May 11, 2020
May 11, 2020

How to price a soccer match

Why price a soccer match yourself?

An example of how to price a soccer match

What do you do when you start to win?

How to price a soccer match
While there is nothing wrong with betting based on opinion, it isn’t a viable strategy to make a long-term profit. If you’re serious about betting, you’ll analyse an event and create an estimate of what the odds should be to see if there is value on offer. Read this article to learn more about how to price a soccer match.

Why price a soccer match yourself?

In order to find a value bet, you need to compare the odds you are betting on with what you believe to be a more accurate reflection of the true probability for an event. If the available odds underestimate the chance of an outcome in an event compared to your estimate, this will provide you with positive expected value.

While this is a simple enough concept to understand, having something to compare the bookmakers’ odds against is where most bettors will fall down. Odds comparison will certainly help you find the best odds to bet with, but creating your own probabilities and comparing them against the available odds is what will help you find the right option to bet on in a given market.

A lot of people fail to recognise how difficult winning in betting actually is, and you’re not going to start finding value bets as soon as you start pricing matches yourself. However, you need to start somewhere and it will certainly help improve your understanding of probability. Once you develop your knowledge, get access to better information and experiment with various inputs and pricing methods, you may begin to find legitimate betting opportunities.

Although I’ll run through an example of how to price a soccer match in this article, it’s important to note that you don’t necessarily have to do the “heavy lifting” of pricing a market yourself. Some people will choose to trust the market, using information provided by an efficient bookmaker like Pinnacle and look for discrepancies with other bookmakers.

It could be that you use Pinnacle’s odds for a game in tennis and scale it up to odds for the full match. Or it could be that you use individual team totals in an NBA to calculate the likelihood of each team outscoring the other (and by how many) to calculate odds for a Money Line or Handicap bet. We can save such examples (and their potential pitfalls) for another article though.

You have to start somewhere

The prospect of doing what a bookmaker does (creating odds for an event) with incomparable resources will likely seem daunting to a lot of bettors. Providing you are willing to put the time in to learn and you’re happy to make mistakes and accept failures (because they will happen), there is something to be gained from pricing markets yourself.

The common perception of a successful sports bettor has changed in recent years. While traditional handicappers who base their calculations off of their own knowledge and experience do still exist, it is now more about individuals or groups who build their own models with large data sets and complex algorithms.

If you’re aim is to get to the level of the professional bettors that many aspire to be like, you need to understand that you have to start somewhere. You don’t just fire up Excel, R or Python, put a load of data in and play around until it throws something useful up. Start with the basics, a simple approach with small amounts of data, and work your way up from there.

An example of how to price a soccer match

In order to help explain why pricing a soccer match is important if you want to bet on it, I’ve used a simple example to show how it could be done. It should be noted that this approach has plenty of flaws (which we’ll get on to later) and, when used by itself, it will not help you find value in soccer betting markets.

It’s not enough to simply say it works or it doesn’t work though. The most important thing is to understand why it works or why it doesn’t work.

I have used a Poisson model to create 1X2 odds for a round of fixtures in the Premier League (I have chosen the first round of fixtures from the 2019/20 season for this example). How Poisson Distribution can be used in betting is explained in more detail in a separate article, but I will also cover the basics here.

Using Infogol’s expected goals data from the previous Premier League season (2018/19), I was able to calculate the “Attack Strength” and “Defence Strength” of each team for playing both at home and away.

This provides us with a relative measure of team ability in terms of scoring and conceding goals by using the ratio of a team’s average and the league average. Using expected goals instead of actual goals will give a more accurate reflection of teams’ performances and go some way to removing the randomness and instances of luck we’d see in a 38-game season.

Home Attack Strength =

Team expected goals per home game / League average expected goals per home game

Home Defence Strength =

Team expected goals against per home game / League average expected goals against per home game

Away Attack Strength =

Team expected goals per away game / League average expected goals per away game

Away Defence Strength =

Team expected goals against per away game / League average expected goals against per away game

Premier League 2019/20 Attack Strength and Defence Strength

Team

xGF Home

xGA Home

xGF Away

xGA Away

Home AS

Home DS

Away AS

Away DS

Manchester City

52.6

15.6

37.9

12.9

1.679

0.619

1.505

0.412

Liverpool

43.9

15.7

34.6

18.4

1.401

0.623

1.374

0.587

Chelsea

34.8

16.0

30.1

25.7

1.111

0.635

1.195

0.820

Tottenham

33.3

27.2

27.7

24.7

1.063

1.080

1.100

0.788

Arsenal

35.9

26.2

27.8

32.0

1.146

1.040

1.104

1.021

Manchester United

37.5

23.9

32.2

30.7

1.197

0.949

1.278

0.980

Wolves

34.9

21.4

23.6

25.3

1.114

0.849

0.937

0.808

Everton

33.4

23.8

24.6

27.5

1.066

0.945

0.977

0.878

Leicester City

28.3

20.5

26.9

26.0

0.903

0.814

1.068

0.830

West Ham United

27.6

26.4

23.2

42.1

0.881

1.048

1.076

1.344

Watford

25.2

32.1

27.1

35.9

0.804

1.274

1.076

1.146

Crystal Palace

28.7

26.9

22.1

30.2

0.916

1.068

0.877

0.964

Newcastle United

25.6

29.4

17.5

33.6

0.817

1.167

0.695

1.072

Bournemouth

32.1

27.3

27.9

35.0

1.025

1.084

1.108

1.117

Burnley

28.4

30.5

21.1

38.2

0.906

1.211

0.838

1.219

Southampton

28.3

27

26.3

33.5

0.903

1.072

1.044

1.069

Brighton

22.4

26.3

18.4

39.0

0.715

1.044

0.730

1.245

Norwich

29.1

27.4

19.4

38.4

0.929

1.088

0.770

1.226

Sheffield United

26.8

30.5

19.2

42.6

0.855

1.211

0.762

1.360

Aston Villa

18.0

29.8

16.2

35.4

0.575

1.183

0.643

1.130

Next, we need to break this down into the specific fixtures that we want to price up. We can then use the home team’s home attack strength and the away team’s away defence strength to calculate how many goals the home team would be expected to score (and reverse this – using home defence strength and away attack strength – to calculate how many goals the away team might score).

This is what the process would look like for the match from Gameweek 1 of the 2018/19 Premier League season between Leicester and Wolves.

Leicester goals =

Leicester Home Attack Strength x Wolves Away Defence Strength x League average expected goals per home game

0.903 x 0.808 x 1.649 = 1.203

Wolves goals =

Wolves Away Attack Strength x Leicester Home Defence Strength x League average expected goals per away game

0.937 x 0.814 x 1.326 = 1.011

This then provides us with the number of goals each team would be expected to score if they were to play each other (1.203 for Leicester and 1.011 for Wolves). However, the game can’t finish 1.203 – 1.011 so we need to find a distribution of probability across a range out outcomes.

We can use the Poisson function in Excel to calculate the probability distribution for the different number of goals that each team might score in a match (I used a range of 0-5 to keep things simple). Using the example above, this is what the distribution will look like.

Leicester vs. Wolves Poisson Distribution

Goals

0

1

2

3

4

5

Leicester

0.3002

0.3612

0.2173

0.0871

0.0262

0.0063

Wolves

0.3639

0.3678

0.1858

0.0626

0.0158

0.0031

In order to calculate the probability of just the home win, draw and away win (1X2), we need to work out the probability for each of the potential outcomes.

Leicester 0 - 0 Wolves =

Probability of Leicester scoring 0 x Probability of Wolves scoring 0

0-0 = 0.3002 x 0.3639 = 0.1092 or 10.92%

We then replicate this for all of the possible permutations of a result where both teams can score between 0 and 5 goals (36 in total – six draws, 15 home wins and 15 away wins). This is what the outcomes for this match would look like.

Leicester vs. Wolves possible scores

Leicester

Wolves

Probability

%

0

0

0.109

10.924

1

1

0.133

13.285

2

2

0.040

4.037

3

3

0.005

0.545

4

4

0.000

0.041

5

5

0.000

0.002

Outcome

Draw

0.288

28.835

1

0

0.131

13.144

2

0

0.079

7.908

3

0

0.032

3.170

4

0

0.010

0.953

5

0

0.002

0.229

2

1

0.080

7.992

3

1

0.032

3.204

4

1

0.010

0.964

5

1

0.002

0.232

3

2

0.016

1.618

4

2

0.005

0.487

5

2

0.001

0.117

4

3

0.002

0.164

5

3

0.000

0.039

5

4

0.000

0.010

Outcome

Leicester Win

0.402

40.231

0

1

0.110

11.041

0

2

0.056

5.578

0

3

0.019

1.879

0

4

0.005

0.474

0

5

0.001

0.093

1

2

0.067

6.711

1

3

0.023

2.261

1

4

0.006

0.571

1

5

0.001

0.112

2

3

0.014

1.360

2

4

0.003

0.343

2

5

0.001

0.067

3

4

0.001

0.138

3

5

0.000

0.027

4

5

0.000

0.008

Outcome

Wolves Win

0.307

30.664

This provides us with the following probabilities for each outcome.

Leicester vs. Wolves possible outcomes

Outcome

%

Leicester Win

40.23

Draw

28.84

Wolves Win

30.66

We can then convert these percentages into odds, or convert the bookmakers’ odds into percentages, to compare the two and try to identify any bets that offer value. Below is an odds comparison of Pinnacle’s opening odds for Gameweek 1 of the 2019/20 Premier League season and the odds produces by this expected goals Poisson model.

xG Poisson vs. Pinnacle odds comparison

Home

Away

Pinnacle Open Home

Pinnacle Open Draw

Pinnacle Open Away

xG Poisson Home

xG Poisson Draw

xG Poisson Away

Liverpool

Norwich

1.15

9.59

18.05

1.23

7.89

16.3

West Ham

Man City

11.68

6.53

1.26

10.5

5.34

1.39

Bournemouth

Sheffield United

2.04

3.57

3.9

1.58

5.13

5.81

Burnley

Southampton

2.71

3.31

2.81

2.72

4.27

2.51

Crystal Palace

Everton

3.21

3.37

2.39

2.8

3.86

2.61

Watford

Brighton

1.98

3.44

4.37

2.13

4.09

3.49

Tottenham

Aston Villa

1.3

5.84

10.96

1.63

4.64

5.85

Leicester

Wolves

2.21

3.34

3.66

2.48

3.46

3.25

Newcastle

Arsenal

4.58

3.93

1.81

3.13

4.09

2.38

Man United

Chelsea

2.21

3.37

3.63

2.47

4.17

2.81

Identify your weaknesses and maximise your edge

If this was a real example we were experimenting with, you would then need to take some time to assess how accurate these odds are compared to those provided by the bookmaker. It’s all well and good finding discrepancies, but if the bookmaker is more accurate than you, you aren’t going to win in the long run.

The temptation might be to start placing money on what you believe to be the value bets produced, but even with small stakes this could prove to be a costly endeavour (we’d need a lot of bets to begin to make any meaningful observations). Therefore, back testing is the most efficient approach to see how viable this method is.

Comparing the odds this model would have produced for past events and comparing them against Pinnacle’s closing line will help us see how good this pricing strategy is. It’s not enough to simply say it works or it doesn’t work though. The most important thing is to understand why it works or why it doesn’t work.

There are plenty of reasons why the expected goals Poisson model used above isn’t a good way of pricing a soccer match. Using last season’s data and not using rolling data means it will quickly become outdated. Not accounting for transfers and new managers could skew the measure of team strength and their chance of winning a match. I’ve also used data from relegated teams for the newly promoted teams. These are just a few examples that should be considered.

If, hypothetically speaking, we did uncover some sort of edge with this model. It’s important to understand why. Is it just something that the bookmaker or other bettors haven’t considered? Is it dependant on when you place the bet? Can you improve the quality of the data to magnify the edge? Once we have a legitimate edge and we know how it is created, it is imperative that you manage your bankroll to maximise this edge.

What do you do when you start to win?

It may come as a surprise to some but the hard work isn’t over once you find a successful betting strategy. In fact, for a lot of people, this is when the hard work really begins.Unfortunately, some bookmakers will ban or restrict those that manage to make more accurate predictions than the odds being offered.

This makes it even more important that you maximise your edge while you can and that you work to keep improving your model so you test yourself at bookmakers where you won’t be banned or restricted, no matter how much you win. A bookmaker like Pinnacle.

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