Betting Strategy & Psychology

Using quantitative methods to examine effects of small sample

What do you do when we are still at the beginning of the season and there is not enough data to draw reliable conclusions? In this article Dominic explains how bootstrapping can be used to minimize the effect of any parametric error due to small samples by giving two examples.

The importance of measuring performance against the closing line

One way to distinguish winning from losing players is to look at the odds a player received when they made their bet and compare it with the Pinnacle Sports closing line. Consistently beating the closing odds at Pinnacle Sports can be a strong indicator of long-term betting profits.

Can Pythagorean expectation relate to points won in the next season?

Predominantly used for betting on US sports such as baseball and basketball, can Pythagorean expectation be used for betting on soccer? Analytics expert, Mark Taylor explains why this could be a potentially profitable strategy when betting on long-term markets.

Unwrapping exciting betting focused R packages

Gaining an edge in betting often boils down to intelligent data analysis, but faced with daunting amounts of data it can be hard to know where to start. If this sounds familiar, R – an increasingly popular statistical programming language widely used for data analysis – could be just what you’re looking for.

Using Chi-Square to make more informed betting decisions

In order to have a successful betting model, all bettors should collect as much data as possible. But just how well does data fit into certain expected scenarios? Dominic Cortis explains how important 'the goodness of fit' is in analysing data.