The cumulative number of water sport and boating-related deaths from 2015 to 2019 varied widely from month to month. The number of reported fatalities or missing persons peaked in the summer, particularly in August with nearly 140 fatalities. However, these figures must be weighed against the number of interventions by the regional operational surveillance and rescue centers (CROSS). The number of sea rescue operations is also much higher during the summer period. The number of deaths per intervention is higher in January than in August.

ABSTRACT Objectives: To analyze the results of pre-participation tests applied to soccer players from a professional club, aiming to compare the cardiographic findings with the literature and encourage the development of new strategies for the prevention of sudden death. Methods: We used a sample group of 110 male soccer players. Stages of the study: 1) collection of data from the pre-participation tests (cardiac history, electrocardiogram, exercise test and echocardiogram) using a form covering three years (2015 to 2017); 2) tabulation of data using Word and Excel Office 2010 software; 3) comparison with the literature. Results: Of the athletes studied, 55.5% had sinus bradycardia and 14.5% had ventricular repolarization abnormalities, 33.3% showed evidence of minimal tricuspid regurgitation, and 45.7% had physiological pulmonary regurgitation. The echocardiogram presented some interesting data when compared to the adult non-athlete population. In the ergometric test, 53.6% of the athletes reached the maximum stage and 46.4% discontinued the test due to physical fatigue. Regarding arrhythmias, in 21.8% of the patients we observed rare isolated ventricular extrasystoles and in 8.2% rare isolated supraventricular extrasystoles. Conclusion: The findings corroborate data from the literature on exercise and sports cardiology, since they mainly represent physiological adaptations of the athlete's heart. The sports physician is responsible for monitoring athletes to prevent sudden death. Level of Evidence II; Retrospective study.

Between 2000 and 2023, mountain hiking was the sporting activity that registered the highest number of fatal accidents in Switzerland, counting more than 1,000. Mountaineering was the second sport in the list, with more than *** fatalities.

Number of injury deaths by different types of sport and exercise during leisure time (n = 1192).

Overall incidence rate of sports-related fatalities in male athletes reported to Japan Sports Council that occurred during high school organized sports between 2009 and 2018 in Japan.

"The Equine Death and Breakdown report lists horses that have broken down, been injured, or have died at New York State race tracks."

https://data.ny.gov/widgets/q6ts-kwhk

IMAGE by: https://pt.scribd.com/article/408514386/Why-Are-So-Many-Racehorses-Dying

Big Data Derby 2022 Analystics Competition - Context

"Injury prevention is a critical component in modern athletics. Sports that involve animals, such as horse racing, are no different than human sport. Typically, efficiency in movement correlates to both improvements in performance and injury prevention."

"A wealth of data is now collected, including measures for heart rate, EKG, longitudinal movement, dorsal/ventral movement, medial/lateral deviation, total power and total landing vibration. Your data science skills and analysis are needed to decipher what makes the most positive impact."

https://www.kaggle.com/competitions/big-data-derby-2022/overview/description

The year 2014 was particularly deadly for diving enthusiasts. Indeed, the regional operational center of surveillance and rescue (CROSS) of France listed 39 disappearances or death of a diver that year. Scuba diving appears to be the most dangerous since there have been more than 70 deaths related to this practice since 2013.

Valorant Champions Tour 2023 All Tournament Datasets

This dataset contain basic statistics of each player and team for each game, containing 200 columns and 6000+ rows, statistic about team, map and agent will be updated later

Data Dictionary

Player Stats

• match_id : ID of matches played
• game_id : Game ID of each map played from each match
• team : Team name
• score_team : Final score achieved by the team
• opponent : Opponent team name
• score_opp : Final score achieved by the opponent
• win_lose : Result of the game
• map : Name of map played
• map_pick : Which team picked the map
• player_id : ID of player
• player : Player name
• agent : Agent used in the game
• rating : Rating by player for the game
• acs : Average Combat Score
• kill : Number of Kill Accumulated
• death : Number of Death Accumulated
• assist : Number of Assist Accumulated
• kast% : Percentage of Kill, Assist, Survive, Trade
• adr : Average damage dealt during a game
• hs% ; Headshot percentage
• fk : First kill count
• fd : First death count

This statistic depicts the distribution of drowning deaths in Canada between 2011 to 2015, by recreational activity. According to the data, ** percent of drowning deaths occurred during swimming activities.

This statistic shows the number of deaths per 100,000 population in the European Union in 2016 from various causes including; lightening terror attack, homicide, consumer product deaths, pedestrian deaths, sporting accidents, heat wave, traffic accident, suicide, respiratory illness, heart disease and cancer. The cause of death with the greatest likelihood of death was cancer, which occurred in 265 people out of every 100,000 people. This statistic also shows that the likelihood of an EU residents becoming a victim of terrorist activity is infinitesimal.

Number of injury deaths in Australian sport and recreation from 2000 to 2019, incidence rate per 100 000 population with 95% confidence interval (n = 1192).

Heart conditions were the most common causes of death in Mexico in 2023. During that period, more than ******* people died in the North American country as a result from said conditions. Diabetes mellitus ranked second, with over ******* deaths registered that year. Obesity in MexicoObesity and being overweight can worsen many risk factors for developing heart conditions, prediabetes, type 2 diabetes, and gestational diabetes, which in the case of a COVID-19 infection can lead to a severe course of the disease. In 2020, Mexico was reported as having one of the largest overweight and/or obese population in Latin America, with ** percent of people in the country having a body mass index higher than 25. In 2022, obesity was announced as being one of the most common illnesses experienced in Mexico, with over ******* cases estimated. In a decade from now, it is predicted that about *** million children in Mexico will suffer from obesity. If estimations are correct, this North American country will belong to the world’s top 10 countries with the most obese children in 2030. Physical activity in MexicoIt is not only a matter of food intake. A 2023 survey found, for instance, that only **** percent of Mexican population practiced sports and physical activities in their free time, a figure that has decreased in comparison to 2013. Less than ** percent of the physically active Mexicans practice sports for fun. However, the vast majority were motivated by health reasons.

Bathing facilities and health phronesis: a preliminary English investigation. Mixed methods sequential research in five phases.
Research questions and hypotheses • RQ1: Does the geospatial distribution of swimming facilities impact health? (Nomothetic). (H10: Pools is insignificant vs. H1A: Pools is significant) • RQ2: Is the construction of swimming pools adequate for national health need? (Nomothetic). (H20: Forecast pool construction stable vs. H2A: Forecast increase in pool construction) • RQ3: What policy learning emerges from idiosyncratic cases? (Idiographic & qualitative) Approach After problematisation (1) and structured literature review (2), the study conducted cross-sectional analysis of excess mortality and swimming pools (3a & 3b) and longitudinal analysis of pool construction (3c-e). Cross-sectional investigation involved factor analysis (3a) to explore and regression to analysis (3b) to investigate English mortality and its covariates (3b). The For the time series analysis, the study analysed 120 years of English pool construction data using autoregressive distributed lag models - ARIMA (3c), ADL (3d) and ECM (3e).
Data Cross sectional analysis Deaths (DV, Yd): ONS standardised mortality ratio (2013-2017). Observed total deaths from all causes (by five year age and gender band) as a percentage of expected deaths.
Access Leisure (IV, X1): reflects accessibility to 727 leisure centres, swimming baths or 2,738 health clubs in kilometres. Liverpool University’s Consumer Data Research Centre, Access to Healthy Assets and Hazards (AHAH) index. Obesity (IV, X2): percentage of adult population with a body mass index (BMI) of 30 kg/m2 or higher, age-standardized, WHO 2389 NCD_BMI_30 (2020). Deprivation (IV, X3): deprivation score for English small areas, sourced from Index of Multiple Deprivation (2019). Environment (IV, X4) measures accessible blue and green space, sourced via SE (2020), data constitutes an element of AHAH (2017).
Pools (IV, X5): reflects pools per 10,000 in 2018. Data extracted from SE Active Places Power (APP) Time series analysis Pools constructed (PC & ∆PC): English swimming pools constructed each year during a 120 year period since 1900, SE Active Places Power (2020) database. English output (GDP & ∆GDP): Bank of England millennium of macroeconomic data UK (2017) provides historical macroeconomic and financial statistics.
English population (Pop & ∆Pop): English population and population growth 1900-2020, Office for National Statistics (ONS): Total population (2018).

Notable findings The evidence from cross sectional regression analysis (3b) supports the alternative hypothesis, H1A, that pool density significantly influences excess mortality in England. All three times series models project an increase in pool construction which lends support to H2A of an increased pool construction need. For RQ2 then, current levels of swimming pool construction appears inadequate.

The Statistics of U.S. Businesses (SUSB) provides detailed annual data for all U.S. business establishments with paid employees by geography, industry, and enterprise size. This program covers all NAICS industries except crop and animal production; rail transportation; National Postal Service; pension, health, welfare, and vacation funds; trusts, estates, and agency accounts; private households; and public administration. The SUSB also excludes most government employees. Further, SUSB data for years 1988-1997 were tabulated based on the Standard Industrial Classification (SIC) system. The SUSB features several arts-related NAICS industries, including the following: Arts, entertainment, and recreation (NAICS Code 71) Performing arts companies Spectator sports Promoters of performing arts, sports, and similar events Independent artists, writers, and performers Museums, historical sites, and similar institutions Amusement parks and arcades Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic Design Services Landscape architectural services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, and musical instrument stores Sewing, needlework, and piece goods stores Book stores Art dealers Also, the SUSB features several arts related SIC industries, including the following: Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums and art galleries (SIC Code 8412) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Production & Services (SIC Code 7810) Data compiled for the SUSB are extracted from the Business Register (BR). The BR contains continuously updated data from the Census Bureau's economic censuses and currently business surveys, quarterly and annual Federal tax records and other department and federal statistics. SUSB data are available approximately 24 months after each reference year and are available for the United States, each state, and Metropolitan Statistical Areas (MSA). The annual SUSB consist of number of firms, number of establishments, annual payroll, and employment during the week of March 12. In addition, estimated receipts data are included for years ending in 2 and 7. Dynamic data, which are created from the Business Information Tracking Series (BITS), consist of the number of establishments and corresponding employment change for births, deaths, expansions, and contractions. The SUSB is important because it provides the only source of annual, complete, and consistent enterprise-level data for U.S. businesses, with industry detail. Private businesses use the data for market research, strategic business planning, and managing sales territories. State and local governments, as well as, budget, economic development, and planning offices use the data to assess business changes, develop fiscal policies, and plan future policies and programs. In addition, the data are the standard reference source for small business statistics. Users can view the latest SUSB annual data and employment change data on the main SUSB page. For more detailed industry and employment size classes, users can download additional data in comma-delimited format. Annual data are tabulated back to 1988 and employment change data back to 1989-1990. Data users can find news and updates about the SUSB data via the News & Updates section.

Context

This dataset presents statistics from each match from CSGO Major. So, after match ended is generated statistics, like scoreboard for each map played, who team was the winner and loser and players stats, K/D, ADR, etc.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

The dataset consists of 75 columns, which are:

Overview

This dataset contains comprehensive player statistics from the Valorant Champions Tour 2024, Riot Games' official tournament circuit for their tactical hero shooter, Valorant. The data is sourced from vlr.gg, a reputable Valorant esports website.

Dataset Contents

The dataset includes detailed performance metrics for players participating in both regional and international events of the Valorant Champions Tour 2024. It is provided in CSV format, making it easily accessible for analysis.

Key Statistics

The dataset captures a wide range of player performance indicators, including but not limited to:

Basic Information: - Event Region (Region) - Event Name (Event) - Player Name (Player) - Team Name abbreviation (Team Abbreviated) - Team Name complete (Team Complete) - Rounds Played (RND)

Overall Performance Metrics: - Rating (R) - Average Combat Score (ACS) - Kill:Death Ratio (K:D) - Kill, Assist, Survive, Trade Percentage (KAST)

Damage and Elimination Stats: - Average Damage per Round (ADR) - Kills per Round (KPR) - Assists per Round (APR) - First Kills per Round (FKPR) - First Deaths per Round (FDPR)

Accuracy and Skill Indicators: - Headshot Percentage (HS%) - Clutch Success Percentage (CL%) - Clutches Won/Played (CL) - Clutches Won (CW) - Clutches Played (CP)

Match Highlights: - Maximum Kills in a Single Map (KMax)

Aggregate Statistics: - Total Kills (K) - Total Deaths (D) - Total Assists (A) - Total First Kills (FK) - Total First Deaths (FD)

Notes: The dataset covers all events in the Valorant Champions Tour 2024, providing a comprehensive view of the competitive season. As Valorant is a free-to-play first-person tactical hero shooter, this dataset offers insights into the highest level of play for this popular esport. Users of this dataset should consider the context of different roles and agents in Valorant when interpreting these statistics.

Black Lives Matter (BLM) is a social movement advocating for an end to police brutality and violence against Black people. The death of George Floyd in police custody in Minneapolis in May 2020 caused a renewed wave of public outrage and, following his death, many athletes threw their support behind the BLM movement and advocated social change. During a June 2020 survey in the United States, ** percent of NBA fans stated that they supported the Black Lives Matter movement.

Objective: Cycling is associated with numerous health benefits but also the risk of traumatic injury. Recent data demonstrate an increase in overall cycling injuries as well as hospital admissions from 1997 to 2013 in the United States. We seek to better understand the causes of the increase in cycling injuries and hospital admissions. Methods: Data regarding cycling-related injuries and hospital admissions were obtained from the National Electronic Injury Surveillance System (NEISS). Participation data were derived from the National Sporting Goods Association Sports Participation Survey, and fatality data were collected from the Fatality Analysis Reporting System (FARS). Population estimates were obtained using a complex survey design. Linear regression was used to evaluate univariate relationships between cycling injuries, hospital admissions, deaths, and participation. To evaluate factors associated with hospital admission, we developed a multivariable logistic regression model that included year, age, gender, body part injured, and injury type (i.e., contusion, fracture, or laceration). Results: The number of individuals who cycle did not change significantly over time, but there was a substantial increase in cycling-related injuries, leading to an increase in per participant injuries from 701/100,000 in 1997 to 1,164/100,000 in 2013. When the injuries were evaluated by age group, younger cyclists have an increased risk for injury, whereas the rise in injuries among older cyclists stemmed from an increase in ridership rather than a unique susceptibility to injury. Trends in hospital admissions and fatalities appeared to be driven by increases in the older age groups. In the multivariable model evaluating factors related to hospital admission, the odds of hospital admission increased for each decade after age 25, as well as male gender and body part injured. Conclusion: On a per participant basis, the rate of cycling-related injuries and hospital admissions increased between 1997 and 2013. This trend likely reflects a combination of shifting demographics among cyclists with an increase in older cyclists who are at increased risk of severe injury.

# Stats and Definitions A Total assists AGT Average game time/duration, in minutes APG Assists per game B% Percentage of games in which the champion was banned (not tied to a specific role) BLND% Blind-pick rate: percentage of games in which this player/champion was picked before their lane opponent (not always available) BN% Baron control rate CCPM Crowd control dealt to champions per minute Champion Champion name CKPM Average combined kills per minute (team kills + opponent kills) CS%P15 Average share of team's total CS post-15-minutes CSD10 Average creep score difference at 10 minutes CSD15 Average creep score difference at 15 minutes CSD20 Average creep score difference at 20 minutes CSPM Average monsters + minions killed per minute CTR% Counter-pick rate: percentage of games in which this player/champion was picked after their lane opponent (not always available) CWPM Control wards purchased per minute D Total deaths D%P15 Average share of team's damage to champions post-15-minutes DMG% Damage Share: average share of team’s total damage to champions DMG%P15 Average share of team's damage to champions post-15-minutes DPG Deaths per game DPM Average damage to champions per minute DRG% Dragon control rate: percent of all Dragons killed that were taken by the team, reflecting only elemental drakes if ELD% is present DTH% Average share of team’s deaths EGPM Average earned gold per minute (excludes starting gold and inherent gold generation) EGR Early-Game Rating ELD% Elder dragon control rate Event Event name F3T% First-to-three-towers rate (percentage of games in which team was the first to 3 tower kills FB% First Blood rate -- for players/champions, percent of games earning a First Blood participation (kill or assist) FBN% First Baron rate FBV% First Blood Victim rate -- percent of games player/champion was killed for First Blood FD% First dragon rate FT% First tower rate GD10 Average gold difference at 10 minutes GD15 Average gold difference at 15 minutes GD20 Average gold difference at 20 minutes GOLD% Gold Share: average share of team’s total gold earned (excludes starting gold and inherent gold generation) GP Games Played GPM Average gold per minute GPR Gold percent rating (average amount of game’s total gold held, relative to 50%) GSPD Average gold spent percentage difference GXD10 Average gold+experience difference at 10 minutes GXD15 Average gold+experience difference at 15 minutes GXD20 Average gold+experience difference at 20 minutes HLD% Rift Herald control rate IWC% Average percentage of opponent’s invisible wards cleared JNG% Jungle Control: average share of game’s total jungle CS K Total kills KD Kill-to-Death Ratio KDA Total Kill/Death/Assist ratio KP Kill participation: percentage of team's kills in which player earned a Kill or Assist KPG Kills per game KS% Kill share: player's percentage of their team's total kills L Losses LNE% Lane Control: average share of game’s total lane CS Losses Total Losses LP Ladder Points MLR Mid/Late Rating OE Rating Oracle’s Elixir Performance Rating OE Rtg Oracle’s Elixir Performance Rating P% Percentage of games champion was picked in this role. P+B% Percentage of games in which the champion was either banned or picked in any role Player Player's in-game name Pos Position PPG Turret plates destroyed per game Rank Official Leaderboard Rank STL Neutral objectives stolen STLPG Neutral objectives stolen per game StPG Neutral objectives stolen per game Team Team name VSPM Vision score per minute VWC% Average percentage of opponent’s visible wards cleared W Wins W% Win percentage WC% Average percentage of opponent wards cleared WCPM Average wards cleared per minute Wins Total Wins WPM Average wards placed per minute XPD10 Average experience difference at 10 minutes XPD15 Average experience difference at 15 minutes XPD20 Average experience difference at 20 minutes