**Home In On The Best Picks And Tips From Hundreds Each Week:**

Many football (soccer to our American friends) picks and tips sites provide only a few picks/tips a week, some only one, with many charging huge amounts for the privilege. In this article I will show you how to get the very best from hundreds of free and low cost picks and tips every week by answering these four questions.

What if you were able to pick the absolute best picks from hundreds of weekly picks/tips greatly increasing your chances of success?

What if those picks/tips are chosen based on the past performance of similar picks/tips and those picks/tips are all created using a combination of several tried and tested statistical methods?

What if you could know whether draw predictions, home predictions or away predictions are more successful for the English Premier League, the Italian Serie A, the German Bundesliga, or many other leagues across Europe?

What if you could do it all for FREE or very low cost?

Well now you can. If you’re interested then read on.

**Some Tips Are Better Than Others:**

Using well established statistical methods along with automated software it’s possible to generate hundreds of soccer tips every week for many leagues, theoretically you could cover all of the major leagues in the world. So what, why would you want to do that? Surely many of the tips will be grossly inaccurate but on the other hand many will be correct so how can you determine which will be successful and which not? It would be much better to just concentrate on one or two matches and predict their outcome by intensive and careful focused analysis.

On the face of it the above responses that I have seen over the years have some merit and deserve careful consideration, there is a good argument for focussed analysis of a single match with the aim of trying to predict its outcome. However, consider this, when a scientist runs a statistical analysis how many data items do they select as a representative sample? One, two… or more? When carrying out statistical analysis the more data you have to work on the better the outcome. For example,if you wanted to calculate the average height of a class of school children you could just take the first two or three as a sample. But if they are all six feet tall they are going to be highly unrepresentative so obviously you would get all their heights and calculate the average from those, the result is a much more accurate answer. It’s a simplistic example but hopefully you see my point. Obviously you can apply that argument to a single match by collecting past results for each side and carrying out statistical analysis techniques using that data, but why restrict your analysis to that one match?

We know that if we make hundreds of automated tips, based on sound tried and tested statistical methods, that some will be successful and others won’t. So how do we target in on the best tips, the ones most likely to be correct, and how do we do it week after week? Well, the answer is to keep a record of how each and every tip performs, some tips are better than others and we want to know which ones. At this stage, if your thinking how can I possibly calculate all of that information for every game, in every league I want to cover, and do it every week, then don’t worry I’ll show you how it’s all done for you at the end of the article.

**Results Are Not Always The Same:**

Simply keeping a record of how each of the hundreds of tips we make actually perform against the eventual result is not enough, what we need now is a way of analysing that data and grouping it logically to get the best from it. Results are not always the same, in other words a tip that shows one possible outcome for match A and the same possible outcome for match B will not necessarily produce the same result (i.e. a correct prediction or a wrong prediction). Why is this? Well there are hundreds of reasons why and you will never be able to account for them all, if you could you would no doubt be a millionaire. When trying to predict the outcome of a match you may look at such qualitative things as the current injury list of each team, the team sheet, morale of the players, etc. We can also look at Quantitative factors using our statistical methods to predict the outcome of the match, so we may look at such things as past performance, position in the league, or more tried and tested statistical methods such as the Rateform method. We can use all of this information to predict the outcome of match A and the outcome of match B and still not have the same result, part of the reason for this is, as explained before, that we can not account for all the factors in a match, it’s impossible. But there’s something else, something we can account for which we have not yet thought about.

When we look at one match in isolation we only look at the factors concerning each of the two teams in the match, but why not expand this to look at how the other teams they have played are also performing? ‘Why would we want to do that?’ I hear some of you say. Because results are not always the same. Let’s say our prediction for match A and match B is a home win (forgetting about the predicted score for the moment). What else can we take into account to improve the prediction of a home win? We can look at the performance of all the home win tips made for the same competition that the match is being played in and then make a judgement based on that new information. This is great as it gives us an extra factoring level to take into account that we did not have before.

Looking across all the home win predictions in a single league will give us a percentage success rate for home wins for that particular league, but we can improve on this even further. We can do this by doing the exact same exercise across many different leagues and obtaining a percentage success rate for each league. This means we can now look for the league which produces the best overall home win prediction success rate and look for home win predictions for the coming fixtures. By default we know that that league is more likely to produce a successful outcome for a home prediction than any other. Of course we can employ this technique for away win and draw predictions as well.

**How Tight Is The League?:**

Why does this difference between the leagues occur? As with trying to predict the outcome of a single match there are many factors that make up this phenomenon, but there are just a few major factors that influence why one league should produce more home wins through a season than another. The most obvious of these could be described as the ‘tightness’ of the league. What do I mean by ‘tightness’? In any league there is often a gap in the skills and abilities of those teams consistently at the top of the league and those at the bottom, this is often expressed as a ‘difference in class’. This difference in class varies markedly between different leagues with some leagues being much more competitive than others due to a closer level of skills throughout the league, ‘a tight league’. In the case of a tight league the instances of drawn games will be more noticeable than with a ‘not so tight league’ and home wins will most likely be of a lower frequency.

So, let’s say we are interested in predicting a home win, armed with our new information about the ‘tightness’ of leagues we could make predictions for matches throughout a season for as many leagues as we can manage, and watch how those predictions perform in each league. You will find that the success of the predictions will closely match the ‘tightness’ of a particular league, so where a particular league produces more home wins then we will have more success with our home predictions. Don’t be misled, this does not mean that just because there are more home wins we are bound to be more accurate, what I am taking about is a success rate in percentage terms of the number of home predictions made which has nothing directly to do with how many actual home wins there are. For example, let’s say we make one hundred home predictions in league A and one hundred in league B, and let’s say that seventy five percent are correct in league A but only sixty percent in league B. We have made the same number of predictions in each league with differing results, and those difference are most likely due to the ‘tightness’ of each league. League B will be a ‘tight’ league with more teams having similar levels of ‘class’, whereas league A has a wider margin of class when it comes to the teams within it. Therefore we should pick out the best performing league concerning home wins and make our home win selections from that league.

**We Have To Be Consistent:**

Of course there is more to it than that. It’s no good just taking each tip and recording how it performed we have to apply the same rules to each and every tip made. You have to make sure that the parameters you set for each predictive method you use (e.g. Rateform, Score Prediction, etc.) remain constant. So choose your best settings for each method and stick to them for each and every prediction, for every league, and for the whole season. You must do this in order to retain consistency of predictions within leagues, between leagues, and over time. There is nothing stopping you using several different sets of parameters as long as you keep the data produced from each separate.

If you are wondering what the parameters are then take the Rateform method as an example. Using this method we produce an integer number that represents the possible outcome of a match (I’m not going to go into detail about the Rateform method here as that’s the subject of another of my articles). You can set break points that represent a home win and an away win, so if the resulting rateform output for a match is higher than the upper breakpoint then that match could be deemed a home win. Similarly, if the resulting rateform output for a match is lower than the lower breakpoint then that match could be deemed as an away win. Anything that falls in-between is deemed a draw.

Footyforecast.com (now 1X2Monster.com) has been delivering this kind of information, week in week out, on its website since 1999. It covers eighteen leagues across Europe including; English Premiership, Scottish Premiership, Italian Serie A, German Bundesliga, Dutch Eredivisie, Spain, France, to name but a few. A total of seven different statistical methods are used to determine the outcome of each game played in each league, and a comprehensive record of how each method in each game performed is kept. Apart from how each tip performed within its respective league Footyforecast also provides the league tables of how each league has performed in successfully predicting outcomes of games. The league tables of prediction performance are produced for home win predictions, draw predictions, away win predictions, and for overall predictions and are invaluable tools to the soccer punter when deciding where to target their European soccer predictions.

So there you have it. Hopefully I have shown you how to target in on the best leagues in order to raise your chances of success when predicting 1X2 results, and, although I offer no guarantees, I’m fairly confident that this method will improve your profits.