Football betting is extremely popular among gamblers. However, if you’re just starting with soccer betting, then you might need a strategy to increase your winning chances. A mathematical approach can help you create football betting prediction models to boost your chances.

That being said, it might take years for a betting system to be perfect. Applying statistics to sports betting is not as easy as it might seem, which is why we are here with the step-by-step approach of modelling soccer odds for beginners.

**Grading Systems**

For beginners who want to model their own soccer odds using statistics, a grading system is a good starting point.

There can be several kinds of grading systems for football prediction, and they vary in terms of complexity and scope. You can start with simple models which are effective in forming predictions; however, you will be somewhat limited in identifying value.

Let’s take a closer look at how a grading system works.

A grading system is based on the grades that you assign to something. We have been graded throughout our school life. For example, we usually assign ‘1’ or ‘A’ to the top tier category, followed by ‘2’ or ‘B’ to second-tier categories and so on. You can make a grading model for football teams within the same league. How many grades you decide to give is entirely up to you. But, we would recommend you to Google a known method called k-clustering that easily identifies natural groups and reduces bias in your model.

Teams can be graded based on their ability which is determined by their past performances. An easy example would be to grade teams based on their performance in the previous season. You can easily get the data which will make it easy to grade teams like A, B and so on.

**How to use grades for football prediction?**

The real advantage of grading teams based on the performance is that it helps to form generalisations about ‘types’ of teams that face each other. Interestingly, a lot of casual football bettors use this soccer odds prediction model to select their bets without actually realising it.

If you are able to assign fair and accurate grades to teams, then it will become easier for you to produce useful statistics on the outcome when teams of different grades are put against each other in a match.

To make such a football odds prediction model, you will need to have access to some past data. You can get such data from websites like football-data.co.uk. Open the data in an Excel file and label the teams with grades to produce the stats. Even with the simplest stats, you can have answers for historical questions such as:

How often a team with lower grade beats a higher grade team?

How a team performs at home?

Performance of the team over the last three years?

And so on. This is an example of what kind of questions you can answer with a simple football prediction model.

Now that you have an idea of how this model works, you can start by creating a great of stats for the results of every grade compared to each other. Here is an example of four groups- A, B, C and D, when putting them into the grid we have the outcome for 16 fixtures.

For each of the fixture there can be three outcomes-win, draw or lose. Hence your model will have 16 x 3 = 48 total outcomes for which you will have to calculate percentage values based on the historical data about performance.

This is the simplest form, and you can add more factors to your football prediction model in order to tweak the percentage values. The reason being adding more complexity improves the accuracy of the prediction model.

Another requirement to produce the stats-based soccer betting model is that you are competent in Excel.

**Creating football prediction odds from statistics**

Once you have produced percentage stats, it becomes easier to translate them into odds. We would be discussing soccer odds in the following example, but it is true for any sports betting model related to any sports.

Suppose, in your football prediction model you find out that the grade B team beats a grade A team 30% of the time at home and 20% of the time it ends in a draw while 50% of the time team A wins. The most important factor to consider here is that whenever you produce percentage stats for the three outcomes of a football match, it should add up to be 100% (30% + 20% + 50% =100% in this case).

Now to predict the outcome for when upcoming match, we need to convert these percentage stats into decimal odds.

The formula used is:

1 / (% chance of each outcome based on past data)

For the above scenario, the decimal odds would be: 3.3 (H), 5.0 (D) and 2.0(A)

Now that you have got the estimated odds, you can proceed to next step.

If you are confident that your estimated odds are accurately predicting results, you can proceed to find value bets based on these odds. Backing any higher soccer odds then your estimate is a value bet while laying below your estimated odds will also be a value bet.

**Grading system suffer from weaknesses **

A grading football prediction model is a simple approach that anyone can use, but it has its limitations.

Short-lived predictions: with this model, if you base your data on a small window of historical data, then you will be making weak predictions based on short-lived winning or losing streaks. This can skew your outcome estimates.

Same grade teams are treated as equals: all the teams in a group with the same grade are treated as equals when, in fact, some teams might be superior to the others in the same group. Such a generalisation can weaken your predictions that you should try to avoid.

Group structure can be confusing: in some scenarios are football prediction model based on grades can be less defined when all the teams come closer in terms of performance. This can cause problems with predictions.

Realistically speaking, basic grading football prediction models are too simplified to identify values in the tournaments like the Premier League. To improve your chances of winning, you have to improve the concept and incorporate more factors that help you identify values.

That being said, the skills you develop by creating football prediction models with this approach impart valuable knowledge that is useful in other methods discussed below.

**Rule-Based Systems**

A rule-based betting system is usually found in conjunction with the grading system or any other sports betting system. The ‘rules’ are used to improve the efficiency of a model by introducing restrictions to decide what bets can be placed. To create rules, sports bettors have to look for patterns in the past data.

When we analyse the past data, then we are easily able to identify a combination of rules that could have easily turned us profits on over selected bets. In hindsight, it is easy to do, but we would recommend that you do not get too excited as things are not always what they seem to be.

Let’s take playing a videogame, for example. In theory, there is a combination of buttons that you press precisely at the right time to take your player to the next level. The combination of buttons might be outrageously complex and far-fetched for a beginner, but when you play through a level enough times, it becomes easier for you to work it out.

However, there is a limitation because one combination might work perfectly for one level, but if we use the same on the next level, the player dies. This is because the winning combination that we have designed doesn’t apply to the rest of the game.

So, what is the context here?

In football, it is roughly easy to analyse past data and naïvely identify a pattern. The pattern can be anything like “so far in the season Liverpool have won every game away from home whenever bookmakers offer more than 3.0 odds at kick-off” or “so far Chelsea have beaten every team they have lost to in the previous two seasons.” It might be that these statements are true and betting on those specific selections you made money, but the important question remains: have you found value?

There is no guarantee that the pattern you have identified is the winning trend, just like the winning combination of keys at one level. In a rule-based betting model using very specific rules for placing bets has no advantage, and the presumed trend doesn’t continue to be a winning choice. This dilemma is known as data overfitting. It poses a danger in driving conclusion from past data.

**How to avoid overfitting your data**

There are some rules that you can implement to avoid data overfitting:

Do not include too strict rules in your model. If you’re too specific, then you will end up making weak assumptions based on a small set of data.

Always involve a large set of data to analyse and identify patterns that have a much wider scope and not just short-lived assumptions.

Base your rules on logic and not gut feelings. There must be a practical logic behind every rule you make.

Always remember that you can be easily blinkered by your own analysis, especially when your calculations show huge profits. But if you follow these three simple tips, you will have a much better chance of using rules in your football prediction model.

**Poisson Distribution**

To take things up a notch, we recommend the Poisson Distribution approach. If you want the football prediction model for the likely number of goals that will be scored in a football match, the Poisson Distribution can provide a method for calculating the same by using historical data.

Don’t be anxious as you do not have to fully understand Poisson Distribution in order to be profitable. Fortunately, Microsoft Excel automatically works out Poisson Distribution. The information you need to know is that you can use this approach to calculate odds on the probability of outcomes for a soccer match in a goal-based market. This model can be used for markets such as Match Odds (1×2), Both Teams To Score, Correct Score, Over / Under Match Goals and Asian Handicap.

A football prediction model based on the Poisson Distribution has its limitations and faults, but still, it can be very useful to understand the fundamentals of creating odds. You can use this method with some of the grading systems and improve them as you avoid generalised grouping of teams together.

**Basics of the Poisson Distribution football prediction**

To get started with the Poisson Distribution model, you will need historical results to calculate the average number of goals for the teams both scored and conceded within a specific timeframe for both home and away games. The averages you generate are compared with the league average to create values for the attacking strength and to defend the strength of each team.

You can easily calculate the figures for attack and defence by:

Average Goals For (or) Average Goals Against / The league average

Let us take an example where the average Goals For in the league is 1.45, and the Liverpool has an average of 1.88, then the team is 29% above the league average for the attack, implying they are a goal-scoring threat.

Here’s how calculation done:

1.88/1.45 = 1.29

1.29 = 129%

**129% – 100% = 29% above average. **

Now we will put these metrics including the opponents’ into a Poisson Distribution formula. The outcome will be the probability of every result when the two teams play each other. Then you can convert these percentage probabilities into odds using the same method we discussed above. Furthermore, the odds can be used to identify value at a bookmaker or betting exchange.

While the Poisson Distribution method produces fairly accurate football predictions, you will be mistaken to assume that other people are not using it, because they are. In a market, you will have thousands of people using this approach and several thousand others using some other model. Therefore, you can use this distribution only for the basis of your model and not the market as a whole.

We would recommend you to go in-depth of the Poisson Distribution to fully understand how it works and how calculations are done.

**How Many Games Can We Use to Calculate the Goal Expectation Figures?**

There is no definite answer. You need to experiment because there are some teams like Leicester that have such a varied performance that a large window might not be enough to produce stats that might truly represent them. The same goes with a very small window of games as you don’t have much data to work with. This is why it is tough to decide how many games can be used for calculation. Some of the experts find that around ten games into a new season give you a starting point to work with.

**Shortcomings of the Poisson Distribution football prediction**

Just like most other stats-based betting approaches, this one also considers measurable results, but it is not uncommon to see a dominant team failing to score. There have been games where a dominant team lost the match to unexpected goals such as late penalty. The match results only tell us the final outcome and not what happened during the game.

Another weakness of the Poisson Distribution is that it is believed that it underestimates the probability of draws and zeros for a football prediction. However, you can rectify this shortcoming by using a method known as zero-inflation to increase the probability of no goals.

**Combining Expected Goals (xG) Data with The Poisson Distribution**

Poisson distribution can be vastly improved by using sophisticated statistics known as the Expected Goals (xG). This stat quantifies attempts on goal. The expected goal approach gets rid of the sentiments and evaluates performance from a scientific standpoint. Incorporating xG into your football betting model improves its accuracy and maximises expected value.

There are many online courses available that teach you how to incorporate expected goals into your own football betting model. You can learn from those and improve the accuracy of your outcome.

**Are these football prediction methods actually useful? **

The stats-based football prediction methods have weaknesses which we have highlighted above. We have focused more on the shortcomings of these models because the top-flight football betting markets have very inconsistent values, and it will not be easy to find profitable odds with simple models.

There is a lot to consider when working on a football prediction model.

To understand why predicting football outcomes is so difficult, you have to compare the sport with other sports—for example, horse racing. In horse racing, the past statistics are much more relevant for an upcoming event. The horse is the same as well as the track. In most scenarios, the trainer and the jockeys are the same as well. There is a lot of consistency in horse racing.

Single person sports like bowling and darts are even more consistent because there are only two outcomes, and the opponents never physically impact one another. The dart players always play the same board that they have played for decades which gives them consistency.

Now think about football-there are no two leagues or even seasons that are alike. The squad, playing 11 players, the coaches and managers and even the stadiums change frequently. And we have not even included injuries, relegations and promotions, player bans and transfers.

Is there a way for our betting models to even keep up?

Fortunately, there is hope amidst the complexity.

Just because there are too many complexities, it doesn’t mean that stats-based football prediction models can’t work for you. There is an enormous array of leagues and markets across bookmakers and sportsbooks for football betting. You can be highly selective without compromising on turnover.

Also, soccer has a lot more hype, interference and the noise surrounding the matches than any other sport which account for countless variables that influence the odds. This is why there is always scope for markets to miss on factors that can actually impact the game itself. This is the exact reason why some of the stats-based football prediction models thrive.

What Really Influences Football?

This is again the ambiguous question, and there is no one view. We can help you with improving your chances of winning in soccer betting by sharing some tips that we already have done here:

**Foundational Live Soccer Betting Tips To Improve Winning Chances **

**Betting Tips for Winning At Major League Soccer**

**Is there a ‘Perfect’ Strategy? **

You have to realise that thousands of gamblers create the odds by applying the methods that we have outlined here. This makes the market to favour one direction as it is based on opinions of the bettors.

We would recommend that you lean towards the ‘cold’ market-based approaches where the response to the news is much quicker. A perfect betting model is one that can distinguish between the hype and what would actually impact the game.

It might seem daunting from a programming perspective, but there are already trading platforms that do the same in the financial markets. It’s only a matter of time before such platforms are developed for sports, and they would be the perfect models.