Football Live Betting: Useful Tips for you to Know

The Internet has made it extremely easy to deal with live betting for everyone. As a result, more and more gamblers find themselves placing wagers in-play in order to be able to control the betting process and enjoy sports events at a maximum level. With this, some gamblers often wonder how to calculate live odds in order to get an edge over bookmakers. For soccer live betting, it is possible to apply the Poisson distribution.

Probabilities of this or that outcome to happen change as a referee blows the whistle. For instance, a draw is more likely if a game goes without goals over a long period. Though there are a few incidents that are able to influence the probabilities, it is goals that exert the greatest impact on live odds.

For the gamblers, it is extremely important to know pre-match odds for the event if they want to question live odds published by bookmakers following game incidents. In this situation, the Poisson model is the best variant because it allows to predict goals scored by a team under certain conditions.

Poisson Distribution & Live Odds Alterations

To better understand how it works, let us consider a football match between Team A (an underdog) and Team B (an obvious favorite). According to the teams’ previous performance, Team A is expected to score 0.85 goals, while the rival scores the average of 1.9 goals per game. Applying the Poisson approach reveals that the favorite has the probability of 65% to win, a Team A winning probability is estimated as 15%, while a draw has a 23%. These probabilities must reflect the odds offered by oddsmakers.

Every gambler should understand that goal scoring becomes higher as the time elapses. The thing is that footballers are losing their concentration, they become nervous and can make key mistakes. This idea is supported by statistics stating that about 55% of all the goals are scored after the referee whistles to denote the end of the break.

Now, it is necessary to know how to calculate a goal expectation for the rest of a game that witnessed a key incident. For this, it is necessary to apply a simple formula that is as follows:

Remaining Goal Expectation = Initial Expectation * Proportion of Time Remaining / Goals per Game

For instance, let us calculate the remaining goal expectation for Team A with the initial expectation of 1 after half-time.

Remaining Goal Expectation = 1(0.5 / 0.85) = 0.56

Here, we can see that the team has a 56% probability to score a goal until the match ends. To tell the truth, it is possible to calculate the figure at any point of a match, which is said to be rather profitable according to professional punters.

As for our example, let us imagine that the first goal is scored by Team A at the 21st minute of the game. The goalless game before this period resulted in decreased goal expectations for the both teams calculated according to the formula above and changed the probabilities for different outcomes. With Team A winning chances staying almost the same, the probability of drawing increased to 26%, while the chances of the favorite to win felt down to 61%.

So, this was the probability until a goal scored by the home team, which changed the situation significantly. Since then, it is necessary to use the decreased goal expectation that depends on the time left. If we ignore the new information, all the calculations will lead to the same results.

Now, the goalless remainder of 70 minutes will result in the win of Team A. That is why the new conditions of the game result in the increased winning probabilities for the team. To tell the truth, it rises to 42% from 16%. Here, you can see that even a single goal can have a huge impact on probabilities. To explain, the above-mentioned winning probability consists of the 16% chance of Team A to score the second goal and the 26% possibility to neither score nor concede a goal.

For further analysis, let us imagine that Team A manages to score the second goal at the 75th minute. With this, it is extremely interesting to analyze the chances of Team B to score at least two goals and make the comeback. Nevertheless, even scoring the first goal does not change the situation dramatically. Thus, Team A has the 70% winning probability shortly after the goal is scored at the 77th minute. With this, it can be noticed that the time left is more important for the Poisson distribution to calculate teams’ probabilities.

Conclusion

In fact, the Poisson approach can be used in order to make in-play betting more sophisticated and lucrative. To prove, home teams scoring the first goal of the game at the 21st minute have the average 70% probability to win the game. As you remember, however, we noticed that chances of Team A amounted to 42% only. It can be explained by the fact that Team A played against more skillful opponents, which decreased their chances to win.

The information stated above allows us to conclude that though the given approach is quite useful to analyze teams’ chances when betting in-play, it is impossible to use the Poisson distribution in complete isolation. Paying attention to external factors and teams’ current form will contribute to more successful results.