How To Make A Profit From FREE 1X2 Soccer Picks/Tips
Many football betting and prediction sites (football for our American friends) provide only a few predictions/predictions per week, some only one, with many charging huge sums for the privilege.
What if you were able to choose the absolute best choices from hundreds of weekly choices/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 proven statistical methods?
What if you could find out whether draw, home 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 at a very low cost?
Well, now you can. If you are interested, read on.
Some tips are better than others
Using established statistical methods together with automated software it is possible to generate hundreds of football predictions every week for many leagues, theoretically you could cover all the major leagues in the world. So what, why would you want to do that? Sure 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 will not? It would be far better to focus on just one or two games and predict their outcome with focused and careful analysis.
On the face of it the answers above that I have seen over the years have some merit and deserve careful consideration, there is a good argument for a focused analysis of a single match with the aim of trying to predict its outcome. However, consider this, when a scientist performs a statistical analysis how many data elements does he or she select as a representative sample? One, two… or more? When performing statistical analysis, the more data you have, the better the result. For example, if you wanted to calculate the average height of a class of school children, you might take only the first two or three as a sample. But if they’re all six feet tall they’ll be highly unrepresentative, so obviously you’d have to get all their heights and calculate the average from those, the result being a much more accurate answer. That’s a simplistic example, but I hope you see my point. Obviously you can apply that argument to a single match by collecting past results for each side and performing statistical analysis techniques using that data, but why limit your analysis to that match?
We know that if we create hundreds of automated suggestions, based on solid proven statistical methods, some will be successful and some will not. So how do we focus on the best tips, the ones that are most likely to be correct, and how do we do this week after week? Well, the answer is to keep track of how each individual tip performs, some tips are better than others and we want to know which ones. At this point, if you’re thinking how can I calculate all 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.
How tight is the league?
Why does this difference between the leagues occur? As with trying to predict the outcome of a single game, there are many factors that make up this phenomenon, but there are just a few main factors that influence why one league should produce more home wins during 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 table and those at the bottom, this is often expressed as a “class difference”. This class difference varies greatly between different leagues with some leagues being much more competitive than others due to a closer level of skill across the league, ‘a tight league’. In the case of a tight league, the instances of draws will be more pronounced than in a “not so tight league” and home wins will most likely be of lower frequency.
So, let’s say we are interested in predicting a home win, armed with our new information on the “tightness” of leagues, we could make predictions for games in a season for as many leagues as we can handle and look at how those predictions perform in each league. You will find that the success of the predictions will correspond closely to the ‘tightness’ of a particular league, so where a particular league produces more home wins, then we will be more successful with our home predictions. Don’t be fooled, this doesn’t mean that just because there are more home wins we are bound to be more accurate, what I’m taking into account is a success rate in percentage terms of the number of predictions made at home which has nothing directly to do with how many actual home wins there are. For example, suppose we make one hundred home predictions in league A and one hundred in league B, and let’s say seventy-five percent are correct in the league but only sixty percent in league B. We made the same number of predictions in each league with different results, and these differences are most likely due to the “tightness” of each league. League B will be a “tight” league with more teams with similar levels of “class”, while League A has a wider class margin when it comes to the teams within it. Therefore we should choose the league with the best performance in terms of home wins and make our selections for home wins from that league.
We must be consistent
Obviously there is more to it than that. It’s not good to take every tip and record how it went, we have to apply the same rules to every tip we make. You need to make sure that the parameters you set for each predictive method you use (e.g. Rateform, Score Prediction, etc.) remain consistent. So choose your best settings for each method and stick to them for each prediction, for each league and for the whole season. You have to do this to maintain the consistency of the predictions within leagues, between leagues and over time. There is nothing to stop you using different sets of parameters as long as you keep the data produced by each separate.
If you are wondering what the parameters are, take the Rateform method as an example. Using this method we produce an integer representing the possible result of a match (I won’t go into detail about the Rateform method here as that’s the subject of another of my articles). It is possible to set breakpoints representing a home win and an away win, so if the resulting rateform output for a match is above the upper breakpoint, that match could be considered a home win. Similarly, if the resulting rateform output for a match is lower than lower breakpoint then that match could be considered an away win. Anything falling in between is considered a draw.
Do it for FREE or at a very low cost
So how do I get all this information without having to calculate it all myself?
Predictions.football has been providing this kind of information, week after week, on its website since 2015. It covers all 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 full record is kept of how each method is performed in each game. Much of this information is totally free to visitors to the site, but for a small subscription fee it is possible to access data from all eighteen leagues. In addition to the performance of each prediction within its respective league, Predictions.football also provides rankings of how each league has performed in successfully predicting match results. Prediction performance rankings are produced for home win predictions, draw predictions, away win predictions and general predictions and are valuable tools for the football bettor when deciding where to direct their European football predictions. You can visit the Predictions.football website using the link below: