Rating and Predicting Football with Recurrent Neural Network and JAX

Introduction

When it comes to talking about sports or competition, the rating is always brought to the table. We always want to rank and measure how a player or a team compares to each other. This is exactly what rating does.

Over the last decades, many rating methods have emerged. For…

Introduction

Football has always been a challenging sport to model. The most famous model is the Dixon-Coles¹ which leverages the Poisson distribution as a prior to model goal scoring. Rating models based on pairwise² comparisons and ranking³ have emerged as an alternative way of making predictions. For instance, the Elo rating⁴…

Quantitative Betting With Python: How To Backtest a Value Bet Strategy

Using data to beat the bookmakers

Quantitative betting consists in using mathematics and algorithm to place a bet as it can be done in finance. To achieve this there are two important steps. First, you need to have predictions, second, you need to backtest your strategy. …

How To Measure Football Prediction Model Quality

Introduction

In predictive modeling, one of the most important steps is measuring your model quality. More importantly, you need to have a measure that is understandable and generalizable. There are a lot of metrics out there that you can use to train a model, select the parameters, or do cross-validation. …

Football Prediction Performance: How to Calculate Hit-ratio and Log-loss

You will never do it wrong

Measuring the performance of a model is an essential step whether you are doing research, betting, or simply comparing predictions. This short article shows how to compute the hit ratio and the log loss for 1x2 football predictions with code and examples. These two losses are commonly used in football…

Introduction

Many websites and data providers sell or give free football/soccer predictions. We have not checked every one of them but 99% won’t give you any performance measure.

Introduction

Logistic regression is a statistical model similar to linear regression in many aspects. But, contrary to linear regression, logistic regression involves strong probabilities theory concepts like the log-likelihood, the Bayes theorem, or conditional probabilities. …

Introduction

Sports events are random, even the best players or teams can experience bad luck and bad times. This randomness turns betting into estimating probabilities. The prediction of the event issue is a result of this estimation as you will predict the most probable outcome.

Everyone betting against bookmakers thinks that…

Introduction

When we are interested in betting there is always a time where we deal with probabilities. These probabilities can be derived from a mathematical model using data analysis or we can directly imply them from the bookmaker odds. Today we are conducting a study with the latter.

The dynamics of the odd market

Let’s take a… octosport.io

We write about machine learning for football prediction and deliver our data through an API.