Date: 08.09.2022     Day: Thursday

League: EUROPE Europa League – Group Stage
Match: PSV  vs  Bodo/Glimt
Odds: 1.50     Fulltime 1:1


Date: 08.09.2022     Day: Thursday

League: EUROPE Europa Conference League – Group Stage
Match: Fiorentina  VS  RFS
Odds: 1.20          Fulltime 1:1


Date: 08.09.2022     Day: Thursday

League: EUROPE Europa Conference League – Group Stage
Match: Villarreal  vs  Lech Poznan
Odds: 1.20    Result: 4:3


Date: 08.09.2022     Day: Thursday

League: EUROPE Europa Conference League – Group Stage
Match: Basel vs  Pyunik Yerevan
Odds: 1.50    Result: 3:1

FREE FIXED MATCHES 1/2  [email protected]
WhatsApp number: +46 73 149 05 65


best accurate source for matches

FREE FIXED MATCHES 1/2. The Expected Value of a bet shows us how much we can expect to win (on average) per bet. Is the most valuable calculation a bettor can make during comparing bookmakers odds. How can you calculate Expected Value in FREE FIXED MATCHES 1/2 in order to predict your winnings ? Read on to find out.

A simple example of Expected Value (EV) put into practice. If you were to bet $10 on heads in a coin toss, and you were to receive $11 every time you got it right, the EV would be 0.5.

This means that if you were to make the same bet on heads over and over again, you can expect to win an average of $0.50 for each bet of $10.

How to Calculate Expected Value of FREE FIXED MATCHES 1/2

The formula for calculating Expected Value is relatively easy.  Simply multiply your probability of winning with the amount you could win per bet, and subtract the probability of losing multiplied by the amount lost per bet:

(Probability of Winning) x (Amount Won per Bet) – (Probability of Losing) x (Amount Lost per Bet)

To calculate the expected value for FREE FIXED MATCHES 1/2, you can fill in the above formula with decimals odds with a few calculations:

  • Find the decimal odds for each outcome (win, lose, draw)
  • Calculate the potential winnings for each outcome by multiplying your stake by the decimal, and then subtract the stake.
  • Divide 1 by the odds of an outcome to calculate the probability of that outcome.
  • Substitute this information into the above formula.


Gambling big sure odds 

For example, when Manchester United (1.263) play Wigan (13.500), with a draw at 6.500, a bet of $10 on Wigan to win would provide potential winnings of $125, with the probability of that happening at 0.074 or 7.4%.

The probability of this outcome not occurring is the sum of Man Utd and a draw. The amount lost per bet is the initial wager – $10. Therefore the complete formula looks like:

(0.074 x $125) – (0.946 x $10) = -$0.20

The EV is negative for this bet, suggesting that you will lose an average of $0.20 for every $10 staked.

How Does FREE FIXED MATCHES 1/2 for Sports Betting Help?

Remember, a negative EV doesn’t mean you’re going to lose money. Unlike a coin toss, sports betting odds are subjective, and therefore if you outsmart the bookmaker, you’re likely to make money.

betting correct scores

If you calculate your own probability for a match that differs from the implied probability of the odds, you could see where to find a positive EV, and therefore the best chance to win.

For example, while the odds imply that Wigan only have a 7.4% chance of winning. If you calculate that Wigan has a 10% chance of winning then the EV for betting on a Wigan win jumps to $3.262.

It’s also a perfect measure such as comparing odds in arbitrage betting. Which is discussed in our article What is arbitrage betting.

Calculating the EV of bets gives bettors more information about the value of their bookmaker. While low-margin bookmakers like bet365.com have EVs of around -$0.20, it’s not uncommon for typical bookmakers to have an EV of -$1.00 – for every $10 stake you would be likely to lose a $1.




Date: 24.08.2022     Day: Wednesday

League: DENMARKLandspokal Cup
Match: Helsingor  vs  Lyngby
Tip: Over 2.5 goals
Odds: 1.65    Result: 2:0

correct scores agreed results  [email protected]
WhatsApp number: +46 73 149 05 65


Gambling sure fixed matches

The ACCURATE SINGLE FIXED MATCHES  are a hot topic in the soccer community. What is an expectation in goals and how can this stat be used to improve analysis ? Read on to find out everything you need to know about the expected goals stat.


Expected goals (xG) – the number of goals a team or player would be expected to score base on the quality and quantity of shots taken.

Expected goals per 90 (xG/90) – Expect goals per 90 minutes played by a specific player.

Non-penalty expected goals (npxG) – Total expect goals minus expect goals from penalties.

Expected goals for (xGf) – The number of goals a team expect to have scored based on the quality and quantity of shots taken.

Expect goals against (xGa) – The number of goals a team expect to have concede based on the quality and quantity of shots they have taken.

Expect goals assisted (xA) – The number of assists a player expect to have made based on the quality and quantity of the shots taken directly from their passes.

Expected points (xPts) – The number of points a team is expected to have won base on expect goals data.

What is expected goals? Expect goals explained

Expected goals is a metric which assesses the chance of a shot becoming a ACCURATE SINGLE FIXED MATCHES. It provides a good way to judge the quality of shots since a shot with a 0.4 expected goal (xG) value should be score 40% of the time. An xG is the maximum value which signifies that a player has a 100% chance of scoring. Depending on the model, several factors are taken into account while evaluating expected goals.

Betting best safe soccer matches


Expected goals can be beneficial because they increase the sample size used when analysing soccer. Soccer is a low-scoring game and goals are a rare event. As a result, pure goals data can sometimes be misleading.

There are numerous examples every season where the team who created more chances ultimately loses the match. Basic goal data has trouble reflecting this and may be unrepresentative of the actual game as a result. Expect goals take chances into account by calculating the number of goals that, on average, are score from each position.


Prior to expect goals, metrics like total shots or shots on target were use to attempt to analyse games. Like goals, these stats can be misleading. Total shots count an attempt from the halfway line as equal to a shot from inside the six-yard box.

During the 2014/15 Bundesliga season Borussia Monchengladbach’s Granit Xhaka and Mainz 05’s Yunus Malli took a similar number of shots per 90.

Despite taking more ACCURATE SINGLE FIXED MATCHES per 90 minutes played, Xhaka was unable to match Malli’s xG output. Malli scored 6 goals to Xhaka’s 0 despite playing fewer minutes. Malli primarily shot from close range with 70.7% of his attempts taken from inside the penalty area. In contrast, 64% of Xhaka’s were from outside the box.

Malli shot less than Xhaka but from better locations. Expected goals shows the higher value of these attempts and provides a quick and easy way to factor. The players shot locations and types into the data. This allows for a better analysis of the difference between the two players.

Agreed football results 

Factors Used To Determine Expected Goals

As discussed above, the location of the shot is a big part of a shot’s xG rating. We take several factors into account since matches have become more sophisticated than ever before and our team aims to make them more accurate than any other information provider.

Some models now factor in everything from the body part used to take the shot through to defensive positioning, attack speed and where the first possession of the attack started.

Which expected goals model is most accurate?

There is some debate as to which expected goals model is most accurate. Fortunately, fixedmatch.bet have an article discussing the merits of different expected goals models.


.A good example of how a basic goals analysis can be misleading is the August 2017 Premier League game between Arsenal and Stoke. The 1-1 result suggested the game was evenly match. It is evident that using this draw as a predictor of future Premier League success would be problematic.

Using expected goals analysis we can see that in the long-run Arsenal could expect to win this game 55% of the time. This is much more useful for predicting future performance since we can minimise the effect of scoreline variance. Using this data it is clearer that Arsenal are likely to perform better than Stoke in the long-run