BEST CORRECT SCORE FIXED MATCHES

*BEST CORRECT SCORE FIXED MATCHES*

*BEST CORRECT SCORE FIXED MATCHES*

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**Date: 09.10.2022 Day: Sunday**

**League: FRANCE Ligue 1**

**Match: Rennes vs Nantes**

**Tip: HOME WIN
**

**Odds: 1.55 Fulltime 3:0**

**Date: 09.10.2022 Day: Sunday**

**League: KAZAKHSTAN Premier League**

**Match: FC Astana vs Ordabasy**

**Tip: HOME WIN
**

**Odds: 1.45 Fulltime 6:0**

**Date: 09.10.2022 Day: Sunday**

**League: ITALY Serie A**

**Match: AS Roma vs Lecce**

**Tip: HOME WIN
**

**Odds: 1.40 Fulltime 2:1**

**Date: 09.10.2022 Day: Sunday**

**League: NETHERLANDS Eredivisie**

**Match: Utrecht vs AZ Alkmaar**

**Tip: OVER 2.5 goals
**

**Odds: 1.70 Fulltime 1:2**

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**Correct scores big odds **

**Correct scores big odds**

With * BEST CORRECT SCORE FIXED MATCHES* As a bettor are you aware that you can use standard deviation to predict

**outcomes ? Find out what the standard deviation is, how to calculate it and apply it to your**

*sure betting***.**

*betting correct scores* In ** BEST CORRECT SCORE FIXED MATCHES**, we explained why

**should not solely rely on the average, given its tendency to be influenced by outliers, and its inability to show the dispersion within a set of numbers.**

*professional bettors* A quantity expressing by how much the value of a group differ from the mean value for the group. Different metrics are either used directly or are input parameters for a function or distribution of **BEST CORRECT SCORE FIXED MATCHES. **

Poisson vs. Normal Distribution

For example, ** vip bettors** are known to use a Poisson distribution model to predict the number of goals score per team in a

*. However, this distribution has just one input parameter – the average – and is a discrete distribution – produces outputs as whole numbers.*

**sure soccer game** Predicting goals spread in the **Premier League**

As a test case let’s look at game goal difference in soccer. The goal difference per match seems to be normally distribute. The goal difference is the number of goals score by the home team minus the goals score by the away team, with a zero resulting in a draw.

**Betting fixed matches **

**Betting fixed matches**

Lets look at the data from the 2013/14 Premier League season:

- Man City recorded the biggest home win – 7-0 against Norwich
- Liverpool’s 5-0 win at Tottenham was the biggest away victory
- The average goal difference was 0.3789 (median & mode = 0)
- The standard deviation was 1.9188.

A number of conclusions can be take from the data. Primarily the most popular goal difference is a draw, and the distribution is close to symmetric, with a favour towards home wins. However, our focus for the article is the standard deviation.

Calculating *BEST CORRECT SCORE FIXED MATCHES *

The normal distribution uses the two parameters (average and standard deviation) to create a standardised curve. In this, around 68% of the distribution lies within one standard deviation away from the mean, and 95% lies within 2 standard deviations.

In this case we expect 68% of games to end up between -1.5399 and 2.2977 goals (i.e. 0.3789 + 1.9188). The continuous nature of the curve does have its limitations: -1.5399 goal difference is not possible.

**Rigged soccer games**

**Rigged soccer games**

In order to estimate a home win by a goal difference of 1. 1 can be move from a discrete (whole) value of 1, to represent the continuous range between 0.5 and 1.5. For each value we can then calculate its difference from the mean in terms of standard deviations.

*BEST CORRECT SCORE FIXED MATCHES *

The great thing about this is that we can now remodel the normal distribution as shown. In this case we’d need to find the area of the region shaded in orange.

The area shade in blue, showing the probability of less than 1 goal (or its continuous equivalent being less than 0.5 goals) can be find to be 52.15%.

While it is not the aim to delve deep into the BEST CORRECT SCORE FIXED MATCHES of this. It can be find using most spreadsheet software (in MS Excel: =NORM.DIST(0.5,0.3789, 1.9188,1). Similarly the probability of under 1.5 goals is 72.05%. Therefore we expect 19.53% between these two values.

Consequently out of 380 matches, we would have estimated 74.22 games ending with the home team winning with just one goal difference. In reality there were 75 games, so this was very close.