Category Archives: Data Analytics

Calculating distance from Lat & Lon coordinates using Python & Pandas

Posted on February 2, 2019 by

Given a GPS log file structured as follows (column separators omitted for clarity): Speed Latitud Longitud Time 2019-01-07 06:15:27 0 59.649582 17.721365 2019-01-07 06:16:28 0 59.649583 17.721372 2019-01-07 06:17:28 0 59.649583 17.721370 2019-01-07 06:18:28 0 59.649583 17.721372 2019-01-07 06:19:29 0 … Continue reading →

Posted in Data Analytics, Math, Nautical Information Systems, Pandas, Python | Tagged , , , , | Leave a comment

Geolocation with Google API’s & Python – mapping addresses to GPS coordinates

Posted on January 19, 2019 by

Google does some pretty impressive things – not just all the web-based search stuff, but they also have lot’s and lot’s of really cool API’s for programmers to peruse. In order to access these API’s, you need a personal authorization … Continue reading →

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Vasaloppet 2018 – race time analysis

Posted on January 10, 2019 by

An analysis of race times for the ~11000 men and ~2000 women that participated in 2018 Vasaloppet. For explanations of the graphs, see earlier posts on Marcialonga or Tour de Ski. [Btw, the weird looking vertical orange/blue “spike” in the … Continue reading →

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Tour de Ski Final climb – does age matter for performance ?

Posted on January 6, 2019 by

In an earlier post, I analyzed data from the Marcialonga Ski race. Marcialonga is one of the classic long distance ski races, where both elite’ as well as amateurs compete together. In fact, the vast majority of the competitors in … Continue reading →

Posted in Bayes, Data Analytics, Math, Numpy, Pandas, Probability, PYMC, Python, SNA, sports, Statistics | Tagged , , , , , , , , | Leave a comment

Tour de Ski 2019 Final Climb Analysis

Posted on January 6, 2019 by

Just a quickie analysis on the just finished race, comparing the climb times for women vs men, the top-28 of both genders. Once again, the results are consistent with my earlier findings on this topic: at elite’ level, the difference … Continue reading →

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Marcialonga Ski 2018 – some Analytics

Posted on January 5, 2019 by

Now, with the power grid finally – after 62 hours! – back in business, I’m able to continue my stats/analytics exploration of the past Marcialonga ski race. First, some basic stats about the race: Total number of participants: 5558, of … Continue reading →

Posted in Bayes, Data Analytics, Numpy, Pandas, Probability, PYMC, Python, sports, Statistics | Tagged , , , , , , , , , | Leave a comment

Ugly soup with Python, requests & Beautiful Soup

Posted on January 3, 2019 by

Web scraping has never been a coveted nor favorite discipline of mine; in fact, for me web scraping is an unfortunate, but sometimes necessary evil. Scraping web-pages, at least for me, is a very unstructured process, basically pure Trial & … Continue reading →

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Gender is not a social construct, but a biological reality, clearly demonstrated in sports

Posted on January 1, 2019 by

Just a quick demo to debunk the contemporary notion that “Gender is a social construct”. Data taken from today’s Tour the Ski sprint qualification times, for the top 30 women vs men, where both men & women used the same … Continue reading →

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Bayesian Linear Regression with PYMC

Posted on December 31, 2018 by

Python, Pandas & PYMC example on Bayesian Linear Regression, adopted from Richard McElreath’s “Statistical Rethinking” class, where he uses R as modeling language instead of PYMC. Data in a csv-file describe various attributes such as weight, height, age, gender etc … Continue reading →

Posted in Bayes, Data Analytics, Numpy, Pandas, Probability, PYMC, Python, Statistics | Tagged , , , , , , , | Leave a comment

Global Warming – soon in a place near you

Posted on September 29, 2018 by

Just downloaded hourly temperature data from SMHI, the Swedish Meteorological & Hydrological Institute, taken on Svenska Högarna, one of the islands in the remote Stockholm Archipelago, for the years 1949 – 2018. The plots below present max/min/mean daily temps for … Continue reading →

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