Home In On The Best Picks And Tips From Hundreds Each Week
Many soccer (soccer to our American friends) picks and tips sites provide just a few picks/tips a week, some only one, with lots of charging substantial quantities for the privilege. Within this article, I will show you how to get the very best from hundreds of free and reduced cost selections and tips every week by answering these four questions.
Imagine if you could pick the absolute best picks from countless weekly picks/tips greatly increasing your odds of succeeding?
Imagine if these picks/tips are chosen based on the past performance of similar picks/tips and those picks/tips are all created with a mix of several tried and tested statistical techniques?
What if you could do all of it for a FREE or very low price?
If you’re interested then read on.
Using well established statistical methods combined with automatic software it is possible to generate hundreds of soccer tips every week for many leagues, theoretically, you can cover all of the major leagues in the world. So what, why do you want to do that? Surely a number 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 a lot better to just concentrate on one or two matches and predict their result by intensive and cautious focused evaluation.
On the face of it the aforementioned responses that I have observed over the years have some merit and deserve careful consideration, there is a good debate for focused analysis of one game with the purpose of attempting to predict its outcome. However, consider this, when a scientist runs a statistical evaluation how many data items do they pick a representative sample? One, two… or longer? When carrying out statistical evaluation the more data you have to work to the better your outcome. As an instance, in case you wanted to figure out the normal height of a class of school kids you could just choose the first two or three as a sample. But if they are all six feet tall they will be highly unrepresentative so obviously you’d get all their peaks and figure out the average from these, the outcome is a much more accurate response. Obviously, you are able to apply this argument to a single match by collecting past results for every side and carrying out statistical analysis techniques using that information, but why restrict your analysis to that 1 match?
We are aware that if we create hundreds of automatic tips, based on sound tried and tested statistical procedures, that a few will be successful and many others won’t. So how can we target in on the best tips, the ones most likely to be right, and how do we do it week after week? Well, the answer is to maintain a record of how each and every tip performs, a few hints are much better than others and we want to know which ones. At this point, in case your thinking how can I possibly compute all of that information for every single game, in every league I want to cover and do it each week, then don’t worry I’ll explain to you how it is all done for you at the close of the article.
Results Are Not Always The Same
Just maintaining a record of how each of the hundreds of hints we make really performs against the eventual result is not enough, what we want today is a way of analyzing that data and group it logically to find the best from it. Results are not always the same, in other words, a tip that shows you potential outcome for match A and the same possible outcome for match B will not necessarily produce exactly the same outcome (i.e. a right forecast or a wrong forecast). 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’d undoubtedly be a millionaire. When attempting to predict the outcome of a game you may look at such qualitative things as the current injury list of every team, the team sheet, morale of the players, etc.. We could also look at Quantitative facets using our statistical procedures to predict the results of the game, so we may look at such matters as past performance, standing in the league, or more tried and tested statistical techniques such as the Rateform technique. We could use all of this information to forecast the results of match A as well as the outcome of game B and still not have exactly the identical outcome, part of the reason for this is, as explained before, that we can’t account for all the variables in a game, it’s hopeless. But there’s something else, something we can account for which we haven’t yet thought about.
When we look at the same match in isolation we only look at the factors concerning each of the two teams in the match, but why not extend this to look at how the other teams they’ve played are also doing? ‘Why would we need to do this?’ I hear some of you say. Because results are not always exactly the same. Let’s state our forecast for match A and match B is a home win (forgetting about the predicted score for the moment). What else can we take into consideration to enhance the forecast of a home triumph? We can have a look at the operation of all the home win hints made for exactly the same competition that the match has been played in and then make a judgment based on that new information. This is great as it gives us an excess factoring level to consider that we did not have before.
Looking across all of the house win predictions in one league provides us a percent success rate for house wins for that particular league, but we could improve on this even further. We can do it by doing the specific same exercise across several different leagues and getting a percentage success rate for each league. This implies we can now look for the league which produces the best overall home win forecast success rate and searches for house win forecasts for the coming fixtures. By default, we know that that league is more likely to produce a successful result for a home prediction than any other. Obviously, we can use this technique to away win and draw predictions as well.
Why does this gap between the leagues happen? Like trying to predict the outcome of one match there are many facets which make up this occurrence, however, there are just a few major factors that influence why one league should produce more home wins through a year than another. The most obvious of them could be called the ‘tightness’ of this league. What do I mean by ‘tightness’? In any league, there is frequently a gap in the skills and abilities of those teams always at the top of the league and those at the bottom, this is often expressed as a ‘difference in class’. This gap in class varies markedly between different leagues with a few leagues being much more competitive than others due to a closer level of abilities throughout the league, ‘a tight league’. In the case of a tight league, the cases of games that are drawn will be more noticeable than with a ‘not so tight league’ and home wins will most likely be of a lower frequency.
Thus, let us say we’re interested in predicting a home win, armed with our newest info regarding the ‘tightness’ of championships we can make predictions for matches throughout a year for as many leagues as we could manage, and watch how those predictions work in each league. You will find that the success of the predictions will carefully fit the ‘tightness’ of a particular league, so in which a particular league generates more home wins then we will have more success with our house forecasts. Don’t be fooled, this doesn’t necessarily mean simply because there are more home wins we’re sure to be more accurate, what I am talking about is that a success rate in percentage terms of the number of home predictions made which has nothing directly related to just how many actual homes wins you will find. For instance, let’s say we create one hundred house predictions in league A and one hundred in league B, and let’s say that seventy-five percent are right in league A but just 1 percentage in league B. We have made the exact same variety of predictions in each league with differing results, and those difference are most likely because of the ‘tightness’ of every league. League B will be a ‘tight’ league using more teams with equal levels of ‘course’, whereas league A has a wider margin of course when it comes to the teams within it. Therefore we ought to pick out the best performing team concerning home wins and create our house win selections from this league.
It is no good just taking each suggestion and documenting how it performed we have to apply the very same rules to each and every tip made. You need to make certain 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 every method and adhere to them for each and every forecast, for every league, and also for the whole season. You have to do it in order to keep the consistency of predictions within leagues, involving leagues, and over time. There is nothing stopping you using a number of distinct sets of parameters as long as you keep the data produced by every individual.
If you’re wondering what the parameters are then spent the Rateform method as an example. Using this method we produce an integer number that represents the possible result of a match (I’m not going to go into detail about the Rateform method here since that’s the topic of another of my articles). You can set breakpoints that represent a home win and an away win, therefore if the consequent rateform output for a match is greater than the top breakpoint then that match could be deemed a house win. Similarly, if the consequent rateform output to get a match is lower than the lower breakpoint then that match could be deemed as an away win. Whatever drops between is deemed a draw.
Do It For FREE Or Very Low Cost
So how do I get all of this information without having to compute it all myself?
Footyforecast.com has been delivering this type of advice, week in week out, on its site since 1999. It covers eighteen leagues around Europe such as; 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 results of every game played in each league, and a thorough record of how each system in every game performed is maintained. A lot of this information is completely free to site visitors but for a small subscription fee, you can gain access to this data from all eighteen leagues. Apart from how every tip performed within its various league Footyforecast also gives the league tables of how every league has performed in successfully predicting results of games. The league tables of prediction performance are made for home win predictions, draw predictions, away win predictions, and also for overall predictions and are invaluable tools to the soccer punter when determining where to target their European soccer predictions. You can visit the Footyforecast website by using the link below:
Hopefully, I have shown you the way you can target in on the top leagues in order to raise your chances of success when calling 1X2 outcomes, also, although I offer no guarantees, I’m pretty confident that this method will enhance your profits.