**Predicting football matches yourself is a good method for developing a value betting strategy. However, it can be challenging to figure out how to correctly predict football matches. Many articles have been written about this subject, but most of them do not provide you with the required tools to actually start predicting matches yourself. In this guide I will show you how to apply George’s Football Club Ratings for predicting football matches without the use of any statistical models. Predicting football matches yourself won’t get any easier than this.**

**Note – This article includes a handy Excel spreadsheet (download link at the bottom of this page). By downloading this Excel spreadsheet you can start predicting football matches right away*!

## ► Introducing George’s Football Club Ratings

A little while ago I have introduced a new rating system on this blog, called George’s Football Club Ratings (GFCR). This rating system basically indicates how strong each team is from an offensive, defensive and overall perspective. The higher the rating, the stronger the team . Each club’s strength level is based on a statistical analysis of all the matches they have played over the past four years. The statistical method that I have used to calculate the ratings is somewhat similar to the well-known Massey’s method (I have used a similar method in this article as well). In the future I might write a more in-depth article about the methodology behind George’s Football Club Ratings, but in this guide I first show you how to use GFCR for predicting matches yourself. Below you can see which clubs currently dominate the Premier League.

As you can see Manchester City is the strongest Premier League club at the moment. The overall strength rating of *The Citizens *is the sum of their offensive (1.187) and defensive (0.553) strength ratings:

## ► How to interpret George’s Football Club Ratings

In order to understand how we can predict football matches using George’s Football Club Ratings, we first need to know more about what the club ratings actually mean. Let’s start by defining each component:

**OFF –**The offensive strength rating of a club indicates (1) the number of goals that a club is expected to score » (2) on top of the average number of goals scored by home/away*****teams » (3) against an opponent with an average defense.**DEF –**The defensive strength rating of a club indicates (1) the number of goals that a team is expected to concede » (2) on top of the average number of goals conceded by home/away*****teams » (3) against an opponent with an average offense.**TOT –**The overall strength rating of a club indicates (1) the expected goal difference » (2) on top of the average goal difference between home/away*****teams » (3) against an opponent with an average overall (TOT) rating.

**Note – The average number of goals scored by a team depends on whether they play their match at home or away from home. In general, clubs score more goals at home while conceding more goals away from home. This is called the home field advantage.*

### ► Practical example – How to apply GFCR

The following practical example should make the use of GFCR more clear. Assume that Liverpool plays their next match at home against an average opponent (i.e. an opponent for which all ratings are exactly equal to zero). We know that Liverpool has an offensive rating of 0.938. We also know that they play against an average opponent (i.e. with a defensive rating of exactly zero). Next, assume that the average Premier League home team scores 1.50 goals per match. By combining all three parts we can calculate the expected number of goals scored by Liverpool:

As you can see it is very easy to use GFCR for predicting the number of goals (per team) in football matches. Moreover, once we know how many goals we can expect both teams to score, we can also start predicting the outcome of football matches. Again, let’s show how this works by using a practical example.

## ► First – Predict goals in football matches

Assume now that Liverpool will play Manchester City at home. Looking at the GFCR of both clubs we can see that they have very similar ratings, although Manchester City is slightly stronger. However, Liverpool has the advantage of playing at home. Taking into account both elements, how can we calculate the probability for a Liverpool win if they play a home match against Man City?

##### How to predict the expected number of goals for Liverpool and Man City

As you can see we first need to know the average number of home and away goals that are expected to be scored in a football match (avgGSH; avgGSA). Next, we also need to know the offensive and defensive strength ratings for both teams (OFF; DEF).

Let’s start with the first part: determining how many goals we can expect both teams to score *on average*, while also taking into account the home field advantage. Let’s not make things too complicated and use the average number of goals scored in the current (2019-2020) English Premier League season. According to this website the average number of goals scored by home teams (avgGSH) and away teams (avgGSA) equals 1.50 and 1.21 respectively. Next, we can plug the values of all parameters into the formula:

##### Expected number of goals for Liverpool and Man City

As you can see Liverpool is expected to score ±1.9 goals, while Man City is expected to score ±1.8 goals. Now the next step is to transform the above into match outcome predictions. The nice thing about this part is that you don’t have to apply any statistics at all. Simply download the Excel spreadsheet at the bottom of this page, and Excel will do all the calculation work for you.

##### ► More about predicting football matches in Excel

If you want to read a more in-depth article about the statistical methods that I have used, check this previous guide of mine. This previous guide covers everything on how to predict football matches by applying the Poisson distribution in Excel. The methods used in both guides are very similar. In fact, some elements of the corresponding Excel spreadsheets for both guides are exactly the same. Thus, if you want to learn more about the statistical methods used for predicting football matches: read my previous guide.

## ► Next – Use GFCR for predicting football matches

Alright, let’s continue predicting the match Liverpool versus Manchester City. Below you can see some print screens from the corresponding Excel spreadsheet. The only things you have to enter into the spreadsheet are the correct club ratings (Step 1). Once you have done that, Excel will calculate all the predictions for you (Step 2 and Step 3). As you can see from the below images, the Excel spreadsheet predicts both the correct score and the match odds market.

From the last image (Step 3) we can see that Liverpool has a 41.3% chance of winning the match. On the other hand, Man City has a 37% chance of winning the match. From Step 2 we can see that the most likely score is 1-1, with a probability of 8.6%. A 2-1 win for Liverpool is also relatively likely (8.1%).

## ► Is this method profitable already?

Obviously predicting football matches is fun, but it is even more fun to make some money out of it. Below you can see how much money we could have made by applying this method to all matches for I expected to be profitable on the long-run (* see the below notes). Keep in mind that I have not placed any *real* bets. The below results are based on betting simulations over the last 10 Premier League seasons.

* **How to determine whether we can expect to win or not?** Think of the following example. We have just predicted Liverpool to win against Man City with a 41.3% probability. Now let’s say that the bookmakers think that Liverpool has 35% chance of winning the match. Thus, our predictions indicate that the fair odds are 1/0.413=**2.42**. However, we can get our money matched at odds of 1/0.35=**2.86**. On the long-term we can therefore expect this bet to be profitable, since the bookmakers offer better odds than what we predicted to be the *correct odds*.

##### Cumulative betting profit in last 10 Premier League seasons

The above image shows that the simulated results are not too bad. There is no clear upwards trend, but we did actually make some money (approximately *£*1500 in ten years). Moreover, there is enough room for improvement (but that is up to you to figure out!). I can already mention the following improvements that would likely have a positive impact on your returns:

- It is important to mention that I have used the
**closing odds**from only 4 of the biggest bookmakers. First, you can improve the results by placing your bets longer before the start of the match. These odds are assumed to be less accurate than the closing odds. Next to that, you might incorporate more bookmakers in your portfolio in order to get matched at better odds. - The above results are based on a betting strategy that placed a bet on literally every match that was expected to be profitable, even by the tiniest of margins. This fact leaves room for many improvements. For example, you could restrict yourself to only place a bet whenever the expected profit is above a certain threshold. Another possibility is to place bets only if the bookmaker’s odds are within a certain range.

## ► Concluding

Now this was easy, right? The best part is the fact that you can accurately start predicting football matches without the use of any statistical models. You don’t need to know anything about the machine learning techniques that I have used to calculate George’s Football Club Ratings, while you still benefit from the predictions as much as I do.

Below I have added the corresponding Excel spreadsheet. By following the contents of this guide you will be able to reproduce everything that we have covered – within minutes. Hopefully, this guide can bring you closer to a money-making betting career. Good luck!

Thank you! Allways best content

Hi George soccerstats.com page where to find (avgGSH; avgGSA) ??

Hi Péter,

You can find the stats a little above the bottom of that webpage.

HI George says I can not find it would make an instructional video about all this I would be very grateful to.