Equations evaluation until January 31.

by John Kamaras

02/02/2021

Summary

After almost three and a half 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 Oct 18 till Jan 31, we examined 2143 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. Finally, you can find the Covid and VAR effect to football outcomes here.

Section one – Results equations as a classifier.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

1572

698

44.4%

409

26.0%

465

29.6%

1448

-8.6%

2

367

94

25.6%

88

24.0%

185

50.4%

395

7.2%

X

204

72

35.3%

61

29.9%

71

34.8%

210

2.9%

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 571 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 8.6% loss.

Section two – Results equations as value bets.

outcome

games

1

per1

X

perX

2

per2

ret

prof%

1

1479

656

44.4%

385

26.0%

438

29.6%

1359

-8.9%

2

353

88

24.9%

84

23.8%

181

51.3%

385

8.4%

X

204

72

35.3%

61

29.9%

71

34.8%

210

2.9%

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 557 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

1727

749

43.4%

451

26.1%

527

30.5%

1565

-10.3%

2

405

111

27.4%

101

24.9%

193

47.7%

410

1.3%

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

1555

686

44.1%

392

25.2%

477

30.7%

1434

-8.4%

2

375

104

27.7%

91

24.3%

180

48.0%

380

1.3%

We had a slight improvement for home 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

1468

666

45.4%

382

26.0%

420

28.6%

1351

-8.7%

2

242

62

25.6%

55

22.7%

125

51.7%

261

7.2%

We observe a minor decrease of away win predictions regarding the capital return percentage and a slight increase 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

692

405

58.5%

149

21.5%

138

19.9%

669

-3.4%

Much better picture for the home teams but still under expectations. 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

880

293

33.3%

260

29.5%

327

37.2%

778

-13.1%

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!
You can find an article about the Covid and VAR effect to football outcomes here.

Final thoughts

We can establish a pool of predicted outcomes with positive or near positive expected returns from our analysis. During these three and a half months of football activity, the method gave us over 550 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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