Market Study “Both Teams to Score”

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In order to detect valuable odds e thorough study is necessary, and this method is based fundamentally in statistical study.

To get you to know my method I think it’s easier to explain through an example.

With that said, the example I’m going to use is the match between Marítimo – Paços de Ferreira from the last fixture of the Liga ZON Sagres 11/12.

To calculate a probability (or give a better probability, better this way) of the event “Both Teams to Score” of a match, we have to give a probability that we think is plausible to each team to score. With that said, my method starts here.

1 – Calculating the probability for Marítimo (visited) to score one goal to Paços de Ferreira (visitor), by pure statistic

Note again, that the probability result for this first step is just based in pure statistic.

To calculate this first probability I have attention to two data: the percentage of games in which Marítimo scored goals as visited and the percentage of games in which Paços de Ferreria conceded playing as visitor.

Market Study “Both Teams to Score”
Analyzing the table above, Marítimo scored in 79% of the games played at home (100-21) and Paços de Ferreira conceded goals in 86% (100-14) of the games away counting for the Liga ZON Sagres.

To calculate the probability of Marítimo to score in this match, I then make an average between both referred probabilities. Therefore:

Marítimo probability (visited) to score one goal to Paços de Ferreira (visitor), by pure statistic: 82.5% [(79+86)/2 = 82.5]

2 – Calculating the probability for Paços de Ferreira (visitor) to score one goal to Marítimo (visited), by pure statistic

This step is equal to 1st, so:

Paços scored In 57% of his games playing as the away team and Marítimo conceded goals in 71% of his games while playing at home. Then:

Paços probability (visitor) to score one goal to Marítimo (visited), by pure statistic: 65%.

Of course, the reliability of these first two steps will as big as the number of the games made by each team on the competition grows bigger (Law of Large Numbers), as well as having to take in consideration which were the opponent’s they both faced.

As we are in the last fixture, this is a perfect match to verify the “Both Teams to Score” probability, just by counting the home and away form throughout the season from each team.

Therefore: 0.825 x 0.65 = 0.536 = 53.6%

3 – Other variable analysis

From here on, the thing starts to become more subjective. Obviously, it’s not enough to look at the rankings and statistics to reach practical conclusions about the teams and therefore, it’s necessary to make a study in the variables that can affect our prognostic.

In the goals market, the more preponderant are:

  • Absences or returns from important players

In the case were a team is with a lot of injured players, and these players are preponderant in the offensive maneuver of the team, then the team will have an expected worse offensive performance, and that way, its probability to score on the match in question will be reduced.

In the case where that team has also important players absent in the defensive maneuver of the team, then it’s expected that the opponent team has a bigger probability to score.

There can also be situations when there are returns of important players that were absent for too much time, that can also improve the performance of the team we are studying.

In this case, Paços is in full strength with all the team available and Marítimo, despite having summoned all the available players, has Roberge (almost always in the starting team), Fábio Felício and Pouga injured.

  • Teams form

Despite a long term review of both teams, a short term analysis is as, or so important.

A team can have an excellent record in terms of goals and yet be in a results or goals crisis, and when that happens, the probabilities calculated in steps 1 and 2 will lose value.

For this match, Marítimo has had regular performances, so in the short term analysis for the islanders is consistent with the long term. On the other hand, the same doesn’t happen with Paços team.

Since Henrique Calisto got ahead of the team, the beavers have been presenting a more confident football, and although Paços has scored only 57% out of home, the fact is that the most part of those negative results happened before the actual coach, when Paços was the red lantern of the championship.

Although in the last match out of home they weren’t able to score, Paços managed to score playing as the away team for 4 consecutive times in the games before.

  • Weather and turf status

If there’s information about the turf state, the worse its state, the lower the probabilities to score goals.

If heavy rain is expected, it can make the pitch heavier, so it’s more difficult to score goals. Excessively negative or high temperatures can also bring the match to a slower pace (and in the first case, even a frozen turf), meaning, less goals.

The good conditions of the turf and the weather forecast to Madeira don’t seem enough to me to influence the match.

  • Team breakdown

If the difference of value between the teams is too high, then the probabilities for the stronger team to score, or go up, or keep the same, although, the probability for the weaker team to score can be lower.

This parameter, in my opinion, only makes sense if we analyze matches between a very strong team and a very weak team (most of whose previous games were against teams of similar level), or in confronts between teams of different leagues and with significant difference of quality.

Despite the difference in the table, I don’t consider there’s a big gap of quality between Marítimo and Paços.

  • Statements by coaches, players and managers

They may have little meaning, but many of the times the intervenient statements about the game can reveal what is the expectation to the game and the way they will approach it.

Henrique Calisto told the press that he expects an “open game”, so I believe we can infer through this statement that the probability to have goals will be higher.

  • Players motivation

If the teams fight or not for objectives, if the game is decisive, or if they need several goals to change the classification by goal average, or if the draw is a good result to both teams, for example. The lower the importance of the match, the greater is the probability for the match to have more goals.

As both teams aren’t fighting for objectives, both teams will play relaxed, and for that the probability to have goals from each side will be bigger.

  • Another curious or important data

4 – Reconcile between statistical analysis and theoretical (subjective) and assign an odd

The objective of this step is to assign a percentage that we think is fair to the event we are studying, through reconcile of the 3 previous data.

Here, the weight each one gives to the facts studied is relative, and the more accurate is the “measuring”, better will be our performance in the long term.

As I am not very experienced in the matter, I will make the distribution of the data my way and, if you don’t agree with something, comment. Attention this is the most important part, but also the most debatable.

  • Roberge isn’t playing for Marítimo, fundamental piece in the defense for most of the matches played by Marítimo. For that I raise the probability of Paços to score by 1%;
  • Paços recent form makes me raise their probability to score in 5%;
  • The statements of Paços coach make me believe that the probability of Marítimo and Paços to score increase by 2.5%, due to the fact that this match is of low importance to both.


Probability (assigned by me) for Marítimo (visited) to score one goal to Paços de Ferreira (visitor): 85%

Probability (assigned by me) for Paços (visitor) to score one goal to Marítimo (visited): 73.5%

Probability (assigned by me) of both teams to score: 62.5%

My odd: 1 / 0.625 = 1.60

5 – Check if there are value bets for the event that we studied

If the plates of the balance were well weighed, that is, if we feel that the percentage of both teams to score is fair (here yes, is the difficult, because only with lots of skill and experience can we know if our predictions stay true to reality), the next step is to look for value bets in various bookies.

If the odd offered by the bookies is similar to the ones we calculated or lower, then it isn’t worth to bet as the bet has no value. But if the value of the odd offered is bigger (preferably, considerably bigger, because we can have judgment errors in the calculations we did) then we take advantage and bet.

Of course we will not always hit, but I’m convinced that if we use this method correctly, at the end of many bets there will be profit.

At this moment, Bet365 offers the odd at 1.80 for the referred event. As our odd, the one we consider fair is 1.60, then, through my analysis, this is a value bet. 

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