briefing note on Sport England’s website. The 'Base' refers to the sample size, i.e. the number of respondents. http://activepeople.sportengland.org/

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 Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People survey is a school-based survey (i.e., historically always completed at school as part of lessons). Academic years 2020-2021 and 2019-20 have both been disrupted by the coronavirus pandemic, resulting in school sites being closed to many pupils for some of the year (e.g., during national lockdown periods, and during summer term for 2019-20). Due to the closure of school sites, the Active Lives Children and Young People Survey, 2020-2021 was adapted to allow at-home completion. Despite the disruption, the survey has still received a sufficient volume of responses for analysis.

The adaptions involved minor questionnaire changes (e.g., to ensure the wording was appropriate for those not attending school and to enable completion at home), and communication changes. For further details on the survey changes, please see the accompanying User Guide document. Academic year 2020-21 saw a more even split of responses by term across the year, compared to 2019-20 which had a reduced proportion of summer term responses due to the disruption caused by Covid-19. It is recommended to analyse the data within term, as well as at an overall level, because of the changes in termly distribution.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1) Main dataset – this file includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

2) Year 1-2 dataset – this file include responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).

3) Teacher dataset – this file includes response from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets. More general information about the study can be found on the Sport England Active Lives Survey webpages.

Latest edition information

For the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these data.

Sports characteristics.

Percentage of young people in England participating in sport. Young people are all those aged 5-19. All 5-16 year olds will have the chance to do 5 hours of high quality Physical Education (PE) and Sport.

Sport England is committed to the creation of a world-leading community sport environment. This means focusing our investment on organisations and projects that will grow and sustain participation in grassroots sport and create opportunities for people to excel at their chosen sport. Details of our funded projects from April 2009 to December 2013 are available on the links below.

Career longevity is dependent on various factors for any player in all the games and so for NBA Rookies. The factors like games played, count of games played, and other statistics of the player during the game.

Data Description

The dataset contains player statistics for NRB Rookies. There are 1100+ observations in the train dataset with 19 variables excluding the target variable (i.e. Target).

GP: Games Played (here you might find some values in decimal, consider them to be the floor integer, for example, if the value is 12.789, the number of games played by the player is 12)

The values for given attributes are averaged over all the games played by players

MIN: Minutes Played

PTS: Number of points per game

FGM: Field goals made

FGA: Field goals attempt

FG%: field goals percent

3P Made: 3 point made

3PA: 3 points attempt

3P%: 3 point percent

FTM: Free throw made

FTA: Free throw attempts

FT%: Free throw percent

OREB: Offensive rebounds

DREB: Defensive rebounds

REB: Rebounds

AST: Assists

STL: Steals

BLK: Blocks

TOV: Turnovers

Target: 0 if career years played < 5, 1 if career years played >= 5