To create the cubes and other data visualisations
Posted: Mon Dec 23, 2024 4:04 am
British Athletics has collected data since the 2006 season, so there are as of now (September 2020) 14 complete years of data, together with an as yet incomplete 15th year for the 2020 season (although this has been somewhat curtailed due to the Coronavirus pandemic).
In this blog post, we demonstrate using athletic performance data to show how analytical methods like pattern matching and expressions can be applied to real-world data. We croatia phone number will look at the season best performances for athletes and look for interesting insights and trends, and see if we can find the best athletic performances of the last 15 years! within this blog, we have used our data analytics software solution Apteco FastStats.
Data Collection
The ‘Power of 10’ website has a rankings page in which you can query the data for a particular event in a particular season. For example, we can view the rankings page for the Men’s 100m in 2019 and we will see a page that starts off like this:
Power of 10 overview
I wrote R scripts to query these pages and to obtain the relevant performance information from each page, and turn them into an R data frame. There is a lot of data to collect, and the pages differ in terms of their formats and fields (for example there are times in track events and only some have wind readings, distances in jumps, heights, points etc.) so this is not a trivial task. There is 14 years’ worth of data for 46 different events, some of which are competed in by both genders, others which are specific to men or women (e.g. 100 hurdles v 110 hurdles). This dataset comprises all the events which are on the Olympic programme and also the 10k and Half Marathon road times.
In this blog post, we demonstrate using athletic performance data to show how analytical methods like pattern matching and expressions can be applied to real-world data. We croatia phone number will look at the season best performances for athletes and look for interesting insights and trends, and see if we can find the best athletic performances of the last 15 years! within this blog, we have used our data analytics software solution Apteco FastStats.
Data Collection
The ‘Power of 10’ website has a rankings page in which you can query the data for a particular event in a particular season. For example, we can view the rankings page for the Men’s 100m in 2019 and we will see a page that starts off like this:
Power of 10 overview
I wrote R scripts to query these pages and to obtain the relevant performance information from each page, and turn them into an R data frame. There is a lot of data to collect, and the pages differ in terms of their formats and fields (for example there are times in track events and only some have wind readings, distances in jumps, heights, points etc.) so this is not a trivial task. There is 14 years’ worth of data for 46 different events, some of which are competed in by both genders, others which are specific to men or women (e.g. 100 hurdles v 110 hurdles). This dataset comprises all the events which are on the Olympic programme and also the 10k and Half Marathon road times.