Tag Archives: Scipy

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

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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

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Bayesian Multi-predictor Regression – Valet2018

[Continuing my exploration of the Swedish election results, but I thought this might be of interest also for those of you not very interested in the Swedish elections, simply because the potential MatStat’s  insights – thus, the text is in … Continue reading

Posted in Bayes, Big Data, Data Analytics, Data Driven Management, Numpy, Politik, Probability, PYMC, Python, Research, Society, Statistics, Sverige | Tagged , , , , , , , , , , | 1 Comment

PYMC – Markov Chain Monte Carlo regression – canonical example

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Val2018 Bayesian Inference – sammanfattning

Nu är ju inte den slutgiltiga rösträkningen klar, men resultatet ur ett statistiskt / matematiskt perspektiv är ändå så stabilt att jag väljer att summera mina resultat redan nu. I graferna nedan har jag använt mig av samtliga opinionsinstituts prognoser … Continue reading

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Scientific Gambling – Ice Hockey World Championships starting tomorrow

The tournament is starting tomorrow with four games. From now on, future posts on this topic on the public Facebook group Scientific Gambling on Ice Hockey World Championships 2018 only. So, I you want to continue following how my Bayesian Inference engine … Continue reading

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Scientific Gambling – how do betting shops make money….?

Betting shops are commercial businesses, that is, they want to and must make money in order to survive. Like any other business. So take a casino as an example: they make money – in the long run – by having … Continue reading

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Scientific gambling – How to identify potentially profitable odds/plays ?

In all sports gambling, success or failure is determined by a number of factors, luck not being the least of them, since in any sport there are loads of “Unknown Unknowns“, which we could also call “Uncertainty”. And then there … Continue reading

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Scientific Gambling on Ice Hockey Worlds – identifying potentially exploitable games

One of the most difficult aspects of dealing with lots of data, is to present the information obtained from various computations in a clear and meaningful way. For instance, in order to identify games where there is a potentially exploitable … Continue reading

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Scientific Gambling on Ice Hockey Worlds – Bets for games of May 5th

Summary I’m using mathematical & statistical methods, more specifically, Bayesian Inference, Markov Chain Monte Carlo, simulation and Probabilistic Programming, attempting to predict the game outcomes of the upcoming Ice Hockey World Championships, starting May 4th. Based on the findings of … Continue reading

Posted in AI, Bayes, Big Data, Business, Data Analytics, Data Driven Management, Gambling, HOCKEY-2018, Math, Numpy, Probability, PYMC, Python, Statistics | Tagged , , , , , , , , , , , , , | Leave a comment