Learn the best picks and tips from hundreds every week:
Many soccer picks and tips sites (soccer for our American friends) provide only a few picks/tips a week, some just one, and many charge huge amounts for the privilege. In this article, I’ll show you how to get the best of hundreds of free and low-cost picks and tips every week by answering these four questions.
What if you could pick the absolute best picks from hundreds of weekly picks/tips that would greatly increase your chances of success?
What if those teams/councils are chosen based on the past performance of similar teams/councils and all those picks/forecasts are created by a combination of various proven statistical methods?
What if you could find out if draw predictions, home predictions or away predictions are most successful for English Premier League, Italian Serie A, German Bundesliga or many other leagues across Europe?
What if you could do it all for FREE or at very little cost?
Well now you can. If you are interested, keep reading.
Some tips are better than others:
Using well-established statistical methods in conjunction with automated software, it is possible to generate hundreds of soccer forecasts each week for many leagues; in theory, it could cover all the major leagues in the world. So why would you want to do that? Surely a lot of the advice will be wildly inaccurate but on the other hand, a lot of it will be correct, so how can you determine which one will be successful and which one will not? It would be much better to concentrate on one or two games and predict their outcome through intensive and careful analysis.
At first glance, the previous responses I’ve seen over the years have some merit and deserve careful consideration, there’s a good case for focused analysis of a single match with the aim of trying to predict its outcome. Consider this though, when a scientist performs a statistical analysis, how many data items do they select as a representative sample? One, two… or more? When performing statistical analysis, the more data you have to work with, the better the result will be. For example, if you want to calculate the average height of a class of school-age children, you might take the top two or three as your sample. But if they’re all six feet tall, they’re going to be very unrepresentative, so obviously you’d take all of their heights and average them, the result is a much more accurate answer. It’s a simplistic example, but I hope you get my point. You can obviously apply that argument to a single match by collecting each side’s past results and carrying out statistical analysis techniques using that data, but why restrict your analysis to that match?
We know that if we make hundreds of automated suggestions, based on tried and tested statistical methods, some will be successful and some will not. So how do we target the best advice, the ones most likely to be correct, and how do we do it week after week? Well the answer is to keep track of the performance of each and every tip, some tips are better than others and we want to know which ones. At this stage, if you’re thinking about how I can 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.
The results are not always the same:
Simply keeping track of how each of the hundreds of tips we make actually performs against the bottom line isn’t enough, what we need now is a way to analyze that data and logically group it to get the best out of it. The outcomes are not always the same, in other words, a forecast that shows a possible outcome for match A and the same possible outcome for match B will not necessarily produce the same outcome (i.e. a correct prediction or an incorrect prediction). . Why is this? Well, there are hundreds of reasons why you will never be able to explain them all, if you could, you would certainly be a millionaire. When trying to predict the outcome of a match, you can look at things as qualitative as each team’s current injury list, team sheet, player morale, etc. We can also look at quantitative factors using our statistical methods to predict the outcome of the match, so we can look at things like past performance, league position, or more tried and tested statistical methods like 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 outcome, part of the reason for this is, as explained above, that we cannot account for all factors in a party, it is impossible. But there is something else, something we can explain that we haven’t thought of yet.
When we look at a match in isolation, we only look at the factors related to each of the two teams in the match, but why not expand this to see how the other teams they have played against are performing? ‘Why would we want to do that?’ I hear some of you say. Because the results are not always the same. Let’s say our prediction for Game A and Game B is a home win (forgetting the predicted score for the moment). What else can we take into account to improve the forecast of a home win? We can look at the performance of all home win predictions made for the same competition in which the match is being played and then make a judgment based on that new information. This is great as it gives us an extra level of factoring to take into account that we didn’t have before.
Checking 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 this further. We can do this by doing the exact same drill in many different leagues and getting a success rate for each league. This means that we can now search for the league that produces the best overall home win prediction success rate and search for home win predictions for upcoming matches. By default, we know that that league is more likely to produce a successful result for a home prediction than any other. Of course, we can employ this technique for away wins and tie predictions as well.
How tight is the league?:
Why does this difference between the leagues occur? Just like when it comes to predicting the outcome of a single game, there are many factors that make up this phenomenon, but there are only a few important factors that go into why a league should produce more home wins during one season than another. The most obvious of these could be described as the ‘tightness’ of the league. What do I mean by ‘tight’? In any league, there is often a gap in the skills and abilities of the teams that are at the top of the league and those that are at the bottom, this is often expressed as a ‘difference in class’. This class difference varies markedly between different leagues, with some leagues being much more competitive than others due to a closer level of abilities across the league, “a tight league”. In the case of a close league, instances of tied games will be more noticeable than with a ‘not so close league’ and home wins will probably be less frequent.
So, suppose we’re interested in predicting a home win, armed with our new information on the ‘resistance’ of leagues, we can make predictions for games over the course of a season for as many leagues as we can manage, and see how those predictions work in each league. You will find that the success of the predictions will closely match the “fit” of a particular league, so when a particular league produces more home wins, we will be more successful with our home predictions. Don’t be fooled, this does not mean that just because there are more home wins we are forced to be more accurate, what I am talking about is a success rate in percentage terms of the number of home predictions made that has nothing to do directly with with seeing how many real home wins there are. For example, let’s say we make one hundred home tips in league A and one hundred in league B, and let’s say seventy-five percent are correct in league A but only sixty percent in league B. We’ve done the same number of picks in each league with different results, and those differences are probably due to the ‘narrowness’ of each league. League B will be a ‘restricted’ league with more teams with similar levels of ‘class’, while League A has a wider margin of class when it comes to the teams within it. So we need to pick the best performing league in terms of home wins and make our home win picks from that league.
We have to be consistent:
Of course there is more than that. It’s not good to just take every suggestion and record how it performed, we have to apply the same rules to each and every suggestion made. You must ensure that the parameters you set for each predictive method you use (eg Rateform, Score Prediction, etc.) remain constant. So choose your best settings for each method and stick to it for each and every prediction, for each league and for the entire season. You should do this to maintain consistency of predictions within leagues, between leagues, and over time. There’s nothing stopping you from using several different sets of parameters, as long as you keep the data produced by each one separate.
If you are wondering what the parameters are, take the Rateform method as an example. Using this method, we produce an integer that represents the possible result of a match (I won’t go into detail about the Rateform method here, as that is 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 rate shape output for a match is higher than the top break point, that match could be considered a home win. House. Similarly, if the resulting rate out-of-form for a match is lower than the lower cutoff, then that match could be considered an away win. Anything that falls in between is considered a tie.
Footyforecast.com (now 1X2Monster.com) has provided this type of information, week after week, 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 just a few. A total of seven different statistical methods are used to determine the result of each match played in each league, and a full record is kept of how each method is carried out in each match. In addition to how each forecast performed within their respective league, Footyforecast also provides the leaderboards for how each league has performed in successfully predicting game outcomes. Prediction Performance League Tables are produced for Home Win Predictions, Draw Predictions, Away Win Predictions and for General Predictions and are invaluable tools for soccer punters when deciding where to target their European soccer predictions. .
So there you have it. I hope I’ve shown you how to target the best leagues to increase your chances of success when predicting 1X2 results and while I make no guarantees, I’m pretty sure this method will improve your profits.