Equations evaluation until November 30, 2020



Equations Evaluation until November 30, 2020

Equations Evaluation until November 30, 2020

John Kamaras

3/12/2020

Summary

After almost a full month of football activity, I want to evaluate my equations’ performance in real life. I remind you that I have two sets of equations; the results equations are easier to apply and predict only match result (1, X, 2). The expected goals equations, more complicated but with the ability to predict more outcomes.
In this article, I will evaluate the equations’ results as a classification method and as a value betting method. The classification method considers the more probable results according to equations when the value betting method uses the estimations with lower odds than the bookmakers’.
You can find the optimal equations for each division in my book at Amazon bookstore.
From Nov 01 till Nov 30, we examined 723 games; you can download the predictions of the equations along with the game info and odds from here.
The columns with names eq1,eq2,eqX are the predicted probabilities for each outcome from the results equations. The column eqC is the most probable outcome. The columns wa1, waX, wa2 are the predicted probabilities for each outcome from the expected goals equations, and the column waC is the most probable outcome. The columns waO, waU, waOU referring to Over/Under 2.5 goals predictions.
I’ll remind you that the bookmakers’ odds Home, Draw, Away, Over, Under are the average ones; you can find better odds for sure.

Section one – Results equations as a classifier.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

496

219

44.2%

125

25.2%

152

30.6%

445

-11.5%

2

135

37

27.4%

33

24.4%

65

48.1%

153

11.9%

X

92

36

39.1%

29

31.5%

27

29.3%

97

4.8%

As you can see on the table, we had excellent results, even for blind betting, when the results equations suggest as the most probable outcome, the draw or the away win.
We had 227 games with a positive expected value, a pool from which we can “safely” pick our bets. A major problem with the home win prediction which, for blind betting, returns over 11% loss.

Section two – Results equations as value bets.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

470

203

43.2%

117

24.9%

150

31.9%

411

-14.3%

2

127

34

26.8%

29

22.8%

64

50.4%

151

15.7%

X

92

36

39.1%

29

31.5%

27

29.3%

97

4.8%

The picture is almost the same, great results for the draw and away win predictions and very poor results for the home win. We had 219 games with a positive expected value, a pool from which we can “safely” pick our bets but a very large number of home win predictions we must avoid; I’ll try to address this issue later.

Section three – Expected goals equations as a classifier.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

567

241

42.5%

148

26.1%

178

31.4%

495

-14.6%

2

150

48

32.0%

36

24.0%

66

44.0%

157

4.2%

The expected goals equations never (or rarely) predict a draw as a most probable result because they follow the bookmakers’ approach to the game more. We have slightly worse results from the results equations, although the away win prediction still has a positive expected value. The problem with the home win gets bigger.

Section four – Expected goals equations as value bets.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

512

220

43.0%

128

25.0%

164

32.0%

452

-13.2%

2

134

46

34.3%

30

22.4%

58

43.3%

138

2.8%

We had a slight improvement for home win predictions followed by a smaller positive return in away win predictions.

Section five – Combining the two methods.

This section presents the games’ results that two methods predict the same outcome as most probable.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

468

208

44.4%

115

24.6%

145

31.0%

416

-12.5%

2

91

26

28.6%

18

19.8%

47

51.6%

113

19.7%

We observe a major improvement of away win predictions regarding the capital return percentage and a slight improvement to home win returns. The problem with the home wins has troubled me enough; although the equations are not suitable for blind betting, the negative return is too big. Of course, because of the closed stadiums, we might have a very smaller home team advantage or even a zero one.
A slight increase in home team advantage (about 3-4 percent) is more than enough to reverse the picture, but I decided to do a little more digging. Below is the table for home win predictions when the home team is a clear favorite, when it is a better team, in other words, when the home team advantage doesn’t play so much role in the game’s outcome. I choose the odds from one to 2.10 ,and I’ll use the results equations, as a classifier, because they seem to do a little better than expected goals this year.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

223

136

61.0%

39

17.5%

48

21.5%

229

2.6%

Voula, this is the picture we expect to see; a small positive return or even around zero is fine. The method is made to help you improve your game, not to replace you; there is no such thing as an automated system to make you rich! Now let’s see the games’ results with home team odds bigger than 2.10 when the home team advantage starts to kick in the game’s reality.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

273

83

30.4%

86

31.5%

104

38.1%

216

-26.4%

The loses for the home team are so big that I am sure that there is value on draw and away win, but I’m not going to look it up because this is not the right way to do things!

Final thoughts

We can establish a pool of predicted outcomes with positive or near positive expected returns from our analysis. During a nearly full month of football activity, the method gave us about 450 games from which we could select our bets more safely. Applying your style to game selection is essential; in my book, I present 40 real-life examples that describe my betting style. You can use them as it is or to give you an idea.


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