NBA Draft Combine Measurement Data from 2012-2019

Examining the Physical Attributes of Basketball Prospects

By Andrew Chou [source]

About this dataset

This dataset contains vital statistics from the NBA Draft Combine for the period of 2012 to 2019. It includes player information, height (both with and without shoes), wingspan, vertical jump, weight, body fat percentage, hand size, bench press reps, agility score and sprint time. Through these gathered statistics, a unique snapshot is offered insight into an athlete's physical performance prior to the draft in order to form an informed decision on their potential relative to other draft prospects. This wealth of data is essential in understanding how players can differ and may influence both their importance in the league as well as their potential value drafted. The combination of variables paints a detailed portrait of each athlete which further acts as great resource for both scouts and analysts alike when predicting future impact at the professional level

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How to use the dataset

This dataset contains measurements from the NBA Draft Combine from 2012 to 2019, including player information, height, wingspan, vertical jump, weight, body fat percentage, hand size, bench press reps and agility & sprint times. In order to use this dataset effectively and gain valuable insights from it to understand the trends of NBA pre-draft training around each year during the 2012-2019 Draft Combines.

• Firstly analyze the necessary data fields available in this which is essential for exploring certain tendencies between players and predicting their potential as a professional athlete based on their draft combine measure experiences: Player name; Year; Weight; Height (No Shoes); Vertical (Max Reach); Body Fat %; Hand Length; Bench Press Reps & Agility & Sprint Times.
• Secondly evaluate Raw Data closely by plotting graphs with selected fields e

Research Ideas

• Comparisons of draft prospects from year to year. For example, by analyzing the data from multiple years, patterns in the data could be identified that suggest trends in preferred measurements for certain player types and positions.
• Predictive analytics for predicting where a player might be drafted based on their talent level, athletic abilities and measurements taken at the NBA Draft Combine.
• Visualization of the various categories (height, wingspan, body fat percentage) and how these correlate with player performance on the court as well as scouting reports/opinions about particular players/positions

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 contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: nba_draft_combine_all_years.csv | Column name | Description | |:-----------------------------|:---------------------------------------------------| | Player | Name of the player. (String) | | Year | Year of the NBA Draft Combine. (Integer) | | Draft pick | Draft pick of the player. (Integer) | | Height (No Shoes) | Height of the player without shoes. (Float) | | Height (With Shoes) | Height of the player with shoes. (Float) | | Wingspan | Wingspan of the player. (Float) | | Standing reach | Standing reach of the player. (Float) | | Vertical (Max) | Maximum vertical jump of the player. (Float) | | Vertical (Max Reach) | Maximum vertical jump reach of the player. (Float) | | Vertical (No Step) | No step vertical jump of the player. (Float) | | Vertical (No Step Reach) | No step vertical jump reach of the player. (Float) | | Weight ...

Data set:

Validation of the French version of mental toughness measurement among athletes

Abstract

The objective of this study was to evaluate the factorial validity of the translated French version of the questionnaire measuring mental toughness "MTQ48" according to the 4/6Cs (four and six components) theoretical constructs of Clough et al. (2002). The 48 items assess the following components: challenge; commitment; emotional and life control; ability and interpersonal confidence; and overall mental toughness (MT) score. The sample consisted of 302 athletes (153 males and 149 females), aged 14-27 years (M 20.25; SD 3.8). The confirmatory factor analysis (CFA) performed by principal component analysis (PCA) with the PROMAX method did not confirm the four and six component theoretical structure of the MTQ48, but a good statistical fit could be obtained with a reduced version of 37 items and three components MTQ37_3C : Challenge-Commitment CC, Confidence CF and Control CT. Cronbach's alpha suggests that each of the three factors has adequate internal consistency (alpha CC = .991; alpha CF = .989; alpha CT = .813). The Kaiser-Meyer-Olkin index for measuring sampling quality was (.991) and Bartlett's sphericity index was (df = 666; p < .001), indicating that CFA is appropriate. MES structural equation modelling confirmed that the 3 factors represent a good model fit (χ² = 678 564, df = 628, CFI = .997, SRMR = .019, RMSEA = .017). In conclusion, our results allowed us to propose a valid and reliable French measure of three components of mental toughness among athletes.

Keywords: mental toughness, French version, validation, confirmatory factor analysis, structural equation modeling.

Validation de la version française de la mesure de la force mentale chez les sportifs

Résumé

L'objectif de cette étude était d'évaluer la validité factorielle de la version française traduite du questionnaire qui mesure la force mentale "MTQ48" selon les constructions théoriques 4/6Cs (quatre et six composantes) du modèle de Clough et al. (2002). Les 48 items évaluent les composantes suivantes : le défi ; l'engagement ; le contrôle des émotions et de la vie ; la confiance en capacités et interpersonnelle, ainsi que le score global de la force mentale (FM). L'échantillon était composé de 302 sportifs (153 hommes et 149 femmes), âgés de 14 à 27 ans (M 20.25 ; ET 3.8). L'analyse factorielle confirmatoire (AFC) effectuée par l'analyse des composantes principales (ACP) avec la méthode PROMAX n'a pas confirmé la structure théorique à quatre et six composantes du MTQ48, mais un bon ajustement statistique a pu être obtenu avec une version réduite de 37 items et de trois composantes MTQ37_3C : le Défi- Engagement DE, la Confiance CF et le Contrôle CT. Le coefficient alpha de Cronbach suggère que chacun des trois composantes possède une cohérence interne adéquate (alpha DE = .991 ; alpha CF = .989 ; alpha CT = .813). L'indice de Kaiser-Meyer-Olkin pour mesurer la qualité de l'échantillonnage était de (.991) et l'indice de sphéricité de Bartlett était (df = 666 ; p <.001), indiquant que l'AFC est approprié. La modélisation des équations structurelles MES a confirmé que les 3 composantes représentent un bon ajustement du modèle (χ² = 678 564, df = 628, CFI = .997, SRMR = .019, RMSEA = .017). En conclusion, nos résultats ont permis de proposer une mesure française valide et fiable de trois composantes de la force mentale chez les sportifs.

Mots clefs : force mentale, version française, validation, analyse factorielle confirmatoire modélisation des équations structurelles..

Men's Mile Run World Record Progression History (1861-Present)

Examining the Athlete, Nationality and Venue Influence on Race Times

By Ben Jones [source]

About this dataset

This remarkable dataset chronicles the world record progression of the men's mile run, containing detailed information on each athlete's time, their name, nationality, date of their accomplishment and the location of their event. It allows us to look back in history and get a comprehensive overview of how this track event has progressed over time. Analyzing this information can help us understand how training and technology have improved the event over the years; as well as study different athletes' performances and learn how some athletes have pushed beyond their limits or fell short. This valuable resource is an essential source for anyone intrigued by the cutting edge achievements in men's mile running world records. Discovering powerful insights from this dataset can allow us to gain perspective into not only our own personal goals but also uncover ideas on how we could continue pushing our physical boundaries by watching past successes. Explore and comprehend for yourself what it means to be a true athlete at heart!

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How to use the dataset

This guide provides an introduction on how best to use this dataset in order to analyze various aspects involving the men’s mile run world records. We will focus on analyzing specific fields such as date, athlete name & nationality, time taken for completion and auto status by using statistical methods and graphical displays of data.

In order to use this data effectively it is important that you understand what each field measures: • Time: The amount of time it took for an athlete to finish a race - measured in minutes and seconds (example: 3:54).
• Auto: Whether or not a pacemaker was used during a specific race (example ; yes/no).
• Athlete Name & Nationality: The name and nationality associated with an athlete who set \record(example; Usain Bolt - Jamaica).
• Date : Year representing when a specific record was set by an individual( example-2021 ). •Venue : Location at which the record is set.(example; London Olympic Stadium )

Now that you understand which fields measure what let’s discuss various ways that you can use these datasets features. Analyzing trends in historical sporting performances has long been utilized as means for understanding changes brought about by new training methods/technologies etc., over time . This can be done with our dataset by using basic statistical displays like bar graphs & average analysis or more advanced methods such as regression analysis or even Bayesian approaches etc..The first thing anyone interested should do when dealing with this sort of data is inspect any wacky outliers before beginning more rigorous analysis; if one discovers any potential unreasonable values it would be best to discard them before building after models or readings based off them (this sort of elimination is common practice).After cleaning your work space let’s move onto building interactive visual display through graphics ,plotting different columns against one another e.g., – plotting time against date allows us see changes overtime from 1861 until now . Additionally plotting time vs Auto allows us see any

Research Ideas

• Comparing individual athletes and identifying those who have consistently pushed the event to higher levels of performance.
• Analyzing national trends related to improvement in track records over time, based on differences in training and technology.
• Creating a heatmap to visualize the progression of track records around the world and locate regions with a particularly strong historical performance in this event

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 contributions under the same license as the original. -...

NBA Players Performance

Players Performance & Statistics

By [source]

About this dataset

This dataset contains comprehensive performance data of National Basketball Association (NBA) players during the 2019-20 season. It includes all the crucial performance metrics crucial to assess a player’s quality of play. Here, you can compare players across teams, positions and categories and gain deeper insight into their overall performance. This dataset includes useful statistics such as GP (Games Played), Player name, Position, Assists Turnovers Ratio, Blocks per Game, Fouls per Minutes Played, Rebounds per Game and more. Dive in to this detailed overview of NBA player performance and take your understanding of athletes within the organization to another level!

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How to use the dataset

This dataset provides an in-depth look into the performance of NBA Players throughout the 2019-20 season, allowing an informed analysis of various important statistics. There are a number of ways to use this dataset to both observe and compare players, teams and positions.

• By looking at the data you can get an idea of how players are performing across all metrics. The “Points Per Game” metric is particularly useful as it allows quick comparison between different players and teams on their offensive ability. Additionally, exploratory analysis can be conducted by looking at metrics like rebounds or assists per game which allows one to make interesting observations within the game itself such as ball movement being a significant factor for team success.

• This dataset also enables further comparison between players from different positions on particular metrics that might be position orientated or generic across all positions such as points per game (ppg). This includes adjusting for positional skill sets; For example guard’s field goal attempts might include more three point shots because it would benefit them more than larger forwards or centres who rely more heavily on in close shot attempts due to their size advantage over their opponents.

• This dataset also allows for simple visualisation of player performance with respect to each other; For example one can view points scored against assists ratio when comparing multiple point guards etc., providing further insight into individual performances on certain metrics which otherwise could not be analysed quickly with traditional methods like statistical analysis only within similarly situated groups (e.g.: same position). Furthermore this data set could aid further research in emerging areas such as targeted marketing analytics where identify potential customers based off publically available data regarding factors like ppg et cetera which may highly affect team success orotemode profitability dynamicsincreasedancefficiencyoftheirownopponentteams etcet

Research Ideas

• Develop an AI-powered recommendation system that can suggest optimal players to fill out a team based on their performances in the past season.
• Examine trends in player performance across teams and positions, allowing coaches and scouts to make informed decisions when evaluating talent.
• Create a web or mobile app that can compare the performances of multiple players, allowing users to explore different performance metrics head-to-head

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: assists-turnovers.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: blocks.csv | Column name | Description | |:--------------|:----------------------------------| | GP | Number of games played. (Integer) | | Player | Player name. (String) | | Position | Player position. (String) |

File: fouls-minutes.csv | Column name | Description | |:--------------|:----------------------...

General Description:

This dataset offers a detailed look at the biomechanics of boxing punches, specifically the jab and rear cross, by capturing the acceleration of different parts of the arm and the force generated during these movements. The integration of IMU and force plate data provides a rich source of information for analyzing the efficiency, power, and technique of athletes, offering valuable insights for coaches, researchers, and athletes in the field of sports science and physical culture.

This dataset comprises a collection of Excel files containing measurements of right hand pads from various participants. Each file is named to reflect the participant's identifier followed by the specific measurement context (e.g., "participant1_right hand pad.xlsx"). The original dataset included participant surnames, but these have been replaced with unique numerical identifiers (e.g., "participant1", "participant2", etc.) to ensure anonymity. The dataset is intended for use in physical culture and sports science research, providing valuable data for studies on hand measurements and their implications in sports and physical activities.

File Naming Convention:

Files within this dataset follow a standardized naming convention: . The is a unique numerical identifier assigned to each participant (e.g., "participant1"), and describes the specific measurement focus of the file (e.g., "right hand pad").

Column Descriptions:

• Time: The timestamp or duration associated with each measurement session, indicating when each set of measurements was taken, typically essential for analyzing movement or force over time.

• 1x, 1y, 1z: Acceleration data (in milli-g) for the IMU placed on the fist. These columns capture the three-dimensional acceleration of the fist during boxing techniques, with 'x', 'y', and 'z' representing the acceleration along the horizontal, vertical, and depth axes, respectively. This data is crucial for understanding the speed and direction of the punch.

• 2x, 2y, 2z: Acceleration data (in milli-g) for the IMU on the forearm. Similar to the fist data, these columns provide insights into the forearm's movement dynamics during the execution of boxing techniques, offering a comprehensive view of the arm's acceleration.

• 3x, 3y, 3z: Acceleration data (in milli-g) for the IMU placed on the upper arm. These measurements complement the fist and forearm data, providing a complete picture of the arm's acceleration and movement patterns during different boxing punches.

• fx, fy, fz: Force measurements (in Newtons) from the force plate. These columns represent the force exerted in the x (horizontal), y (vertical), and z (depth or forward/backward) directions. Force plate data is essential for analyzing the power and effectiveness of boxing techniques, as well as the athlete's balance and stability during the punch execution.

Files are related to code on github : https://github.com/Dareczin/boxing_biomechanics

Based on this code, data is saved in two folders. Original data capture (5 strikes in one measurement) and files after computing, to extract each event (strike) as separate file.

Context

College presidents across the nation recognized a need to track how student-athletes are doing academically prior to graduation. Starting in 2003, colleges and universities in NCAA Division I — the largest and highest profile athletics programs — implemented a comprehensive academic reform package designed to improve the academic success and graduation of all student-athletes. The centerpiece of the academic reform package was the development of a real-time academic measurement for sports teams, known as the Academic Progress Rate (APR).

The APR includes student-athlete eligibility, retention and graduation as factors in a formula that yields a single number, providing a much clearer picture of the current academic culture on each Division I sports team in the country. Since its inception, the APR has become an important measure of student-athlete academic success. For high APR scores, the NCAA recognizes member institutions for ensuring that student-athletes succeed in the classroom. If, however, low APR scores are earned consistently, member institutions can be subjected to penalties including scholarship reductions and the loss of eligibility to compete in championships.

Content

This study was created, by the National Collegiate Athletic Association (NCAA), to provide public access to team-level APR scores, eligibility rates, retention rates, and athlete counts on Division I athletic programs starting with the 2003-2004 season through the 2013-2014 season

Inspiration

Which sport or school has the highest academic score? Which schools' scores have increased or decreased significantly in the past decade? Are men's or women's team academic performance better? What about public and private colleges?

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 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.

“Faster, higher, stronger” is the motto of any professional athlete. Does that apply to brain dynamics as well? In our paper, we performed a series of EEG experiments on Visually Evoked Potentials and a series of cognitive tests—reaction time and visual search, with professional eSport players in Counter-Strike: Global Offensive (CS:GO) and novices (control group) in order to find important differences between them. EEG data were studied in a temporal domain by Event-Related Potentials (ERPs) and in a frequency domain by Variational Mode Decomposition. The EEG analysis showed that the brain reaction of eSport players is faster (P300 latency is earlier on average by 20-70 ms, p < 0.005) and stronger (P300 peak amplitude is higher on average by 7-9 mkV, p < 0.01). Professional eSport players also exhibit stronger stimulus-locked alpha-band power. Besides, the Spearman correlation analysis showed a significant correlation between hours spend in CS:GO and mean amplitude of P200 and N200 for the professional players. The comparison of cognitive test results showed the superiority of the professional players to the novices in reaction time (faster) and choice reaction time—faster reaction, but similar correctness, while a significant difference in visual search skills was not detected. Thus, significant differences in EEG signals (in spectrograms and ERPs) and cognitive test results (reaction time) were detected between the professional players and the control group. Cognitive tests could be used to separate skilled players from novices, while EEG testing can help to understand the skilled player’s level. The results can contribute to understanding the impact of eSport on a player’s cognitive state and associating eSport with a real sport. Moreover, the presented results can be useful for evaluating eSport team members and making training plans.

University Football Injury Prediction Dataset

🎯 Overview

This dataset contains comprehensive data from 800 Chinese university football players participating in collegiate and provincial leagues. The goal is to predict whether a player will suffer an injury in the next academic season using machine learning classification methods.

📊 Dataset Details

• Samples: 800 university football players
• Features: 18 input features + 1 target label
• Task: Binary classification (0 = No injury, 1 = Injury)
• Balance: Well-balanced dataset
• Age Range: 18-24 years (typical university students)

🏃‍♂️ Feature Categories

Physical Characteristics (4 features)

• Age, Height, Weight, BMI
• Measured through standard university health checkups

Football-Specific Metrics (4 features)

• Playing position, training hours per week, matches played, injury history
• Collected from official records and coach evaluations

Physical Fitness Assessment (6 features)

• Knee strength, hamstring flexibility, reaction time, balance, sprint speed, agility
• Professional fitness testing using standardized protocols

Lifestyle Factors (3 features)

• Sleep hours, stress level, nutrition quality
• Self-reported surveys and validated questionnaires

Training Compliance (1 feature)

• Warm-up routine adherence (0=Poor, 1=Good)

🎯 Target Variable

Injury_Next_Season: Binary classification where injury is defined as training/competition-related injury causing ≥7 consecutive days of absence, verified by university medical center and coaching staff.

🔬 Data Quality

• Collection Period: Within 4 weeks of each academic year start
• Validation: Multi-source verification (medical records, coach reports, student surveys)
• Quality Control: Reviewed by sports medicine professionals
• Missing Data: Minimal, verified through multiple channels

🚀 Potential Applications

• Sports Medicine Research: Identify key injury risk factors
• Preventive Healthcare: Data-driven injury prevention strategies
• University Sports Management: Risk assessment for student athletes
• Machine Learning: Healthcare classification algorithm validation

📈 Suggested Approaches

• Classical ML: Logistic Regression, Random Forest, SVM, XGBoost
• Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, AUC-ROC
• Validation: Cross-validation with stratified sampling

🏆 Research Value

This dataset bridges sports science and machine learning, offering insights into university-level athletic injury prediction. It's particularly valuable for researchers in sports medicine, preventive healthcare, and applied machine learning.

This dataset is intended for academic research and educational purposes. Please respect data privacy and usage guidelines.

This dataset contains comprehensive annual data from 30 elite female race walkers collected during 2021–2024. The dataset includes anthropometric variables (e.g., body mass, height, fat mass), physiological indicators (e.g., VO₂max, heart rate, lactate threshold, oxygen pulse), and biomechanical measures (e.g., step length, walking speed), as well as neuromuscular performance parameters (e.g., 1RM, power output, RFD).

The dataset is structured across four Excel sheets representing consecutive years. Each sheet includes anonymized rows for each athlete and columns for the assessed variables. These data were used in the study: "Optimizing Race Walking Performance through Advanced Modeling and AI-based Training Analysis."

This resource supports time-series analysis, seasonality modeling, and development of machine learning algorithms in elite sport research.

The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data.

The main file, dat.cleaned.csv, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.

Two sets of additional text files (per_2017.txt, per_2018.txt, rpm_2017.txt, rpm_2018.txt) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.

However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset.

With the aim to investigate psychological resilience factors and vulnerabilities of a dual career construction in late adolescence and how they impact the life course, we have collected longitudinal mixed methods data from 391/453 student-athletes, aged 16/17 at the baseline (T1/T2) and enrolled in 6/7 elite sport upper secondary schools across Finland, their parents/guardians, and coaches. The current dataset consists of: (1) self-report questionnaires of student-athletes gathered 6 times (a) at the beginning of upper secondary school, (b) at the end of the first year, (c) at the end of the second school year, (d) at the beginning of the third school year, (e) at the end of the third school year, and (f) at the beginning of the fourth year; (2) life story interviews, including art-based, visual storytelling, with 18 talented and elite student-athletes at the matching time-points during the upper secondary; (3) self-report questionnaires of the participants’ parents/legal guardians at the beginning and the end of the upper secondary; (4) one-time, semi-structured interviews with 10 male, youth ice-hockey coaches from a club in Finland; (5) one-time, semi-structured interviews with 10 female and male cross-country ski coaches in Finland; and (6) one-time, semi-structured interviews with 15 female and male athletics (track-and-field) coaches from a club in Finland. Student-athlete questionnaires were constructed to examine development of the participants’ motivation, identity, psychological well-being, future orientation and career adaptability resources in and across sport and school contexts. Parental questionnaires were constructed to examine, for example, the role of parenting styles, expectations, own education, athletic background and well-being in the outcome measures of adolescent participants. Life story interviews were designed to obtain a deeper understanding of how young people make sense of their life trajectories in particular socio-cultural contexts, marked by concrete events, relationships, and transitions. Coaches were interviewed to explore the discourses that underpin their coaching philosophy and views on holistic development (e.g., dual career, lifelong participation, life-skills) of gendered athletes and how the derived meanings shape their coaching practice. Data were collected in 2015-2018. Follow-up data on dual career status, athletic and academic achievements, employment, economic situation, dimensions of emerging adulthood (IDEA), mood states, motivation, identity, psychological well-being, career adaptability resources, and impact of COVID-19 on young people’s life course were collected in Autumn 2021, when they transitioned to early adulthood (n=238).

This dataset contains a novel collection of data from 21 elite female footballers who were continuously monitored for 17 days. The dataset includes measures of actigraphy, well-being, caffeine consumption, screen time and daily hand strength tests. The main objective is to gain a deeper understanding of the interactions between lifestyle, sleep and athletic performance.

Sleep is essential for physical and mental recovery, memory performance and brain development. Athletes' sleep quality can be significantly affected by various factors, such as rigorous training schedules, stress, light exposure and caffeine consumption. By closely examining these factors, this dataset supports the creation of personalised training models that take into account the individual sleep patterns and recovery needs of each athlete. Such personalised approaches aim to optimise training and recovery strategies to ultimately improve the overall performance and well-being of athletes.

Context: Valorant, developed by Riot Games, has quickly become one of the most popular tactical first-person shooter games since its release. The game emphasizes strategic team play, individual skills, and tactical execution, making it a fascinating subject for performance analysis. Understanding the various metrics that contribute to player success can offer insights into effective strategies and gameplay techniques. This dataset was created to help players, coaches, and analysts delve into the detailed aspects of player performance and identify key areas for improvement.

Sources: The data for this dataset was collected from various online sources, including:

In-Game Statistics: Aggregated from player profiles and match histories available within the game client. Third-Party Valorant Trackers: Websites and tools that track player statistics and match performance, such as Tracker.gg and Blitz.gg. Community Contributions: Insights and data shared by the Valorant community, including professional players, streamers, and analysts, who often provide detailed breakdowns of their gameplay. Inspiration: The inspiration for compiling this dataset stems from several key areas:

Performance Analysis: In competitive gaming, understanding the granular details of player performance is crucial for improvement. Metrics like win rate, damage per round, and headshot percentage provide actionable insights. Strategic Development: By analyzing this data, players and teams can develop better strategies, identify strengths and weaknesses, and tailor their training regimes accordingly. Predictive Modeling: The dataset serves as a foundation for building predictive models to forecast future performance, which can be useful for coaching, match preparation, and scouting new talent. Community Engagement: Providing this dataset to the wider Valorant community fosters engagement and encourages collaborative analysis. It allows enthusiasts to test hypotheses, share findings, and contribute to a deeper understanding of the game. Educational Purposes: For educators and students in data science, sports analytics, and game design, this dataset offers a real-world application of data analysis techniques and methodologies. Future Directions: The dataset can be expanded by including additional metrics such as agent pick rates, map-specific performance, and team composition analysis. Incorporating more granular data over longer periods can also enhance the depth of analysis and provide a more comprehensive view of player performance trends.

By sharing this dataset, we aim to empower the Valorant community with data-driven insights that can elevate gameplay, inform strategic decisions, and contribute to the overall growth of the esports ecosystem.

Abstract Background: Maximal oxygen consumption (VO2max) and ventilatory threshold (VT) obtained during a cardiopulmonary exercise test (CPX) are used in the evaluation of athletes. However, the identification of these variables may sometimes be unreliable, which limits their use. In contrast, the cardiorespiratory optimal point (COP) is a submaximal variable derived from CPX with objective measurement and prognostic significance. However, its behavior in athletes is unknown. Objective: To describe the behavior of COP in professional soccer players and its association with VO2max and VT. Methods: VO2max, VT and COP were obtained retrospectively from 198 soccer players undergoing maximal treadmill CPX using ramp protocol. COP was defined as the lowest value of the ventilation/oxygen consumption ratio in a given minute of the CPX. The soccer players were stratified according to their field position: goalkeeper, center-defender, left/right-back, midfielder and forwarder. Continuous variables were compared using unpaired Student t test or ANOVA, or Mann-Whitney test or Kruskal-Wallis test depending on their distribution, and categorical variables were compared using chi-square test. Pearson correlation was used to test the association between COP and other ventilatory variables. A level of 5% was used for statistical significance. Results: COP (mean ± SD) was 18.2 ± 2.1 and was achieved at a speed 4.3 ± 1.4 km.h-1 lower than that achieved at the VT. While VO2max (62.1 ± 6.2 mL.kg-1.min-1) tended to be lower in goalkeepers (p < 0.05), the COP did not vary according to field position (p = 0.41). No significant association was observed between COP and VO2max (r = 0.032, p = 0.65) or between COP and VT (r = -0.003, p = 0.96). Conclusion: COP can be easily determined during submaximal exercise performed with incremental speed in soccer players and does not vary according to the athlete’s field position. The absence of association with VO2max and VT indicates that COP provides distinct and complementary information to these variables. Future studies are needed to determine the practical implications of COP in assessing athletes. (Int J Cardiovasc Sci. 2018; [online].ahead print, PP.0-0)

Muscle-tendon unit (MTU) assessments can be categorised into local (e.g. tendon strain) or global (e.g. jump height) assessments. Although menstrual cycle phase may be a key consideration when implementing these assessments in female athletes, the reliability of many MTU assessments is not well defined within female populations. Therefore, the purpose of this study was to report the test-retest reliability of local and global MTU assessments during the early follicular phase of the menstrual cycle. Seventeen naturally menstruating females (age 28.5 ± 7.3 years) completed local and global MTU assessments during two testing sessions separated over 24-72 hours. Local tests included Achilles’ tendon mechanical testing and isometric strength of ankle plantar flexors and knee extensors, whereas global tests included countermovement, squat, and drop jumps, and the isometric midthigh pull. Based on intraclass correlation coefficient (ICC) statistics, poor to excellent reliability was found for local measures (ICC: 0.096-0.936). Good to excellent reliability was found for all global measures (ICC: 0.788-0.985), excluding the eccentric utilisation ratio (ICC 0.738) and most rate of force development metrics (ICC: 0.635-0.912). Isometric midthigh pull peak force displayed excellent reliability (ICC: 0.966), whereas force-time metrics ranged from moderate to excellent (ICC: 0.635-0.970). Excluding rate of force development (coefficient of variation [CV]: 10.6-35.9%), global measures (CV: 1.6-12.9%) were more reproducible than local measures (CV: 3.6-64.5%). However, local metrics directly measure specific properties of the MTU, and therefore provide valuable information despite lower reproducibility. The novel data reported here provides insight into the natural variability of MTU assessments within female athletes, which can be used to enhance the interpretation of other female athlete data, especially that which aims to investigate other aspects of variability, such as the menstrual cycle.

The Arena of Valor game dataset contains information on individual player performance during matches of the popular mobile multiplayer online battle arena (MOBA) game. The dataset includes details on player IDs, team IDs, chosen heroes, positions played, game stats (such as level, gold, KDA, damage dealt, damage taken, and time played), and match IDs.

Column Details

• Match ID: The unique identifier for each Arena of Valor match.

• Player ID: A unique identifier for each player participating in a match.

• Team ID: A unique identifier for each team in a match.

• Hero: The hero chosen by the player for the match.

• Position: The position played by the player in the match (such as top, mid, jungle, or bottom).

• Level: The level of the player's hero at the end of the match.

• Gold: The amount of gold earned by the player during the match.

• KDA: A measure of the player's performance, including kills, deaths, and assists.

• Damage Dealt: The amount of damage dealt by the player to enemy players during the match.

• Damage Taken: The amount of damage taken by the player from enemy players during the match.

• Time Played: The amount of time played by the player in the match.

You could use this dataset to analyze how different heroes perform in different positions, which players are the most effective in each position, which teams are the most successful, and many other factors related to Arena of Valor gameplay

Note: The dataset is an example and may not accurately represent the actual data structure of an Arena of Valor game dataset.

** The purpose of creating this dataset is solely for educational use, and any commercial use is strictly prohibited and this dataset was large language models generated and not collected from actual data sources.

cover image: https://www.4gamers.co.th/news/detail/236/rov-battlefield

This dataset includes right and left hand anthropometric measurements such as hand length, hand width, palm length, third finger length, shape index, finger index, and hand surface area. Measurements were collected from 160 professional volleyball players (80 females, 80 males) and 160 sedentary university students (80 females, 80 males), aged between 18 and 25 years. The data were obtained through standardized digital imaging and analyzed using the ImageJ software program. This dataset can be used for sports science research, anthropometric studies, and hand morphology comparisons between volleyball players and non-athletic young adults.

Abstract To evaluate the knowledge of Brazilian dentists of sports doping through the development, validation and application of the Brazilian Knowledge Scale about Sports Doping in Dentistry (B-KSSDD). A scale with 12 items was developed to assess a dentist’s ability to determine whether the use of a medication characterised sports doping according to the World Anti-Doping Agency. A preliminary study to validate the B-KSSDD was carried out with 135 dentists, allowing the evaluation of ceiling and floor effects, convergent and discriminant validity, test-retest reliability and internal consistency of the instrument. A sample size calculation using the results of the preliminary study and the B-KSSDD was completed online using SurveyMonkey® by 270 participants from all regions of the country. The B-KSSDD showed evidence of convergent and discriminant validity, good temporal stability (ICC = 0.75) and internal consistency (alpha = 0.89). In the main study, the participants obtained an average score of 4.19/12 points on the B-KSSDD, suggesting that these professionals have insufficient knowledge about sports doping. The age of participants showed a negative association with knowledge about doping, while frequency of treating athletes and frequency of performing surgeries showed positive associations with knowledge about doping. The dentists had insufficient knowledge of the subject. Age of participants and frequency with which they attend to athletes are associated with knowledge about sports doping. Professional updating and education policies on doping are necessary for dentists, as athlete patients are at risk for severe sporting and financial penalties.