Making accurate football predictions consistently is challenging, even for experienced sports bettors. However, employing data-driven analysis can significantly enhance your ability to make informed choices. Given its importance, our guide offers analytical tips and strategies to improve your chances of making predictions.
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Are you afraid that making predictions using statistical analyses may be confusing? We promise to keep our explanations simple. Keep on reading to find out.
Poisson Distribution
Any discussion about data-driven analysis for making football predictions will be incomplete without considering Poisson Distribution. Using this method, you can estimate the likelihood of various goal outcomes in future matches by analyzing past performance.
The Poisson Distribution Formula
You can calculate the probability of a football team scoring with this formula:
P(X=n) = (e–r rn )/n!
In relation to football score predictions:
- P(X= n) is the probability of scoring “n” goals;
- e is the base of the natural algorithm, equivalent to 2.71828;
- r is the average number of goals a team has managed in the last meetings;
- n is the specific number of goals you predict a team will score;
- n! is the factorial of n.
To avoid unnecessary complications, you can use the Poisson Distribution online calculators. These calculators will work out the math and provide an answer within seconds. Below is a simple explanation of how to do the math yourself.
Working Out the Math Yourself
Let us use an upcoming 2023/24 Premier League match between Manchester City and Arsenal. Using past data, we will estimate the probability of Arsenal scoring 0, 1, 2, and 3 goals.
Let us work out each function in our Poisson formula:
- Arsenal has averaged 1 goal per game against Manchester City in the last 20 meetings. So, our r= 1.
- We want the probabilities of Arsenal scoring 0, 1, 2, and 3 goals. So, our n= 0,1,2,3.
Now, we can work out the needed probability using the parameters above. So, Let’s begin.
The probability of Arsenal scoring 0 goals against Manchester City
P(X= n) = (e-r rn)/n!
P(X= 0) = (e-1 10)/0!
P(X= 0) = e-1
P(X= 0) = 1/e
P(X= 0) = 1/2.71828
P(X= 0) = 0.3679
The probability of Arsenal scoring 1 goal against Manchester City
P(X= n) = (e-r rn)/n!
P(X= 1) = (e-1 11)/1!
P(X= 1) = e-1
P(X= 1) = 1/e
P(X= 1) = 1/2.71828
P(X= 1) = 0.3679
The probability of Arsenal scoring 2 goals against Manchester City
P(X= n) = (e-r rn)/n!
P(X= 2) = (e-1 12)/2!
P(X= 2) = e-1/2
P(X= 2) = 1/2e
P(X= 2) = 1/(2*2.71828)
P(X= 2) = 1/(5.43656)
P(X= 2) = 0.1839
The probability of Arsenal scoring 3 goals against Manchester City
P(X= n) = (e-r rn)/n!
P(X= 3) = (e-1 13)/3!
P(X= 3) = e-1/6
P(X= 3) = 1/6e
P(X= 2) = 1/(6*2.71828)
P(X= 2) = 1/(16.30968)
P(X= 3) = 0.0613
Our Conclusion
- The chances of Arsenal scoring 0, 1, 2, and 3 goals against Manchester City are 0.3679, 0.3679, 0.1839, and 0.0613.
- In percentages, the probabilities are approximately 36.8%, 36.8%, 18.4%, and 6.1%. Thus, it is most likely Arsenal will end the game with 1 goal or none at all.
- You can also determine the betting odds based on the probabilities already calculated. The odds of Arsenal scoring 0, 1, 2, and 3 goals are approximately 2.71, 2.71, 5.44, and 16.31.
These poison-determined odds do not include bet margins. A bet margin is a built-in advantage that ensures a bookmaker's profits. It is like a hidden fee that the bookmaker takes, no matter who wins the bet.
How Reliable is the Poisson Distribution?
As simple as this strategy is, it fails to consider many factors that may affect the bearing of a football match. Examples include player availability, tactics, home-field advantage, injuries, and locker room atmosphere.
This tool is most useful for predictions in low-ranked leagues and competitions. Down there, you can gain an edge over sportsbooks. The strategy works best with clubs that do not possess players who can affect the outcome of matches with their brilliance.
Using Team Metrics for Score Predictions
With this strategy, we can cover more bases. Instead of limiting ourselves to the goal stats of two teams, we can bring in the entire season's goal stats. These statistics include average home/away goals. The system also considers conceded goals, whether home or away.
By considering these metrics, we are factoring in playing form. Home/away goals fall under attacking strength, and goals conceded at home/away fall under defensive strength. So, let us take the Arsenal vs. Manchester City 2023/24 season game as an example.
Calculating Attacking Strength
We will calculate the attacking strength of Arsenal and Manchester City (M.City). We need the following 2023/24 data to proceed:
- Arsenal’s home goals (AHG) = 36.
- Arsenal’s home matches (AHM) = 14.
- M.City’s away goals (MAG) = 29.
- M.City’s away matches (MAM) = 14.
- Premier League home goals (PLHG) = 498.
- Premier League home matches (PLHM) = 283.
- Premier League away goals (PLAG) = 419.
- Premier League away matches (PLAM) = 283.
The formula for calculating Arsenal’s attacking strength at home
(AHG/AHM) / (PLHG/PLHM)
(36/14) / (498/283)
(2.5714) / (1.7597)
= 1.4613
The formula for calculating M.City’s attacking strength away
(MAG/MAM) / (PLAG/PLAM)
(29/14) / (419/283)
(2.0714) / (1.4806)
= 1.3991
Calculating Defensive Strength
We will calculate the defensive strength of Arsenal and Manchester City (M.City). We need the following 2023/24 data to proceed:
- Arsenal’s home goals conceded (AHGC) = 13.
- Arsenal’s home matches (AHM) = 14.
- M.City’s away goals conceded (MAGC) = 16.
- M.City’s away matches (MAM) = 14.
- Premier League home goals conceded (PLHGC) = 419.
- Premier League home matches (PLHM) = 283.
- Premier League away goals conceded (PLAGC) = 498.
- Premier League away matches (PLAM) = 283.
The formula for calculating Arsenal’s defensive strength at home
(AHGC/AHM) / (PLHGC/PLHM)
(13/14) / (419/283)
(0.9286) / (1.4806)
= 0.6272
The formula for calculating M.City’s defensive strength away
(MAGC/MAM) / (PLAGC/PLAM)
(16/14) / (498/283)
(1.1429) / (1.7597)
= 0.6495
Our Results
- Arsenal’s attacking strength at home (AASH) = 1.4613
- Arsenal’s defensive strength at home (ADSH) = 0.6272
- M.City’s attacking strength away (MASA) = 1.3991
- M.City’s defensive strength away ( MDSA) = 0.6485
Other Relevant Statistics
- Average Premier League home goals (APLHG) = (PLHG/PLHM) = 1.7597
- Average Premier League away goals (APLAG) = (PLAG/PLAM) = 1.4806
- Average Premier League home goals conceded (APLHC) = (PLHGC/PLHM) = 1.4806
- Average Premier League away goals conceded (APLAGC) = (PLAGC/PLAM) = 1.7597
Now, let us calculate the possible goals by each team.
Possible Arsenal goals at home against M.City
AASH *MDSA * APLHG
1.4613 * 0.6485 * 1.7587
= 1.7
Possible M.City goals away against Arsenal
MASA *ADSH * APLAG
1.3991 * 0.6272 * 1.4806
= 1.3
Our Conclusion
Arsenal can score 1.7 possible goals, approximately two goals, against Manchester City. Manchester City can score 1.3 possible goals, approximately 1 goal, against Arsenal.
So, there is the possibility that the final score between Arsenal and Manchester City is 2:1.
Strategies for Improving Prediction Accuracy
Other methods exist for football analysis and predictions. Again, these strategies will only help you make informed decisions. Success is never 100% guaranteed.
Rating Systems
Using rating systems may give you a glimpse of the strength or form of a team and its players. With this information, you could make an educated guess of the winner of a football match. These systems incorporate a lot of performance-related factors to generate ratings.
Expected Goals/Assists
xG is a value representing every shot taken in a match. The result depends on the quality of the shot and the probability of resulting in a goal. Information on xGs for goals for and against can help paint a picture of who the match winner could be.
Analysis of Sportsbook Market and Odds
Accessing bookmaker odds could provide information on expectations from a football game. The higher the odds, the lesser the chance of the team winning the game. Bookmakers give ultimate favorites very low odds, between 1.1 and 1.4.
Considering Player and Coach Influence
Lineups and tactics change from time to time. Being informed of these changes beforehand can help make more accurate predictions. The presence of a star player in a lineup raises a team’s winning chances. Information on injured players can also help your decision-making process.
Avoiding Bias
It is safe to stay away from one’s favorite team when it comes to football predictions. This way, you can abstain from bias and get clarity on the teams involved. Eliminating emotions can go a long way toward promoting informed decision-making.
Conclusion
We looked at two major strategies that could help with accurate football predictions. In the later sections, we provided tips you can use besides these statistical models. Using all these strategies can raise the accuracy of your predictions.
We use these strategies and other analytical tools to curate our sure odds. Sign up on the EaglePredict website to access our daily football sure odds.
