# Category Archives: Numpy

## More Mindbending Probability

In a previous post, I discussed the seemingly unintuitive logic of the famous Monty Hall Problem. However, with some careful thinking, even without resorting to Monte Carlo Simulation, I’m able to make sense of that apparent paradox. However, the paradox … Continue reading

Posted in Math, Numpy, Pandas, Probability, Python | Tagged , | 2 Comments

## Corona : slowing down…?

Some further analysis on Corona, with data up and including yesterday. First, let’s look at the global spread: The first of these plots has linear (lin/lin) scale, and should be familiar from any of the many sites that post data … Continue reading

## Vasaloppet 2018 – race time analysis

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

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

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 | | Leave a comment

## Marcialonga Ski 2019 – some Analytics

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 | | Leave a comment

## Bayesian Linear Regression with PYMC

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

## Python & Pandas to map gps coordinates to known locations

Assume you have a gps log file, with time and position (Lat,Lon in columns 9,10) info, like: 2018.12.12 00:41:20;0;0;0;0;0;1;0;25.8;59.348978;17.969643;0;0; 2018.12.12 01:41:21;0;0;0;0;0;1;0;25.7;59.348962;17.969627;0;0; 2018.12.12 02:41:21;0;0;0;0;0;1;0;25.7;59.349;17.969688;0;0; 2018.12.12 03:41:21;0;0;0;0;0;1;0;25.7;59.349;17.96966;0;0; 2018.12.12 04:41:22;0;0;0;0;0;1;0;25.6;59.349007;17.969618;0;0; 2018.12.12 04:48:50;0;0;0;0;1;1;1;25.2;59.349007;17.969635;0;0; 2018.12.12 04:49:51;0;0.001;0;0;1;1;1;28.3;59.349;17.969642;0;0; Assume further that you’d like to map each of … Continue reading

## Using Bayesian Inference to predict and bet on Italian Serie A Fotball

As my old timer readers know, I’be been using Bayesian Inference to predict and bet on various sporting events, such as FIFA World Cup, and IIHF World Championships. With some success. When the Italian premier division started for about a … Continue reading