Athletic Skill Demand Ranking for 60 Sports

An ESPN Expert-Rated Comparison

By Corey Hermanson [source]

About this dataset

Welcome to the Toughest Sport dataset! Here we are bringing you a complete breakdown of 60 sports and their demands for 10 distinct skills that make up athletic capabilities. We collected data from 8 expert panelists and asked them to rate each sport on a scale of 1-10 for every skill ranging from Strength and Speed to Nerve, Hand-eye Coordination, and more. By totalling up the opinions of our experts, we have created an overall degree of difficulty score for each sport in the dataset between 1-100. If you're curious as to which sports require what skill sets, or if you're wondering which is the toughest sport across all ten skillsets - this is your place! Get ready to explore how athleticism guides our understanding of what makes 'Toughest Sport'!

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How to use the dataset

This dataset contains the rankings of 60 sports across 10 skills, as well as the total score and rank of each sport. It is intended to provide an overview of the relative athleticism required across a variety of different competitive sports and can be used to identify which physical attributes are most important in each sport.

To use this dataset, you will need to understand what the different sports measured by this data set represent. Sports like skiing, boxing, wrestling, and football all require very different mental and physical abilities in order to compete successfully. For example, Alpine Skiing will require greater skill related to speed, agility and power than Cross Country running does; while Swimming may rely more heavily on durability than Football does. Once familiar with the included sports then it becomes easier to utilize the scores assigned for each skill in order identify which skills might benefit a particular athlete most when considering a new athletic challenge.

The dataset also provides useful information about how difficult it might be for any one individual athlete or competitor if they were looking at taking up one particular sport from scratch versus another with similar momentum when compared in terms of its overall scores across 10 areas relating specifically as they relate athletics. This could help indicate whether that athlete has a better chance or worse chance when competing against others who may have trained in or specialised within their chosen field longer or shorter than themselves respectively before stepping onto this same playing field together; simply by comparing total Athletics Skill Demand (ASD) Numbers over between both their desired sporting choices (the higher number representing higher difficulty).

Research Ideas

• Identifying the most requested athletic traits by sport. By analyzing the data, one can uncover patterns within certain sports that require certain skills or abilities more than others.
• Determining which sports offer the best opportunity for balance and development of all skillsets by athletes. Specifically, this dataset could be used to identify which sports encourage holistic athletic development and what combination of skill demands those particular sports have in common.
• Developing an optimal training program for athletes interested in perfecting their craft in a particular sport by geotargeting regions with more advanced competition based on their specific skill needs relative to that region’s average competition level as demonstrated by this dataset

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: toughestsport.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | SPORT | Name of the sport. (String) | | END | Endurance score for the sport. (Integer) | | STR | Strength score for the sport. (Integer) | | PWR | Power score for the sport. (Integer) | | SPD | Speed score for the sport. (Integer) | | AGI | Agility score for the sport. (Integer) | | FLX | Flexibility score for the sport. (Integer) | | NER | Nerve score for the sport. (Integer) | | DUR | Durability score ...

Context

This is a historical dataset on the modern Olympic Games, including all the Games from Athens 1896 to Rio 2016. I scraped this data from www.sports-reference.com in May 2018. The R code I used to scrape and wrangle the data is on GitHub. I recommend checking my kernel before starting your own analysis.

Note that the Winter and Summer Games were held in the same year up until 1992. After that, they staggered them such that Winter Games occur on a four year cycle starting with 1994, then Summer in 1996, then Winter in 1998, and so on. A common mistake people make when analyzing this data is to assume that the Summer and Winter Games have always been staggered.

Content

The file athlete_events.csv contains 271116 rows and 15 columns. Each row corresponds to an individual athlete competing in an individual Olympic event (athlete-events). The columns are:

1. ID - Unique number for each athlete
2. Name - Athlete's name
3. Sex - M or F
4. Age - Integer
5. Height - In centimeters
6. Weight - In kilograms
7. Team - Team name
8. NOC - National Olympic Committee 3-letter code
9. Games - Year and season
10. Year - Integer
11. Season - Summer or Winter
12. City - Host city
13. Sport - Sport
14. Event - Event
15. Medal - Gold, Silver, Bronze, or NA

Acknowledgements

The Olympic data on www.sports-reference.com is the result of an incredible amount of research by a group of Olympic history enthusiasts and self-proclaimed 'statistorians'. Check out their blog for more information. All I did was consolidated their decades of work into a convenient format for data analysis.

Inspiration

This dataset provides an opportunity to ask questions about how the Olympics have evolved over time, including questions about the participation and performance of women, different nations, and different sports and events.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society. The survey is created for both individuals and businesses. It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

Description of the data in this data set: structure of the survey and pre-defined answers (if any) 1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed} 2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high 3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question) 4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility} 5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available 6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies} 8. How would you assess the value of the following data categories? 8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable 9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question 10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question 11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question 12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)} 13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable 14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)} 15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company 16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company} 17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”} 18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

Format of the file .xls, .csv (for the first spreadsheet only), .odt

Licenses or restrictions CC-BY

Esports Performance Rankings and Results

Performance Rankings and Results from Multiple Esports Platforms

By [source]

About this dataset

This dataset provides a detailed look into the world of competitive video gaming in universities. It covers a wide range of topics, from performance rankings and results across multiple esports platforms to the individual team and university rankings within each tournament. With an incredible wealth of data, fans can discover statistics on their favorite teams or explore the challenges placed upon university gamers as they battle it out to be the best. Dive into the information provided and get an inside view into the world of collegiate esports tournaments as you assess all things from Match ID, Team 1, University affiliations, Points earned or lost in each match and special Seeds or UniSeeds for exceptional teams. Of course don't forget about exploring all the great Team Names along with their corresponding websites for further details on stats across tournaments!

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How to use the dataset

Download Files First, make sure you have downloaded the CS_week1, CS_week2, CS_week3 and seeds datasets on Kaggle. You will also need to download the currentRankings file for each week of competition. All files should be saved using their originally assigned name in order for your analysis tools to read them properly (ie: CS_week1.csv).

Understand File Structure Once all data has been collected and organized into separate files on your desktop/laptop computer/mobile device/etc., it's time to become familiar with what type of information is included in each file. The main folder contains three main data files: week1-3 and seedings. The week1-3 contain teams matched against one another according to university, point score from match results as well as team name and website URL associated with university entry; whereas the seedings include a ranking system amongst university entries which are accompanied by information regarding team names, website URLs etc.. Furthermore, there is additional file featured which contains currentRankings scores for each individual player/teams for an first given period of competition (ie: first week).

Analyzing Data Now that everything is set up on your end it’s time explore! You can dive deep into trends amongst universities or individual players in regards to specific match performances or standings overall throughout weeks of competition etc… Furthermore you may also jumpstart insights via further creation of graphs based off compiled date from sources taken from BUECTracker dataset! For example let us say we wanted compare two universities- let's say Harvard University v Cornell University - against one another since beginning of event i we shall extract respective points(column),dates(column)(found under result tab) ,regions(csilluminating North America vs Europe etc)general stats such as maps played etc.. As well any other custom ideas which would come along in regards when dealing with similar datasets!

Research Ideas

• Analyze the performance of teams and identify areas for improvement for better performance in future competitions.
• Assess which esports platforms are the most popular among gamers.
• Gain a better understanding of player rankings across different regions, based on rankings system, to create targeted strategies that could boost individual players' scoring potential or team overall success in competitive gaming events

Acknowledgements

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

License

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

Columns

File: CS_week1.csv | Column name | Description | |:---------------|:----------------------------------------------| | Match ID | Unique identifier for each match. (Integer) | | Team 1 | Name of the first team in the match. (String) | | University | University associated with the team. (String) |

File: CS_week1_currentRankings.csv | Column name | Description | |:--------------|:-----------------------------------------------------------|...

Athletic Achievements of Adaptive Sports Athletes from 2000-2016

Success in International Events and UK-based Paralympic Spending

By DiversityinData [source]

About this dataset

At #DiversityInData, #ProjectHealthViz, and #SportsVizSunday, we have partnered up to create a dataset focusing on adaptive sports athletes with the goal of raising awareness of their incredible achievements. We aim to promote adaptive sports programs that provide a safe and supportive environment for people with disabilities who would otherwise not get access to physical activity.

These adaptive sports such as biking, golfing, skiing and snowboarding give participants both physical and mental benefits. We hope this dataset motivates others to join in on the diverse range of these activities! With this dataset on Adaptive Sports Athletes including marathon wheelchair results from the Boston, Chicago, London & New York Marathons; historical information from the Paralympics; UK Paralympic spending & results for UK athletes - you will be able to visualize the amazing feats accomplished by adaptive sports athletes around the world. We look forward to seeing your visualizations that display different perspectives of this valuable data! Be sure tag all three projects on Twitter (DiversityInData/ProjectHealthViz/SportsVizSunday) and let us know if you have any questions!

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How to use the dataset

This dataset contains records of the athletic achievements of adaptive sports athletes from 2000 to 2016. It includes athlete, gender, nationality and sport information, as well as details about medals won and race times recorded.

The data is organized in columns for easy access. The first column contains the name of the athlete. The second contains information about their year of competition (from 2000-2016). The third (nationality) holds information about their country of origin. The fourth column holds the time it took them to complete the race. This is followed by total time taken in seconds, then a column for the title of race completed, after that is gender and sport name columns following thereafter are all medal-related ones: gold, silver and bronze respectivey with lastly an overview total number won and finally games they participated in completing 2019 Olympics trial races while other seasons held participative world championships in 2020 pandemic cancelations etc..

We encourage all to explore this data according to your particular interests - whether you would like to investigate which countries produce most successful adaptation athletes or look into most performed event individual athletes partake in in this category - whatever way you use it do not forget root purpose behind generating such records: paying tribute those amazing individuals who despite physical limitations have been able achieve excellence on par with other global professional athletes! Best wishes!

Research Ideas

• Creating an interactive map visualization showing the locations of global adaptive sports events.
• Developing a data dashboard that compares success and funding for UK Paralympic athletes over time.
• Analyzing trends in the medal counts of countries with high investments in Paralympic programs to determine which ones provide the greatest return on investment

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Marathon Wheelchair Winners.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------| | Athlete | The name of the athlete. (String) | | Year | The year in which the athlete competed. (Integer) | | Nationality | The country of origin of the athlete. (String) | | Time | The time it took the athlete to complete the race. (String) | | Total Time in Seconds | The total time it took the athlete to complete the race in seconds. (Integer) | | Race | The type of race the athlete competed in. (String) | | Gender | The gender of the athlete. (String) |

**File...

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 sportsme...

This dataset contains anonymized individual- and community-level data used to analyze changes in walking practice among adults (≥19 years) living in Busan, South Korea, before (2018–2019) and after (2020–2021) the COVID-19 pandemic. Individual-level variables were derived from the Korea Community Health Survey (CHS) and include socio-demographic characteristics, health behaviors, health status, and subjective perceptions of the neighborhood environment. Community-level variables were constructed by linking administrative and open-government data on urban parks, pedestrian paths, public and private sports facilities, and social network difficulty at the district level. The binary outcome variable indicates whether respondents met the CHS definition of “walking practice” (≥30 minutes of walking per day on ≥5 days in the past week). All direct personal identifiers have been removed, and districts are coded with anonymized identifiers.

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

While sample sizes in elite sports are necessarily small, so are the effects that may be relevant. This conundrum is complicated by an understandable reluctance of athletes to comply with extensive study requirements. In Bayesian analyses, pre-existing knowledge (e.g., from sub-elite trials) can be formally included to supplement scarce data. Moreover, some design specifics for small sample research extend to the extreme case of a single subject. This provides the basis for actionable feedback (e.g., about individual responses) thereby incentivising participation. As a proof-of-concept, we conducted a replicated cross-over trial on the effect of cold-water immersion (CWI) on sprint performance recovery in soccer players. Times for 30 m linear sprint and the initial 5 m section, respectively, were measured by light gates before and 24 h after induction of fatigue. Data were analysed by Bayesian and by standard frequentist methods. Informative priors are based on a published metaanalysis. Seven players completed the trial. Sprint performance was 4.156 ± 0.193 s for 30 m linear sprint and 0.978 ± 0.064 s for the initial 5 m section. CWI improved recovery of sprint time for the initial 5 m section (difference to control: −0.060 ± 0.060 s, p = 0.004) but not for the full 30 m sprint (0.002 ± 0.115 s, p = 0.959), with general agreement between Bayesian and frequentist interval estimates. On the individual level, relevant differences between analytical approaches were present for most players. Changes in the two performance measures are correlated (p = 0.009) with a fairly good reproducibility of individual response patterns. Bayesian analyses with informative priors may be a practicable and meaningful option particularly for very small samples and when the analytical aim is decision making (use / don't use in the specific setting) rather than generalizable inference.

Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

Historical data on the modern Olympic Games, from Athens 1896 to Rio 2016. Each row corresponds to an individual athlete competing in an individual event, including the athlete's name, sex, age, height, weight, country, and medal, and the event's name, sport, games, year, and city.

Analysis on: 1.Analyze and visualize the % of both Male and Female athletes over time. 2.Compare and contrast the summer and the winter games: - How many athletes compete? - How many countries compete? - How many events are there? 3.Analyze and visualize country-level trends: - Which countries send the most athletes to the olympics? - Do they also tend to win the most medals? - How have these trends changed over time?

FILE TYPES: CSV

TAGS: Sports Time Series Geospatial

Source: https://mavenanalytics.io/data-playground?page=8&pageSize=5

Sports Reference

Context

Forbes is not just one of the most popular business magazines!! It contains countless articles on numerous subjects (e.g., business, investing, technology, entrepreneurship, etc.), reporting valuable data and insights.

For instance, Forbes publishes annual lists of wealthy people reporting their worth such as "Forbes 400" and "Forbes World's Billionaires list".

Athletes are not an exception, and every year lists are published for the top highest paid individuals.

Content

The data are scrapped manually from Forbes articles listing the top 10 highest-paid athletes in tennis, NBA, and soccer.

Athletes can have multiple sources of income. • Team sports athletes earn a salary paid by their team whereas individual sports athletes compete in tournaments for prize money (such as tennis players). • Most of the time, brands are paying athletes to promote their products (on and off the court) as a marketing promotional strategy to reach a wider target audience and boost their sales / profit.

The dataset contains 11 years of data starting from 2011.

Source

Forbes official website: https://www.forbes.com/ (dataset last updated on 3rd of January 2022)

Inspiration

• Which sport rewarded its athletes the most in each year? • Is there a trend across years for the total earnings of the top 10 highest-paid athletes of each sport? • Does this trend change when looking into salaries (or prize money) and endorsements separately? • Which country 'earns' the most out of those three sports each year?

These are examples of interesting questions that could be answered by analysing this dataset.

If you are interested, please have a look at the Tableau dashboard that I have created to help answer the above questions, and report some of my insights. Tableau dashboard: https://public.tableau.com/views/AthelesSaleries/SportsEarningsAnalysis?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

College Football 2022 (Wins, Losses, Rankings)

Team Performance and Game Results

By [source]

About this dataset

This dataset contains comprehensive data about the 2022 season of college football in the United States, providing researchers and analysts alike with a powerful tool for studying the sport. The dataset includes rankings and games data that highlight individual teams, as well as game results, conference game data, and neutral-site games information. By examining this data through an in-depth analysis of team wins and losses across all levels of competition, users can gain deeper insights into how college football is played. This dataset offers a unique opportunity to explore collegiate sports in ways never before possible - uncovering new trends and helping to paint a picture of some of America's favorite pastimes!

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This dataset is an excellent resource for researchers and analysts interested in studying college football during the 2022 season. The compiled data includes team rankings, game results, conference game data, and neutral-site games data. The dataset is useful for both qualitative and quantitative analysis of college football teams’s performances throughout the season.

In order to get started with this dataset exploring it, users should first become familiar with the columns that make up the data set: - Season: This column indicates which year of college football (2022 in this case) it pertains to - Week: Indicates which week of the season a certain game was played
- Season Type: Regular or post season - Start Date: Date when a particular game was played - Neutral Site: Whether or not the featured game was on a “neutral site” (not home field for either team) such as a bowl or championship games - Conference Game Status/Type: Indicates if this particular matchup is an interconference matchup like divisional playoffs / championship title bout etc.
- Home Team/ Points / Level & Away Team/ Points / Level information will have some combination of these elements indicate which two teams competed against one another in any given instance and how they did was according to their number of points scored and their level(Division I, Division II etc.).

After becoming familiar with all columns included in this particular dataset, users can begin more detailed analysis by creating pivot tables that focus on different aspects that analyze wins & losses such as wins vs losses overall each team as well as by division within conferences (for example). The section below titled ‘Filtering For Desired Elements Of Analysis’ will provide instructions on how to do so within Microsoft Excel but similar concepts exist across other programs such as Tableau & Google Sheets too! Furthermore filtering could also be used across other fields such characteristics like start date / regular versus post season fixtures etc. To illustrate what types outcomes are possible via filtering let's say we wanted take closer look at all wins achieved by division one teams throughout course regular 2022 then we apply relevant filter conditions established within table would result overview containing only results related specification!.

After developing accurate Filters it possible extract only desired elements analysis produce visual displays reflect findings further gathered insights gain clearer understanding patterns behavior see here . Lastly aggregate statistics provided not just adequate help formulate thoughts hypothesis but also contribute towards various models predict future outcomes!

Research Ideas

• Analyzing the Season Winners – By analyzing the results, rating, volatility and other statistics of teams in this dataset, it is possible to predict which team(s) may take home their respective conference title(s) for the 2022 season and beyond.
• Receiver Performance Analysis – Several stats from this dataset can be compared to each other in order to analyze how receivers perform under varying levels of competition or for games on neutral sites, for example.
• Ranking the Best Seasons – This dataset can be used to rank the best college football seasons in terms of wins/losses over time by a specific school or within a given conference or division. This could provide insights into which teams have had long-term success across multiple years and which teams may need additional support going forward in order to compete with top-tier schools year after year

Acknowledgements

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

Medal Ranks of Countries from Winter Olympics

Investigating Country Medal Performance in the Winter Olympics

By Andy Kriebel [source]

About this dataset

This dataset focuses on the medal rankings of various countries at Winter Olympics. It contains an exhaustive list of country, gender, event and athlete or team details with accompanying statistics. This data can be used to analyse which countries performed better than others in different events and sports, as well as to gain further insights into individual athletes or teams. With this detailed dataset you will be able to answer questions such as “which country had the highest medal rank?”, “which events yielded a majority of medals for a particular nation?”, or even “which older athletes were most successful at this Olympics?”. By examining the details provided in this dataset it is possible for anyone – regardless of whether they were a part of the Olympics themselves – to get an in-depth look at all things related to its performance and results!

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How to use the dataset

This dataset contains information about the medal rankings of countries in the Winter Olympics. It contains years, sports, events, countries, gender, medal rank and name of athlete or team and age of athletes.

Research Ideas

• Analyzing the gender divide in Olympic medal wins - this data can be used to determine whether particular countries have a higher ratio of medal wins going to one gender over another.
• Analyzing trends in medal rankings over time - this data could be used to analyze whether nations are consistently regularly at the top of the leaderboard or whether their successes are more sporadic and unpredictable.
• Defining the age range of athletes that most frequently win medals - this data would show which age group is typically bringing home most of the medals for any given nation, allowing for more targeted athletic training programs in that age range within that country if desired

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Winer Olympic Medals.csv | Column name | Description | |:----------------------------|:--------------------------------------------------------------| | Year | The year in which the Winter Olympics took place. (Integer) | | Sport | The sport in which the medal was won. (String) | | Event | The event in which the medal was won. (String) | | Country | The country in which the medal was won. (String) | | Gender | The gender of the athlete or team who won the medal. (String) | | Medal Rank | The rank of the medal won. (Integer) | | Name of Athlete or Team | The name of the athlete or team who won the medal. (String) | | Age of Athlete | The age of the athlete or team who won the medal. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Andy Kriebel.

Power BI Dashboard : https://www.mavenanalytics.io/project/3776

The IPL (Indian Premier League) is one of the most popular and widely followed cricket leagues in the world. It features top cricket players from around the world playing for various franchise teams in India. The league is known for its high-scoring matches, intense rivalries, and innovative marketing strategies.

If you are a data enthusiast or a cricket fan, you will be excited to know that there is a dataset available on Kaggle that contains comprehensive information about the IPL matches played over the years. This dataset is a valuable resource for anyone interested in analyzing the performance of players and teams in the league.

The IPL dataset on Kaggle contains information on over 800 IPL matches played from 2008 to 2020. It includes details on the date, time, venue, teams, players, and various statistics such as runs scored, wickets taken, and more. The dataset also contains information on the individual performances of players and teams, as well as the overall performance of the league over the years.

The IPL dataset is a goldmine for data analysts and cricket enthusiasts alike. It provides a wealth of information that can be used to uncover insights about the league and its players. For example, you can use the dataset to analyze the performance of a particular player or team over the years, or to identify trends in the league such as changes in team strategies or the emergence of new players.

If you are new to data analysis, the IPL dataset is a great place to start. You can use it to learn how to use tools such as Excel or Power BI to create visualizations and gain insights from data. With the right skills and tools, you can use the IPL dataset to create interactive dashboards and reports that provide valuable insights into the world of cricket.

Overall, the IPL dataset on Kaggle is an excellent resource for anyone interested in cricket or data analysis. It contains a wealth of information that can be used to analyze and gain insights into the performance of players and teams in one of the most exciting cricket leagues in the world.

This dataset contains points table and player Information. To view more data such as Match stats, Ball_by_ball data & Player innings data, Please visit the below links:

Match stats, Ball_by_ball data: https://www.kaggle.com/datasets/biswajitbrahmma/ipl-complete-dataset-2008-2022

Player innings data: https://www.kaggle.com/datasets/paritosh712/cricket-every-single-ipl-inning-20082022

Thanks to Biswajit Brahmma & Paritosh Anand for their dataset.

This dataset includes the individual results from the Chicago Marathon from 1996 to the present. The dataset also includes the dates of each race and the weather on race day.

The finisher data comes from directly from the Chicago Marathon website and the weather data comes from Weather Spark.

A third file in the dataset contains the qualifying times for the Chicago Marathon that were implemented in 2014, 2018, and 2025.

This data was originally collected and prepared as part of an analysis that was published on Medium. You can read find more information about that analysis here.

The feature image on this dataset is Chad Veal, CC BY-SA 4.0, via Wikimedia Commons.

This dataset investigates the impact of player injuries on team performance across seven Premier League clubs from 2019 to 2023, including Tottenham, Aston Villa, Brighton, Arsenal, Brentford, Everton, Burnley, and Manchester City. The dataset contains over 600 injury records, offering insights into how player absences influence match results and individual performance metrics.

Data Sources Transfer Market: Provided player injury records and durations. Football Critic: Offered player ratings for pre- and post-injury matches. Sky Sports: Supplemented additional match statistics and player performance data.

Dataset Overview Each entry includes: Player Information: Name, position, age, FIFA rating (spanning five years). Injury Details: Type of injury, date of injury, date of return. Performance Data: Match results (win, draw, loss), opposition, and goal difference (GD) for three matches before the injury, during missed matches, and for three matches after the player's return. Player ratings for each match, before and after the injury.

Key Data Points Performance fluctuations around injury events. Match outcomes during player absences. Ratings of players over time to observe any decline or improvement post-injury. This dataset is ideal for sports analytics, performance modeling, and evaluating the broader implications of player injuries on Premier League teams. Explore how injuries disrupt team dynamics and contribute to competitive outcomes in one of the world’s top football leagues.

Introduction

The lack of publicly available National Football League (NFL) data sources has been a major obstacle in the creation of modern, reproducible research in football analytics. While clean play-by-play data is available via open-source software packages in other sports (e.g. nhlscrapr for hockey; PitchF/x data in baseball; the Basketball Reference for basketball), the equivalent datasets are not freely available for researchers interested in the statistical analysis of the NFL. To solve this issue, a group of Carnegie Mellon University statistical researchers including Maksim Horowitz, Ron Yurko, and Sam Ventura, built and released nflscrapR an R package which uses an API maintained by the NFL to scrape, clean, parse, and output clean datasets at the individual play, player, game, and season levels. Using the data outputted by the package, the trio went on to develop reproducible methods for building expected point and win probability models for the NFL. The outputs of these models are included in this dataset and can be accessed using the nflscrapR package.

Content

The dataset made available on Kaggle contains all the regular season plays from the 2009-2016 NFL seasons. The dataset has 356,768 rows and 100 columns. Each play is broken down into great detail containing information on: game situation, players involved, results, and advanced metrics such as expected point and win probability values. Detailed information about the dataset can be found at the following web page, along with more NFL data: https://github.com/ryurko/nflscrapR-data.

Acknowledgements

This dataset was compiled by Ron Yurko, Sam Ventura, and myself. Special shout-out to Ron for improving our current expected points and win probability models and compiling this dataset. All three of us are proud founders of the Carnegie Mellon Sports Analytics Club.

Inspiration

This dataset is meant to both grow and bring together the community of sports analytics by providing clean and easily accessible NFL data that has never been availabe on this scale for free.

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

• Tokyo 2020 Olympics
• Tokyo 2020 Horses
• Olympic Games, 1986-2018
• 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.

this graph was created in R :

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This dataset provides comprehensive information on more than 2400 One Day International (ODI) cricket matches obtained from Cricsheet and includes detailed batting and bowling statistics match summaries and individual player performances with the exception of matches involving Afghanistan’s men’s team or those played in the Afghanistan Premier League due to Cricsheet’s data policy making it an excellent resource for sports analytics machine learning and cricket strategy modeling allowing users to analyze player consistency evaluate team performance predict fantasy cricket outcomes and assess match results the dataset is divided into several files including batter_player_stats.csv which contains batting data such as total runs strike rate matches played and player of the match awards bowler_player_stats.csv which offers bowling data including total wickets economy rate overs bowled and matches played as a bowler detailed_player_data.csv which provides per-match player performance data such as runs scored balls faced wickets taken catches and fantasy points and match_summary.csv which includes match-level information such as toss results match outcomes either by runs or wickets player of the match and venue details potential use cases include player performance analysis to identify the most consistent batters and bowlers across various seasons match outcome prediction by developing models that leverage historical performance data fantasy cricket strategy optimization by selecting teams based on previous player performance and cricket analytics and visualization to explore trends in runs wickets and match-winning performances enabling deeper insights into the game and supporting advanced sports research and data-driven decision-making.