The Second Edition of The Hundered 2022 Ball by Ball Data for both Male and Female Games. Each ball detail along with extras is given. Two seperate files are made for male and female games.

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This is the Olympic Winter Games dataset that describes medals, results, athletes, coaches, and other records for Beijing 2020. MoreThe data was created from Beijing Olympics.

Almost 3000 Athletes, 200 Coaches and Technical Officials (with some personal data: date and place of birth, height, etc.), 600 Medas, 600 Events, 15 Disciplines, and Results (Hockey & Curling) of the XXIV Olympic Winter Games you can find here.

Disciplines

Alpine Skiing, Biathlon, Bobsleigh, Cross-Country Skiing, Curling, Figure Skating, Freestyle Skiing, Ice Hockey, Luge, Nordic Combined, Short Track Speed Skating, Skeleton, Ski Jumping, Snowboard, Speed Skating

Dataset Description

Related Datasets

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

Dataset History

2022-02-20 - dataset is updated (Game Days: 15 & 16 (Last Day))
2022-02-18 - dataset is updated (Game Days: 13 & 14)
2022-02-16 - dataset is updated (Game Days: 10, 11 & 12)
2022-02-13 - dataset is updated (Game Days: 5, 6, 7, 8 & 9)
2022-02-08 - dataset is updated (Game Days: 3 & 4)
2022-02-06 - dataset is updated (Game Day 2)
2022-02-05 - dataset is updated (Game Day 1 (First Day))
2022-02-02 - dataset is created.

Q&A

If you have some questions please start a discussion.

BackgroundThe sex difference in athletic performance has been thoroughly investigated in single sport disciplines such as swimming, cycling, and running. In contrast, only small samples of long-distance triathlons, such as the IRONMAN® triathlon, have been investigated so far.AimThe aim of the study was to examine potential sex differences in the three split disciplines by age groups in 5-year intervals in a very large data set of IRONMAN® age group triathletes.MethodsData from 687,696 (553,608 men and 134,088 women) IRONMAN® age group triathletes (in 5-year intervals from 18–24 to 75+ years) finishing successfully between 2002 and 2022 an official IRONMAN® race worldwide were analyzed. The differences in performance between women and men were determined for each split discipline and for the overall race distance.ResultsMost finishers were in the age group 40–44 years. The fastest women were in the age group 25–29 years, and the fastest men were in the age group 30–34 years. For all split disciplines and overall race time, men were always faster than women in all groups. The performance difference between the sexes was more pronounced in cycling compared to swimming and running. From the age group 35–39 years until 60–64 years, the sex differences were nearly identical in swimming and running. For both women and men, the smallest sex difference was least significant in age group 18–24 years for all split disciplines and increased in a U-shaped manner until age group 70–74 years. For age groups 75 years and older, the sex difference decreased in swimming and cycling but increased in running. Considering the different characteristics of the race courses, the smallest performance gaps between men and women were found in river swimming, flat surface cycling and rolling running courses.ConclusionsThe sex difference in the IRONMAN® triathlon was least significant in age group 18–24 years for all split disciplines and increased in a U-shaped manner until age group 70–74 years. For 75 years and older, the sex difference decreased in swimming and cycling but increased in running.

Introduction

The World Athletics, previously known as the International Amateur Athletic Federation and is the international governing organization for the sport of athletics covering from track and field and several running modalities (road, race walking, ultra, mountain running, etc). One of the World Atthletics tasks is to organize and publish a global ranking system to compare multiple athletes performances across a range of sports categories. By applying standardised compilation methods (under specific rules), it is therefore possible to evaluate the comparative quality of the participating fields at competitions of the same type and to produce competition performance rankings. The rankings are designed to recognize and celebrate the achievements of athletes participating in marathon events worldwide. The list takes into account various factors such as race results, timing, and the competitive level of the event.

In this analysis we will focus on the World Athletics Marathon ranking list from 2019 until June 2023. Our goal is to evaluate the outstanding performances of the best marathon runners in the world. It is important to notice that this analysis will be limited to the listed athletes's performances acrosss different races and events recognized by the World Athletics organization. Many answers we will attempt to answer, such as the top countries that displays on the top 100 marathon runners, the countries evolution (based on the nationalities) on ranking from 2019-2023 (is Kenya really the country with the most top runners in the world ?), the age distribution for male and women and curiosities such the performance of Eliud Kipchoge (the fastest marathon runner in the world), the Brazilian performances and even for how long the athletes can keep his name in the ranking list.

Motivation

My name is Marcel Caraciolo, and currently doing a Data Science Specialization at the Cesar School, a famous technology university at Recife, Pernambuco Brazil. This project is part of the evaluation of a discipline named 'Data Visualization' ministered by the professor Eronides Neto. The initial reason is to apply data exploratory and visualization techniques on in sports analytics, and since I am marathon enthusiast and a passioned runner, I would like to understand the athetes profiles of the best marathoners in the world. This analyis could be useful for anyone interested to get a current data snapshot of the marathon performances and furthermore as basis for enthusiasts and journalists interested in data sports analytics.

Datasets

For this study, I had to scrape the website of World of Athletics, the organization that provides the marathon ranking lists. The data in original form can be found here. The parsed data can be found here at Kaggle webpage.

• World Athletics Marathon Performance Ranking list 2019-2023

Parsing and preparing the data provided was a little challenging, wince I needed to loop over all the marathon ranking lists organized by month-date and sex. For each ranking list I also had to loop over all the pages since the ranking was split into a table of 50 rows per page. All the data result files of the World Athletics ranking list over the past 4 years (January 2019 - June 2023) is saved as comma-separated text files. After a second analysis at the ranking lists I could also find some stats about the races considered to compute the ranking score. I could extract the race description, the date of the event and the race type (marathon (42km) or half-marathon (21km)).

The data scraping notebook can be found following this link:

Data Dictionary

Data Dictionary for worldathletics/RANKINGDATE_SEX_WORLDATHLETICS_MARATHON_RANKINGS.csv

rank,competitor,dob,nat,score,events,competitor_id,sex,rank_date

First 25 female and male finishers running data Stuttgart Lauf Years: 2019, 2020 (Virtual), 2021 (Virtual), 2022 and 2023 Data was retreived from the official event website.