Tag Archives: Matplotlib

Scientific Gambling – Ice Hockey World Championships starting tomorrow

Posted on May 3, 2018 by

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….?

Posted on May 1, 2018 by

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 ?

Posted on April 30, 2018 by

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

Posted on April 30, 2018 by

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 Hockey Worlds – Expected profits from games of day 1 & 2

Posted on April 27, 2018 by

An Expected Value-calculation gives the expected gains from my bets on the games played during the first two days of the tournament as follows: OUTCOME U_ODDS U_P P P_DELTA EV_PER_UNIT HOME AWAY CZE SVK DRAW 5.20 0.192308 0.243738 0.051430 0.267438 … Continue reading →

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

Posted on April 26, 2018 by

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

Bayesian Prediction – wanna bet…? Putting your money where your mouth is…

Posted on April 24, 2018 by

[A disclaimer: I know virtually nothing about contemporary ice hockey, my interest faded when Börje Salming decided to put his skates on the shelf for a couple of decades ago, so I have not included any personal hockey insights into … Continue reading →

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Bayesian updating with PYMC

Posted on April 21, 2018 by

I’ve been looking for neat ways to update a Bayesian Prior from a posterior sample for a while, and just the other day managed to find what I was looking for: a code example that shows how to make a … Continue reading →

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Poor Man’s Betting Shop – Using Baysian Inference to setup your own Betting Shop

Posted on April 18, 2018 by

Further exploration of Bayesian Inference, applied to the upcoming 2018 Ice Hockey World Championships. This time, I’m trying to understand how the professional betting shops set their odds, and how they make a profit. It took some ‘research’ into the … Continue reading →

Posted in Bayes, Complex Systems, Data Analytics, development, Gambling, Math, Numpy, Probability, PYMC, Python, Statistics | Tagged , , , , , , , , , | Leave a comment

Bayesian Inference 2018 Ice Hockey World Cup outcomes

Posted on April 15, 2018 by

I’ve tuned my Bayesian model a bit. Previously, it used the cumulative sum of historical results point spread as its data input, now it uses each individual game spread. Perhaps an example can make this clearer: Consider two teams, A … Continue reading →

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