Minecraft Statistics: The reports say that the gaming industry is expected to reach $431.87 billion by the year 2030. Since technological developments, not only there are laptops and PCs which are gaming-oriented but mobile devices have become compatible with many advanced games today. The recent release of the Harry Potter game ‘ Hogwarts Legacy is already doing its magic on the muggle world. These Minecraft Statistics include insights from various aspects that provide light on why Minecraft is one of the best games today. Editor’s Choice In Minecraft, 24 hours of the game is 20 minutes in real life. As of January 2023, the recorded number of players is 173.5 million. On average, 110,000 concurrent viewers are found on Twitch. Revenue generated from mobile downloads excluding in-game transactions counts for up to 41% of total Minecraft revenue. The Chinese edition of Minecraft has been downloaded more than 400 million times. To heal the players’ health healing potions have been used more than 1.1 billion times. Before launching Minecraft, the game was almost named a ‘Cave Game’. The game sometimes misspells its name by changing the order of words ‘C’ and ‘E’ with ‘Minecraft’. During the initial years of the pandemic, the database of total players increased by more than 14 million. The average age of a player is 24 years.

Dataset

This dataset was created by Ezgi Turalı

Contents

European Sold Production of Games and Toys by Country, 2023 Discover more data with ReportLinker!

Content Which Olympic athletes have the most gold medals? Which countries are they from and how has it changed over time?

More than 35,000 medals have been awarded at the Olympics since 1896. The first two Olympiads awarded silver medals and an olive wreath for the winner, and the IOC retrospectively awarded gold, silver, and bronze to athletes based on their rankings. This dataset includes a row for every Olympic athlete that has won a medal since the first games.

Acknowledgements Original Data up to 2014 was provided by the IOC Research and Reference Service and published by The Guardian's Datablog, and was taken from: https://www.kaggle.com/the-guardian/olympic-games Data from 2016 to 2021 was scraped from http://www.olympedia.org/ (there could be some missing data and minor errors) Unlike the original dataset, each team only shows up once per medal, and not once per athlete!

A survey conducted in the third quarter of 2024 found that over 92 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 83 percent of internet users were gamers.

The dataset contains year-, country-, athlete- and sport-wise historical data on gold, silver and bronze medals won in Olympics, along with details of host-country and city in which Olympics were held

Notes:

1) Medal-wise winner details for some events/sports before 1980 are not available on the Official International Olympic Committee (IOC) Website, and hence, the sport-wise, medal-wise data may not match with the country-wise medals dataset (https://dataful.in/datasets/19674/).

2) Both datasets are sourced from the Official International Olympic Committee (IOC) Website.

European Toys and Games Production Value by Country, 2023 Discover more data with ReportLinker!

This is a Paralympic Games dataset that describes medals and athletes for Tokyo 2020. The data was created from Tokyo Paralympics.

All medals and more than 4,500 athletes (with some personal data: date and place of birth, height, etc.) of the Paralympic Games you can find here. Apart from it coaches and technical officials are present.

Please, click on the ticker to the right top of the dataset to cast an upvote. It will help be on the top.

Data: 1. medals_total.csv - dataset contains all medals grouped by country as here. 2. medals.csv - dataset includes general information on all athletes who won a medal. 3. athletes.csv - dataset includes some personal information of all athletes. 4. coaches.csv - dataset includes some personal information of all coaches. 5. technical_officials - dataset includes some personal information of all technical officials.

Related Datasets

• Olympic Games, 1986-2020
• Beijing 2022 Olympics
• Tokyo 2020 Olympics
• Tokyo 2020 Horses
• Olympic Games Hosts

Data Visualization

Tokyo 2020 Paralympics

Dataset History

2021-09-05 - dataset is updated. Contains full information. 2021-08-30 - dataset is updated. Contains information for the first 6 days of competitions. 2021-08-27 - dataset is created. Contains information for the first 3 days of competitions.

Q&A

If you have some questions please start a discussion.

Analysis of ‘Cricket Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/notkrishna/cricket-statistics-for-all-formats on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Cricket is a bat-and-ball game played between two teams of eleven players on a field at the centre of which is a 22-yard (20-metre) pitch with a wicket at each end, each comprising two bails balanced on three stumps. The game proceeds when a player on the fielding team, called the bowler, "bowls" (propels) the ball from one end of the pitch towards the wicket at the other end. The batting side's players score runs by striking the bowled ball with a bat and running between the wickets, while the fielding side tries to prevent this by keeping the ball within the field and getting it to either wicket, and also tries to dismiss each batter (so they are "out"). Means of dismissal include being bowled, when the ball hits the stumps and dislodges the bails, and by the fielding side either catching a hit ball before it touches the ground, or hitting a wicket with the ball before a batter can cross the crease line in front of the wicket to complete a run. When ten batters have been dismissed, the innings ends and the teams swap roles. The game is adjudicated by two umpires, aided by a third umpire and match referee in international matches.

Forms of cricket range from Twenty20, with each team batting for a single innings of 20 overs and the game generally lasting three hours, to Test matches played over five days. Traditionally cricketers play in all-white kit, but in limited overs cricket they wear club or team colours. In addition to the basic kit, some players wear protective gear to prevent injury caused by the ball, which is a hard, solid spheroid made of compressed leather with a slightly raised sewn seam enclosing a cork core layered with tightly wound string.

The earliest reference to cricket is in South East England in the mid-16th century. It spread globally with the expansion of the British Empire, with the first international matches in the second half of the 19th century. The game's governing body is the International Cricket Council (ICC), which has over 100 members, twelve of which are full members who play Test matches. The game's rules, the Laws of Cricket, are maintained by Marylebone Cricket Club (MCC) in London. The sport is followed primarily in South Asia, Australasia, the United Kingdom, southern Africa and the West Indies.[1] Women's cricket, which is organised and played separately, has also achieved international standard. The most successful side playing international cricket is Australia, which has won seven One Day International trophies, including five World Cups, more than any other country and has been the top-rated Test side more than any other country.

Content

Cricket as any sport is full of important data and stats. Given, the game is generally is played in three different formats, one day (50 overs for each team to score and bowl), test (no limitations on overs but played for max 5 days with each team having two innings to score), and newest format twenty20 (each team has 20 overs to score).

Dataset contains 9 files (3 for each format). Each group of three files contains best stats for batsmen, bowlers and series/tournaments.

Source https://www.espncricinfo.com/

Play with it as you like.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Tokyo 2020 Olympics Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aliaamiri/2020-summer-olympics-dataset on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

The Olympic games are not just sport events, but some social and economic factors have effects on every nation's performance. In order to measure performance, the first step is collecting data. There is a comprehensive dataset by @heesoo37 which covers Olympic games from 1896 to 2016. I tried in vain to find a similar dataset for 2020 Summer Olympics. Therefore, I decided to make one from the data available on official Olympics website www.olympics.com. rvest, jasonlite and tidyverse packages of R language were used to web scrape the desired data.

Content

This dataset consists of every event in which an athlete participated together with age, nationality, ranks and medals. There two clear differences between current dataset and similar ones. First, in addition to medals, ranks are also included for every event an athlete took part. Second, each event is labeled in a way one can easily confer whether it is team or individual event. I will explain my incentive for doing this way in a separate notebook, however, in a nutshell, measuring performance just by counting medals and treating each team medal as an individual medal is not an accurate way. So, defining a new Key Performance Index is necessary. Although the data offered by www.olympics.com is not perfect, this website is the most comprehensive reference for 2020 Summer Olympics. www.olympedia.com is another good resource for historical data collection of past Olympic games which is maintained by a number Olympics historians and statisticians. In the process of establishing the current dataset, the main reference was www.olympics.com. In some cases there were dubious entries which was corrected or omitted after verifying them by referring to www.olympedia.com and www.wikipedia.com.

Inspiration

This dataset can be utilised to understand which countries performed better in 2020 Summer Olympics and what factors affected their success.

--- Original source retains full ownership of the source dataset ---

Context This dataset is about top 17 female football players who have made 100th and more international goals for their country. So, this group is also known as "Century Club".

Content This dataset is easy and good enough to start the visualizations for beginners. This datset contains name of the player, their country, goals, position and their years (how many they played/playing football).

Analysis of ‘Tokyo 2020 Olympics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/piterfm/tokyo-2020-olympics on 13 February 2022.

--- Dataset description provided by original source is as follows ---

This is an Olympic Games dataset that describes medals and athletes for Tokyo 2020. The data was created from Tokyo Olympics.

More than 2,400 medals, and 11,000 athletes (with some personal data: date and place of birth, height, etc.) of the XXXII Olympic Games you can find here. Apart from it coaches and technical officials are present.

Please, click on the ticker to the right top of the dataset to cast an upvote. It will help be on the top.

Data: 1. medals_total.csv - dataset contains all medals grouped by country as here. 2. medals.csv - dataset includes general information on all athletes who won a medal. 3. athletes.csv - dataset includes some personal information of all athletes. 4. coaches.csv - dataset includes some personal information of all coaches. 5. technical_officials - dataset includes some personal information of all technical officials.

Related Datasets

• Olympic Games, 1986-2020
• Beijing 2022 Olympics
• Tokyo 2020 Paralympics
• Tokyo 2020 Horses
• Olympic Games Hosts

Dataset History

2021-08-30 - country column is fixed. 2021-08-29 - dataset is created.

Q&A

If you have some questions please start a discussion.

--- Original source retains full ownership of the source dataset ---

It is often believed that the Olympic Games have become more migratory. The number of Olympic athletes representing countries in which they weren’t born, is thought to be on the rise. It should, however, be noted that migration in the context of sports is hardly a new phenomenon. To study the question of whether the Olympic Games have become more migratory, we constructed a dataset consisting of approximately 40,000 participants from eleven countries that participated in the Summer Olympics between 1948 and 2012.Based on the data, J. Jansen and G. Engbersen have written an academic paper. The paper is published in the international, peer-reviewed open access journal Comparative Migration Studies. In the paper, the authors show that, as a reflection of global migration patterns and trends, the number of foreign-born Olympians hasn’t necessarily increased in all countries. Rather, the direction of Olympic migration has changed and most teams have become more diverse. Olympic migration is thus primarily a reflection of global migration patterns instead of a discontinuity with the past.

European Turnover of Computer Games Publishing by Country, 2023 Discover more data with ReportLinker!

European Production Value of Computer Games Publishing by Country, 2023 Discover more data with ReportLinker!

The 17th of 20 years of International Social Survey Programme (ISSP) surveys within New Zealand by Professor Philip Gendall, Department of Marketing, Massey University.A verbose rundown on topics covered follows.Leisure time: activities and satisfaction. The meaning of time and leisure, and its relation to work and other spheres of life. Sport/game activities and subjective functions of sport and games. Sociological aspects of sports. Social and political participation. Social determinants and consequences of leisure.Frequency of leisure activities in respondent’s free time; main purpose of free time activities; enjoyment from reading books, getting together with friends, taking part in physical activities, and watching TV or DVDs; motivation for leisure time activities: establishing useful contacts, relaxing, and developing skills in free time.Frequency of feeling bored, feeling rushed, and thinking about work during free time; preference for sharing time with other people or being alone; wishes for: more time in a paid job, more time doing household work, more time with family, and more time in leisure activities; number of nights the respondent stayed away from home for holiday or social visits; days of leave from work; most frequent exercises or physical activity.Preferred type of games rather than sports; most important reasons for taking part in sports or games: physical or mental health, meeting other people, competing against others or physical attractiveness; most frequently watched sport on TV; feeling of national pride when respondent’s country does well at international sports or games competition; attitudes towards sport (scale); social and political participation; trust in people; interest in politics; reasons for staying away from doing free time activities: lack of facilities nearby, lack of money and time, personal health or responsibility to take care of someone; perception of happiness; estimation of personal health. Whether the day before questioning was a working-day or a holiday; time of getting up and going to sleep on the day before; height and weight of respondent; wishes to gain or to lose weight; conception of an ideal shape of a man and a women on the bases of presented pictures.Demography: Sex; age; marital status; steady life partner; years of schooling; highest education level; country specific education and degree; current employment status (respondent and partner); hours worked weekly; occupation (ISCO 1988) (respondent and partner); supervising function at work; working for private or public sector or self-employed (respondent and partner); if self-employed: number of employees; trade union membership; earnings of respondent (country specific); family income (country specific); size of household; household composition; party affiliation (left-right); country specific party affiliation; participation in last election; religious denomination; religious main groups; attendance of religious services; self-placement on a top-bottom scale; region (country specific); size of community (country specific); type of community: urban-rural area; country of origin or ethnic group affiliation. Additionally coded: administrative mode of data-collection; weighting factor; case substitution.

Results from the Evolution Fighting Game Tournament (EVO) for the critically acclaimed fighting game series, Tekken.

Covers tournament years 2005 - 2024 for both the mainline championship and Japan subseries. Data was gathered from a combination of Liquipedia and match VODs, entered by hand.

This data set is suited for a wide variety of exploratory sports analytics, including character win-loss comparisons, player match prediction, and general trend analysis.

Note: Tekken Tag Tournament results for EVO 2005 not included due to lack of available references.

Attributes

• EVO Year + Series: The specific tournament (string)
• Game: The year's competition Tekken title (string)
• Tournament Round: The specific round of the finals bracket (string)
• Format: The match format in player game win target (FT2 = First to 2, FT3 = First to 3) (string)
• P1: The first player (string)
• P1 Score: The number of games P1 won in the match (0-2 for FT2, 0-3 for FT3) (integer)
• P1 Char: The character P1 played (string)
• P1 Team: The team P1 played for in the specific tournament (string)
• P1 Country: P1's country of origin (string, 2 letter country code)
• P2: The second player (string)
• P2 Score: The number of games P2 won in the match (0-2 for FT2, 0-3 for FT3) (integer)
• P2 Char: The character P2 played (string)
• P2 Team: The team P2 played for in the specific tournament (string)
• P2 Country: P2's country of origin (string, 2 letter country code)
• Winner: The winner of the tournament round (string)
• Special Condition: Any special properties of the tournament round (Character Switch = One or more players switched characters, Reset = Loser's side of Grand Finals won and triggered a round reset)

Tags: Fighting-Games, Sports, Video-Games

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Context

Most publicly available football (soccer) statistics are limited to aggregated data such as Goals, Shots, Fouls, Cards. When assessing performance or building predictive models, this simple aggregation, without any context, can be misleading. For example, a team that produced 10 shots on target from long range has a lower chance of scoring than a club that produced the same amount of shots from inside the box. However, metrics derived from this simple count of shots will similarly asses the two teams.

A football game generates hundreds of events and it is very important and interesting to take into account the context in which those events were generated. This incredibly rich data set should keep football analytics enthusiasts awake for long hours as the size of the data set and number of questions that can be asked is huge.

Content

There are 4 main files containing the data: 1) Competition data: Contains information regarding competetion id, competition name, season id, season name, country and gender.

2)Match data: Match information for each match including competition and season information, stadium and referee information, home and away team information as well as the data version the match was collected under.

3) Lineup data: Records the lineup information for the players, managers and referees involved with each match. The following variables are collected in the lineups of each match - team id, team name and lineup. The lineup array is a nested data frame inside of the lineup object, the lineup array contains the following information for each team- player id, player name, player nickname, jersey number and country

4) Event data: Event Data comprises of general attributes and event specific attributes. General attributes are recorded for most event types, depending only on applicability. Event specific attributes help describe the event type in more detail as well as describe the outcome of the event type.

The open data specification document in the doc folder describes the structure of the data along with all attributes in great detail. Take a look at this file for deeper understanding of the data.

Acknowledgements

This data is from the StatsBomb Open Data repository. StatsBomb are committed to sharing new data and research publicly to enhance understanding of the game of Football. They want to actively encourage new research and analysis at all levels. Therefore they have made certain leagues of StatsBomb Data freely available for public use for research projects and genuine interest in football analytics.

Inspiration

There are many many questions we can ask with such detailed event data. Here are just a few examples: What is the value of a shot? Or what is the probability of a shot being a goal given it's location, shooter, league, assist method, gamestate, number of players on the pitch, time - known as expected goals (xG) models When are teams more likely to score? Which teams are the best or sloppiest at holding the lead? Which teams or players make the best use of set pieces? How do players compare when they shoot with their week foot versus strong foot? Or which players are ambidextrous? Identify different styles of plays (shooting from long range vs shooting from the box, crossing the ball vs passing the ball, use of headers) Which teams have a bias for attacking on a particular flank?

European Sold Production of Games and Toys Share by Country (Units (Items)), 2023 Discover more data with ReportLinker!

European Turnover of Computer Games Publishing Share by Country (Million Euros), 2023 Discover more data with ReportLinker!