Weighting of more recent behaviour of the teams is replaced by modelling
Differing from our previous football prediction model, the gradually diminishing impact of the matches played in the distant past to the team performance is learned through the modelling. It's not a weighting. The learning algorithm decides by itself, how much importance it gives to the various factors.

The development of the model continues. You can read more about the underlying bayesian method here.

The UEFA prediction model was created using BayMiner BayMiner is a browser-enabled tool for analysis of data in tabular format. It finds dependencies in complex multi-dimensional situations and visualizes their co-occurrences in a format easily understandable by humans. The data does not need to be numeric and the source table does not need to be complete. Read more on frequently asked questions on BayMiner.

Using BayMiner for business
Understanding complex situations and timely reaction to changing situations is a common theme in business. Instead of classical statistics producing a multitude of curves and pie-charts about a particular set of data, BayMiner uses machine-learning to produce an easy to use knowledge model. BayMiner makes the information of even very complex data-sets easily accessible for the user. Unlike conventional data mining tools, BayMiner is very easy to use and does not require knowledge about statistics or the technology used.

Benefits of Bayesian Networks
Bayesian Networks are the best technology for mastering uncertainty. They make possible to grasp complex cause-dependencies in multi-dimensional environments such as quality disturbances, or project risk management. One of the reasons for using Bayesian Networks in this football application, as well as in many business applications, is that a knowledge model using Bayesian Networks can be successfully used for predictions when a time series is not long enough to be used with classical statistical (frequentistic) methods.

Working in a modelled environment gives you the opportunity to make trustworthy decisions quickly, in response to various needs as they arise. The fast implementation of knowledge models is a major factor behind the success of BayMiner in many applications.

BayMiner allows better analysis of latent variables
Latent variables are variables that are not directly observable but may be inferred from, usually several unknown other variables that are observed and measured. It is not always possible to identify and name a latent variable, as it sometimes represents a very complex phenomenon. Latent variables are also called hidden variables. Examples in football context include “fighting spirit” and in business “risk”.

Examples of successful business applications
The BayMiner solution is widely acknowledged as the most flexible solution available for business analysis today. With its probabilistic modelling suite BayMiner PRO, the dedicated applications EWS (Early Warning Signals) and QVM (Quality Variance Management), BayesIT has the technology and services that fit literally any need of an organization.

EWS
Knowledge models based on data about operations in the past and developed in BayMiner can predict future more accurately than most other technologies. For the purpose of simplicity of use, BayesIT developed the BayMiner EWS (Early Warning System) product. EWS applications are usable with rudimentary skills in using computers. The EWS spearhead application steers the user using traffic lights away from risky tenders in an early stage of a project or contract service sales. For more information, See our pages on EWS.

QVM
Probabilistic Modeling solutions coupled with 3D visualization help users identify the connection between e.g. quality, customer satisfaction and process parameters. For this purpose BayesIT developed the QVM concept. It is especially useful in detecting root causes to claims and punctuality variations (delays in production). For more information, See our pages on QVM.

Other applications
Significant benefits in various business applications has been achieved, including:

• Reduced customer dissatisfaction through identification of root causes to field problems.
• Definition of right customer service levels using multi-dimensional segmentation.
• Improved profitability through identification of non-profitable market and feature combinations.
• Improved delivery performance through identification of causes to excessive order information changes.
• Fraud detection.

Go to the predictor