A survey conducted in the second quarter of 2025 found that around 91.5 percent of female internet users aged 16 to 24 years worldwide played video games on any kind of device. During the survey period, 93 percent of male respondents in the same age group stated that they played video games. Worldwide, over 82 percent of internet users were gamers.

The dataset comprises responses from a study designed to explore the relationship between gaming addiction and motivational factors among Polish gamers. It includes various data points gathered from a diverse sample of participants.

1. Instruments and Validation: Gaming Addiction Test (GAT): This dataset includes scores from an adapted version of the Internet Addiction Test (IAT) by K. Young, tailored specifically for gaming addiction. The adaptation aligns with Griffiths' Gaming Addiction Criteria, ICD-11 Criteria, and DSM-V criteria. The reliability of this test was assessed using Cronbach's Alpha and McDonald's Omega indicators, ensuring its robustness and consistency in measuring gaming addiction. Motivation to Play Online Games Test: The dataset also contains scores from a modified version of Nick Yee's (2007) Motivation to Play Online Games test. This adaptation measures various motivational factors for gaming, such as escapism, social interaction, and immersion. The reliability of this test was similarly confirmed using Cronbach's Alpha and McDonald's Omega indicators.

2. Participants: Sample Size: The dataset includes data from 520 Polish participants. Data Collection Method: Data was collected using an online surveying tool. The survey was distributed through community channels of popular online games, including World of Warcraft, League of Legends, Dota 2, CS:GO, Apex Legends, and Diablo III and IV.

3. Data Points: Scores: The dataset includes the sums of raw scores and standardized scores (Standard Ten and Z-value) for both the Gaming Addiction Test and the Motivation to Play Online Games test. Descriptive Statistics: Required demographic information is provided, including participants' age, gender, and gaming habits (online vs. offline gaming). Gender was self-identified within four categories: man, woman, other (specify), and prefer not to tell. However, for this study, the descriptors were consolidated to Male and Female, as none of the 520 participants selected 'other' or 'prefer not to tell'.

4. Reliability Indicators: Cronbach's Alpha and McDonald's Omega: These indicators are included in the dataset to validate the internal consistency and reliability of the adapted psychometric tests.

5. Survey Distribution: Community Channels: The survey was disseminated through various online platforms and community channels associated with popular games.

The dataset provides valuable insights into the demographics and gaming habits of students, capturing various attributes that could be analyzed to uncover meaningful correlations. Each entry in the dataset includes the student's gender, identified as either "Female" or "Male," along with a unique school code that serves as an identifier for each institution. This allows for the categorization and grouping of students based on their respective schools, enabling comparative analyses across different educational institutions.

One of the key aspects covered in the dataset is the student's gaming experience, which includes the number of years they have been playing games. This attribute can indicate whether gaming is a long-term habit or a relatively new activity for the student. Additionally, the dataset records how frequently students engage in gaming, likely measured on a scale from 1 to 5, providing a quantitative representation of their gaming intensity. To further elaborate on gaming engagement, the dataset also tracks the average number of hours a student spends playing games daily. This metric can be crucial in understanding whether extended gaming sessions have an impact on academic performance. Moreover, the dataset distinguishes whether a student actively plays games or not, which can be particularly useful in comparative studies assessing the behaviors of gaming versus non-gaming students.

Beyond gaming habits, the dataset delves into socioeconomic factors by including the annual income of the student's family. This "Parent Revenue" variable can help researchers examine the potential influence of economic background on a student's gaming behavior and academic performance. Additionally, the education levels of both the student's father and mother are recorded, offering insights into whether parental education has any correlation with the student's gaming frequency, academic performance, or gaming choices.

Academic performance is another critical component of this dataset, represented by the "Grade" variable, which provides a measure of the student's academic standing. This information can be instrumental in investigating how gaming habits, parental background, and socioeconomic status contribute to or hinder academic success.

This dataset presents an excellent opportunity for analysis on platforms like Kaggle. Potential research directions could include exploring the relationship between gaming frequency and academic performance, investigating whether students from higher-income families spend more or fewer hours gaming, or analyzing if parental education has any impact on the types of games students play or their duration of play. By leveraging this dataset, researchers can identify trends and generate insights that may inform policies on gaming habits, parental involvement, and educational strategies.

College Basketball Game Statistics (2014-2024)

Dataset Description

This dataset contains detailed game-by-game statistics for NCAA Division I men's basketball games from the 2013-14 season through the 2023-24 season (excluding 2019-20). The data was sourced from barttorvik.com, a respected resource for college basketball analytics.

Temporal Coverage

• Start: 2013-14 season
• End: 2023-24 season
• Note: 2019-20 season is excluded due to the COVID-19 pandemic's impact on that season

Column Descriptions

Game Information

• Date: Game date in YYYY-MM-DD format
• Type: Type of game
  • N: Non-conference regular season game
  • C: Conference regular season game
  • CT: Conference Tournament game
  • P: NIT Tournament game
  • T: NCAA Tournament game
• Team: Name of the team
• Conf.: Conference affiliation of the team
• Opp.: Opponent team name
• Venue: Game location (Home/Away/Neutral)

Game Outcomes

• Win: Binary indicator of game result (1 = Win, 0 = Loss)
• Points_For: Points scored by the team
• Points_Against: Points scored by the opponent
• Point_Differential: Point difference (Points_For minus Points_Against)

Team Performance Metrics

• Adj. O: Adjusted offensive efficiency
• Adj. D: Adjusted defensive efficiency
• T: Game tempo (possessions per 40 minutes)

Offensive Statistics (prefixed with O.)

• O.EFF: Offensive efficiency (points per 100 possessions)
• O.eFG%: Effective field goal percentage
• O.TO%: Turnover percentage
• O.Reb%: Offensive rebounding percentage
• O.FTR: Free throw rate

Defensive Statistics (prefixed with D.)

• D.EFF: Defensive efficiency (points allowed per 100 possessions)
• D.eFG%: Opponent's effective field goal percentage
• D.TO%: Forced turnover percentage
• D.Reb%: Defensive rebounding percentage
• D.FTR: Opponent's free throw rate

Performance Metrics

• G-Sc: Game Score (a measure of game performance)

Usage Notes

• All efficiency metrics are tempo-adjusted
• Conference affiliations reflect the team's conference for that specific season
• Neutral site games include both tournament and non-tournament games
• The dataset includes regular season and postseason games

ObjectiveThis study tested the mediation effect of maladaptive cognition of internet gaming and moderation effect of internet gaming history in the relationship between internet gaming engagement and internet gaming disorder in adolescents.MethodA total of 2,902 secondary school students were surveyed in Hong Kong from February 2021 to December 2021. The proposed moderated mediation model was tested by PROCESS.ResultsInternet gaming engagement, internet gaming history and maladaptive cognition were positively associated with internet gaming disorder symptoms. Maladaptive cognition significantly mediated the association between internet gaming engagement and internet gaming disorder symptoms in both males and females. In addition, a significant interaction between internet gaming engagement and internet gaming history was detected among females but not for males, namely, the positive relationships of internet gaming engagement with maladaptive cognition and internet gaming disorder symptoms were weaker with the increased years of internet gaming.ConclusionsOur study provides a better understanding of the underlying mechanism and boundary condition in the association between internet gaming engagement and internet gaming disorder among adolescents. Preventing interventions should aim to reduce maladaptive cognition and internet gaming engagement. Interventions targeting internet gaming engagement maybe more effective among female gamers who are beginners and all male gamers.

Motivation

Ultimate, also known as Ultimate Frisbee, is a non-contact sport where 2 teams of 7 players pass a frisbee to each other and try to reach the opposing teams ‘end zone’ and score a point without dropping the frisbee. Each point starts with a pull, where the defensive team throws the frisbee from their end zone to the end zone of the offensive team. The goal of the defensive team is to force the offensive team to turnover the disc by dropping it or throwing it out of bounds. If the defensive team forces a turnover, they can then try to score while the offensive team tries to stop them. The team that scores a point then executes the next pull to the team that got scored on.

College ultimate games follow the USA Ultimate rules, which dictate a game to 15 goals, with halftime occurring when a team has scored 8 goals. The rules also allow for a time cap, where once the cap is reached the game is played to a score determined by the score of the team that is winning. Like other sports, there are two main positions in ultimate called handler and cutter. Teams often have 2 or 3 handlers playing at a time and they are players who are versatile throwers and orchestrate how the offense is run. Cutters are players who run around and try to escape defenders in order to receive the frisbee from the handlers. Teams have different formations and offensive schemes they use to try and find openings to make gaining yardage and scoring points easier. Teams tend to have offensive and defensive lines in order to save players from playing every point and split the load of games.

Measured Statistics Data This data comes from the 2024 Division 1 and 3 Men’s and Women’s College Ultimate Championships. It contains scoring and defensive statistics for players from each game played at the two tournaments. This table has 1665 rows and 16 columns, 1 row for each player that contains all the statistics for that player.

Variable Descriptions

-player player name - level the level that the player was competing at - gender gender of the player’s division - division level and gender of the competing player’s division - team_name full name of the players team - Turns the number of turnovers the player threw - D s the number of defensive blocks the player made - Assists the number of assists the player threw - Points the number of points the player scored - plus_minus the point differential of the player for offensive points - team_games the number of games played - turns_per_game the average turnovers per game -ds_per_game the average defensive blocks per game - ast_per_game the average assists per game - pts_per_game the average pts per game - pls_mns_per_game the average plus minus per game

Questions

• What are some ways to graphically represent variables together to compare divisions?
• What might strengths in different variable mean about players/teams?
• What variables differ discernibly between different divisions?

References

Statistics found on USA Ultimate and taken from a data visualization, USA Ultimate 2024 Nationals Stats Dashboard done by Ben Ayres.

Acknowledgement

Foto von ALEXANDRE LALLEMAND auf Unsplash

Descriptive statistics and correlations between the variables in female and male gamers group.

ObjectivesHip and groin pain is common in Gaelic games players, but data are limited to elite males with poor representation of females. The aim of this study was to examine the prevalence, severity and factors associated with hip and groin pain and Femoroacetabular Impingement Syndrome (FAIS) in male and female Gaelic games players.MethodsA representative national sample of Gaelic games players completed a survey providing demographic information and details related to self-reported episodes of hip and groin pain and FAIS diagnosis within the last year. Players from multiple age grades, codes (Football/Hurling/Camogie) and levels of Gaelic games were included.ResultsA total of 775 players responded to the survey. The annual prevalence of hip and groin pain was 54.8%. Almost half of players (48.8%) continued to participate in sport, while 18.7% ceased participation and 32.5% reported reduced participation. Although 40% of episodes lasted no longer than 3 weeks, there was a high recurrence rate (33.5%). FAIS was reported by eight players, representing 1.9% of hip and groin complaints. Logistic regression models indicate male sex, playing both codes of Gaelic games and participating in additional sport were significant factors in predicting hip and groin pain.ConclusionHip and groin pain is prevalent in Gaelic Games with FAIS accounting for a small proportion of cases. However, consideration of indicators of severity (participation impact/symptom duration/medical attention) is essential in understanding the context and magnitude of these hip and groin issues. Male players and players engaging in multiple sports are more likely to experience hip and groin pain.

Context

With the rise of the popularity of machine learning, this is a good opportunity to share a wide database of the even more popular video-game Pokémon by Nintendo, Game freak, and Creatures, originally released in 1996.

Pokémon started as a Role Playing Game (RPG), but due to its increasing popularity, its owners ended up producing many TV series, manga comics, and so on, as well as other types of video-games (like the famous Pokémon Go!).

This dataset is focused on the stats and features of the Pokémon in the RPGs. Until now (08/01/2017) seven generations of Pokémon have been published. All in all, this dataset does not include the data corresponding to the last generation, since 1) I created the databased when the seventh generation was not released yet, and 2) this database is a modification+extension of the database "721 Pokemon with stats" by Alberto Barradas (https://www.kaggle.com/abcsds/pokemon), which does not include (of course) the latest generation either.

Content

This database includes 21 variables per each of the 721 Pokémon of the first six generations, plus the Pokémon ID and its name. These variables are briefly described next:

• Number. Pokémon ID in the Pokédex.
• Name. Name of the Pokémon.
• Type_1. Primary type.
• Type_2. Second type, in case the Pokémon has it.
• Total. Sum of all the base stats (Health Points, Attack, Defense, Special Attack, Special Defense, and Speed).
• HP. Base Health Points.
• Attack. Base Attack.
• Defense. Base Defense.
• Sp_Atk. Base Special Attack.
• Sp_Def. Base Special Defense.
• Speed. Base Speed.
• Generation. Number of the generation when the Pokémon was introduced.
• isLegendary. Boolean that indicates whether the Pokémon is Legendary or not.
• Color. Color of the Pokémon according to the Pokédex.
• hasGender. Boolean that indicates if the Pokémon can be classified as female or male.
• Pr_male. In case the Pokémon has Gender, the probability of its being male. The probability of being female is, of course, 1 minus this value.
• Egg_Group_1. Egg Group of the Pokémon.
• Egg_Group_2. Second Egg Group of the Pokémon, in case it has two.
• hasMegaEvolution. Boolean that indicates whether the Pokémon is able to Mega-evolve or not.
• Height_m. Height of the Pokémon, in meters.
• Weight_kg. Weight of the Pokémon, in kilograms.
• Catch_Rate. Catch Rate.
• Body_Style. Body Style of the Pokémon according to the Pokédex.

Notes

Please note that many Pokémon are multi-form, and also some of them can Mega-evolve. I wanted to keep the structure of the dataset as simple and general as possible, as well as the Number variable (the ID of the Pokémon) unique. Hence, in the cases of the multi-form Pokémon, or the ones capable of Mega-evolve, I just chose one of the forms, the one I (and my brother) considered the standard and/or the most common. The specific choice for each of this Pokémon are shown below:

• Mega-Evolutions are not considered as Pokémon.
• Kyogre, Groudon. Primal forms not considered.
• Deoxis. Only normal form considered.
• Wormadam. Only plant form considered.
• Rotom. Only normal form considered, the one with types Electric and Ghost.
• Giratina. Origin form considered.
• Shaymin. Land form considered.
• Darmanitan. Standard mode considered.
• Tornadus, Thundurus, Landorus. Incarnate form considered.
• Kyurem. Normal form considered, not white or black forms.
• Meloetta. Aria form considered.
• Mewstic. Both female and male forms are equal in the considered variables.
• Aegislash. Shield form considered.
• Pumpkaboo, Gourgeist. Average size considered.
• Zygarde. 50% form considered.
• Hoopa. Confined form considered.

Acknowledgements

As said at the beginning, this database was based on the Kaggle database "721 Pokemon with stats" by Alberto Barradas (https://www.kaggle.com/abcsds/pokemon). The other resources I mainly used are listed below:

• WikiDex (http://es.pokemon.wikia.com/wiki/WikiDex).
• Bulbapedia, the community driven Pokémon encyclopedia (http://bulbapedia.bulbagarden.net/wiki/Main_Page).
• Smogon University (http://www.smogon.com/).

Possible future work

This dataset can be used with different objectives, such as, Pokémon clustering, trying to find relations or dependencies between the variables, and also for supervised classification purposes, where the class could be the Primary Type, but also many of the other variables.

Author

Asier López Zorrilla

This DataSet includes all pokémon of 8 generations ( count to Sword & Shield) - pokedex_number: the id of pokémon in National Pokédex - name: the name of pokémon - generation: separating Pokémon games based on the Pokémon they include
- status: Legendary/ Mythical/ Normal/ Sub Legendary - species
- type_number: the total type
- type_1: the first type
- type_2: the second type
- height_m: height of pokémon (meters)
- weight_kg: weight of pokémon (kilograms)
- abilities_number: the total ability of pokémon (ability provides a passive effect in battle or in the overworld) - ability_1: the first ability - ability_2: the second ability - ability_hidden: the special ability - total_points: the total of base stats - hp: heal point stats - attack: physical attack stats - defense: physical defense stats - sp_attack: special attack stats - sp_defense: special defense stats - speed: speed stats - catch_rate: a formula to determine the chances of catching that Pokémo
- growth_rate: There is: + Erratic: 600,000 exp at level 100 + Fast: 800,000 exp at level 100 + medium fast: 1,000,000 exp at level 100 + medium slow: 1,059,860 exp at level 100 + slow: 1,250,000 exp at level 100 + Fluctuating: 1,640,000 exp at level 100
- egg_type number: the total of egg types (categories that determine which Pokémon are able to interbreed) - egg_type 1: the first egg type
- egg_type 2: the second egg type
- percentage_male: percentage male (the rest is female) - egg_cycles: an internal value used for tracking how long until a Pokémon Egg hatches
VS ( effectiveness and weakness (in battle) 0: No effect 0.5: Not very effective 1: Normal 2: Super-effective (200%) other: depend on dual type) - vs_normal - vs_fire
- vs_water
- vs_electric
- vs_grass - vs_ice - vs_fight
- vs_poison - vs_ground - vs_flying - vs_psychic
- vs_bug
- vs_rock
- vs_ghost
- vs_dragon - vs_dark
- vs_steel
- vs_fairy
- vs_total: the total of all vs columns above source: Bulbapedia

Context

Dataset of the original 151 Pokemon with stats only from Generation 1. Data was scraped from https://serebii.net/.

Content

• Number: Pokedex Index Number
• Name: Name of the Pokemon
• Types: The number of types a Pokemon has
• Type1: The primary type
• Type2: The secondary type if applicable
• Height: The height of the Pokemon in meters
• Weight: The weight of the Pokemon in kilograms
• Male_Pct: The probability of encountering a male in percentage
• Female_Pct: The probability of encountering a female in percentage
• Capt_Rate: Measures how difficult it is too capture the Pokemon (Higher value = Higher difficulty
• Exp_Points: Experience points needed to fully level up
• Exp_Speed: How fast a Pokemon fully levels up
• Base_Total: The sum all the base stats (HP, Attack, Defense, Special, Speed)
• HP - Speed: The base stats of a Pokemon
• Normal_Dmg - Dragon_Dmg: The multiplicative damage it takes from certain types
• Evolutions: The number of evolutions the base Pokemon has

Acknowledgements

Scraped from https://serebii.net/.

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

This dataset contains all match results and bets for all tennis tournaments. ATP Men´s Tour from 2000 till 2023. WTA Women´s Tour from 2007 till 2023.

Original Data

Data Dictionary

Key to results data:

ATP = Tournament number (men) WTA = Tournament number (women) Location = Venue of tournament Tournament = Name of tounament (including sponsor if relevant) Data = Date of match (note: prior to 2003 the date shown for all matches played in a single tournament is the start date) Series = Name of ATP tennis series (Grand Slam, Masters, International or International Gold) Tier = Tier (tournament ranking) of WTA tennis series. Court = Type of court (outdoors or indoors) Surface = Type of surface (clay, hard, carpet or grass) Round = Round of match Best of = Maximum number of sets playable in match Winner = Match winner Loser = Match loser WRank = ATP Entry ranking of the match winner as of the start of the tournament LRank = ATP Entry ranking of the match loser as of the start of the tournament WPts = ATP Entry points of the match winner as of the start of the tournament LPts = ATP Entry points of the match loser as of the start of the tournament W1 = Number of games won in 1st set by match winner L1 = Number of games won in 1st set by match loser W2 = Number of games won in 2nd set by match winner L2 = Number of games won in 2nd set by match loser W3 = Number of games won in 3rd set by match winner L3 = Number of games won in 3rd set by match loser W4 = Number of games won in 4th set by match winner L4 = Number of games won in 4th set by match loser W5 = Number of games won in 5th set by match winner L5 = Number of games won in 5th set by match loser Wsets = Number of sets won by match winner Lsets = Number of sets won by match loser Comment = Comment on the match (Completed, won through retirement of loser, or via Walkover)

Key to match betting odds data:

B365W = Bet365 odds of match winner B365L = Bet365 odds of match loser B&WW = Bet&Win odds of match winner B&WL = Bet&Win odds of match loser CBW = Centrebet odds of match winner CBL = Centrebet odds of match loser EXW = Expekt odds of match winner EXL = Expekt odds of match loser LBW = Ladbrokes odds of match winner LBL = Ladbrokes odds of match loser GBW = Gamebookers odds of match winner GBL = Gamebookers odds of match loser IWW = Interwetten odds of match winner IWL = Interwetten odds of match loser PSW = Pinnacles Sports odds of match winner PSL = Pinnacles Sports odds of match loser SBW = Sportingbet odds of match winner SBL = Sportingbet odds of match loser SJW = Stan James odds of match winner SJL = Stan James odds of match loser UBW = Unibet odds of match winner UBL = Unibet odds of match loser

MaxW= Maximum odds of match winner (as shown by Oddsportal.com) MaxL= Maximum odds of match loser (as shown by Oddsportal.com) AvgW= Average odds of match winner (as shown by Oddsportal.com) AvgL= Average odds of match loser (as shown by Oddsportal.com)

Acknowlegement

Foto von Matthias David auf Unsplash

Tennis-Data would like to acknowledge the following sources which are currently utilised in the compilation of Tennis-Data's results and odds files.

Results: Xscores - http://www.xscores.com/ ATPtennis.com - http://www.atptennis.com/ ATP Tour Rankings and Results Page - http://www.stevegtennis.com/ Livescore - http://www.livescore.net/

Rankings: ATPtennis.com - http://www.atptennis.com/ ATP Tour Rankings and Results Page - http://www.stevegtennis.com/ WTA TOur Rankings - http://www.sonyericssonwtatour.com

Betting odds for matches generally represent the most recent before play starts, as reported by oddsportal.com and the individual bookmakers.

IntroductionThe CrossFit® Open (CFO) acts a preliminary round that qualifies men and women for later stages of its annual Games competition. The CFO typically consists of 4–6 workouts that variably challenge an athlete’s weightlifting strength, gymnastic skill, and endurance capacity. Except for differences in prescribed intensity loads, workouts are designed the same for men and women to elicit a similar challenge. While all workouts within a single year are unique to each other, one has been repeated from a previous CFO each year between 2012 and 2021. Because previous CFO workouts are often integrated into training, improvements are expected when a workout is officially repeated. However, besides documented record performances, it is unclear whether most athletes are improving, if these improvements affect ranking, or if differences exist between men and women.PurposeTo examine sex-division differences and performance changes across repeated CFO workouts, as well as their effect on CFO and workout ranking.MethodsEleven separate samples of 500 men and 500 women, who were representative of the same overall percent rank within each year involving one of the nine repeated CFO workouts (2011–2021) were drawn for this study. Each athlete’s age (18–54 years), rank (overall and within each workout), and reported workout scores were collected from the competition’s publicly-available leaderboard. Each sample had excluded any athlete who had not met minimum performance criteria (e.g., at least one completed round) for all prescribed (Rx) workouts within a given year (including those not analyzed). Since some workouts could be scored as repetitions completed or time-to-completion (TTC), and because programming was often scaled between men and women, all scores were converted to a repetition completion rate (repetitions divided by TTC [in minutes]).ResultsSeparate sex-division x time analyses of variance with repeated measures revealed significant (p < 0.05) interactions in all but one repeated workout comparison. Initially, men were faster in four workouts (~18.5%, range = 3.9–35.0%, p < 0.001), women in two (~7.1%, range = 5.2–9.0%, p < 0.001), and they tied in the remaining three workouts. When workouts were repeated in subsequent years, men were faster in three workouts (~5.4%, range = 0.9–7.8%, p < 0.05), while women were faster in two (~3.8%, range = 3.5–4.1%, p < 0.01). Though performance improved in seven of the nine workouts (~14.3%, p < 0.001) and percentile rank was controlled, athletes earned a lower rank (overall and within workout) on each repeated workout (p < 0.001).ConclusionsPerformance (measured as repetition completion rate) has improved in most repeated CFO workouts, particularly for women. However, improvements seen among all athletes, along with increased participation, have made it more difficult for athletes to improve their overall rank. To rank higher, individual athletes must improve their pace to a greater degree than the average improvements seen across the competitive field.

https://upload.wikimedia.org/wikipedia/en/9/91/Football%2C_Tokyo_2020.svg">

2020–21 UEFA Champions League

This dataset has all information related to the Football 2020 Summer Olympics Please feel free to share and use the dataset.

Columns

Inspiration

The inspiration for creating this dataset is to analyze the performance of teams during the competition and relate them to the bet on other platforms around the world.

Source

All data were taken from One Football platform. The images were taken from Wikipedia.