**BEST CORRECT SCORE FIXED MATCHES**

**BEST CORRECT SCORE FIXED MATCHES**

**BEST CORRECT SCORE FIXED MATCHES**

**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**

**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

*. Betting Resources already has many articles covering Expected Value or EV, so let’s just summarize*

**beating all odds***briefly with an equation:*

**BEST CORRECT SCORE FIXED MATCHES**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%.

*are only interest if the expect value is greater than 0%, or when the bookmaker offers odds that are longer than the*

**Serious bettors***.*

**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**

**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

*, I divided fixedmatches.cc ’s initial odds by their closing odds, having removed their margin from the closing odds, and subtracted one.*

**betting odds**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**

**Best correct scores soccer games**

How meaningful is** BEST CORRECT SCORE FIXED MATCHES**?

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.