Predicting the outcome of football matches and making profitable bets is immensely difficult even for the most experienced bettors. The world of football is rife with uncertainty, noise, and numerous factors influencing the final result. Fortunately, football betting systems provide a structured approach to help bettors make informed decisions and increase their chances of success.
In this article, I will explore what football betting systems are, their benefits, and the key considerations that should be taken into account when creating one. I will also discuss the importance of probability in football prediction and examine three popular types of football betting systems: grading systems, rule-based systems, and the Poisson distribution.
Article Contents
What Is A Football Betting System?
A football betting system is a strategy or set of rules that guides your football betting decisions. It can help you identify profitable betting opportunities, manage your bankroll, and increase your chances of winning.
There are many types of football betting systems out there, and each one has its strengths and weaknesses. In this article, we’ll take a look at some of the most popular approaches to football betting systems and how they work.
Key Considerations For Any Betting System
When making a betting system, football or otherwise, bettors should consider the following factors:
- Profitability: The primary objective of any betting system is to make a profit. A good betting system should have a positive expected value, which means that the expected returns from the system should be greater than the total amount of money invested.
- Bankroll Management: A good betting system should take into account the size of your bankroll and provide guidelines for managing your stake sizes accordingly. It should also have a plan for dealing with losing streaks, as they are inevitable in any betting system.
- Consistency: A good betting system should be consistent in its approach and not rely on short-term trends or streaks. It should be based on a sound mathematical principle or betting strategy that can be applied over the long term.
- Risk Management: A good betting system should also take into account the risk associated with each bet and provide guidelines for managing risk. This includes setting stop-loss limits and avoiding bets with high risk-to-reward ratios.
- Adaptability: A good betting system should be adaptable to changing market conditions and should be able to adjust to new information or developments. It should be able to evolve over time to remain effective and profitable.
- Testability: A good betting system should be testable, which means that it should have a clearly defined set of rules and be able to produce consistent results over a large sample size. It should also be able to withstand statistical tests and be free of bias or data mining.
Creating a football betting system that satisfies all of the above factors can be challenging, so I invite you to check out the Strategies section of this site for a complete overview of what it takes to succeed as a bettor.
The Importance Of Probability
Probability plays a critical role in football prediction because it provides a measure of the likelihood of a particular outcome occurring.
In order to make informed decisions about when to bet, it’s vital to have a good understanding of the probabilities at play. This involves taking into account a wide range of factors, including team form, player availability, head-to-head records, or perhaps even less obvious influences on the game. By analysing this information, bettors can assess the value in the betting markets.
So what is “value”?
A bet is said to have “value” if the odds offered by the bookmaker/betting exchange underestimate the true probability of an outcome occurring. For example, if a bookmaker is offering odds of 5.0 on a particular team to win, but the fair odds for that outcome would be 4.0, then those available odds have value. This is what every football prediction model should seek to find in order to succeed in the long run.
Therefore an important and fundamental skill in football prediction is knowing how to convert bookmaker odds into probabilities and vice versa. The formulas are as follows.
To convert a percentage into decimal odds:
1 / percentage expressed as decimal
For example, an outcome with a 25% chance has decimal odds of: 1 / 0.24 = 4.0
To convert decimal odds into a percentage:
1 / decimal odds
For example, an outcome with decimal odds of 5.0 has an implied probability of: 1 / 5.0 = 0.2 = 20%
The remainder of this article provides an overview of a few basic approaches for any bettors seeking to develop their own football betting system.
1. Grading Systems
A grading system is an excellent starting point for bettors seeking to calculate their own odds to identify potential value in the betting markets. It involves assigning grades or groups to relevant factors, such as team quality or performance, to help make more informed predictions about future events.
In football, a common approach is to group teams within a league based on their level of quality. This usually means assigning a numerical or alphabetical grade to each team, with higher grades indicating better quality. However, it is important to make every effort to avoid bias and ensure accuracy by grouping teams based on statistics rather than relying on personal opinion. One statistical method for identifying “natural groupings” is called k-clustering.
When grading teams based on performance, consider a reasonable time frame that accurately reflects current form. While past performance is a useful indicator, you need to avoid relying too heavily on outdated data as a team’s performance can change rapidly.
When done well, grading teams based on their performance enables the identification of patterns and tendencies among different types of teams that face each other. This approach is often used by both casual and experienced football bettors (whether they realise it or not), as a means of making informed betting decisions.
Using Gradings for Football Prediction
To begin using graded teams for football prediction, you’ll need to gather past results (try football-data.co.uk). Then you’ll have to assign the teams with a grade in order to produce a forecast for upcoming fixtures.
I recommend using Excel to generate a grid of stats for the results of every grade vs each other. For example, if you have chosen 4 groups in your grading system (let’s say A, B, C & D) then you will have the following 16 fixture ‘types’ to account for.
Every potential fixture Type
| A vs A | A vs B | A vs C | A vs D |
| B vs A | B vs B | B vs C | B vs D |
| C vs A | C vs B | C vs C | C vs D |
| D vs A | D vs B | D vs C | D vs D |
Within each of the above 16 fixture types there are 3 possible results: Win, Draw or Lose. This means there are 16 x 3 = 48 total outcomes that you need to create statistics for, based on historical performance.
For example, for A vs D (where A is at home) you could determine the following:
- 70% of the time the A grade wins.
- 20% of the time it’s a draw.
- 10% of the D grade wins.
Note that the percentages need to add up to 100% for every fixture type.
In this case, your odds would translate to 1.42 (home win), 5.0 (draw), 10.0 (away win). If you believe these odds are accurate, then you would determine that greater odds available at sportsbooks or betting exchanges would represent a value bet.
Adding More Complexity
Once you have established a basic grading system for football predictions, you can add additional factors to further refine and improve its accuracy. By introducing more complexity to the model, you can gain a more comprehensive understanding of the likelihood of different outcomes.
One possible factor to consider is a goals metric, which takes into account the typical winning margins when teams of different grades face each other. By incorporating this information, you can strengthen your predictions and potentially identify opportunities in the goals markets.
Another important factor to consider when developing a football betting strategy is the head-to-head record between specific clubs. This record can reveal patterns and tendencies that go beyond a team’s overall performance or grade. For example, a club may struggle to pick up results at a particular stadium or against a particular team, even if they are performing better overall.
Weaknesses In Grading Systems
When creating a grading system, it’s important to consider the potential pitfalls that can arise from the data used.
- Runs of form: Analysing a small window of historical data, such as the last 4 matches, can lead to weak predictions based on short-lived winning or losing streaks. Therefore, it’s essential to consider a larger dataset to gain a more accurate understanding of a team’s form.
- Teams of the same grade are not necessarily equal: For example, some Grade A teams may be superior to others in their group. It’s important to avoid making generalizations that weaken predictions by treating all teams within a grade as equal. Instead, consider a more nuanced approach to grading teams based on their strengths and weaknesses.
- Group structures change between seasons: In one season there may be well a defined number of groups. Currently in the Premier League it’s well recognised that there’s a Top 6. But that’s not always the case.
Realistically, using basic grading systems alone may not be sufficient to identify value in highly competitive betting markets — such as the Premier League Match Odds market. While it is possible to break even with a simple approach, a wide range of factors and influences might be needed to achieve long-term profitability. However, mastering the skills required to create odds using this approach can be invaluable when developing and implementing other more advanced betting systems.
2. Rule-Based Systems
Rule-based systems for football prediction involve creating a set of rules or criteria that are used to identify potential outcomes of football matches. These systems are based on the premise that certain factors or conditions are more likely to lead to a particular outcome.
A rule-based system might use factors such as team form, head-to-head record, injuries, and home advantage to predict the winner of a particular match. The system would be programmed with a set of rules that assign weights to each of these factors and then use them to calculate the probability of each team winning.
One of the main advantages of rule-based systems is that they are transparent and easy to understand. The rules used to make predictions can be clearly defined and the system can be tested against historical data to assess its potential profitability.
Rule-based betting systems can be used in conjunction with grading systems — or any other betting system for that matter.
Using Rules for Football Prediction
Here are some examples of rules that might be used in a football betting system:
- “If a team has won their last three matches and is playing at home, then they are likely to win their next match.”
- “If a team has lost their last three matches and is playing away, then they are likely to lose their next match.”
- “If a team is playing against an opponent who is in the bottom three of the league table, then they are likely to win the match.”
- “If a team has a good record against their opponent in previous meetings, then they are likely to win the match.”
- “If a team has a key player injured and is playing against a strong opponent, then they are likely to lose the match.”
These are just a few examples, but there are countless other rules that can be created based on different factors such as team form, player availability, head-to-head records, home and away form, and more.
Weaknesses of Rule-Based Systems
The main drawbacks of rule-based systems is that they are rigid and inflexible. They struggle to account for unforeseen circumstances or factors that are not explicitly included in the set of rules. You need to ask yourself: how much “power of hindsight” does my rule-based system actually provide?
When analysing past data, it’s possible to identify a combination of “rules” that could have turned a profit if used for placing bets. However, it’s important not to get excited too quickly, as things aren’t always as they seem. To illustrate this, consider playing the classic Sonic video game…
In theory, there will be a combination of buttons that can be pressed at precisely the right time to get Sonic through a level without being hit by spikes or villains, falling down a hole, or drowning. The string of buttons may be complex and far-fetched, but it’s possible to work it out through repeated playthroughs.
A button combination may work perfectly on one level.
However, this one, very complex, death-dodging combination does not help in completing the rest of the game. It’s useless.
So what am I getting at?
The point is, while it may be tempting to analyse past data to identify patterns or rules that “would have worked had I done that”, it doesn’t always form the basis of a good predictive method. So it’s important to approach rule-based systems with caution.
For example, you may discover incidental patterns in football such as:
- “Chelsea have won every away game where bookmakers offered more than 3.0 odds at kick-off and they drew their previous fixture” or
- “Spurs have beaten every team that lost their previous 2 home fixtures,
Even if these statements are true, it doesn’t necessarily mean you’ve found valuable betting opportunities. The danger lies in overfitting the data, where you’re tailoring your analysis to fit the data too closely, and as a result, your analysis does not apply to future outcomes.
Be careful…
Tips to Avoid Over-Fitting Your Data
It’s easy to fall into the trap of being overly confident in our own analysis, especially when it seems to show substantial profits. But there are three critical steps you should follow to increase your chances of effectively using ‘rules’ for football prediction:
- Avoid making your rules too strict. If you’re too specific, you risk making weak assumptions based on a small subset of data. Keep your rules general enough to capture relevant patterns.
- Always analyse a large sample size of data. As I explain in my post, “The Paramount Importance Of Sample Size In Betting Analysis,” small samples can lead to erroneous conclusions.
- Ask yourself whether your rules make sense. Keep an open mind, but scrutinise your rules to ensure they are based on sound logic. Be careful not to let hindsight bias influence your analysis.
3. Poisson Distribution
The Poisson distribution is commonly used in football betting models to estimate the probability of a team scoring a certain number of goals in a particular match, taking into account their average goals per game. By utilising historical data, this distribution can help predict the team’s “goal expectation” for the upcoming match.
For example, if a team has an average of 1.5 goals per game, the Poisson distribution will calculate the probability of them scoring 0, 1, 2, 3, or more goals in a particular match. This information can then be used to determine the most likely outcome of the match and to make informed betting decisions.
Bettors can use Microsoft Excel or comparable program to develop a Poisson Distribution betting model for use in various goal-based betting markets such as Match Odds (1×2), Correct Score, Over/Under Match Goals, Both Teams To Score and Asian Handicap.
Based on my experience working on football prediction projects involving the Poisson Distribution, I have found it to be a more accurate method than using the basic grading and rule-based systems described earlier in this post. This is primarily because the Poisson Distribution avoids generalising by “grouping” or relying on far-fetched trends. Instead, it relies on concise and meaningful data to provide a more accurate estimation of potential outcomes.
Using the Poisson Distribution for Football Prediction
Pinnacle has published a useful entry-level article on how to use the Poisson Distribution. I’ll elaborate on some of the key points.
To begin, you’ll need to gather historical football results in order to calculate the average number of goals scored and conceded by each team, both in home and away games, within a specific timeframe such as one season. These averages are then compared to the league average to determine the attacking and defensive strengths of each team.
To calculate the attacking and defensive strengths, divide the Average Goals For or Average Goals Against by the league average. For instance, if the Average Goals For in the Premier League is 1.45 and Manchester City has an average of 1.97, they are 35% above the league average for attack, indicating their prowess in scoring goals. Here’s how it’s calculated:
1.97 / 1.45 = 1.35 1.35 = 135% 135% - 100% = 35% above average
These metrics, along with the opponent’s corresponding values, are then incorporated into a Poisson Distribution formula. This formula determines the probability of each possible result when two teams face each other. By converting these percentage probabilities into odds, as demonstrated earlier, it becomes possible to identify potentially valuable bets at bookmakers or exchanges.
Optimal Number of Games to Calculate Goal Expectation
The optimal number of games to use for calculating goal expectation figures is a subjective matter that requires experimentation. For instance, teams such as Leicester have undergone significant changes over the past decade, making a large window of five seasons less representative of their current form. Conversely, a small window of games (e.g., the past three fixtures) provides limited data to work with.
From my experience, after around ten games into a new season, you have a sufficient amount of current data to work with. However, the smaller the sample, the more likely you are to make poor decisions based on variance.
Combining Poisson with Expected Goals (xG)
Expected Goals (xG) is a more sophisticated statistic that quantifies the goal-scoring likelihood of attempts on goal, providing a scientific evaluation of performances. It goes beyond goals, which don not always tell the entire story of a match.
By incorporating xG data into your football betting model, you can produce a more comprehensive analysis of a team’s performance and goal-scoring ability. This will improve the accuracy of your predictions.
Weaknesses of the Poisson Distribution
While stats-based approaches to betting, like Poisson Distribution, can be effective, they do have their limitations. Firstly, the Poisson only considers measurable results — but there are instances where a team dominates a match but fails to score or loses due to an unexpected goal, such as a late penalty. The final score of a match may not necessarily reflect what occurred during the game.
Another limitation of using Poisson Distribution for football prediction is that it tends to underestimate the probability of draws and the probability of zero. However, this can be rectified using a technique called zero-inflation, which increases the probability of no goals. With this method, the model can account for games where neither team scores and better predict the likelihood of a draw.
While utilising the Poisson Distribution method can generate reasonably accurate football predictions, it’s crucial to recognise that many others are likely using this approach as well. Therefore it is essential to consider the Poisson distribution as a foundation for your model, and to consider incorporating other statistical methods and variables into your analysis. Additionally, always keep in mind that there are variables in football that cannot be predicted or quantified, such as injuries, team morale, and weather conditions.
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The Challenge of Football Prediction
The world of football prediction is rife with challenges due to the constantly changing nature of the sport. Each season sees teams undergoing significant changes, such as new managers, players, and stadiums, while injuries, player bans, and transfers can all impact team cohesion and strategy. Accurately predicting match outcomes can be a daunting under such circumstances.
Moreover, the media hype and noise surrounding football can further complicate predictions, as public opinion is easily swayed by sensational stories. Meanwhile countless variables such as player form, team morale, and weather conditions can legitimately influence the outcome of a games, making it essential for bettors to separate fact from fiction to make accurate predictions.
While statistical models are a far more reliable method of prediction than “gut feeling” or pure guesswork, they too have limitations in accounting for every variable and situation that can arise. Therefore building a successful betting model can be helped with a deeper understanding of the game, and the ability to quickly respond to breaking news and events.
Ultimately, achieving success in football prediction depends on accurately interpreting all factors that will impact the outcome of a match, and capitalising on value in the markets.
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I’m not too sure that i’d ever bother trying anything with the EPL. I’d want to dive straight into some obscure foreign league/market. Much more chance of finding decent odds there.
Sounds like a good approach. However, sometimes those obscure markets lack liquidity and the participants tend to be people like you i.e. looking for value opportunities as opposed to a punt. Swings and roundabouts. Striking the right balance is important.
Wonderful Article.
I would like to suggest a combined strategy between the Grading System and the Poisson Distribution.
First of all, I would use the Total Shots in the Box Ratio (TSBR) to grade a football team.
Since about 85% of goals come from shots in the box, it helps to grade each team based on their ability to make and avoid shots in the box.
In this way, you can assign a group for each team according to the TSBR and get the results usually play out when teams of different groups play one another.
Then, you can put a result for a specific fixture type in a probability calculator based on Poisson distribution, to get the “fair odds” for that match.
From here, just like the article says, you can back at higher odds than your estimates and lay below your estimates.
Finally, another weakness I see concerns the probability of a draw, given that the Poisson distribution calculates it with a slight imprecision.
However, it comes to help the data set available for each potential fixture type to calculate the percentage of draws.
How many draws happened between the Group A team and the Group B team?
Especially when the sample size is very large and you have at least 150 results for a fixture type.
I completely missed your comment here, so I’m a year late to respond.
But thank you for your input – you’ve given it a lot a thought and I’m sure it’ll help someone out.
TSBR data could be easier to obtain than XG. Plus there’s no ambiguity about it. XG would also differ depending on how it was calculated (i.e. how chances are assessed).
However, using TSBR would lose some accuracy by assuming all shots in the box are equal. In reality a shot from two yards stands a much better chance than an ambitious (or simply weak) effort from far edge of the box that would require a lot more skill to convert.
These kinds of things are well worth trying though, if only to compare results from different approaches.
Thanks again.
Dear Toby,
Please check my comment below about the Orio Sports course.
Much obliged!
Dear Toby,
how are You?
I would like to buy Orio Sports course, but, their website is down.
I have no idea whether they are still offering the course.
On the top of that, what is the best way to get xG data? Buying course won’t be useful if I cannot get xG data.
Much obliged!