You're working as a sports journalist at a major online sports media company, specializing in soccer analysis and reporting. You've been watching both men's and women's international soccer matches for a number of years, and your gut instinct tells you that more goals are scored in women's international football matches than men's. This would make an interesting investigative article that your subscribers are bound to love, but you'll need to perform a valid statistical hypothesis test to be sure!

While scoping this project, you acknowledge that the sport has changed a lot over the years, and performances likely vary a lot depending on the tournament, so you decide to limit the data used in the analysis to only official FIFA World Cup matches (not including qualifiers) since 2002-01-01.

You create two datasets containing the results of every official men's and women's international football match since the 19th century, which you scraped from a reliable online source. This data is stored in two CSV files: women_results.csv and men_results.csv.

The question you are trying to determine the answer to is:

Are more goals scored in women's international soccer matches than men's?

You assume a 10% significance level, and use the following null and alternative hypotheses:

The mean number of goals scored in women's international soccer matches is the same as men's.

The mean number of goals scored in women's international soccer matches is greater than men's.

This data is collected on over 600 Norwegian athletes from different sports performing 40m sprint tests under highly controlled conditions. The data was collected as part of training monitoring. The data forms the background for several published studies.

Number and percentage of individuals who participate regularly in sport activities by sex, age group and other demographic characteristics, Canada, Geographical region of Canada, province or territory.

Whole-organism performance capacity is thought to play a key role in sexual selection, through its impacts on both intrasexual competition and intersexual mate choice. Based on data from elite sports, several studies have reported a positive association between facial attractiveness and athletic performance in humans, leading to claims that facial correlates of sporting prowess in men reveal heritable or non-heritable mate quality. However, for most of the sports studied (soccer, ice hockey, American football and cycling) it is not possible to separate individual performance from team performance. Here, using photographs of athletes who compete annually in a multi-event World Cup, we examine the relationship between facial attractiveness and individual career-best performance metrics in the biathlon, a multidisciplinary sport that combines target shooting and cross-country skiing. Unlike all previous studies, which considered only male athletes, we report relationships for both sportsmen and sportswomen. As predicted by evolutionary arguments, we found that male biathletes were judged more attractive if (unknown to the raters) they had achieved a higher peak performance (World Cup points score) in their career, whereas there was no significant relationship for female biathletes. Our findings show that elite male athletes display visible, attractive cues that reliably reflect their athletic performance.

Electrocardiogram Data in Male and Female Athletes.

As a fan of the game, I enjoy the analysis of player stats from the game, to make comparisons and generate interesting insight. This dataset has all the player stats from the new FC 24 game, a continuation of FIFA 23, including male and female players. The details include the following: - name - nation - club - position - alternative positions - age - overall - PAC - SHO - PAS - DRI - DEF - PHY - Acceleration - Sprint Speed - Positioning - Finishing - Shot Power - Long Shots - Volleys - Penalties - Vision - Crossing - Free Kick Accuracy - Short Passing - Long Passing - Curve - Agility - Balance - Reactions - Ball Control - Dribbling - Composure - Interceptions - Heading Accuracy - Def Awareness - Standing Tackle - Sliding Tackle - Jumping - Stamina - Strength - Aggression - height - weight - preferred foot - weak foot - league - att work rate - def work rate - skill moves - url - GK Diving - GK Handling - GK Kicking - GK Positioning - GK Reflexes - Gender

This dataset contains the number of working and non-working members in youth sport institutions in the State of Qatar, categorized by gender (male/female). The data reflects the engagement of youth members in operational and non-operational roles within sports institutions.The dataset is valuable for analyzing gender participation trends, labor force involvement in the youth sports sector, and planning future programs aimed at youth empowerment and inclusive sports development. It aligns with national youth and sports policy objectives under the Ministry of Sports and Youth.

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

Context

ncaa conference files from men's competition (for use in women's competition) Refer to links below for source, use restrictions, and license information: https://www.kaggle.com/c/mens-machine-learning-competition-2018/data https://www.kaggle.com/c/mens-machine-learning-competition-2018/rules

Content

see men's competition page for description Refer to links below for source, use restrictions, and license information: https://www.kaggle.com/c/mens-machine-learning-competition-2018/data https://www.kaggle.com/c/mens-machine-learning-competition-2018/rules

Acknowledgements

see men's competition page for acknowledgements Refer to links below for source, use restrictions, and license information: https://www.kaggle.com/c/mens-machine-learning-competition-2018/data https://www.kaggle.com/c/mens-machine-learning-competition-2018/rules

Abstract Aims: This study investigated the existence of Relative Age Effect (RAE) in the selection process of male and female athletes in the Brazilian national handball teams according to age categories (U-18, U-21, and senior) and playing position (wings, backs, pivots, and goalkeepers). Methods: In order to determine RAE, athletes were divided based on their months of birth; quarters Q1 (January-March), Q2 (April-June), Q3 (July-September), and Q4 (October-December). Data were collected from the official Brazilian Handball Confederation (CBHb) website and included the athletes that participated in training and/or competitions composing the Brazilian national teams from 2014 to 2018. To determine the RAE on playing positions, age categories of male and female groups were pooled. Chi-squared tests were performed to investigate the RAE. Results: An over-representation of players born in Q1 and Q2 in the U-18, U-20, and senior categories of male teams and the U-20, and senior female teams were found. In the male teams, as the age category increased, RAE decreased, but still existed. Such distribution was reversed in the female athletes, with a higher RAE magnitude in the senior category as compared to U-18 and U-21. Additionally, it seems that RAE is dependent on the playing position only for male athletes (wings and backs) whereas RAE was found for all playing positions in female athletes. Conclusion: Overall, RAE was found in Brazilian national handball teams, but its magnitude and form of manifestation seem to be influenced by sex, category, and playing position.

Women's Soccer Participation in High Schools

Women's Soccer Participation in High Schools

By Eva Murray [source]

About this dataset

This dataset contains information on participation in high school soccer in the United States from 2006 to 2014. It includes data on the number of schools participating, the number of students participating, and the gender split of participants. This dataset can be used to understand the popularity of soccer among high school students and compare participation rates between boys and girls

How to use the dataset

Research Ideas

• Analyzing the correlation between boys and girls soccer participation in high school and the level of success of each gender's national soccer team.
• Determining which states have the largest disparities between boys and girls soccer participation rates.
• Analyzing how participation rates have changed over time, both nationally and by state

Acknowledgements

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: partcipation_statistics_06_14_2020 10_22.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------| | Year | The year the data was collected. (Integer) | | State | The state the data was collected from. (String) | | Sport | The sport the data is for. (String) | | Boys School | The number of schools that offered a boys soccer program. (Integer) | | Girls School | The number of schools that offered a girls soccer program. (Integer) | | Boys Participation | The number of boys who participated in soccer. (Integer) | | Girls Participation | The number of girls who participated in soccer. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit Eva Murray.

This dataset provides detailed statistics on individuals who participated in activities organized by youth and sports institutions in the State of Qatar. Data is disaggregated by type of activity, age group, nationality (Qatari, non-Qatari), and gender (male, female). It enables analysis of participation trends in various recreational, cultural, scientific, and physical activities among different population segments.

The dataset provides comprehensive information on FC 25 players, focusing on their in-game ratings, attributes, and additional statistics. It is derived from EA SPORTS Website using web scraping as shown in the notebook here Here’s a detailed breakdown of the columns in the dataset along with their meanings:

• Rank: Player’s ranking based on overall rating (OVR) within the FC 25 group.
• Name: The full name of the player.
• Height: The player’s height
• Weight: The player’s weight
• Position: The primary position the player plays on the field
• Alternative positions: Other positions the player is capable of playing effectively.
• Age: The player’s age.
• Nation: The country the player represents in international competitions.
• League: The football league in which the player currently plays.
• Team: The club team the player is part of.
• Play style: Specific gameplay traits or tendencies that define the player’s behavior and skillset on the field (e.g., "Quick Step", "Finesse Shot").
• URL: A link to the player's detailed profile.

Player Attributes

• Acceleration: The player’s ability to reach maximum speed quickly.
• Sprint Speed: The top speed the player can achieve when sprinting.
• Positioning: The player's awareness and positioning in attack.
• Finishing: The player’s ability to convert scoring chances into goals.
• Shot Power: The strength of the player’s shots on goal.
• Long Shots: The accuracy and power of shots taken from outside the penalty area.
• Volleys: The player’s ability to strike the ball cleanly from mid-air.
• Penalties: The player's skill at taking penalty kicks.

Passing and Vision

• Vision: The player's ability to make accurate passes and see plays develop.
• Crossing: The ability to deliver accurate crosses from wide areas.
• Free Kick Accuracy: The player’s precision when taking free kicks.
• Short Passing: The accuracy and skill in making short-distance passes.
• Long Passing: The ability to deliver accurate long-range passes.
• Curve: The player’s ability to bend the ball during passes or shots.

Dribbling and Agility

• Dribbling: The player’s ball control and ability to maneuver in tight spaces.
• Agility: How quickly and smoothly the player can change direction.
• Balance: The player’s stability and ability to stay on their feet under pressure.
• Reactions: The player’s responsiveness to unpredictable events during the game.
• Ball Control: How well the player controls the ball while moving.

Mentality and Defense

• Composure: The player’s calmness under pressure.
• Interceptions: The player’s ability to read and intercept passes.
• Heading Accuracy: The player's precision when attempting to head the ball.
• Defensive Awareness (Def Awareness): The player’s positioning and ability to anticipate defensive situations.
• Standing Tackle: The player’s ability to win the ball with a standing tackle.
• Sliding Tackle: The skill and accuracy of the player’s sliding tackles.

Physical Attributes

• Jumping: The player’s ability to jump high during headers or challenges.
• Stamina: The player’s endurance and ability to perform at a high level throughout the match.
• Strength: The player’s physical power and ability to win physical challenges.
• Aggression: The player’s determination and intensity in winning challenges and duels.

Technical Skills

• Weak foot: The player’s proficiency with their non-dominant foot (rated from 1 to 5 stars).
• Skill moves: The player’s ability to perform advanced dribbling moves (rated from 1 to 5 stars).
• Preferred foot: Indicates whether the player prefers using their left or right foot.

Goalkeeping Attributes (if applicable)

• GK Diving: The goalkeeper’s ability to dive and make saves.
• GK Handling: The goalkeeper’s skill in catching or holding onto the ball.
• GK Kicking: The accuracy and power of the goalkeeper’s kicks when distributing the ball.
• GK Positioning: The goalkeeper’s ability to position themselves effectively during defensive situations.
• GK Reflexes: The goalkeeper’s quickness in reacting to shots.

Comprehensive Roster and Individual Player Statistics Dataset (2021-2023 Seasons)

Introduction
Welcome to the comprehensive dataset capturing detailed roster information and individual player statistics across the 2021-2023 VNL seasons for both men's and women's teams. This dataset is structured to provide a deep dive into player dynamics, performance metrics, and team compositions, making it an invaluable resource for sports analysts, data scientists, and enthusiasts interested in sports statistics and team performance analysis,

Dataset Overview

The dataset is organized into four CSV files, each tailored to present a unique aspect of the player and team data across the specified seasons. Here's what each file contains:

Men's Teams

• df_mens_rosters_21_23.csv: This file presents the rosters for each men's team over the specified seasons, detailing Age, Birthdate, Height, Position, Jersey Number, and yearly statistics including the coach's name.
• df_mens_indv_21_23.csv: Focuses on the individual performance metrics for each player per season, covering key statistical areas such as blocks, serves, attacks, receptions, digs, and sets.

Women's Teams

• df_womens_rosters_21_23.csv: Mirroring the men's roster file, this includes comprehensive roster information for each women's team, providing insights into player demographics, positional data, and annual statistics alongside head coach details.
• df_womens_indv_21_23.csv: Dedicated to individual women's player statistics, this file encapsulates performance data across blocks, serves, attacks, receptions, digs, and sets for each season.

Position Mapping and Data Structure

For ease of analysis and to ensure consistency across the dataset, positions have been mapped to standardize terminologies and roles.

Dataset Creation

This dataset was created by using publicly available information on https://en.volleyballworld.com/ using Python 3 (Pandas, BeautifulSoup and Selenium).

ABSTRACT Objective To compare factors of disordered eating to non-athlete and athlete groups aesthetic, endurance or weight class division sports. Methods One hundred and eighty-seven athletes and 200 subjects who comprised the non-athlete group were participated. We used the Eating Attitudes Test (EAT-26) to evaluate the disordered eating. We conducted multivariate analysis of covariance to compare the EAT-26 subscales according to group and sex. Results The results showed that all the scores of the EAT-26 subscales were higher in females compared to males, regardless of group (p < 0,05). Furthermore, the EAT-26 subscales were similar between athletes and non-athletes in females and in males, showed up high scores for the Diet and Self-control Oral subscales in the non-athlete group when compared to athletes (p < 0,05). Conclusion The athletes did not show higher scores on the EAT-26 subscales when compared to the non-athlete group.

The Physiological Analysis of Female Basketball players (PHYAFB) database is a valuable resource for studying the physiological demands placed on female amateur basketball players during official games. This database includes heart rate data from ten female players aged between 18 and 26 years, which were collected during ten high-stress matches played during a crucial relegation phase. In total, more than 50000 heart rate samples have been reported. The data is stored in CSV format, facilitating easy access and analysis, and the database comes with \textit{Python} source code to support initial examination.

The primary aim of the PHYAFB database is to provide a useful reference for other teams facing similar situations. Furthermore, the database represents a unique and valuable resource for sports scientists, coaches, and trainers seeking to comprehend the physiological demands of female basketball players during official competitions. Through the analysis of heart rate data, coaches and trainers can identify the intensity and duration of physical activity during games, enabling the development of more effective training programs. Additionally, the database can be used to compare the physiological demands placed on male and female basketball players or to investigate the impact of different game strategies on player performance.

Section 1: Introduction

Brief overview of dataset contents:

Current database contains anonymised data collected during exercise testing services performed on male and female participants (cycling, rowing, kayaking and running) provided by the Human Performance Laboratory, School of Medicine, Trinity College Dublin, Dublin 2, Ireland.

835 graded incremental exercise test files (285 cycling, 266 rowing / kayaking, 284 running)

Description file with each row representing a test file - COLUMNS: file name (AXXX), sport (cycling, running, rowing or kayaking)

Anthropometric data of participants by sport (age, gender, height, body mass, BMI, skinfold thickness,% body fat, lean body mass and haematological data; namely, haemoglobin concentration (Hb), haematocrit (Hct), red blood cell (RBC) count and white blood cell (WBC) count )

Test data (HR, VO2 and lactate data) at rest and across a range of exercise intensities

Derived physiological indices quantifying each individual’s endurance profile

Following a request from athletes seeking assessment by phone or e-mail the test protocol, risks, benefits and test and medical requirements, were explained verbally or by return e-mail. Subsequently, an appointment for an exercise assessment was arranged following the regulatory reflection period (7 days). Following this regulatory period each participant’s verbal consent was obtained pre-test, for participants under 18 years of age parent / guardian consent was obtained in writing. Ethics approval was obtained from the Faculty of Health Sciences ethics committee and all testing procedures were performed in compliance with Declaration of Helsinki guidelines.

All consenting participants were required to attend the laboratory on one occasion in a rested, carbohydrate loaded and well-hydrated state, and for male participants’ clean shaven in the facial region. All participants underwent a pre-test medical examination, including assessment of resting blood pressure, pulmonary function testing and haematological (Coulter Counter Act Diff, Beckmann Coulter, CA,US) review performed by a qualified medical doctor prior to exercise testing. Any person presenting with any cardiac abnormalities, respiratory difficulties, symptoms of cold or influenza, musculoskeletal injury that could impair performance, diabetes, hypertension, metabolic disorders, or any other contra-indicatory symptoms were excluded. In addition, participants completed a medical questionnaire detailing training history, previous personal and family health abnormalities, recent illness or injury, menstrual status for female participants, as well as details of recent travel and current vaccination status, and current medications, supplements and allergies. Barefoot height in metre (Holtain, Crymych, UK), body mass (counter balanced scales) in kilogram (Seca, Hamburg, Germany) and skinfold thickness in millimetre using a Harpenden skinfold caliper (Bath International, West Sussex, UK) were recorded pre-exercise.

Section 2: Testing protocols

2.1: Cycling

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on an electromagnetically braked cycle ergometer (Lode Excalibur Sport, Groningen, The Netherlands). Participants initially identified a cycling position in which they were most comfortable by adjusting saddle height, saddle fore-aft position relative to the crank axis, saddle to handlebar distance and handlebar height. Participant’s feet were secured to the ergometer using their own cycling shoes with cleats and accompanying pedals. The protocol commenced with a 15-min warm-up at a workload of 120 Watt (W), followed by a 10-min rest. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a workload of 100 or 120 W for female and male participants, respectively, and subsequently increasing by a 20, 30 or 40 W incremental increase every 3-min depending on gender and current competition category. During assessment participants maintained a constant self-selected cadence chosen during their warm-up (permitted window was 5 rev.min−1 within a permitted absolute range of 75 to 95 rev.min−1) and the test was terminated when a participant was no longer able to maintain a constant cadence.

Heart rate (HR) data were recorded continuously by radio-telemetry using a Cosmed HR monitor (Cosmed, Rome, Italy). During the test, blood samples were collected from the middle finger of the right hand at the end of the second minute of each 3-min interval. The fingertip was cleaned to remove any sweat or blood and lanced using a long point sterile lancet (Braun, Melsungen, Germany). The blood sample was collected into a heparinised capillary tube (Brand, Wertheim, Germany) by holding the tube horizontal to the droplet and allowing transfer by capillary action. Subsequently, a 25μL aliquot of whole blood was drawn from the capillary tube using a YSI syringepet (YSI, OH, USA) and added into the chamber of a YSI 1500 Sport lactate analyser (YSI, OH, USA) for determination of non-lysed [Lac] in mmol.L−1. The lactate analyser was calibrated to the manufacturer’s requirements (± 0.05 mmol.L−1) before each test using a standard solution (YSI, OH, USA) of known concentration (5 mmol.L−1) and analyser linearity was confirmed using either a 15 or 30 mmol.L-1 standard solution (YSI, OH, USA).

Gas exchange variables including respiration rate (Rf in breaths.min-1), minute ventilation (VE in L.min-1), oxygen consumption (VO2 in L.min-1 and in mL.kg-1.min-1) and carbon dioxide production (VCO2 in L.min-1), were measured on a breath-by-breath basis throughout the test, using a cardiopulmonary exercise testing unit (CPET) and an associated software package (Cosmed, Rome, Italy). Participants wore a face mask (Hans Rudolf, KA, USA) which was connected to the CPET unit. The metabolic unit was calibrated prior to each test using ambient air and an alpha certified gas mixture containing 16% O2, 5% CO2 and 79% N2 (Cosmed, Rome, Italy). Volume calibration was performed using a 3L gas calibration syringe (Cosmed, Rome, Italy). Barometric pressure recorded by the CPET was confirmed by recording barometric pressure using a laboratory grade barometer.

Following testing mean HR and mean VO2 data at rest and during each exercise increment were computed and tabulated over the final minute of each 3-min interval. A graphical plot of [Lac], mean VO2 and mean HR versus cycling workload was constructed and analysed to quantify physiological endurance indices, see Data Analysis section. Data for VO2 peak in L.min-1 (absolute) and in mL.kg-1.min-1 (relative) and VE peak in L.min-1 were reported as the peak data recorded over any 10 consecutive breaths recorded during the last minute of the final exercise increment.

2.2: Running protocol

A continuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a motorised treadmill (Powerjog, Birmingham, UK). The running protocol, performed at a gradient of 0%, commenced with a 15-min warm-up at a velocity (km.h-1) which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity. Subsequently, the warm-up was followed by a 10 minute rest / dynamic stretching phase. From a safety perspective during all running GxT participants wore a suspended lightweight safety harness to minimise any potential falls risk. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal running velocity which was lower than the participant’s reported typical weekly long run (>60 min) on-road training velocity, and subsequently increased by ≥ 1 km.h-1 every 3-min depending on gender and current competition category. The test was terminated when a participant was no longer able to maintain the imposed treadmill.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

2.3: Rowing / kayaking protocol

A discontinuous graded incremental exercise test (GxT) to volitional exhaustion was performed on a Concept 2C rowing ergometer (Concept, VA, US) in rowers or a Dansprint kayak ergometer (Dansprint, Hvidovre, Denmark) in flat-water kayakers. The protocol commenced with a 15-min low-intensity warm-up at a workload (W) dependent on gender, sport and competition category, followed by a 10-min rest. For rowing the flywheel damping (120, 125 or 130W) was set dependent on gender and competition category. For kayaking the bungee cord tension was adjusted by individual participants to suit their requirements. A discontinuous protocol of 3-min exercise at a targeted load followed by a 1-min rest phase to facilitate stationary earlobe capillary blood sample collection and resetting of ergometer display (Dansprint ergometer) was used. The GxT began with a 3-min stationary phase for resting data collection, followed by an active phase commencing at a sub-maximal load 80 to 120 W for rowing, 50 to 90 W for kayaking and subsequently increased by 20,30 or 40 W every 3-min depending on gender, sport and current competition category. The test was terminated when a participant was no longer able to maintain the targeted workload.

Measurement variables, equipment and pre-test calibration procedures, timing and procedure for measurement of selected variables and subsequent data analysis were as outlined in Section 2.1.

3.1: Data analysis

Constructed graphical plots (HR, VO2 and [Lac] versus load / velocity) were analysed to quantify the following; load / velocity at TLac, HR at TLac, [Lac] at TLac, % of VO2 peak at TLac, % of HRmax at TLac, load / velocity and HR at a nominal [Lac] of 2 mmol.L-1, load / velocity, VO2 and [Lac} at a nominal HR of

ABSTRACT Bradycardia in athletes can range from moderate to severe, and the factors that contribute to slow heart rate are complex. Studies investigating the mechanisms associated with this condition are controversial, and may be linked to the form of exercise practiced. A systematic literature review was conducted to discuss bradycardia mechanisms in athletes who practice different forms of sport. The databases consulted were Pubmed (MEDLINE), Clinical Trials, Cochrane, Scopus, Web of Science, SciELO, Sport Discus and PEDro. The search included English language articles published up to January 2019, that evaluated athletes who practiced different forms of sport. One hundred and ninety-three articles were found, ten of which met the inclusion criteria, with 1549 male and female athletes who practiced diverse forms of sport. Resting heart rate and cardiac structure were studied in association with the form of sport practiced, through heart rate variability, electrocardiogram, echocardiogram and pharmacological blockade. The studies suggest that a slow resting heart rate cannot be explained by increased vagal modulation alone, but also includes changes in cardiac structure. According to the studies, different sports seem to produce different cardiac responses, and the bradycardia found in athletes can be explained by non-autonomic and autonomic mechanisms, depending on the type of effort or the form of sport practiced. However, the mechanism underlying the slow heart rate in each form of sport is still unclear. Level of evidence II; Prognostic studies - Investigating the effect of a patient characteristic on the outcome of disease.

Long jump results, women and men, for all Olympics between 2008 and 2024: 2008, 2012, 2016, 2020, and 2024.

The dataset is ideal for those interested in sports analytics, performance trends, track and fied, athletics or long jump statistics, as it offers comprehensivelong jump data across multiple Olympic Games.

Dataset Highlights - Results from multiple Olympic Games (2008–2024) - Detailed jump-by-jump performance data for athletes - Separate records for preliminary and final rounds - Data from both men's and women's long jump events

Competition format - In the preliminary round, all athletes get three jumps. - The top athletes in the preliminary round proceed to the final round. This is typically the top 12 athletes from the preliminary round. - In the final round, all athletes get three jumps. The top eight athletes get an additional three jumps for a total of six jumps in the final round. - The winner is determined by the longest distance during the final round. Note that the preliminary round does not count.

Original source - The original source of this data is Wikipedia. - Here is an example page: Wikipedia 2008 Olympic Women's Long Jump Results

Column Descriptions - Rank: Athlete’s rank after the prelim round which consists of three jumps. Note that this is not the final ranking. - Group: Qualifying group (A or B) the athlete competed in during the preliminaries. - Name: Name of the athlete. - Country: Country the athlete represents. - Jump_1_Prelim: Distance (in meters) of the athlete’s first jump in the preliminary round. - Jump_2_Prelim: Distance of the athlete’s second jump in the preliminary round. - - Jump_3_Prelim: Distance of the athlete’s third jump in the preliminary round. - Jump_1_Final: Distance of the athlete’s first jump in the final round. - Jump_2_Final: Distance of the athlete’s second jump in the final round. - Jump_3_Final: Distance of the athlete’s third jump in the final round. - Jump_4_Final: Distance of the athlete’s fourth jump in the final round (if applicable). - Jump_5_Final: Distance of the athlete’s fifth jump in the final round (if applicable). - Jump_6_Final: Distance of the athlete’s sixth jump in the final round (if applicable). "- - Year: Year of the Olympic Games (e.g., 2024). - Gender: Gender of the athlete (Men or Women).

Usage Ideas - Analyze performance trends across multiple Olympic Games. - Compare the performance of male and female athletes in long jump. - Study jump-by-jump performance for individual athletes or countries. - Investigate correlations between jump performance in preliminary and final rounds. - Whether you are a sports enthusiast, data analyst, or machine learning practitioner, this dataset offers a rich source of information for understanding Olympic long jump performances over time

Sample Python notebook: https://www.kaggle.com/code/michaeldelamaza/find-long-jump-results-of-a-particular-athlete/edit

BackgroundCoach-athlete relationship (CAR), thriving and athlete engagement are important psychological variables that affect sports performance. On the basis of self-determination theory, this study constructs a mediation model to examine the influence of CAR on athlete engagement and the mediating effect of thriving between them.MethodsThis cross-sectional study involves a questionnaire survey with 287 Chinese active athletes (M = 19.63, SD = 2.53) aged 14–26 years (64.5% male, 35.5% female) from eight sports. CAR, thriving and athlete engagement were assessed using the CAR Questionnaire, the Thriving Scale, and the Athlete Engagement Questionnaire, respectively.ResultsCAR and its dimensions can significantly and positively predict athlete engagement, complementarity, commitment, and closeness, accounting for 35.1%, 34.6%, and 30.4% of the cumulative variance in dominance analysis, respectively. The direct and indirect paths show that CAR affects athlete engagement through the mediating effect of thriving. The mediating effect model has a good fit and indirect effects account for 56.9% of the total effects.ConclusionThe effect of CAR on athlete engagement reflects a practical application of interpersonal dynamics in competitive sports to a certain extent. The following suggestions can be used to improve athlete engagement. First, setting common goals, emphasizing mutual cooperation, and building trust and support, promote coaches and athletes to have a higher sense of commitment and complementarity to each other, thereby helping improve athlete engagement. Second, meeting the vitality and progress needs of athletes effectively mobilizes CAR resources to promote athlete engagement, which can be manipulated by cultivating closeness, commitment, and complementarity. Third, to ensure the athletes’ sports state and mental health, the sports team should focus on the cultivation of athletes’ capacities to thrive and internally form a dynamic and positive sports atmosphere in their team. In the future, we can track and compare the influence of the improvement of CAR on thriving and athlete engagement can be tracked and compared from the dual perspectives of coaches and athletes.