FanPost

The Bundesliga Winterbreak: A Points Modifier? An Expanded View


Earlier today, FC Bayern dropped a Captain Insano powerbomb on FC Shalke 04 to to the tune of 5-0. This bodes well for the continuation of FC Bayern's first half form and to celebrate this auspicious occasion, I'm going to post lots of MATH! YEAH MATH! More accurately, I am going to present the results from expanding the analysis I did a week and a half ago here. Back then I asked myself the following question

How do the league leaders at the Winter Break fare in post-Winter Break play in the Bundesliga? How should we expect FC Bayern to do in 2013?

However, I had also asked myself the following question:

Do teams tend to perform better, worse after this break?

Directly related to this was the observation that the average Bundesliga league leader (BuLL) at the winter break earned only 31 points after the winter break (When/if you read the article, it will say 32/33, but if you do math on a 9-4-4 record its 31, and 42+31=73 like I stated the final ) (and yes, beer was involved previously) down from the average of 38 prior to the break. The question here is Who did these dropped points go to? Now is where the math parts comes in.

To answer this, I used the previous methodology and expanded my data set to every Bundesliga since they moved to 18 teams after German reunification, or the 1992-1993 season. This does cause changes in the average performance of the top team as compared to the previous model containing only 12 years worth of data.


Bundesliga Average Team Performance Post Winterbreak based on league position at Winterbreak

First Half

Second Half

W

D

L

Pts

W

D

L

Pts

Δ Pts

Ttl Pts

1

11.3

3.6

2.2

37

8.9

4.7

3.5

31

-6

68

2

10.1

4.1

2.9

34

9

3.3

4.7

30

-4

64

3

9.3

4.3

3.5

32

8.2

3.4

5.5

28

-4

60

4

9

3.8

4.3

31

7.2

4.7

5.2

26

-5

57

5

8

4.5

4.5

29

7

4.3

5.8

25

-4

54

6

7.3

5.1

4.6

27

6.6

4.5

5.9

24

-3

51

7

6.9

5.1

5.1

26

5.8

4.7

6.5

22

-4

48

8

6.4

5.1

5.6

24

6.2

4.4

6.5

23

-1

47

9

6.1

4.8

6.2

23

7.1

4

6

25

2

48

10

5.8

4.5

6.8

22

5.1

5

6.9

20

-2

42

11

5.3

4.8

6.9

21

5.8

4.1

7.2

21

0

42

12

4.8

5.3

7

20

5.9

4.2

7

22

2

42

13

4.6

5

7.4

19

5.5

4.6

7

21

2

40

14

4.4

4.4

8.2

18

5

5.2

6.9

20

2

38

15

4.1

4.7

8.3

17

5.8

4.5

6.8

22

5

39

16

3.5

4.4

9.1

15

4.3

4.1

8.6

17

2

32

17

2.6

5.4

9

13

5.6

4.4

7.1

21

8

34

18

2.6

3.6

10.8

11

5.4

4.3

7.3

21

10

32

Sweet Table!

Feel free to peruse the data but seriously who likes tables? What we need is a graph, because everyone loves graphs! If you don't believe me go ask your neighbor. If you don't have neighbor, move and get one. Then realize that neighbors suck, especially if they have really loud "private" time (which I suppose you could no longer consider private) and you live somewhere with threadbare walls. Then you can move to the suburbs and really hate your neighbors because they never take their Christmas lights down and She is a giant trophy (and not the good kind that's silver with big ears) who sucks the soul out of everything, kind of like a Dementor from Harry Potter. Remember, no neighbor is like Wilson on Home Improvement. But I digress from charts, so here's a chart!

Figure1_medium

As shown, there is actually a measurable and very accurate increase in the points earned by lower table teams after the winter break indicating they actually outperform themselves compared to their pre-winter break performance. While there is some variability, the fitness of the line shows a very tight correlation. A caveot of this is that fact that we are looking at the fitness of averages, not the raw data. However, even using the raw data there is still a enough fitness to the line to be confident of an overall trend (R2 of 0.3148).

This speaks to the competitiveness of the Bundesliga in that the worst teams show a marked increase in points earned post winter break. Whatever the reason, whether it be better tactics or more training time for team coordination, but the best teams actually drop points compared to their pre-break performance.

With the linear trend we can actually assess how teams should fare after the Winter Break and what position they should theoretically occupy.

(2nd Half Points)-(1st Half Points) = 0.7872(league position)-7.4314 or as integrated into a model

(Final Points) = (2)*(1st Half Points )+0.7872(Winter Break Table Position)-7.4314

In another post to come, I will assess the viability and accurate predictiveness of this model in determining final league position for all teams. While this model so far is fairly rudimentary and takes into account league position independent of the actual numbers of points earned in the first half, I hope to soon integrate actual points earned as a factor for modifying the above model. Stay tuned for more to come.

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