The early goal catches the worm (Betting School)

Another Betting School article. This time one I originally wrote in April 2013. This one looks at goal times and the impact of an early goal.

The early goal catches the worm

 

I was struggling through Osasuna v Atletico Madrid and trying to find things to do other than watch the match when a goal was scored. The goal was scored in the 35th minute and I wondered what the likelihood of more goals was. Before the goal I had looked at the under 2.5 goal price and it was around 1.55 so goals certainly weren’t expected in large numbers.  Obviously this goal made over 2.5 more likely but was there a way of working out how much more likely?

 

I decided to do some research into it and that is what I am going the share with you. I have used the betexplorer.com  site as they provide odds for matches and the goal times if you click on the link to any match. I always like to look at the major leagues so I have used the Premier League, La Primera in Spain , Serie A in Italy, the Bundesliga in Germany and the French Ligue 1. For all these leagues I have used data from this season. I also added the Championship as there is no doubt more interest in English leagues amongst the readership. This season is heading towards the finish line but there is a lot of football still played in the summer so I have used the MLS in America and the J-League in Japan. For these two leagues I have used data from last season which ran throughout 2012.

 

The main market to look at in all this is the over/under 2.5 goal market as this is the classic goal line. Let’s see on average how many games from each of these leagues are over/under 2.5 goals and how many matches are included for each league in this study.

 

Total Over 2.5 Under 2.5
League Season Games Games % Games %
Eng Champ 2012-13 453 238 53% 215 47%
Eng Prem 2012-13 296 168 57% 128 43%
French Ligue 1 2012-13 290 131 45% 159 55%
German Bundesliga 2012-13 234 124 53% 110 47%
Italy Serie A 2012-13 288 141 49% 147 51%
Japan J-League 2012 306 158 52% 148 48%
Spain La Primera 2012-13 280 148 53% 132 47%
USA MLS 2012 323 165 51% 158 49%
Total 2470 1273 52% 1197 48%

 

Table 1. Number of matches for each league included in study and counts and percentages of games which were over or under 2.5 goals

 

Looking at the table the first thing that stands out is the number of games in the Premier League that have been over 2.5 goals.  I have no idea why, but would presume this is a blip as it is way ahead of all the other leagues. If you remove the Premier League and France from the data then all the rest are pretty consistent lying between 49% and 53%. The average is 52% across all these leagues and I always think this demonstrates why over/under 2.5 goals works so well in betting as the line is so close to 50%. You can see prices for the coming matches on the Bet365 website.

 

We want to look at the time of the first goal and see how that impacts on the over/under market; so let’s first look at the times of the first goal across the leagues  to get an idea of when it occurs. The results are shown in table 2.

 

League 0-10 11-20 21-30 31-40 41-50 51-60 61+ 0-0
Eng Champ 18% 20% 14% 11% 10% 8% 13% 6%
Eng Prem 22% 19% 11% 13% 8% 7% 11% 9%
French Ligue 1 16% 19% 12% 14% 10% 8% 14% 8%
German Bundesliga 25% 14% 17% 14% 12% 4% 7% 8%
Italy Serie A 20% 17% 13% 10% 8% 9% 15% 8%
Japan J-League 19% 19% 11% 11% 12% 7% 14% 8%
Spain La Primera 22% 19% 17% 9% 7% 6% 13% 6%
USA MLS 21% 21% 11% 6% 10% 7% 16% 7%
Total 20% 19% 13% 11% 10% 7% 13% 7%

 

Table 2. Time of the first goal in matches, grouped in 10 minute brackets, as a percentage of the total matches

 

I was surprised by how many matches feature an early goal. Across all leagues on average 1 in 5 matches has a goal before the 10th minute. It feels like I never watch these matches, that’s for sure. The Bundesliga produces some odd figures having the highest percentage in the 0-10 bracket and the lowest in 11-20 but they probably average out like the others if you looked at 0-20 as a whole. If we know there are quite a lot of early goals let’s look at the 4 brackets from 0-40 minutes and see what impact the goal has on the over 2.5 goal expectancy.  The results are shown in table 3.

 

All 0-10 11-20 21-30 31-40
League O U O U O U O U O U
Eng Champ 53% 47% 75% 25% 71% 29% 75% 25% 55% 45%
Eng Prem 57% 43% 83% 17% 75% 25% 58% 42% 57% 43%
French Ligue 1 45% 55% 67% 33% 65% 35% 57% 43% 51% 49%
Ger’ Bundesliga 53% 47% 69% 31% 67% 33% 60% 40% 63% 38%
Italy Serie A 49% 51% 71% 29% 67% 33% 70% 30% 43% 57%
Japan J-League 52% 48% 90% 10% 72% 28% 62% 38% 46% 54%
Spain La Primera 53% 47% 74% 26% 66% 34% 69% 31% 42% 58%
USA MLS 51% 49% 71% 29% 72% 28% 68% 32% 45% 55%
Total 52% 48% 75% 25% 70% 30% 66% 34% 51% 49%

 

Table 3. Breakdown of matches which have over or under 2.5 goals based on the time the goal was scored. Goals are grouped in 10 minute brackets and ‘All’ is included to allow comparison.

 

Very interesting and some possible angles that could be exploited.  It is no surprise that if there is a goal in the first 10 minutes of a match  the likelihood of the game going over 2.5 goals is greatly increased. If we look at all matches the average is that it happens in 75% of matches, so if you got to the 10th minute of a match and a goal had been scored then you would want to be backing over 2.5 goals at a price better than 1.33 to get some value. I calculated that by converting 75% to decimal odds (1/0.75).

 

My view is that by using the odds required we can get a much better picture of the jump that occurs in the 10 minute brackets and this is shown in table 4.

 

Percentage Equivalent Odds
Minute Over Under Over Under
0-10 75% 25% 1.33 4.01
11-20 70% 30% 1.43 3.31
21-30 66% 34% 1.52 2.93
31-40 51% 49% 1.96 2.05
41-50 52% 48% 1.91 2.1
51-60 38% 62% 2.64 1.61

 

Table 4. Equivalent odds based on the percentages of matches that go over or under 2.5 goals based on the time bracket of the first goal

 

The table above shows very well the big drops in the percentage of games that go over 2.5 goals if a goal is not scored before the 30th minute, and then again after the 50th minute. I have charted the results as well in figure 1 to allow further analysis.

 

Figure 1. Line chart showing percentage of games over 2.5 goals based on time of 1st goal.

 

I wanted to visualise this data to see if it is easier to see the change, as from the table it looks to be steady but then dips drastically. In figure 1, I grouped the minutes in pairs (1-2, 3-4 etc) to lessen the spikes in the chart. The chart shows a steady gradual decline to just before the 30th minute and then there does seem to be a drop. The bigger and more clear drop is the next one just after half time and therefore the chances of over 2.5 occurring drops steeply after half time.

 

We have some basic facts but then there are some factors we should probably consider, such as if the team that scores first is the home team or the away team. In table 5, I have produced the same figures we have in table 3 but broken down by if the home team or the away team scored first.

 

All 0-10 11-20 21-30 31-40
League 1st O U O U O U O U O U
Eng Champ Hm 60% 40% 86% 14% 78% 22% 64% 36% 52% 48%
Aw 52% 48% 63% 37% 63% 37% 82% 18% 58% 42%
Eng Prem Hm 67% 33% 91% 9% 79% 21% 67% 33% 50% 50%
Aw 58% 42% 75% 25% 72% 28% 50% 50% 63% 37%
French Ligue 1 Hm 54% 46% 54% 46% 68% 32% 77% 23% 53% 47%
Aw 45% 55% 81% 19% 63% 38% 45% 55% 50% 50%
German Bundesliga Hm 60% 40% 74% 26% 67% 33% 53% 47% 53% 47%
Aw 55% 45% 58% 42% 67% 33% 64% 36% 71% 29%
Italy Serie A Hm 65% 35% 76% 24% 73% 27% 79% 21% 67% 33%
Aw 42% 58% 64% 36% 61% 39% 61% 39% 28% 72%
Japan J-League Hm 54% 46% 84% 16% 74% 26% 77% 23% 29% 71%
Aw 57% 43% 96% 4% 70% 30% 52% 48% 57% 43%
Spain La Primera Hm 63% 37% 83% 17% 81% 19% 85% 15% 36% 64%
Aw 51% 49% 65% 35% 50% 50% 57% 43% 50% 50%
USA MLS Hm 60% 40% 81% 19% 77% 23% 69% 31% 50% 50%
Aw 50% 50% 61% 39% 66% 34% 67% 33% 42% 58%
Total Hm 60% 40% 80% 20% 75% 25% 71% 29% 49% 51%
Aw 51% 49% 70% 30% 64% 36% 62% 38% 53% 47%

 

Table 5. Breakdown of matches which have over or under 2.5 goals based on the time the goal was scored and by which team. Goals are grouped in 10 minute brackets and ‘All’ is included to allow comparison.

 

The table is a little busy but if you start with the total line you immediately get a feel of what happens. If the home team score first then the likelihood of there being more goals is much more likely.

 

If the home team scores first before the 10th minute then in 8/10 matches the game goes on to have over 2.5 goals. Compare this to the away team scoring before the 10th minute and this drops to 7/10. Thinking of the equivalent decimal odds it would mean you would need on average odds of over 1.42 (1/0.7) on over 2.5 goals if the away team scored but just 1.25 (1/0.8) if the home team scores; and that is a big difference. Even if the home team scores between the 11th and 20th minutes there is still a 75% chance of over 2.5 goals.

 

I just want to have one more dig into this data and will stick with the home teams but group them in terms of odds.  I have made 4 brackets for the home team price which is less than 1.5, 1.5 to 1.99, 2-2.99, 3-3.99 and 4+.

 

0-10 11-20 21-30 31-40
Odds O U O U O U O U
<1.5 95% 5% 88% 12% 88% 13% 100% 0%
1.5-1.99 81% 19% 75% 25% 68% 32% 47% 53%
2.0-2.99 77% 23% 73% 27% 70% 30% 53% 47%
3.0-3.99 74% 26% 69% 31% 67% 33% 38% 62%
4+ 76% 24% 79% 21% 75% 25% 21% 79%
Total 80% 20% 75% 25% 71% 29% 49% 51%

 

Table 6. Breakdown of matches which have over or under 2.5 goals when the home team scores first based on  time the goal was scored and the pre-match odds of home team.

 

I grouped all the matches together to give a bigger sample size. Again there is the clear drop off as you pass the 30th minute and this is even more pronounced when the away team has a bigger price.

 

It is interesting in this group that in the matches where the home team was priced below 1.5 and they scored anytime before the 40th minute then over 90% (64 out of the 70 matches) were over 2.5 goals. This is a small sample but there looks likely to be a fair few goals even if the home favourite takes a while to get going.

 

Hopefully this will give you some added insight when a goal goes in. Thinking about the time that is was scored and by which team should enable you to make a decent decision on whether to back over or under 2.5 goals.

Half Time Report (Betting School Article)

Another Betting School article. This time one I originally wrote in November 2010. This one looks at full time outcomes based on half time results.

When the half time whistle goes and you get the urge to make a bet is there anything the first half can tell you? Of course you will have seen how the match has panned out and make judgements on what you think will happen but can stats help? The aim of this article is to look at a few of the in-play markets on Betfair and see if we can get some valuable pointers to the outcome of the game based on the half time result.

The data I am looking at is from the top division in England, Spain and Italy for the seasons from 2005-2006 to 2009-2010. The statistics and pre-match odds for the 5700 matches come from the treasure trove that is www.football-data.co.uk .

The in-play data was downloaded from the Betfair data site and is for the entire 2009-2010 season.  The Betfair data files contain so much information that all I have is the in-play data so the pre-match prices come from football-data. I have taken the half time data as prices that occur 50 minutes after match kick off.

Strong Favourites

The idea for this article came when watching Real Madrid away at Levante this season which was scoreless at half time. Looking at the data for the game I can see at kick off Madrid were matched at 1.24 on Betfair and this increased to 1.39 at half time.  I was convinced they would win and as the odds drifted, I decided they were great value and at half time and made my bet.

Let’s look at the results of some of these strong favourites. In the last 5 seasons in the 3 leagues I am using there were only 13 occasions when the away team was priced 1.3 or below. Interestingly on 6 of those occasions they were drawing at half time and went on to win. An example is Real Madrid on 13/02/2010 at Xerez when they were also drawing 0-0 at half time. On this occasion they started the game at 1.27 and could have been backed at 1.47 at half time before going on to win 3-0.

That’s not enough data so let’s look at home strong favourites, which I classify as teams priced at 1.3 or less using BbMxH (Betbrain maximum home win odds). All in all there have been 344 of these matches and the home team won 293 (85%) of these games.  If we look at games that were draws at half time we are left with 114 matches and the home team won 85 of them (74%).  To break even backing those teams we would need odds of 1.41 taking into account betfair commission of 5%. I have chosen 4 of those games to look at the Betfair data and get an idea of the sort of odds available.

Div

Date

Home

Away

First Match Odds in play

Match odds at HT

Eng Prem

26/12/2009

Liverpool

Wolves

1.3

1.61

Ita Seria A

09/01/2010

Inter

Siena

1.23

1.56

Eng Prem

31/10/2009

Man United

Blackburn

1.22

1.46

Spa Primera

26/09/2009

Real Madrid

Tenerife

1.11

1.3


In 3 out of 4 of these games the price required is reached and the game that didn’t had the home team ridiculously short (only 18 of the 114 matches had home odds lower than 1.2). This looks like a strategy with some promise but are there any angles that strengthen the chances of the game ending in a home win? Let’s check by leagues

Div

HT Draws

Won

Drew

Lost

Win %

Eng Prem

59

42

16

1

71%

Ita Seria A

27

18

8

1

67%

Spa Primera

28

25

1

2

89%

Clearly Spain looks the best league with which to follow this strategy. Spain, like Scotland, has a very unhealthy two team domination and Barcelona or Real Madrid have won the league in all but 4 of the last 26 seasons. The Big 2 therefore have a strong advantage and that would account for these figures.

Looking at games poised at 1-1 as opposed to 0-0 we see

Div

HT Draws

Won

Drew

Lost

Win %

Eng Prem

11

8

3

0

73%

Ita Seria A

6

5

1

0

83%

Spa Primera

10

9

1

0

90%

 Not a huge amount of data but a game that is already 1-1 looks more likely to go on to be a home win than a game that is goalless.  I think this demonstrates that although the chance of a home win decreases slightly when a match is all square at half time, the odds more than make up for this and provide value.

Going back to the Real Madrid game I was watching; the game stayed at 0-0 despite Real Madrid having 25 shots. There are numerous occasions when you watch this type of game and the pressure the favourite exerts increases and increase before the smaller team cracks and I’d back Real Madrid in the same position again.

For the record these are all the teams that started at odds of 1.3 or lower and their performance when drawing at half time. I only included teams with at least 4 matches.

Team

HT Draws

Won

Drew

Lost

Win %

Real Madrid

5

5

100%

Barcelona

13

12

1

92%

Man United

13

11

2

85%

Arsenal

4

3

1

75%

Inter

7

5

2

71%

Milan

3

2

1

67%

Chelsea

12

7

5

58%

Juventus

4

2

2

50%

Liverpool

5

2

3

40%

Scoreless second half

A number of Betfair markets such as next goal, correct score and the over/under markets are affected by the number of second half goals. Let’s look at the chances of a scoreless second half by league.

Div

Scoreless 2nd Half

Matches

% matches scoreless 2nd Half

Eng Prem

455

1900

24%

Spa Primera

434

1900

23%

Ita Seria A

432

1900

23%

Total

1321

5700

23%

 

There seems little difference across these leagues and in general  just under 1 in 4 matches has a scoreless second half. The next step is to see if the half time score affects that.

HT Score

Scoreless 2nd Half

Matches

% matches scoreless 2nd Half

0-0

455

1823

25.0%

1-0

297

1229

24.2%

0-1

165

846

19.5%

1-1

137

591

23.2%

2-0

99

392

25.3%

2-1

46

202

22.8%

0-2

33

193

17.1%

1-2

22

117

18.8%

3-0

20

89

22.5%

2-2

13

55

23.6%

 

The most interesting stat for me here is that a game that is 2-0 at half time is as likely to be scoreless in the second half as a game that is 0-0 and the chance is still 1 in 4.  To investigate this further I dropped all matches with a strong favourite which I classify as teams priced at 1.3 or less using BbMxH (Betbrain maximum home win odds). In the Betfair data for 2009/2010 I was left with 66 games that were 2-0 at half time.

In these games I want to see what sort of profit would have been made backing the no goals being scored in the second half. A 2-0 half time game has a number of markets which are all the same such as 2-0 correct score, no next goal and under 2.5 and it’s the latter I will use as the data is easiest to deal with having just 2 outcomes and also better liquidity.

The full table showing all 66 results is produced at the end of the article. I looked for an under 2.5 goals price matched around 50 minutes after the start of the match. 20 of the 66 games went on to stay 2-0 and backing under 2.5 goals at the half time price I found would have returned a whopping 23 points profit which is an ROI of around 35%. The results also demonstrate the fluctuations that can happen in betting. If you started this idea on the opening Saturday of the season come Valentines Day you would have been in love with it, up 33 points and with an ROI of over 80%! The other people you told about it who started the day after might not have been so pleased with the 9 point loss they were saddled with to the end of the season. The same odds would no doubt be available for backing 2-0 as a correct score or no next goal.

In conclusion it looks like goals do not necessarily mean more goals although the perception of the general betting public is that it does.

Will they draw?

On a similar theme does the half time score affect the chance of the game drawing. Here is a list of scores and the chance of the game ending in a draw.

HT Score

Ends in draw

Matches

% Chance of draw

2-2

19

46

41%

0-0

673

1745

39%

1-1

206

564

37%

1-2

30

116

26%

0-1

194

830

23%

1-0

223

1122

20%

2-1

30

183

16%

0-2

15

191

8%

2-0

21

337

6%

3-0

1

75

1%

Quite clear if a game is a draw at half time then there’s a good chance of the final score being a draw. In this case what stands out to me is the fact that games that are 1-0 or 2-1 have a much lower draw chance and worth looking to see if laying the draw is possible.

In this case checking the Betfair data is not as easy as before as the data is only stamped as to when it first and last occurred.  Therefore an odds value may occur in the first half, half time and the second half but it is not possible to know the half time value as only the first and last time the odds were taken is available without paying.

In this case I have looked at games where the away team started off as a favourite (BbMxA > BbMxH) to give myself an idea of the target odds.

Div

FT Draw

Matches

% FT Draws

Betfair Odds Required

Eng Prem

8

65

12.3%

8.125

Ita Serie A

8

59

13.6%

7.375

Spa Primera

7

49

14.3%

7

It would be nice to prove this as a back or lay strategy but as the data does not allow it and it will have to be a watching brief.

Conclusion

The two clear messages that come out of this are firstly, that if a team are strong pre-game favourites the drift of odds in play can work in your favour. If I see Real Madrid again drawing at half time I will be happy to go in again and back them to win hoping that 25 shots will be enough this time! Secondly, goals do not necessarily mean more goals and if you want to stand out from the crowd go under as that seems to be where the money is.

 

Table showing Betfair in play half time odds for under 2.5 goals when the HT score was 2-0

Div

Date

Home

Away

First In Play Match

Under 2.5 Match at HT

Final Score

Profit less 5%

Eng Prem

15/08/2009

Stoke

Burnley

1.78

3.8

2-0

2.66

Ita Serie A

30/08/2009

Napoli

Livorno

1.88

4.3

3-1

-1

Ita Serie A

30/08/2009

Sampdoria

Udinese

2.14

5

3-1

-1

Ita Serie A

19/09/2009

Juventus

Livorno

2.18

5.7

2-0

4.465

Eng Prem

19/09/2009

Aston Villa

Portsmouth

2.06

4.4

2-0

3.23

Ita Serie A

20/09/2009

Sampdoria

Siena

2

5

4-1

-1

Spa Primera

20/09/2009

Ath Bilbao

Villarreal

1.92

4.2

3-2

-1

Ita Serie A

20/09/2009

Chievo

Genoa

1.77

4.4

3-1

-1

Spa Primera

22/09/2009

Sevilla

Mallorca

2.28

5.3

2-0

4.085

Eng Prem

26/09/2009

Tottenham

Burnley

2.72

5.7

5-0

-1

Spa Primera

27/09/2009

Zaragoza

Getafe

2

4.1

3-0

-1

Ita Serie A

04/10/2009

Palermo

Juventus

2.02

6.2

2-0

4.94

Spa Primera

18/10/2009

Zaragoza

Santander

2.02

4.8

2-2

-1

Ita Serie A

28/10/2009

Juventus

Sampdoria

2.02

4.3

5-1

-1

Eng Prem

31/10/2009

Arsenal

Tottenham

2.74

5.4

3-0

-1

Eng Prem

31/10/2009

Stoke

Wolves

1.74

3.9

2-2

-1

Eng Prem

31/10/2009

Portsmouth

Wigan

1.82

4.3

4-0

-1

Ita Serie A

28/11/2009

Udinese

Livorno

1.7

3.9

2-0

2.755

Spa Primera

29/11/2009

Valladolid

Tenerife

1.95

4.1

3-3

-1

Eng Prem

05/12/2009

Aston Villa

Hull

2.02

4.6

3-0

-1

Spa Primera

19/12/2009

Ath Bilbao

Osasuna

1.71

4.3

2-0

3.135

Eng Prem

26/12/2009

Man City

Stoke

1.94

4.4

2-0

3.23

Eng Prem

11/01/2010

Man City

Blackburn

2.04

4.2

4-1

-1

Spa Primera

16/01/2010

Osasuna

Espanol

1.59

4.1

2-0

2.945

Eng Prem

16/01/2010

Everton

Man City

1.94

4.3

2-0

3.135

Ita Serie A

17/01/2010

Roma

Genoa

2.1

4.8

3-0

-1

Spa Primera

17/01/2010

Valencia

Villarreal

2.14

5.3

4-1

-1

Ita Serie A

24/01/2010

Genoa

Atalanta

1.93

3.95

2-0

2.8025

Ita Serie A

24/01/2010

Palermo

Fiorentina

1.8

4.2

3-0

-1

Eng Prem

27/01/2010

Everton

Sunderland

1.99

5

2-0

3.8

Eng Prem

31/01/2010

Man City

Portsmouth

2.14

4.5

2-0

3.325

Spa Primera

06/02/2010

Valencia

Valladolid

2.46

5.3

2-0

4.085

Eng Prem

06/02/2010

Stoke

Blackburn

1.68

3.45

3-0

-1

Ita Serie A

07/02/2010

Inter

Cagliari

2.26

5.7

3-0

-1

Eng Prem

07/02/2010

Chelsea

Arsenal

1.95

4.2

2-0

3.04

Eng Prem

09/02/2010

Fulham

Burnley

1.73

4.8

3-0

-1

Ita Serie A

13/02/2010

Sampdoria

Fiorentina

1.78

3.9

2-0

2.755

Ita Serie A

14/02/2010

Cagliari

Bari

1.8

4.5

3-1

-1

Spa Primera

21/02/2010

Ath Bilbao

Tenerife

1.93

5.7

4-1

-1

Ita Serie A

21/02/2010

Cagliari

Parma

1.96

4.2

2-0

3.04

Ita Serie A

21/02/2010

Palermo

Lazio

1.87

3.6

3-1

-1

Ita Serie A

27/02/2010

Catania

Bari

1.71

3.7

4-0

-1

Ita Serie A

28/02/2010

Milan

Atalanta

2.24

4.8

3-1

-1

Eng Prem

28/02/2010

Tottenham

Everton

1.82

4.2

2-1

-1

Spa Primera

07/03/2010

Ath Bilbao

Valladolid

1.92

4.3

2-0

3.135

Eng Prem

20/03/2010

Sunderland

Birmingham

1.72

3.75

3-1

-1

Spa Primera

20/03/2010

Xerez

Tenerife

2

5.3

2-1

-1

Eng Prem

28/03/2010

Liverpool

Sunderland

2.3

6.6

3-0

-1

Spa Primera

28/03/2010

Villarreal

Sevilla

1.99

5.4

3-0

-1

Spa Primera

28/03/2010

Xerez

Valladolid

1.74

4.4

3-0

-1

Spa Primera

03/04/2010

Sevilla

Tenerife

2.36

4.4

3-0

-1

Ita Serie A

03/04/2010

Catania

Palermo

1.69

4.7

2-0

3.515

Eng Prem

03/04/2010

Sunderland

Tottenham

1.98

4.7

3-1

-1

Ita Serie A

11/04/2010

Roma

Atalanta

2.48

4.9

2-1

-1

Spa Primera

11/04/2010

Ath Bilbao

Almeria

1.96

4.6

4-1

-1

Spa Primera

11/04/2010

Mallorca

Valencia

2.02

5

3-2

-1

Eng Prem

17/04/2010

Sunderland

Burnley

2.24

5.2

2-1

-1

Spa Primera

17/04/2010

Villarreal

Ath Madrid

2.42

5

2-1

-1

Eng Prem

17/04/2010

Tottenham

Chelsea

2.12

5.9

2-1

-1

Eng Prem

19/04/2010

Liverpool

West Ham

2.02

4.7

3-0

-1

Eng Prem

24/04/2010

Bolton

Portsmouth

2.1

4.8

2-2

-1

Ita Serie A

24/04/2010

Palermo

Milan

2.08

5.1

3-1

-1

Spa Primera

25/04/2010

Ath Madrid

Tenerife

2.1

5.9

3-1

-1

Eng Prem

01/05/2010

Birmingham

Burnley

1.91

4.4

2-1

-1

Spa Primera

08/05/2010

Villarreal

Valencia

2.6

6.4

2-0

5.13

Ita Serie A

15/05/2010

Milan

Juventus

2.38

6

3-0

-1

 

TOTAL

23.21

AVERAGE

2.02

4.73

0.35

 

Hit or Myth: Does the bogey team exist? (Betting School Article)

Another Betting School article. This time one I originally wrote in November 2011. This one examines the idea of bogey teams.

Hit or Myth: Does the bogey team exist?

Continuing on with the theme of myths in football this month I have decided to pick bogeys! I remembered an exchange of emails I had a few years back with someone who was convinced that the idea of bogey teams existed. He gave me some evidence of classic bogey teams but wanted me to check the data. I never did, but this month I want to look into this and see if he was right.

What is a bogey team? A definition I found on the internet was ‘Bogey team is British sports slang for a team which usually manages to win despite an apparent weakness.’

I then searched more on this subject in football and came across some interesting examples such as Bolton being a bogey team for Arsenal in the early 2000s. Looking at the results from the 2002-2003 season to the 2006-2007 season Bolton won 4 of 8 matches at the Reebok stadium against Arsenal and only lost in the FA cup at home during that period so Arsenal managed just 1win in 8 games. They had 4 consecutive wins at one point and were rightly considered their bogey team. This all changed when Arsenal won in the FA Cup at the Reebok in 2007 and they won 10 of the next 11 matches totally turning this so called bogey team myth on its head. The Arsenal – Bolton results were often put down to Bolton’s style of play being the antithesis of Arsenal and the commonly held belief that Arsenal did not like it up ‘em.

Another interesting one is Leeds v Cardiff who drew together earlier this season at Elland Road. That was the 12th time they have met since 1984 and Leeds have not won any of those matches compared with Cardiff who have 10 wins in those 12 matches. The theory this was attributed to was that Cardiff see this as a rivalry and it is almost like a derby for them. I think this rivalry is something to do with hooliganism but Leeds do not see this as a derby, I think they probably think they are better than that, and so are not as “up for it”.

In both cases it seems that the indication is that the style of play a team adopts is the main reason for these bogey problems. I would agree that the style of play a team adopts can have a big bearing on some stats in a match but can certain teams not cope with another team’s style? Could it be geographical factors affect teams like having far to travel or the opposite and a local derby really producing a one sided set of results?

The question is how to measure a bogey team. I have collected 11 years worth of data from the English football leagues using the seasons 2000-2001 to 2010-2011 from the football-data.co.uk site to try and work this out. My plan is to try and find some teams that really struggle against others in the first 10 seasons and see if we could have profited backing them in the season 2010-2011.

One of the problems with looking at all this data is that you often are find stats like Man United have played Wigan 10 times n the last 5 seasons and won them all. That’s not a bogey team but more just a gulf in class. However, in those 10 years Spurs have only won 1 of 20 matches against Man United and United have won 15 of those matches. Could United be considered Spurs’s bogey team? That is a personal decision though and in the end I decided the best course of action was in fact to remove all games featuring Arsenal, Chelsea, Liverpool or Man United from this as they have been very dominant over the time period we are looking at and have numerous bogey teams. I also removed Man City as they are unrecognisable from the team that they were in the early 2000s. Spurs were always a bit of a bogey team for them but that was well and truly wiped out at White Hart Lane this season. In any football analysis I often think the approach of removing the bigger teams gives you more a more consistent set of games to look at.

I decided to cut off the minimum matches the teams needed to have played as 10 matches in the last 10 years which means they would have been matched together for 5 seasons. This meant we did not get Cardiff against Leeds as they only met 6 times. The games I chose had to meet 1 or more of the following criteria

  • The bogey team had won at least 75% of the matches
  • The non-bogey team had lost 15% or less of all the matches and the bogey team had won over half of the matches.

The table below shows the 16 teams that played each other in 2010-2011 and could be considered a bogey team. There were quite a few others which were not in the same division in 2010-2011 so did not play each other.

Bogey Team

Handkerchief

M

Bogey

W D L

H

P/L

A

P/L

Tot P/L

Bolton West Ham

14

10 – 2 – 2

W

1.25

W

2.3

3.55

Cheltenham Lincoln

12

8 – 3 – 1

L

-1

W

2.12

1.12

Coventry Barnsley

10

8 – 2 – 0

W

1

L

-1

0

Darlington Grimsby

12

8 – 3 – 1

L

-1

W

2.4

1.4

Everton Sunderland

14

9 – 3 – 2

W

0.8

D

-1

-0.2

Huddersfield Bournemouth

10

6 – 4 – 0

D

-1

D

-1

-2

Ipswich Coventry

18

11 – 5 – 2

L

-1

D

-1

-2

Lincoln Barnet

12

9 – 0 – 3

W

1.1

L

-1

0.1

Newcastle West Brom

10

6 – 4 – 0

D

-1

L

-1

-2

Norwich Barnsley

10

7 – 2 – 1

W

0.91

W

1.6

2.51

Peterboro Notts County

14

8 – 4 – 2

L

-1

W

1.88

0.88

Preston Coventry

18

10 – 6 – 2

W

1.45

W

3

4.45

Torquay Shrewsbury

12

7 – 4 – 1

W

1.5

D

-1

0.5

Tottenham West Ham

16

10 – 4 – 2

D

-1

L

-1

-2

West Ham Blackburn

14

8 – 4 – 2

D

-1

D

-1

-2

West Ham Fulham

14

8 – 4 – 2

D

-1

W

3.5

2.5

 

TOTAL

     

-0.99

   

6.81

The profit and loss figures are based on the best odds that could have been obtained from the data collected from the football-data site. M is matches, Bogey W D L shows their form in those matches and H and A corresponds to the matches in 2010-2011 when the bogey team was home (H) or away (A).

The first thing to look at is the bottom line and if we had backed the bogey team in all matches we would have come out +5.8 points ahead over the 32 matches which is a healthy 18% ROI. In these 32 games the bogey team won 14 matches, drew 10 and lost just 8.

It is interesting that the away results are exactly the same as home results with 7 wins, 5 draws and 4 losses. That got me thinking about the away results and I then decided to look at the results of teams playing away. This time I looked at teams that had played at least 6 of the 10 seasons in the same division and fitted the following criteria

The home team had won less than 15% of all matches.

The results are shown below and remember the bogey team is the away team in each of these cases. Again M is matches and Bogey W D L shows their form in away matches against the non-bogey team or handkerchief. For example the first line is Bolton v Blackburn and the W D L is 5-5-0 which means Bolton won 5, drew 5 and didn’t lose any of their away games at Ewood Park in the years 2000-01 to 2009-10. A trend Blackburn bucked in 2010-2010 when they won!

Bogey

Handkerchief

M

Bogey

W D L

Res

P/L

Bolton

Blackburn

10

5 – 5 – 0

L

-1

Bristol Rvs

Rochdale

6

3 – 3 – 0

L

-1

Grimsby

Darlington

6

5 – 1 – 0

W

2.4

Everton

Tottenham

10

3 – 6 – 1

L

-1

Lincoln

Shrewsbury

9

3 – 5 – 1

W

1.75

Notts County

Peterboro

7

3 – 3 – 1

W

1.88

Coventry

Cardiff

7

3 – 3 – 1

W

1.4

Sunderland

Aston Villa

7

3 – 3 – 1

L

-1

Nott’m Forest

Watford

7

4 – 2 – 1

L

-1

Fulham

West Ham

7

5 – 1 – 1

W

3.5

Sheffield United

Reading

7

5 – 1 – 1

D

-1

Ipswich

Crystal Palace

7

5 – 1 – 1

L

-1

TOTAL

3.93

There were just 11 matches that we have to look at in this analysis which is a very small sample but once again there are some positive results.  The bogey team won 5 of the 12 matches, drew 1 and lost 6 but as they were away in these games the profit is enhanced and they made a nice 3.93 profit over the 12 matches. If you remove the games where half or more of the previous matches in the periods had been draws you get a 5.18 profit from 8 matches and 4 nice wins but that really is tweaking the results a bit too much.

So is this one a hit or a myth? There could be something in this especially in the lower leagues. The Premier League is so dominated by the big 4, 5 or 6 depending on who you support that they account for a huge amount of the so-called bogey teams. The lower leagues are a more accurate indicator because if one team improves they move up a league and so teams only play against other teams of a similar level and you don’t have the gulf in class as in the Premier League.

My feeling therefore is yes the bogey team does exist. As to why I am stumped. Geographically Bolton and Blackburn are close together and feature in the table above and Fulham and West Ham are both in London but none of the others are. Could it be based on the style of play or could it just be something psychological?

I often check past results between sides before making a bet and will do so more often in the future.

Leon Pidgeon

 

Champions League Hangover? (Betting School Article)

Time for another Betting School article. This time one I originally wrote in September 2011 and looks to see if times struggle after playing in the Champions League.

Hit or Myth: Teams struggle at the weekend after playing in the Champions League in midweek

Over the coming months I intend to look at some of the things you often hear people say connected with football and ask if they are really true. For example when a new manager comes in it is often said it gives the place a lift and results immediately improve. Another, which this article is based on, is the one often trotted out after a team plays in the Champions League that they may perform badly after the midweek exertions.

I was reminded of this after the first round of Champions League matches as Barcelona drew 2-2 at home with AC Milan in the Champions League and then thrashed Osasuna 8-0 at home. On the other hand Real Madrid had travelled to Croatia in midweek winning 1-0 against Zagreb, and then lost away to Levante 1-0 at the weekend. It made me think if there could be some truth in it but only if a team had travelled away in the Champions League. It may be that these teams struggle more if they play away in both the Champions League and their domestic league

To test this I have gone back through the Champions League results from the 2005-2006 season to last season using the Bet Explorer site. I then match this data up to the domestic leagues, thanks to football-data.co.uk, and am using teams from England, France, Germany, Italy, Portugal, Scotland and Spain.

The best way to compare this is to look at individual teams performance over the time period. We can then compare how they performed after playing in the Champions League with their domestic form as a whole. Of the leagues I looked at there were only 10 teams that had played in the Champions League in each of the 6 seasons so I have looked at them in the tables below. The first table looks at how they performed at home the weekend after playing in the Champions League.

Won

Drew

Lost

Team

CL

All

CL

All

CL

All

Arsenal

73%

66%

14%

24%

14%

10%

Barcelona

82%

79%

16%

13%

3%

8%

Bayern Munich

73%

72%

19%

18%

8%

9%

Chelsea

68%

76%

24%

21%

9%

3%

Inter

76%

80%

24%

14%

0%

6%

Lyon

32%

72%

52%

22%

16%

6%

Man United

73%

87%

13%

10%

13%

4%

Milan

55%

71%

32%

17%

14%

12%

Porto

85%

80%

8%

15%

8%

5%

Real Madrid

71%

79%

13%

9%

17%

12%

Grand Total

69%

76%

22%

16%

10%

7%

The column CL shows how the percentage for a team playing at home the weekend after a Champions League game and the All column shows the percentage for that team for all other matches. Lyon stand out in this as they have performed terribly after Champions League matches. Of the 10 teams 6 have performed worse the weekend after playing in the Champions League in terms of victories. The lost column is interesting as 7 of the teams recorded more losses after playing in the Champions League than they did in the other games. However, the totals of these losses were just 25 losses in 263 matches so that’s still a meagre 9.5% losses.

Now let’s look at how teams do when playing away from home the weekend after a Champions League fixture.

Won

Drew

Lost

Team

CL

All

CL

All

CL

All

Arsenal

50%

39%

25%

30%

25%

30%

Barcelona

52%

57%

23%

27%

26%

17%

Bayern Munich

45%

46%

36%

29%

18%

25%

Chelsea

48%

61%

29%

16%

24%

23%

Inter

56%

52%

36%

25%

8%

24%

Lyon

57%

49%

29%

23%

14%

28%

Man United

50%

57%

25%

24%

25%

19%

Milan

64%

43%

12%

31%

24%

26%

Porto

68%

72%

20%

15%

12%

13%

Real Madrid

46%

63%

25%

16%

29%

21%

Grand Total

54%

54%

26%

24%

21%

23%

A mix of results here with 4 teams performing better after Champions League weekends than they have overall and the total values all being pretty similar. The Milan win % stands out here and is very high compared to their normal results (just 12% draws when normally it is 31%) which does level out the overall results a bit. 7 of the 10 teams drew more games after the Champions League matches.

As I mentioned at the beginning the different results Real Madrid and Barcelona had after the first round of Champions League games made me think about a team’s performance when playing away in both the Champions League and at the weekend as I think this would be the hardest schedule.

Weekend

Won

Drew

Lost

Matches

P/L Win

Played at Home in CL Home

66%

20%

14%

252

-3.93

Played Away in CL Home

65%

21%

13%

260

-10.43

Played at Home in CL Away

46%

28%

26%

243

-26.13

Played Away in CL Away

45%

25%

29%

234

-8.56

The table above breaks down all the teams (not just the 10 above) in terms of their percentages and you can see that there is a slight difference. Teams that played away in the Champions League perform worse than those who played at home. This is true whether they play at home the following weekend or away. It is interesting to look at the profit and loss figures (taken to Betbrain average prices) as you almost break even backing teams that played at home in the Champions League and then play at home again. One possible explanation could be the bookies taking into account the very factor this article is about. However, that would not explain the big loss on teams playing at home in the Champions League and then away at the weekend.  Teams playing away after the Champions League do seem to perform worse than those at home but then losses on away teams generally are bigger than those at home.

A few months back I wrote an article on teams travelling long distances and believe this can have an effect on performance. I want to test this a little more and look at teams that had to play away in Eastern Europe, Greece, Turkey or Israel as I thought they all involved some travel. I broke down the results by country and they can be seen below.

Team

Won

Drew

Lost

Matches

P/L

England

77.8%

0.0%

22.2%

9

4.51

France

63.6%

18.2%

18.2%

11

4.7

Germany

37.5%

62.5%

0.0%

8

-0.24

Italy

50.0%

37.5%

12.5%

8

0.27

Portugal

80.0%

20.0%

0.0%

5

1.23

Scotland

0.0%

50.0%

50.0%

2

-2

Spain

22.2%

33.3%

44.4%

9

-4.98

Grand Total

51.9%

28.8%

19.2%

52

3.49

First thing is there is not a particularly big sample size. However, only Spain show real losses here, remember this is to the average price as well. Digging deeper in Spain of the draws and losses only one of the teams that didn’t win was odds on so it looks like there were no shocks  as the other losers were all odds against and my theory is blown to bits!

Whilst looking at this and as the data is available it also makes sense to look at teams performance the weekend before the Champions League. Often teams will rest some of their bigger players so they are fresh for the midweek games. The table below looks first at teams playing at home.

Lyon once again are inexplicable and I wish I had left them out of this now. There is not a lot that stands out in the rest and I also checked the results for teams playing away and they were similar. With Inter Milan having such a high figure I started to wonder about looking at correct scores as I bet there are a fair share of 1-0s in there as teams leading no doubt rest players and break up the rhythm of the match.

Won

Drew

Lost

Team

CL

All

CL

All

CL

All

Arsenal

65%

69%

26%

20%

9%

11%

Barcelona

71%

83%

19%

12%

10%

5%

Bayern Munich

65%

74%

15%

20%

20%

6%

Chelsea

78%

73%

22%

22%

0%

5%

Inter

96%

74%

4%

20%

0%

6%

Lyon

80%

58%

12%

34%

8%

8%

Man United

80%

84%

12%

10%

8%

6%

Milan

67%

68%

19%

21%

15%

11%

Porto

82%

80%

12%

15%

6%

5%

Real Madrid

71%

79%

11%

9%

18%

12%

Grand Total

75%

74%

16%

18%

9%

8%

So, after all that the we need to ask if this is a hit or myth. Personally I think it is a myth and nothing in the data makes me question that assumption. People definitely think there is a bit of truth in it. I think with so many games in a season there is bound to be sometime when it occurs and that is the one people remember. For example the 4 English clubs probably play around 30-40 Champions League games between them in a season so at some stage there should be an upset the weekend after.

If you have any suggestions for any myths to investigate please contact me.

Travel Sickness (Betting School Article)

Another Betting School article. This time one I originally wrote in December 2010. It talks about teams having to travel a lot and seeing if there is anything in it.

Travel Sickness

The first thing I need to say is that I live in Spain (the cold north part) as the next story may not make sense otherwise.

A few weekends back I was walking home on a Saturday late afternoon when I saw the Deportivo La Coruña coach pull up at a hotel near my flat and 2 minutes walk from the Sporting Gijón football stadium. Sporting were playing at home on the Sunday but to Real Madrid and it got me wondering what the Deportivo team were doing there. I got home and checked the fixtures and saw that they were playing away at Mallorca the next day. For those interested in Geography  Mallorca is an island nearly 500 miles off the Spanish East coast and Coruña is around 180 miles from Gijón.

I could see that they would have driven the 180 miles in the afternoon, spent the night in the hotel and then on the Sunday would get up and fly from Asturias airport to Mallorca. The flight would take about an 1½ hr and then the match kicks off at 5pm. Based on the location of Coruña I would guess they have to fly to well over half their away games per season so they should be used to air travel but even so it can’t be good preparation and air travel is tiring.

I started thinking about distances teams have to travel to play away games and then remembered the horrendous away record Newcastle had against London clubs that spanned a number of years as well as Sunderland’s poor form in the capital that they only broke when beating Chelsea this season. Could it be a coincidence that both these teams have further to travel to London than any other teams in the Premier League?

With this article I intend to see if there are any strategies that can be used based on distances teams have to travel and if it helps when making selections. The data I use comes from www.football-data.co.uk and spans 5 seasons from 05-06 to 09-10. The profit/loss is based on the Betbrain maximum home/away win odds from the football data files.

If we start with flying we `can test this by looking at teams based on islands and seeing how their home form is. The idea being that away teams have to fly to play them and could be disadvantaged.  Sticking to the bigger leagues the island teams I have found are shown in the table below:

Club

Island

Country

Ajaccio

Corsica

France

Cagliari

Sardinia

Italy

Catania

Sicily

Italy

Palermo

Sicily

Italy

Maritimo

Madeira

Portugal

Nacional

Madeira

Portugal

Mallorca

Mallorca

Spain

Tenerife

Canary Islands

Spain

If we look at the top line results based on Betbrain maximum home win odds from the football data files we straight away some interesting results:

HomeTeam

Home Profit

Matches

ROI

Ajaccio

-1.6

19

-8.4%

Cagliari

0.78

95

0.8%

Catania

8.27

72

11.5%

Mallorca

20.06

95

21.1%

Maritimo

-8.69

72

-12.1%

Nacional

6.12

72

8.5%

Palermo

19.25

91

21.2%

Tenerife

0.05

19

0.3%

TOTAL

44.24

535

8.3%

Backing all teams from islands over the last 5 seasons would have returned an ROI of just over 8%. If we look at the breakdown by years you can see there have been a few losing seasons and the recent profits may have been based on teams like Mallorca having an exceptional season in 09-10.

Season

Home Profit

Matches

ROI

05-06

-7.37

87

-8.5%

06-07

-11.19

109

-10.3%

07-08

6.08

108

5.6%

08-09

35.08

110

31.9%

09-10

21.64

121

17.9%

TOTAL

44.24

535

8.3%

If these teams are strong at home then the bias should also exist when they play away in that they have to do the travelling. Backing the home team when the away team was from an island produced the following results:

AwayTeam

Home Profit

Matches

ROI

Ajaccio

4.92

19

25.9%

Cagliari

12.24

95

12.9%

Catania

4.67

72

6.5%

Mallorca

-5.18

95

-5.5%

Maritimo

-1.98

72

-2.8%

Nacional

2.64

72

3.7%

Palermo

7.17

91

7.9%

Tenerife

8.41

19

44.3%

TOTAL

32.89

535

6.1%

The theory holds up again. Nowadays some teams will be far better prepared for travelling and do it so often they are used to it. The bigger teams will be playing in European competition each season so doing a fair amount of air travel. These teams also have more money and would not be flying on the day of the game like Deportivo did. I went through a list of the teams in these leagues and excluded teams I thought had played a fair bit in Europe and were a big club in that country. The exclusions were Juventus, AC and Inter Milan, Roma and Fiorentina in Italy, Bordeaux, Lyon and Marseilles in France, Benfica, Porto and Sporting Lisbon in Portugal and Atletico and Real Madrid, Barcelona, Sevilla, Villarreal and Valencia in Spain.

The results with the exclusions are shown below:

HomeTeam

Home Profit

Matches

ROI

Ajaccio

-2.6

16

-16.3%

Cagliari

9.36

71

13.2%

Catania

13.81

53

26.1%

Mallorca

17.09

65

26.3%

Maritimo

-3

57

-5.3%

Nacional

5.42

57

9.5%

Palermo

8.01

67

12.0%

Tenerife

6.05

13

46.5%

TOTAL

54.14

399

13.6%

Only two teams do not show a profit, the ROI has gone up and the results look very good.

The flight theory seems to work, so then I checked if general distance travelled can make a difference. As mentioned earlier Newcastle and Sunderland have a long way to travel to London and if we look at the results you would have made a nice profit backing the home team

AwayTeam

Home Profit

Matches

ROI

Newcastle

4.5

22

20.5%

Sunderland

5.32

21

25.3%

TOTAL

9.82

43

22.8%

Portsmouth is on the South coast and therefore even further for these 2 teams to travel so you would expect to see similar results. There are only 8 games to look at which is a few but backing Pompey each time would have led to a loss of -0.52 so nothing conclusive. As we saw with the Islands if it works one way it should work in reverse and therefore the London clubs should struggle against Newcastle or Sunderland.  Backing Sunderland or Newcastle in these games would have produced a very small profit, but a profit nonetheless, shown below:

HomeTeam

Home Profit

Matches

ROI

Newcastle

0.15

22

0.7%

Sunderland

0.83

21

4.0%

TOTAL

0.98

43

2.3%

Testing this further Spain is an easier country to use as it is so big. I grouped teams from the Northern regions (Galicia, Asturias, Canatbria, Basque Country and Navarra) together and then the same with the teams from the Southern-most region (Andalucia) of Spain as well. Any North v South match or vice versa would mean the opposition having to travel over 500 miles.  From the previous findings I would expect to see a profit for the home team when the visitor was from the other side of the country, but looking at the results this is not the case:

HomeTeam

Home Profit

Matches

ROI

North v South

-15.01

107

-14.0%

South v North

-4.98

107

-4.7%

TOTAL

-19.99

214

-9.3%

We are now showing a loss and if we look at the teams there seems to be little pattern in this. The Northern teams are:

HomeTeam

Home Profit

Matches

ROI

Alaves

3.55

4

88.8%

Ath Bilbao

0.79

20

4.0%

Celta

3.75

7

53.6%

La Coruna

2.59

20

13.0%

Osasuna

-6.47

20

-32.4%

Santander

-9.6

20

-48.0%

Sociedad

-3.05

7

-43.6%

Sp Gijon

3.46

9

38.4%

TOTAL

-4.98

107

-4.7%

Santander’s losses are very big as are Osasuna’s. The 3 most Westerly teams, therefore further from large cities/airports and more remote, are Sporting Gijon, Celta Vigo and Deportivo La Coruña who all show a profit.

The Southern teams are:

HomeTeam

Home Profit

Matches

ROI

Almeria

2.29

14

16.4%

Betis

-5.89

22

-26.8%

Cadiz

-4.57

7

-65.3%

Malaga

-5.76

17

-33.9%

Recreativo

3.13

15

20.9%

Sevilla

0.79

27

2.9%

Xerez

-5

5

-100.0%

TOTAL

-15.01

107

-14.0%

The losses here are bigger and it is hard to put a positive spin on them. The majority of the losses -12.32) are from games when Deportivo La Coruña travelled South and I would have expected them to have a poor record based on the fact they are right out in the North West corner. However, maybe the fact they have to travel at least 180 miles to any game means they are better travellers?

It is hard to take a positive conclusion from this. Maybe some teams are better travellers than others. This may be to do with how often they do it and what sort of budget they have.

However, there does seem to be something in the island idea and that looks the way to go. Backing the island teams at home seems especially profitable in the bigger, and more competitive leagues of Spain and Italy. If we exclude the games against the big teams I mentioned before it produces the following profits.

HomeTeam

Home Profit

Matches

ROI

Cagliari

9.36

71

13.2%

Catania

13.81

53

26.1%

Mallorca

17.09

65

26.3%

Palermo

8.01

67

12.0%

Tenerife

6.05

13

46.5%

TOTAL

54.32

269

20.2%

I would want to look twice before opposing any island team at home but away from home would see the value with the home team.

Football Months (Betting School Article)

Another Betting School article. This time one I originally wrote in January 2010. It talks about the coming months in the season so should still be a good read and valuable.

Football Months

We are now in the new year and the fresh start gives us a chance to look ahead to the rest of the season. Most leagues they have passed the half way point in terms of matches but there is still plenty to come and lots to be decided. In January and February teams are still jostling for position and trying to move up the table but as the season nears the finish some games mean a lot and other games are meaningless.

The aim of this article is to look at the changes that occur over the course of a football season and see if there are any profitable angles

The data I use comes from www.football-data.co.uk and spans 5 seasons from 05-06 to 09-10 for the top leagues in Germany, England, France, Italy and Spain.

When looking at data over time charts come into their own and there is no better way to see patterns. The first areas to look at are goals scored and then shots taken as they go together.

image001image002

Looking at the above you can see some similarities and obvious patterns. At the start of the season the goals are pretty low. However, the number of shots is not lower and so it may be that players take a while to get into the groove and so the early season dip in goals is to do with a bit of rustiness. Then come the months of January and February where there is a clear drop in both the number of goals and number of shots. I can think of no explanation for the drop in shots unless poor pitches and cold weather plays a part. The final part of the season sees a clear increase in both shots and goals possibly down to a lot of teams having little to play for. This brings a more carefree attitude and teams may concede and score more goals as there is less pressure.

As we can see a pattern here the best market to exploit it might be the over under 2.5 market. If we look at percentage of matches under 2.5 goals we hope to see the same pattern again. The big question is do the bookmakers also know that this pattern exists? By charting the average price of under 2.5 goals we should be able to see just that.

image004image003

Although the under 2.5 graph is reversed you can see the same pattern exists in January and February with those 2 months having the highest averages of the season. If we look at the odds for the bookmakers (I have used BbAv<2.5 = Betbrain average under 2.5 goals) we can see a minor drop but nothing like we have seen in any of the other charts. The bookmakers have the climb at the end of the season spot on but there could be a bit of profit to be taken in the months of January and February.

The reason I chose the under market is that it is more profitable than the overs market.  You can see this in the following table as well as a complete breakdown by month for over and under 2.5 results. Again this uses the Betbrain average over and under 2.5 goals price from football-data.co.uk.

Month

Matches

% Over 2.5

Over P/L

Over ROI

% Under 2.5

Under P/L

Under ROI

Aug

627

46.6%

-44.48

-7.1%

53.4%

-42.59

-6.8%

Sep

903

45.2%

-116.22

-12.9%

54.8%

-28.25

-3.1%

Oct

938

48.0%

-71.16

-7.6%

52.0%

-72.57

-7.7%

Nov

916

47.2%

-85.82

-9.4%

52.8%

-60.13

-6.6%

Dec

971

48.4%

-49.46

-5.1%

51.6%

-86.74

-8.9%

Jan

836

42.3%

-143.62

-17.2%

57.7%

5.91

0.7%

Feb

976

44.3%

-130.25

-13.3%

55.7%

-18.01

-1.8%

Mar

1018

45.6%

-102.08

-10.0%

54.4%

-51.02

-5.0%

Apr

1088

48.1%

-60.11

-5.5%

51.9%

-90.32

-8.3%

May

827

54.8%

-5.15

-0.6%

45.2%

-111.96

-13.5%

Total

9100

47.0%

-808.35

-8.9%

53.0%

-555.68

-6.1%

What stands out to me here immediately is that backing all games under 2.5 in January returned a profit and doing the same in February just returns a very small loss. If we had used the maximum odds then those two months combined return an 80pt profit and a ROI of 4.5%.

The other point is that even though the bookies expect more goals in May you can still do well backing overs. Looking at the maximum odds a profit is attainable for this month backing every game as over 2.5 goals.

I think it’s worth digging a little deeper into the January and February under 2.5 results to see if we can find more profitable angles. Maybe some leagues may be better than others so that is a starting point.

Month

Country

Matches

% Under 2.5

Under P/L

Under ROI

Jan

Germany

61

47.5%

-3.32

-5.4%

Feb

Germany

189

48.7%

-4.59

-2.4%

Jan

England

183

57.4%

5.34

2.9%

Feb

England

170

58.2%

3.45

2.0%

Jan

France

172

69.2%

15.14

8.8%

Feb

France

192

58.9%

-14.89

-7.8%

Jan

Italy

207

53.1%

-12.21

-5.9%

Feb

Italy

225

58.2%

6.65

3.0%

Jan

Spain

213

55.9%

0.96

0.5%

Feb

Spain

200

54.5%

-8.63

-4.3%

Very interesting that the Premier League is the only league to show a profit for both months. Serie A and the Bundesliga are the only two leagues not showing a profit in January and that does look a better month to concentrate on. In February, aside from England, Italy is the only country that shows a profit. If we stick with the Premier League in January we’ll see if it’s possible to improve the figures using a rule. If we exclude any match featuring one of the big 3 of Arsenal, Chelsea and Manchester United we get these results

Month

Matches

% Under 2.5

Under P/L

Under ROI

Jan

131

61.1%

16.73

12.8%

Feb

121

59.5%

7.64

6.3%

Normally I would include Liverpool in the big 4 but with their current situation and the poor time they had last season I thought it sensible to stick with a consistent 3 and they make little difference to the results either way. So a simple system of backing under 2.5 goals for Premier League matches in January when one of the big 3 is not playing returns a very nice profit.

It makes sense to look at some other statistics now and see if patterns exist. There is data for corners and cards but sadly no data for the odds so we cannot search for profitable angles but merely speculate.

image005

January again features as the lowest month along with October. I am not sure whay October appears but the drop in January would fit with what we found earlier with less shots. I would assume less shots would contribute to a lower corner count. With corners not being a totally mainstream market there could well be opportunities going lower in January. As in all the charts we see the peak towards the end of the season.

The last area to look at is cards.

image006image007

With the yellow cards there is a clear pattern at the start and end of the season of less cards but from October to March it all looks pretty steady. Refs probably start the season trying to be more lenient or maybe firmer pitches then and at the end of the season play a part. Again I would imagine the bookies may be aware of the lower card counts at the end of the season but the drop is quite considerable and they may not judge it correctly. In terms of red cards there is a drop in November which seems a bit out of character and if we ignore that then the red cards would show a similar pattern to the yellows.

Overall it looks like there are a few distinct sections to a season and we are in the midst of one now in January and February to come. The league may not be decided in these months but the games are very important as everyone still has something to play for. We can expect less goals and less corners it seems as well. Then when we get to May we should see more patterns emerging like more shots, more goals, more corners and less cards.  Further research may be needed to see how the end of season stats are affected by teams playing for nothing or those at the top and bottom of the table.

Leon Pidgeon

Hit or Myth: Does a new manager make his point or points! (Betting School Article)

This is an article I originally wrote for the Betting School in November 2011. Some of the content should be worthwhile despite some of the data being slightly old.

 

Hit or Myth: Does a new manager make his point or points!

You have to love some of the football myths. I remember being asked which player had won the league title with Arsenal and been involved in the Munich air disaster. The answer I was  informed was John Lukic as his mum had been aboard the plane when pregnant and survived the disaster. This is absolute tosh as he was born nearly 3 years after the air crash and was based on the fact that Harry Gregg saved a woman called Mrs Lukic during the disaster who was pregnant.

Thankfully the advent of the internet means it is a lot easier to check this kind of thing and not embarrass yourself with some made up trivia which I have been guilty in the past. My brother in law likes to remind me of some dodgy trivia question I asked him about Imre Varadi and the teams he had played for which he was able to expose as rubbish and declare my knowledge as ‘bobbins’.

This month I want to look at how teams perform with a new manager. Often when a new manager arrives the perception is that the results will pick up as players try to impress the manager. This is the theory I will try to test this by looking at how teams perform in the games before and after the manager arrives to see if there are any profits to be had. I have decided to look at 4 games either side of the appointment. The reason I have chosen 4 games is then we should get two home and two away matches for each team to give a balanced result set.

I am looking at the English Leagues and have taken the information about managers leaving their jobs from the League Managers Association website which has a lot of data for this.

According the league managers association there have been, on average, just over 50 managerial changes in each of the last 5 seasons as shown below.

League

2006-07

2007-08

2008-09

2009-10

2010-11

Barclays Premier League

12

12

14

6

10

npower Championship

11

13

13

19

15

npower League One

13

12

14

12

17

npower League Two

13

9

9

13

16

Total

49

46

50

50

58

I wanted to be able to exclude any changes that happened in the off-season so to do this I only included matches where the next match after hiring was within 14 days and the last match before a firing was within 14 days. Also I have excluded any changes where the new manager was not in place within 30 days of the initial manager leaving. This leaves us with 129 managerial changes over the 5 years.

I have seen some analysis of this kind of thing before and I was expecting to confirm this as a myth but the results are quite surprising. The first thing to look at us how many matches the teams won in the 4 games before and after the managerial change.

Wins

Games

Win %

4 games before

37

129

29%

3 games before

37

129

29%

2 games before

26

129

20%

1 game before

17

129

13%

1 game after

34

129

26%

2 games after

52

129

40%

3 games after

45

129

35%

4 games after

45

129

35%

You can see the win percentage fall as we get closer to the dismissal and then it picks up in the first game but then makes much bigger jumps in the 3 games after. Taking all 4 games together this equates to a 23% win percentage in the period leading up to the change and then a 34% win percentage afterwards.

Another thing to notice is that it is invariably a bad result that triggers the sacking as there is just a 13% win percentage in the game before a sacking. This is not rocket science but with the number of markets available to bet on for managers being sacked it could help.

After seeing the results the next question that needs to be asked is can we profit from this and the answer looks to be yes if we look past the opening match.

Win %

P/L

4 games before

29%

-33.99

3 games before

29%

-31.69

2 games before

20%

-53.66

1 game before

13%

-76.61

1 game after

26%

-15.75

2 games after

40%

24.54

3 games after

35%

8.12

4 games after

35%

-0.28

The second and third game after the managerial change look to be the key games for getting a profit and there is some logic to this. In the first game the manager cannot change very much and sometimes is even just watching from the stands. I also believe in this game the prices reflect the fact that bookmakers expect some improvement. In the second and third game the manager has been able to work with the players and get across any new methods/tactics etc. In the fourth game the win percentage is the same as the third game but no doubt the fact that the team has improved will mean the bookmakers will adjust their prices to be lower and so the profit is eradicated.

I must that admit when I chose this choice of topic I firmly had the idea in my head it was going to be a myth but now it is time to dig deeper and see if we can find any more profitable angles.

Looking at the 129 changes the only manager that was able to produce 4 wins out of 4 was Guus Hiddink when he took over from Big Phil at Chelsea. Maybe there could be some scope in looking at the results by division.

Profit/Loss

Barclays Premier League

npower Championship

npower League One

npower League Two

Conference

4 games before

-10.66

-17.47

-6.26

-1.15

1.55

3 games before

-10.76

-19.42

0.09

-0.17

1.57

2 games before

-21.88

-19.84

-10.74

-8.95

-3

1 game before

-23.86

-26.19

-9.62

-16.91

-0.37

1 game after

-2.31

6.54

-10.66

-4.97

-1.35

2 games after

-2.49

13.89

1.52

12.17

2.45

3 games after

-11.11

0.69

15.82

5.6

0.12

4 games after

7.82

-3.68

-8.83

4.85

2.56

This table above shows the raw profit and loss by division. The Premier League still shows a loss for the 4 games after a managerial change. Of the 4 games it is only the fourth one that shows any profit. If we look at the Championship it is the reverse and only the fourth game does not make a profit and overall there are some decent results. League One shows an overall loss for the games after managerial change but the big loss is in the first game after the change which we discussed earlier as probably being no surprise. League Two shows a decent profit for all but the first game in and is the most profitable of the lot. There are not many results for the Conference and it is hard to take anything positive or negative from there. I will remove the Conference from the rest of the results.

If we look at the 4 games post change as a whole in the table below we can now more clearly see the loss in the Premier League and the fact the other Leagues are all quite consistent in terms of win percentage. Why is the Premier League different? My personal take is that in the Premier League has huge gulfs in class and that a poor team is a poor team and no amount of managerial magic can change that. Yes, some teams over perform but by and large most teams play to their own level. In the other divisions maybe the improvement brought about by a manager can make a very big difference.

4 games post managerial change

Barclays Premier League

npower Championship

npower League One

npower League Two

Won

34

56

45

33

Total

112

164

132

96

Win %

30%

34%

34%

34%

Profit/Loss

-8.09

17.44

-2.15

17.65

So now let’s pull all this together and see if we can find a profitable angle. The Premier League looks worth avoiding and also we have seen that the first game does not seem to yield decent results and that by the fourth game any improvement has been spotted by the bookmakers. The results for the second and third game a new manager has in charge of a team are shown below by the 3 lower divisions.

Won

Total

Win %

Profit/Loss

ROI

npower Championship

29

82

35%

14.58

18%

npower League One

28

66

42%

17.34

26%

npower League Two

19

48

40%

17.77

37%

All

76

196

39%

49.69

25%

Some very nice returns in there and with a total of nearly 200 games spanning 5 seasons there is an average of around 40 games per year that would qualify. Something worth looking at for sure and also on the flipside you would not want to oppose a team with a new manager in these games.

The new broom does look to sweep clean in the lower divisions and I would put this one down as a hit for them. In terms of the Premier League I do not think it makes much difference and would not be too concerned with this factor.

If you have any suggestions for any myths to investigate please contact me.

Leon Pidgeon

Goals Galore Article (Betting Insiders April 2011)

This is an article I originally wrote for the Betting School in April 2011. Some of the content should be worthwhile despite some of the data being slightly old.

When both teams score …

In the last few months I have often come across people talking about the Goals Galore coupon with Betfred and the equivalents that other bookmakers offer. For those who are unfamiliar with it the aim is to find matches where both teams score and Betfred pay 9/4 on 2 correct, 9/2 on 3 correct all the way up to 1150/1 for 12 correct for those keen to lose money.

Working with Betting School we have come up with a console that will hopefully help to pinpoint games that are more likely to have both teams score and that is already available to members.

The idea with this article is to search round the both teams to score angle to see what can be found and how the console can be used with these ides. In this article the abbreviation BTS will be used for both teams score/scored/scoring. The data used is from the English Premiership, Championship, Leagues 1 and 2, the Conference and the top league in Scotland, France, Italy and Spain. I have included the results for Germany in a table below and we see why Betfred don’t include it on their coupon.

Some readers may think this is a bit late in the season to be looking at this type of bet but as I showed in the January article as we get to the end of the season the number of goals increases and therefore so does the number of games where BTS. The table below shows the percentage of games where BTS by month.

 

Month

Matches

BTS

No BTS

% Games where BTS

August

1742

905

837

52%

September

2154

1126

1028

52%

October

2018

1080

938

54%

November

1753

863

890

49%

December

2214

1110

1104

50%

January

1922

957

965

50%

February

2040

977

1063

48%

March

2376

1167

1209

49%

April

2417

1226

1191

51%

May

966

521

445

54%

You can see that the start and end of the seasons look the best times for attacking this angle and overall in 50.7% of matches BTS. Therefore the chance of getting a double with two random games is 1 in 4 which equates to 3/1 and so Betfred only offering 9/4 mean we need to find more angles. To get to a break even situation we need to find games where the chance of BTS is between 55-56%. If we find games where the chance is 56% the decimal odds should be 3.19 on the double but Betfred are offering 3.25 (9/4) so we get value.

If we break the statistics down by the individual leagues we can see if some are better for concentrating on than others.

 

Div

Matches

BTS

No BTS

% Games where BTS

Ger Bund

1530

853

677

56%

Eng Champ

2760

1457

1303

53%

Ita Serie A

1900

998

902

53%

Eng Lge 2

2760

1436

1324

52%

Eng Lge 1

2760

1428

1332

52%

Eng Conference

2624

1343

1281

51%

Spa Primera

1900

966

934

51%

Scot Prem

1140

568

572

50%

Eng Prem

1900

903

997

48%

Fra Ligue 1

1900

859

1041

45%

The German league comes out on top and it’s no surprise that this does not feature on the Betfred coupon. The French league is considerably lower than the others and it’s a bit of a surprise to see the Italian league so high up. The 4 English lower leagues also fair well but in any analysis like this I like to look at results with and without the big teams to see if this makes a difference to the results.

The leagues I am using and the clubs I am classifying as big are as follows:

England – Arsenal, Chelsea, Liverpool and Man United

France – Bordeaux, Lyon and Marseille

Italy – Fiorentina. Inter, Juventus, Milan and Roma

Scotland – Rangers and Celtic

Spain – Atletico Madrid, Barcelona, Real Madrid, Sevilla and Villarreal

If we look at matches involving big teams only we some interesting results.

 

Big Team v Big Team

Matches

BTS

No BTS

% Games where BTS

Eng Prem

60

27

33

45%

Fra Ligue 1

30

22

8

73%

Ita Serie A

92

58

34

63%

Scot Prem

20

8

12

40%

Spa Primera

150

86

64

57%

The two British leagues are the odd ones out in this as all the other leagues have a considerably higher percentage of BTS when it’s just the big teams playing. It is very high in France, and although the sample is very small this would be a pattern I am happy to follow. The same has to be said for Italy where the 63% BTS is well above the 56% threshold we are after.

Normally I would look at games with big teams against non-big teams overall but in this I think it makes sense to break it down depending on who is at home. If we start with games where the big club is at home against a non-big club we get the results below.

 

Big Team at Home

Matches

BTS

No BTS

% Games where BTS

Eng Prem

324

126

198

39%

Fra Ligue 1

255

123

132

48%

Ita Serie A

364

185

179

51%

Scot Prem

170

75

95

44%

Spa Primera

420

206

214

49%

France is the only league where these results are above the average for all games. I would expect this figure to be lower as you would think these teams are more likely to keep clean sheets. I expect most people would not use these games anyway in their coupon but they can be useful when you look back over a team’s form as you may see a couple of games where both teams did not score, but if this was against a big side it may be worth ignoring it. For example on the console I looked at a Bolton away match and both teams had scored in a healthy 10/15 matches. However, the games when they didn’t were against Manchester United, Manchester City, Chelsea, Sunderland and Stoke. The first 3 teams, along with Arsenal fill the top 4 places in the league and have done most of the season so if we ignore games at the top 4 clubs their record becomes an even healthier 9/11. I would imagine these results also work for lower divisions based on the top sides each season but without testing this cannot say so for sure.

The table below shows when the big team is away.

 

Big Team Away

Matches

BTS

No BTS

% Games where BTS

Eng Prem

324

144

180

44%

Fra Ligue 1

255

112

143

44%

Ita Serie A

364

188

176

52%

Scot Prem

170

103

67

61%

Spa Primera

420

217

203

52%

Aside from France the results go up compared to the table above. The Scottish result is a particularly high one and any games with an old firm team away could warrant a second look.

Finally we look at games where none of the big teams are playing below.

 

No Big Team

Matches

BTS

No BTS

% Games where BTS

Eng Prem

1230

623

607

51%

Fra Ligue 1

1360

602

758

44%

Ita Serie A

1080

567

513

53%

Scot Prem

780

382

398

49%

Spa Primera

910

457

453

50%

The Premiership is again an exception here but for all the other leagues we can see that the results for this are more or less equal to the average for all games. Essentially in these games there is nothing outside of norm so we would need to look for patterns with particular teams.

With a bet like this you want to look for teams that follow patterns as they are likely to keep them up. I believe Peterborough and Crewe are often taken off the Goals Galore coupon as they were having BTS so much. Whist writing this article I used the console and saw that Southend host Aldershot on 2nd April and 14/19 Southend home games and 13/19 Aldershot away games had BTS. Both of these teams have a good pattern and would be a decent selection if featured on the coupon.

I looked at individual team patterns a bit more and found some startling results for certain teams over the course of a season. The table below shows 5 teams on either end of the extremes.

 

Season

Div

Home Team

Matches

Both Scored

One or None

% Games where BTS

06-07

Eng Prem

Man City

19

3

16

16%

07-08

Fra Ligue 1

St Etienne

19

3

16

16%

08-09

Eng Champ

Doncaster

23

5

18

22%

09-10

Eng Conference

AFC Wimbledon

22

5

17

23%

08-09

Eng Lge 1

Southend

23

6

17

26%

09-10

Scot Prem

St Johnstone

19

14

5

74%

07-08

Eng Champ

Colchester

23

18

5

78%

07-08

Eng Conference

Forest Green

23

18

5

78%

08-09

Eng Lge 2

Luton

23

18

5

78%

06-07

Ita Serie A

Atalanta

19

15

4

79%

Some of these values are outstanding but show how some teams get a pattern and stick with it all season. That’s not to say you don’t need to be aware of a change in pattern but hopefully the console will help with that. An example of a change in pattern would be Rotherham this season. At home 8 of their first 10 games had BTS but since then just 2 of the last 8 games have had BTS and what’s interesting is that they have kept scoring but have started to keep clean sheets and so those 6 games in the last 8 were all where they kept a clean sheet.

I hope this will all help with anyone interested in trying to make money from the Goals Galore coupon or similar using the console. The other two sections the console has show over/under goal statistics and also the average shots and shots on target. The over/under section just helps to give an idea of how many goals are being scored by these teams and it is all relevant to the both teams scoring market. In the nearly 20,000 games I have been through the games with both teams scoring average 1.4 shots more than those without. 0.95 of that difference is down to the visiting teams having more shots so finding teams with a better than average number of shots, particularly when away can be another factor that will swing whether to bet or not.

I can be contacted with ideas for improvements for the console, problems or any questions to do with it or anything in my articles.

Leon Pidgeon

Betting School Article

Once a month I write an article for Betting School and this month is no exception. I am doing something based on shots and the idea coming out is that is could be a decent indicator of success. So to test it this weekend I have a few selections that I am putting up. I am putting them up for reference as they are in the article and I want to post them

Bundesliga – Hannover look to be a bit lucky being so high up with so few shots but they are at home to Borussia Monchengladbach who also are poor. Freiburg have had a lot of shots and so have Leverkusen and Hoffenheim could be better off than they are. Sadly Leverkusen and Hoffenheim are away from home and the only possible looks to be Freiburg.

 

Eredivisie – Roda look to be in a false position as do Waalwijk. Vitesse and Hercales both play at home and look to have less points than their shot ranking deserves. I like them and also Venlo who have just 1 point at home but are ranked in the top half of average shots.

 

Premier League – West Brom and West Ham look to have been lucky and again Liverpool look to be unlucky! Swansea, Spurs, Arsenal and Villa all look to have less points than their shots deserve. Of these only Villa are at home.

 

Predictions (Best odds available at 18.05 (CET) on Thursday 25th October)

 

Freiburg to bt Dortmund @ 6.0 with WH

Vitesse to bt AZ @ 2.18 with Pinnacle

Heracles to bt Heerenveen @ 2.04 with Pinnacle

Venlo to bt NAC Breda @ 2.28 with Pinnacle

Aston Villa to bt Norwaich @ 1.96 with Pinnacle