This distribution looks as though it has been shifted to the left. The striking characteristics of this graph explain the summary statistics below. The kernel density for the 1500m, years 2012-2021.Ģ020 is not included because of the pandemic year. Right now, this year’s data is most comparable to 2016. While the average of this year is not an outlier compared to years past, the depth of the percentiles shows a shift down, to a concentration of faster times. As results continue to be run and we head into the post season, let’s take note. Finally, we will produce arrays for each year.Ģ020 is not included because of the pandemic year. We will store each year’s data in an array. Now that we have all of the functions written, let’s look at cleaned kernel density plots for each event, 800m to 10,000m, compared from 2012 until now.įor these kernel density plots, we need to compile a list of the times for each event for each year. The code above is sufficient to create plots for all of the archives, and the year 2021. The TFRRS table generator function combines all of the above functions to properly create the table for a specific year and event. DataFrame ( data = all_rows, columns = headings ) new_df = for i in new_df ] new_df = ] return new_df append ( aa_stripped ) # append one row to all_rowsĪll_rows. strip () #append aa to row - note one row entry is being appended # xa0 encodes the flag, \n is the newline and comma separates thousands in numbersĪa = re. # the following regex is to remove \xa0 and \n and comma from row_item.text # row_item.text removes the tags from the entries find_all ( "td" ): #loop through all row entries Row = # this will old entries for one rowįor row_item in body_rows. find_all ( "tr" ) # Head values (Column names) are the first items of the body listīody_rows = body # All other items becomes the rest of the rowsĪll_rows = # will be a list for list for all rowsįor row_num in range ( len ( body_rows )): # A row at a time You input a year as a string, and the function will search the TFRRS archive HTML for the correct link to the outdoor performance list for that year.ĭef table_formatter ( table ): headings = # the head will form our column namesīody = table. The first function below generates the URL on the TFRRS website. The following functions are a cleaned and robust version of several python jupyter notebooks. I spent time studying the TFRRS website in order to properly configure my web scraper. I decided to do the data collection process this way so that I do not have to download each dataset to my computer, but rather I can pull it for each year directly to python. The following functions reproduce those tables from the TFRRS website into pandas dataframes that can be directly manipulated for the purpose of data visualization. The TFRRS website has an archive page that contains the NCAA Track and Field Outdoor Final Qualifying lists for every year since 2012. The project and this page will be updated over time as I continue to do work and develop more overall functionality of the project. I went to work and created the project below, which shows the different statistics from 800m to 10,000m, starting in 2012 and ending with this year. That got me thinking, is there any way that I can prove that the results of this year are statistically different than those of the past? The time? 3:34.09, with a 53.95 final lap.īy comparison, Yared Nuguse just soloed a 3:34.68 in the prelims of the Men’s 1500m at ACC’s. The result? A commanding win by Matthew Centrowitz. The very best runners lined up and went toe to toe in the final of the men’s 1500m. Times that used to be considered “other-worldly” are now commonly run in a prelim heat. This year’s depth across distance events from 800 meters to 10,000 has been remarkable. Those are all questions I, among others, ask myself as a fan of track and field. When new shoe technology is released, people are quick to question the credibility of performance.Īre we experiencing advancements in diet, coaching, and training that account for the sheer depth and difference in times? The shockingness of the results of each meet parallel those of 2017, when Nike first released the Vaporfly. With the release of Nike’s new Dragonfly and Air Zoom Victory spikes, the running world has experienced a new depth of speed across the board. Statistical Updates - Collegiate Track and FieldĪuthor’s Note: This page will be updated weekly as new data becomes available.Īs I watched the times go up on the board, I couldn’t believe my eyes. Statistical Updates - Collegiate Track and Field.Collegiate Track and Field Data, Summarized
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