Over ** million people in the United States went running or jogging at least once in 2024. This figure also represented an increase of roughly 5.7 percent over the previous year.

This statistic illustrates the share of Americans who went jogging or running as of 2021, by generation. In that year, ** percent of Gen Z respondents stated that they went long-distance jogging or running.

Data accompanying manuscript Data of 7 runners during a Marathon is provided to accompany the manuscript “Quantifying and correcting for speed and stride frequency effects on running mechanics in fatiguing outdoor running”. For each runner the following files are provided: - Global Navigation Satellite System (GNSS) running speed - Inertial Measurement unit (IMU) running speed - Acceleration of foot sensor - Acceleration of lower leg/tibia sensor - Knee angles GNSS running speed GNSS running speed during the full marathon was based on different sports watches. Sampling frequencies between sports watches differed but was on average 0.7 (0.4) Hz. In line with the manuscript, missing latitude-longitude data was linearly interpolated before speed was computed as the distance between two latitude-longitude coordinates based on the Haversine formula. GNSS speeds above 20 km/h were deemed extremely unlikely and replaced with spline interpolation. GNSS speed was then resampled to 240 Hz to match the sampling frequency of the IMUs. Note that GNSS and IMU data are not yet time synchronized! - Filename: SubXX_gnss_speed.csv - Size of matrix: [1xN] IMU running speed IMU running speed was solely used for time synchronization of the GNSS running speed with IMU data. The scaled biomechanical model (as described in the manuscript) provided the velocity of the pelvis segment at 240 Hz. Pelvis IMU speed was then computed as the resultant pelvis IMU velocity. - Filename: SubXX_imu_speed.csv - Size of matrix: [Nx1] Acceleration of foot sensor Accelerations of the right foot were used for initial contact detection in the manuscript. 3D accelerations of a sensor on the right foot are provided in a sensor-fixed coordinate system. The sensor was placed on the midfoot within the shoes, the sensor was aligned with the long axis of the foot. The positive axis of the first dimension points towards the center of the ankle joint. The positive axis of the second dimension points to the right. The positive axis of the third dimensions is directed approximately upwards. - Filename: SubXX_rfoot_acc.csv - Size of matrix: [Nx3] Acceleration of lower leg/tibia sensor Accelerations of the lower leg were one of the quantities of interest in the manuscript. 1D acceleration of a sensor on the right lower leg at 240 Hz is provided in a sensor-fixed coordinate system. The sensor was aligned with the axial direction of the tibia. - Filename: SubXX_rtibia_acc.csv - Size of matrix: [Nx1] Knee angles Knee flexion/extension angles were one of the quantities of interest in the manuscript. Knee flexion/extension angles of the right lower leg at 240 Hz are provided. Knee flexion angles were defined 0° when the leg was fully extended during neutral standing. Flexion resulted in positive knee flexion angles. - Filename: SubXX_rknee_angle.csv - Size of matrix: [Nx1]

Between November 2023 and November 2024, over 6.5 million people in England regularly participated in running. This represented an increase over the previous study period's figure of 6.2 million.

The aim of the analysis was to evaluate how single or combinations of weather parameters (temperature, humidity, wind speed, solar load) affect peak performance during endurance running events and identify which events are most vulnerable to varying weather conditions.

Results for the marathon, 50 km race-walk, 20 km race-walk, 10,000 m, 5,000 m and 3,000 m-steeplechase were obtained from the official websites of the largest competitions in the world. Finish times for all races were collected from the first year of each competition for which data were available online until the end of 2019. The collection of these data was completed between February 2016 and September 2020

We obtained the date, time, and location for each race from its official website while the relevant longitude and latitude were obtained from www.locationiq.com. Weather data (air temperature, dew point, wind speed, and cloud coverage) corresponding to the time at half-way in each race were obtained from the closest meteorological station using the official dataset of the National Oceanic and Atmospheric Administration (www.ncei.noaa.gov/data/global-hourly). In cases where these data were not available, we retrieved the information from widely-used meteorology websites (www.wunderground.com and www.weatherspark.com). Wind speed was adjusted for height above the ground and air friction coefficient (i.e., large city with tall buildings). Dew point data were converted to relative humidity. For cases where cloud coverage was not available in the National Oceanic and Atmospheric Administration datasets, the cloud coverage (in okta) was computed using relative humidity data based on previous methodology and applying coefficients of 0.25 for low and high as well as 0.5 for middle clouds, as previously suggested. Solar radiation was calculated using the date, time, and coordinates of each race, while accounting for cloud coverage. Thereafter, the Heat Index, Simplified WBGT and WBGT, were calculated using previous methodology.

🏃 View data in the dashboard

Trail running races are much like regular road running races except that they are off-road by definition, and generally longer than your typical road race. The premier global trail running race is the Ultra-Trail du Mont-Blanc, held in Chamonix-Mont-Blanc, France. It is a 174 km (108 mi) race with 10,000 m (33,000 ft) of elevation gain. This race is organized by the UTMB Group. In addition to the UTMB, they organize 40+ prominent races worldwide called the “UTMB World Series”. Their website, UTMB.world, contains the results of these events. But, to my surprise, it also contains the results of tens of thousands of other trail running races.

Data was scraped from UTMB.world in two steps: First, I had to find all unique race identifiers (race UIDs) since they are erratic, and then I had to scrape each year held. In total, I found 15,679 race UIDs ranging from 4 to 45,846. The total number of races held was 38,460. See the "Scripts used in data collection" notebook for all methods. Second I scraped the content of these all unique race UIDs.

This data collection contains raw scraped data and an easy-to-read CSV file. Please look at the raw file's documentation for more information. The CSV file is much smaller and easy to interpret because: categorical fields were summarized to integers and the array of result times was aggregated to First, Last and Mean finish times as well as the number of participants that did not finish (N DNF).

Includes the raw IMU data for 20 participants performing seven different running exercises each.

20 participants: 16 m, 4 f, 16 to 31 yo, healthy, do sports regularly Seven exercises: Carioca left, carioca right, heel-to-butt, high-knee running, sideskips left, sideskips right, regular running Four IMUs: accelerometer + gyroscope each, two at wrists, two at ankles Ten seconds per recording under supervision One .json file per recording with sensor values and timestamps

The data set consists of a detailed training log from a Dutch high-level running team over a period of seven years (2012-2019). We included the middle and long distance runners of the team, that is, those competing on distances between the 800 meters and the marathon. This design decision is motivated by the fact that these groups have strong endurance based components in their training, making their training regimes comparable. The head coach of the team did not change during the years of data collection. The data set contains samples from 74 runners, of whom 27 are women and 47 are men. At the moment of data collection, they had been in the team for an average of 3.7 years. Most athletes competed on a national level, and some also on an international level. The study was conducted according to the requirements of the Declaration of Helsinki, and was approved by the ethics committee of the second author’s institution (research code: PSY-1920-S-0007).

Context

Every Marathoner has a time goal in mind, and this is the result of all the training done in months of exercises. Long runs, Strides, Kilometers and phisical exercise, all add improvement to the result. Marathon time prediction is an art, generally guided by expert physiologists that prescribe the weekly exercises and the milestones to the marathon.
Unfortunately, Runners have a lot of distractions while preparing the marathon, work, family, illnes, and therefore each one of us arrives to the marathon with his own story. The "simple" approach is to look at data after the competition, the Leaderboard.

But what if we could link the Marathon result to the training history of the Athlete? Could we find that "non orthodox" training plans give good results?

The Athlete Training History

As a start, I'll take just two data from the Athlete History, easy to extract. Two meaningful data, the average km run during the 4 weeks before the marathon, and the average speed that the athlete has run these km.

Meaningful, because in the last month of the training I have the recap of all the previous months that brought me to the marathon.

Easy to extract, because I can go to Strava and I have a "side-by-side" comparison, myself and the reference athlete. I said easy, well, that's not so easy, since I have to search every athlete and write down those numbers, the exact day the marathon happened, otherwise I will put in the average the rest days after the marathon.

I've set my future work in extracting more data and build better algorithms. Thank you for helping me to understand or suggest.

Content

id:
simple counter

Marathon:
the Marathon name where the data were extracted. I use the data coming out from Strava "Side by side comparison" and the data coming from the final marathon result

Name:
The athlete's name, still some problems with UTF-8, I'll fix that soon

Category:
the sex and age group of a runner - MAM Male Athletes under 40 years - WAM Women under 40 Years - M40 Male Athletes between 40 and 45 years

km4week
This is the total number of kilometers run in the last 4 weeks before the marathon, marathon included. If, for example, the km4week is 100, the athlete has run 400 km in the four weeks before the marathon

sp4week
This is the average speed of the athlete in the last 4 training weeks. The average counts all the kilometers done, included the slow kilometers done before and after the training. A typic running session can be of 2km of slow running, then 12-14km of fast running, and finally other 2km of slow running. The average of the speed is this number, and with time this is one of the numbers that has to be refined

cross training:
If the runner is also a cyclist, or a triathlete, does it counts? Use this parameter to see if the athlete is also a cross trainer in other disciplines

Wall21: In decimal. The tricky field. To acknowledge a good performance, as a marathoner, I have to run the first half marathon with the same split of the second half. If, for example, I run the first half marathon in 1h30m, I must finish the marathon in 3h (for doing a good job). If I finish in 3h20m, I started too fast and I hit "the wall". My training history is, therefore, less valid, since I was not estimating my result

Marathon time:
In decimal. This is the final result. Based on my training history, I must predict my expected Marathon time

Category:
This is an ancillary field. It gives some direction, so feel free to use or discard it. It groups in:
- A results under 3h
- B results between 3h and 3h20m
- C results between 3h20m and 3h40m
- D results between 3h40 and 4h

Acknowledgements

Thank you to the main Athletes data sources, GARMIN and STRAVA

The Goal of this Competition:

Based on my training history, I must predict my expected Marathon time. Which other relevant data could help me to be more precise? Heart rate, cadence, speed training, what else? And how could I get those data?

Centre of mass acceleration data was collected while runners ran a long distance overground run. This was for a PhD project investigating running bomechanics related to running performance and runnng injuries.

Centre of mass acceleration data was captured from a tri-axial accelerometer attached to the lower back of runners while they ran for 8 km around an outdoor athletics track.

This statistic shows the number of youth participants in running in the United States from 2006 to 2020. According to the source, the number of youth participants (aged between six and 17 years) in running amounted to approximately 10.9 million in 2020.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. Maryland Sports (http://www.marylandsports.us/) has identified sport venues located within the State of Maryland. These venues offer opportunities to participate in free and fee-based - organized and pick-up - indoor and outdoor sports and physical fitness related activities in the area of Running Sports. Last Updated: 08/2014 Feature Service Layer Link: https://mdgeodata.md.gov/imap/rest/services/Society/MD_SportVenues/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

The data set comprises raw and processed lower extremity gait kinematics and kinetics signals of 39 subjects in different file formats (c3d and txt). A file of metadata (in txt and xls formats), including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. In addition, a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program are also provided. The data were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes.

Dog runs are large, fenced-in areas for dogs to exercise unleashed during park hours. Each record in this dataset is an individual dog run. Planimetrics have ensured good GIS quality of the data. User guide: https://drive.google.com/open?id=1_LB-hb9eJ9Bph7K2Yf06_KOtra8qKppbPnfSyH1lRUg Data Dictionary: https://drive.google.com/open?id=1a3vdtIHUTUl2wokZ1r3fot8wm-lrnKGXYL-6xU5Fj2A

Financial overview and grant giving statistics of West Texas Running Club

This statistic shows the number of participants in trail running in the United States from 2006 to 2017. In 2017, there were approximately 9.15 million participants in trail running in the U.S., up from 8.58 million the previous year.

Financial overview and grant giving statistics of North Carolina Ultra Running Association

Supplementary files for the "Running a Confirmatory Factor Analysis in R: a step-by-step tutorial" consist of an R script and data needed to run the analysis.

Financial overview and grant giving statistics of Vegas Running Connection

R Scripts that generates the data in https://doi.org/10.17632/5rmrxkybbz.1