Monthly Archives: April 2018

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

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

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

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

Scientific Betting on Ice Hockey Worlds now on Facebook

Scientific Gambling on Ice Hockey World Championships 2018

Posted in Bayes, development, Gambling, HOCKEY-2018 | Tagged , , | Leave a comment

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

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

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

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

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

[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

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

Bayesian updating with PYMC

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

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

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

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

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

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

2018 Ice Hockey World Championships – ‘Raw’ Odds Qualifying Round

[logo copyright IIHF] Below the probability distributions from previous post  converted to odds, or more specifically, “Raw” odds, that is, odds based purely on the underlying posterior distributions, not taking into account other aspects, such as the betting distribution, or the … Continue reading

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