Some people think that trying to predict the result of a football match is largely a waste of time. They instinctively know that 'anything can happen in those 90 minutes and often does'. It's true that you can never predict the result of a football match with any degree of certainty, but that doesn't mean that football is wholly unpredictable. A football match is probabilistic in nature and always involves an element of 'God plays dice'. Freak results happen and they are largely unpredictable, but they happen no more often than they should and the best teams win more often than not. To be profitable betting on football it's important to evaluate probabilities rather than predict winners and to use a value based approach to betting. You'll need to establish what factors are important and therefore what information is relevant and you'll need to understand the nature of the problem space. The traditional approach to data processing for football is to use ratings and statistics, however neural networks provide a more sophisticated approach. The subconscious mind can also be very powerful when trained and used in the right way. Some people rely too much on their 'gut feeling' when gambling and other people not enough. Sometimes we feel more certain about a decision than we should and we tend not to think in terms of probabilities, however it is possible to train our minds to be more effective. It's also important to take a disciplined and intelligent approach and to always learn and evolve. One thing that you must accept is that the process involves losing and you can't force your luck. You also need to be realistic: luck is completely random and impersonal. You can be lucky after the event, but not before. You can't see the future in a dream. Dreams are windows to the subconscious, not the future. Whether you plan to take a systematic approach to betting on football or not, this guide will help you to understand what's important and what's not and how best to proceed. There is also a small, easy to understand introduction to neural networks describing what they are and how they work by way of a simple example.