Can Jamie Vardy equal Ruud van Nistelrooy’s record against Newcastle?

When you’re in-form, you’re in-form. Jamie Vardy has scored in nine consecutive Premier League matches, setting a new record for the longest streak in a single season. However, Ruud van Nistelrooy scored in ten successive fixtures for Manchester United over the course of two seasons and now the Leicester City man will have the opportunity to break the Dutchman’s record against Newcastle United on November 21st.

 

walkers stadium

 

Vardy has been simply sensational at the start of the campaign, netting 12 goals in Leicester’s opening 12 fixtures. At the time of writing, he sits five goals above team-mate Riyad Mahrez, Everton’s Romelu Lukaku and Watford striker Odion Ighalo. Vardy looks set to finish as the Premier League’s top goalscorer and will be aiming for at least 25 goals at the end of the season.

 

His phenomenal scoring run has attracted attention from a number of Europe’s elite clubs and the likes of Real Madrid and Arsenal have both been linked with a move for the Foxes striker. On current form, he would certainly be able to play for these clubs but many have their doubts that Vardy could replicate this level of performance at a major club.

 

leicester matchday

 

Many punters will be backing Vardy to score for the tenth successive match against Newcastle next weekend. If you want to place a bet, or simply find out all of the latest news, statistics and odds on Leicester’s main striker, you can find all of this information on Coral’s website. Vardy will be keen to score though, and Leicester will fancy their chances against a suspect Newcastle side.

 

The Foxes striker is full of confidence right now but he mustn’t be too greedy as he seeks to equal Van Nistelrooy’s record. Vardy is widely regarded as one of the Premier League’s top workhorses and his contribution to the team does not go unnoticed. It would be a shame if the distraction of this record affected his usual work ethic, especially if it was detrimental to Leicester’s hopes of winning the match.

 


Jamie Vardy

 

So, can Jamie Vardy equal the record? Of course he can. Leicester have a tough run of matches coming up and Claudio Ranieri’s men will be labelling the Newcastle clash as a ‘must win’ fixture. Vardy has been instrumental to their success throughout the early stages of the season and, with a bit of luck, he will have another key role in the outcome of their clash against the Magpies and could move level with Van Nistelrooy if he performs at his best.

13 Nov, 15 | Posted in: Articles | Comments Closed

Red hot Sanchez hits goalscoring form

While Arsenal fans hadn’t started doubting Alexis Sanchez, the Chilean’s first hat-trick for the club couldn’t have come at a better time.

 

Without a goal to his name yet this season, Sanchez opened his account for the campaign with a sizeable deposit, hitting three goals in Arsenal’s impressive 5-2 victory over Leicester City, and then followed that up with a brace against Manchester United. Currently on five league goals for the season, Sanchez has seen his odds of finishing as the league’s top scorer shorten to 10/1 with some bookmakers when this article was produced.

 

Having made an electric start to life in London following his switch to the Gunners from Barcelona last summer, Sanchez’s goalscoring slipped off somewhat during the second half of last season, and the striker had gone eight games without scoring before his hat-trick against Leicester. Despite failing to continue his early season form in front of goal throughout the entire campaign, the forward still managed to finish his first year in English football with 25 goals to his name, helping the club win the FA Cup for a second successive season.

 

Now back among the goals, Arsenal fans will be hoping Sanchez can play a major role in the club challenging for this season’s Premier League title. While Manchester City might still be many punters’ favourites this season, currently 10/11 with betfair and other major bookmakers, Arsenal are very much in this year’s title race and Sanchez rediscovering his scoring boots certainly strengthens their case.

 

15362272352_65338d8d67_z

CC  by  Ronnie Macdonald 

 

While Sanchez isn’t Arsenal’s only goalscoring threat, the fact is that, when he is in form, Arsenal are a much more dangerous team than when the Chile international is firing on all cylinders. Olivier Giroud has certainly done well over the past three seasons, starting this campaign in solid form with three goals from his opening seven league appearances, and Theo Walcott continues to stake his claim for a role up front, scoring the opening goal against Leicester. But it’s Sanchez who could hold the key this year.

 

giphy

 

As well as his goalscoring, Sanchez also has a big role to play in terms of assists, setting up his team-mates on 12 occasions last season and proving just how important he is to Arsenal from an offensive point of view. If he can get close to 20 goals and 10 assists again – with the likes of Giroud and Walcott weighing in with 10-plus goals apiece, there is no doubt that Arsenal could well push the likes of City much harder than they have managed over the past few seasons – possibly even all the way through to May.

 

Priced at 4/11 for a top-two finish in the league, Arsenal fans might not be satisfied with that this year, especially with Sanchez in this sort of form.

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