Date: 30.09.2022     Day: Tuesday

League: PORTUGAL Liga Portugal
Match: Benfica vs Pacos Ferreira
Odds: 1.55  Halftime 2:1 / Fulltime 3:2


Date: 30.09.2022     Day: Tuesday

League: ITALY Serie A
Match: Inter  vs  Cremonese
Odds: 1.60  Halftime 2:0 / Fulltime 3:1


Date: 30.09.2022     Day: Tuesday

League: ITALY Serie A
Match: AS Roma vs Monza
Odds: 1.30    Result: 3:0

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Rigged football matches 1×2

With 100 WEEKEND PREDICTION TIPS forecasting systems mainly based on past performances, understanding regression to the mean is crucial for sports bettors. The 2015-16 EPL season has been no short of surprises so far, raising the question: Are extreme outcomes sustainable? Here’s what statistics have to say about it.

When dealing with random, or mostly random, systems, variables that are more extreme on an initial measurement show a tendency to be less extreme on a second measurement. This phenomenon is called regression toward the 100 WEEKEND PREDICTION TIPS.

Leicester’s performance during the first of the 2015/16 Premier league season, for example, might gain it a higher team rating than that for Chelsea, who have performed far worse during the same period relative to 100 WEEKEND PREDICTION TIPS. But if much of what contributed to their respective team ratings arose as a consequence of chance factors, the phenomenon of regression to the mean would imply that those ratings might not be sustainable going forward.


One way to measure the performance of a team is to see how it has performed relative to market expectation. For example, if the odds of a team winning are 2.00, this implies that the market believes it has a 50% chance of victory (discounting the influence of the bookmaker’s margin). If it wins, it has overperformed relative to market expectation; if it fails to win, it has underperformed.

Such an approach is qualitatively similar to the Brier Score method, which measures the extent to which a team deviates from what the odds imply.

Betting best soccer matches

The main difference is that it allows us to measure the direction, as well as the magnitude, of the deviation from expectancy. Let’s see how Leicester and Chelsea have performed relative to fixedmatches.cc’ expectation over the first 20 games of the 2015/16 Premiership season. For every game a team wins, it receives a risk adjusted score equal to [1 – 1/odds], whilst for every game it fails to win, it receives a score of [-1/odds].

As the season progresses, these scores are summed cumulatively. The tables below reveal that Leicester has performed far better than fixedmatches.cc betting market expected them to achieve, whilst Chelsea has performed far worse.

How much is performance explained by luck?
100 WEEKEND PREDICTION TIPS      A question now arises: should we expect Leicester’s overperformance and Chelsea’s underperformance relative to market expectations to continue? If these trends were largely a consequence of causal factors like player ability and managerial style, then we might expect little regression back towards market expectation; at least not until the market had fully re-evaluated the teams’ new skill levels. If, on the other hand, they were largely a consequence of luck, regression towards the mean should be more rapid and complete.

To determine how much influence regression to the mean, and by implication luck, has on the outcome of soccer matches, we break our data into two halves – the first and second halves of a season – and compare the two. If regression to the mean is small, we would expect extreme performance in the first half to more readily correlate with similarly extreme performance in the second half.

Gambling 100% sure matches

That is to say, performance would show persistence. Alternatively, if regression to the mean is significant, extreme performance in the first half should show little correlation with extreme performance in the second half.

The chart below illustrates this correlation for English football teams from the Premier and Football Leagues over the 2012/13 to 2014/15 seasons. Each of the 276 data points depicts a first half-second half performance pair for each team during a single season. The dark line represents the average trend of the data points.

Correlation of 1st v. 2nd half season performance

As you can see, there is virtually no correlation and an almost perfect regression to the mean. The value of R2 in a correlation plot like this defines how much the variability in one variable accounts for the variability in the second variable.

A figure of 1 implies perfect correlation whilst a figure of 0 implies no correlation at all. Here we can see that the variability in first half season performances explains virtually none of the variability in the second half season performances, implying there is no causal link between the two, and that deviation away from market expectation is essentially a matter of luck.




Date: 26.08.2022     Day: Friday

League: GERMANY3. Liga
Match: Hallescher  vs  Meppen
Tip: Over 2.5 goals
Odds: 1.75    Result: 1:1

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Accurate agreed football matches

How to Analyze odds using Expected Value

This article will help you understand why getting our PAID fixed matches will help you in beating all odds. Betting Resources already has many articles covering Expected Value or EV, so let’s just summarize BEST CORRECT SCORE FIXED MATCHES  briefly with an equation:

Expected Value = (Bookmaker’s odds / True odds) – 1

If the true odds are 2.00 and the bookmaker’s odds are 2.10, this means the EV is 0.05, or 5%. If the true odds are 4.00 and the bookmaker’s odds are 3.50, the BEST CORRECT SCORE FIXED MATCHES is -0.125, or -12.5%. Serious bettors are only interest if the expect value is greater than 0%, or when the bookmaker offers odds that are longer than the true odds.

Of course, knowing what the true odds are is another matter altogether. Sports betting is not like dice or roulette and the markets are complex systems, for which there are no simple algorithms that will tell you what the true outcome probabilities are. We have to estimate them via data modeling and perhaps a bit of intuition as well. The better your model, the closer your estimate BEST CORRECT SCORE FIXED MATCHES will be to the true probabilities.

fixedmatches.cc model is a combination of their own data analysis and the market information they acquire about their customers’ models, meaning it’s one of the best at estimating true probabilities. Of course, it won’t always be right, but on average it’s surprisingly good. For example, about 50% of their prices of 2.00 (after you’ve removed their margin) win and 25% of their prices of 4.00 win.

Profitable soccer matches

Individually, it’s impossible to know which odds were closer to the true odds and which were not so close, but on average, the errors are broadly cancelled out.

How to apply Expected Value to soccer matches

As the closing odds before a soccer match starts contain more information about the match than the odds when fixedmatches.cc first published them, on average the closing odds are closer to the true odds than the initial ones.

We might propose that the amount by which the odds move provides a measure of how much Expected Value there was available in the initial odds. This is not to argue that the closing odds are always correct, nor that the initial odds are always incorrect.


Instead, this means that the ratio of the two can be used as a proxy measure of Expected Value, given that we can’t know what the true odds are. This can be formulated as follows:

Expected Value = (First odds / Last odds) – 1

Naturally, sometimes both the BEST CORRECT SCORE FIXED MATCHES and the closing odds will be shorter than the true odds.

With this assumption in mind, how much Expected Value exists in soccer betting markets ? For a sample of 158,092 soccer matches and 474,276 home-draw-away betting odds, I divided fixedmatches.cc ’s initial odds by their closing odds, having removed their margin from the closing odds, and subtracted one.

So, how many odds had Expected Value greater than 0%? The figure was 29.7%. That’s actually quite a surprise, as it means nearly a third of fixedmatches.cc bet’s initial odds actually hold some Expected Value.

Best correct scores soccer games


We might expect there to be more occurrences of low Expect Value (e.g. 2%) than high Expected Value (e.g. 20%). But how much more ? I ranked the size of the expected value for each bet in ascending order, then calculated a cumulative percentage.. For example, 29.7% of odds held EV greater than 0%, but this falls to 21.7% for EV greater than 2%. This is how such a trend appears on a graph:

Evidently, there is a lot of expect value to be find in the soccer match betting market. However, don’t expect a lot of big gifts.

Of course, the much bigger problem for bettors is knowing the expected value is there in the first place. Using this proxy measure, you won’t know that it was there until fixedmatches.cc. I have published their closing odds and by then, the initial odds have gone.