How to Predict Football Results
| Updated: November 6, 2025
It is hard to think of a spectator sport more popular than football. Day in and day out, soccer fans take time to watch and discuss matchups. Interestingly, many football devotees get a real kick out of predicting match outcomes. Some do this for fun, while others aim to pocket serious cash.
Ever since the first bookmakers appeared, gambling enthusiasts have done their utmost to find ways to predict football results. Although placing a stake and occasionally winning can be easy, if you want to make money consistently, you must sharpen your skills. That is why here we will introduce one prediction model that can help you rate teams properly and predict results as accurately as possible.
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How to Start with Predicting Football Results
Fans who are about to embark on a sports betting adventure should consider several factors before they start predicting soccer results. When evaluating the outcome of a matchup, it is wise to look at possession, goal differential, shots on goal and on target, and the location of those shots.
In addition, bettors should ask themselves whether the information they use is accurate and readily accessible. The sample size is crucial because it helps you make accurate assessments. Gambling enthusiasts should note how many matches the team in question has played to get a clear picture of its overall performance.
The good news is that today a wide variety of websites provide the necessary data, so you can find everything you need in just a few clicks.
Your technical aptitude also matters when you try to make correct predictions about how a matchup will progress.
Ultimately, let’s return to the data you should consider when making your predictions. One of the first factors to consider is the home-team edge. Whether the home team holds an advantage is one of football’s most debated issues. Many knowledgeable bettors believe that, in most cases, the home team has a better chance to win. Some go even further, claiming certain teams enjoy a greater home-ground edge than others. Others argue that such differences only appear when the sample size is too small and that over a larger number of matches the edge evens out.
Secondly, bettors should always review possession data for the team they plan to back. However, focus on the quality of possession rather than its duration. This can be difficult because the measure is subjective. For example, if your chosen team spends most of the match in the opponent’s penalty area but fails to score, that says a lot about its overall performance. That is why it might be wise to use this information when calculating goal expectancy.
The next thing football bettors should invariably take into consideration is goal differential. It is widely used to gauge a team’s strength because it reveals much about its potential. Again, sample size is essential; with too few events, conclusions can be misleading. Heavy favorites will not win all the time, and underdogs will not always lose. Soccer contains a great deal of randomness, which is exactly what makes it such an alluring sport for gambling.
You might now ask how many matches you need to include to gauge a team’s quality. It is hard to give a precise number, which can hamper decision-making. In some cases a sample of 30 matches is enough to draw conclusions about the team’s quality, but it may not reveal its fluctuations.
Shots on goal are another useful metric, though they are not perfect because no two shots are identical. The likelihood that a shot becomes a goal varies dramatically. Still, shot counts reveal much about a team’s performance and the flow of the match. To improve accuracy, do not focus only on the final score. Thus, if Newcastle has won 2:0 against Sunderland but the shot ratio is 3:8, you might rely more on the shot data than on the final result.
Poisson Distribution
Whether you are a recreational bettor or aim to win serious cash, you want reliable results. One key concept to focus on is goal expectancy. As the name suggests, it is the number of goals that might be scored in a given match, based on each team’s ability to score and to concede. Bettors should consider these values for both the team they back and its opponent. If you calculate goal expectancy accurately, your chances of consistent returns improve.
In short, your success depends on how accurate your forecasts are. Once you have made your predictions, you will need to turn them into odds to decide on the best course of action. That is why we will next focus on the Poisson distribution.
If you have not heard of the Poisson distribution, it is a method for estimating the probability that a set of events will occur within a given time frame. You can estimate these probabilities as long as the events occur at a constant rate. In football betting, the Poisson distribution helps you estimate the likelihood of every possible score line, provided you know each team’s goal expectancy. Once you master this method, your performance can improve dramatically.
You achieve this by converting averages into probabilities, which is not a daunting task. If you know how often an event occurs on average, you can estimate how much other outcomes will diverge from that average.
First, estimate the average number of goals each team might score during the match. To do this, evaluate each team’s offensive and defensive strength. Pay special attention to the time period you use for these calculations; if the period is too short or too long, your conclusions become more vulnerable to outliers.
Now, let’s calculate offensive and defensive strength. Start by calculating the average number of goals scored during your chosen period. To get the average number of goals scored at home during the season, divide the total home goals by the number of home games played. Similarly, to estimate the average goals scored away, divide total away goals by total away games.
Now, let’s assume that home teams scored a total of 567 goals in 380 matches. Therefore, home teams scored an average of 1.492 goals per game. If away teams scored 459 goals in 380 matches, they averaged 1.208 goals per game.
Next, calculate goals conceded over the same period. Suppose home teams conceded 459 goals; they therefore allowed 1.208 goals per game. Suppose away teams conceded 567 goals, yielding 1.492 goals per game. With this data, we can determine each team’s attack strength.
Let’s assume we are analyzing Juventus versus Barcelona. Estimating each team’s attack strength involves two simple steps, but accuracy is essential. First, divide the total goals the home team scored at home by the number of home games it played. Suppose Juventus scored 35 goals in 19 home games, yielding 1.842. To find Juventus’s attack strength, divide its home average (1.842) by the league-wide home average (1.492). The result is 1.235.
Next, estimate Juventus’s defensive strength. Divide the goals Juventus conceded at home (15) by its home games (19) to get 0.789. Divide 0.789 by the average home goals conceded during the season (1.208) to obtain a defensive strength of 0.653.
Now, let’s take a look at Barcelona. Suppose it scored 19 goals in 19 away matches, giving 1.0. Divide this value by the league-wide away average of 1.208 to get an attack strength of 0.828. To calculate Barcelona’s defensive strength, divide the goals it conceded away (31) by its away games (19) to get 1.632. Then divide 1.632 by the league-wide away concessions average of 1.492 to get a defensive strength of 1.094.
Goals Projection
Once we have made all these calculations, we are ready to project each team’s goals. Let’s start with Juventus. To figure out the number of goals the team might potentially score, multiply its attack strength (1.235) by Barcelona’s defensive strength (1.094) and by the average home goals (1.492). This suggests Juventus may score 2.016 goals.
Now, to estimate how many goals Barcelona might score, multiply the attack strength of the away team by the defensive strength of the home team and by the average away goals. Multiply 0.828 by 0.653 by 1.208, which yields 0.653 expected goals.
Of course, no match ends with fractional goals; these values are averages. This is where you need the Poisson distribution to convert averages into probabilities. It is advised to make use of a calculator that will do the job for you, or you can perform the calculations yourself using the formula P(x; μ) = (e^-μ)(μ^x) / x!. After finding the probability for each possible score line, soccer devotees are advised to tabulate the information they have collected. Then compare your Poisson probabilities with the odds offered by bookmakers.
Drawbacks of Poisson Distribution
Although using the Poisson distribution in soccer betting may seem like a guaranteed path to success, punters should take into account that it has its limitations.
A major problem is that this model focuses solely on past results, so current changes such as player transfers are not considered. Another drawback is that the model relies only on final scores, which may not reflect how the match actually unfolded.
The model also ignores factors that might affect the course of the matchup, such as injuries, weather, and so on. Such factors can have a lasting effect on goal expectancy, meaning that your calculations might not be accurate. Any experienced bettor will tell you that pitch conditions are essential as well. Unfortunately, this factor is also overlooked, which can erode your edge.
Conclusion
Once you understand this method of predicting football results, you will see that this model can be the stepping stone to a rewarding betting session. If utilized properly, the Poisson distribution might help you predict the score lines of the matchups you wish to punt on and spot value bets, which can bring you good results as well.