Below is the full account of our FT 1X2 results campaign for the 2019 ‘Summer Leagues’ based around Soccerwidow’s HDAFU ~ Home – Draw – Away – Favourite – Underdog Tables.

2019 Summer League Campaign Profit Curve

 
The systems employed in each league were all picked based on observations of the profit curves produced from the accumulated data (match odds and results) of the previous five complete seasons.

Read on to find out exactly how and why we picked the systems we did…

Campaign Report 2019 Summer Leagues

If you would like the accompaniment for this article please click on the button below to get your FREE 2019 Summer League Campaign spreadsheet, which pares down each system to individual match detail.

>>> 2019 summer league campaign <<<

(Then click on the green Checkout button, fill in your name/email address details and press Submit Order. A download link will then be sent to your email address – and it won’t cost you a penny! Check your junk mail if it doesn’t arrive in your inbox immediately).

How the Systems Were Chosen

Our long-held tried and tested method is to use the HO/AO quotient (i.e. the figure derived from dividing the home odds by the away odds) as the most reliable benchmark for categorising an individual match and allow comparisons or groupings with others.

Employing the quotients of all matches in a league over the previous five complete seasons provides a significant data set with which to carry out in-depth analyses.

Then, by producing graphs for each of the Home-Draw-Away-Favourite-Underdog results, it is easy to visualise betting results and trends.

Constructing a balanced portfolio of ‘systems’ in our chosen leagues is then just a matter of identifying and choosing historically lucrative segments of the profit curves upon which to target our bets in the forthcoming season.

For the purposes of diversification, we chose two systems for each league: one representing the 1st half of the season and the other for the 2nd half of the season.

In all of the leagues summarised below, you will see images taken directly from the 2019 HDAFU tables. (Containing the data from the five seasons prior: from 2014 to 2018)

When deciding upon which segment of the profit/loss curves to choose as our systems in each league, we compared the relevant 1st and 2nd half-season graphs (i.e. representing half of the data set in each case) with that representing the Whole Season (the whole of the data set).

If the segment of interest in, say, the 1st half-season graph was mirrored to some degree in the Whole Season analysis, then it was considered more likely to be influential on the whole-of-season picture. Ditto with the 2nd half-season.

In almost every case, you will see visual similarities between the chosen half-season system and the whole-of-season graph, whereas the graph belonging to the other half of the season is usually very different.

However, in a few examples, the chosen segment of the graph is mirrored in all three graphs (1st, 2nd and Whole-of-season), making it even more likely that the chosen system for the future had a significant influence on the historical picture across the previous five seasons.

Judgment of Risk

You will find a Risk Table (Image 8) and explanatory commentary in our dedicated risk judgment article.

League-by-League Breakdown

In alphabetical order…

League 01) Brazil: Série A

System 1 – 1st Half: Away Win
High-risk

Here, the visually similar peaks in the profit curves between the 1st half and whole-of-season graphs were indicative of a potentially lucrative system:

(Clicking on all of the images below enlarges them in a new tab):

Brazil Série A: 1st Half & Whole-Season Away Win Combined Graphs

By comparison, the 2nd half-season graph looks very different:

Brazil Série A: 2nd Half-Season Away Win Graph

Between odds of 6.00 and 8.00 in the whole-of-season graph above is a less definite picture containing peaks and troughs of ‘statistical noise’.

To conclude, the 1st half-season system of choosing away wins between HO/AO quotients of 0.104 and 0.167 seems to be more influential on the overall picture in this league.


RESULT

In our campaign from 28/04 to 15/09/2019, there was a total of 17 bets satisfying the HO/AO criteria. 15 of these bets lost and only two won. The odds of the bets placed ranged from 8.45 to 12.39.

However, the two bets that won were sufficiently high priced to earn this system a profit.

Profit: £366.00

System 2 – 2nd Half: Home Win
High-risk

Brazil Série A: 2nd Half & Whole-Season Home Win Combined Graphs

And again, the 1st half-season graph looks very different:

Brazil Série A: 1st Half-Season Home Win Graph

In the 1st half graph, this area does show an incline but it represents a gain of fewer than 1,000 units.

By comparison, the 2nd half-season system employed spanned a gain of 2,500 units in the previous five seasons. (Between 2,930 and 5,412 units)

The equivalent whole-of-season graph shows a gain of 3,400 units. (Between 3,979 and 7,394 units)

RESULT

In our campaign from 22/09 to 01/12/2019, there was a total of 13 bets satisfying the HO/AO criteria. Seven of these bets lost and six won. The odds of the bets placed ranged from 3.01 to 3.45.

Profit: £588.00

League 02) China: Super League

System 3 – 1st Half: Away Win
Low-risk

China Super League: 1st Half & Whole-Season Away Win Combined Graphs

But, the away win selections highlighted in the chosen quotient of 3.332 to 15.175 were all odds-on favourites between around 1.20 and 1.80.

The 2nd half-season graph did not show anything as pronounced in this area.

China Super League: 2nd Half-Season Away Win Graph

We knew from the outset that we wouldn’t be playing for massive gains with this 1st half system.

But, when there is little else to find in the analysis, choosing a low-risk system or a home-win or a favourite-based system are the prudent choices.


RESULT

In our campaign from 30/03 to 30/06/2019, there was a total of six bets satisfying the HO/AO criteria. Two of these bets lost and four won. The odds of the bets placed ranged from 1.37 to 1.60.

Profit: £21.00

System 4 – 2nd Half: Draw
High-risk

China Super League: 2nd Half & Whole-Season Draw Combined Graphs

Looking at the 1st half-season graph it was evident that the draw in the second half of the Chinese season showed the stronger trendline.

China Super League: 1st Half-Season Draw Graph

The upper quotient wasn’t extended further: it could have been taken to the next and final peak of around 1.07.

However, it was decided that the final peak represented diminishing returns for a higher risk.

On the 2nd half-season graph you can see before the last peak after 0.988 was a trough of roughly equal size. Therefore, the chances of increasing the profit beyond the 0.988 parameters were roughly 50/50 (i.e. ‘heads’ and the bottom line increases; ‘tails’ and it reduces – this snakes-and-ladders game wasn’t worth playing).

RESULT

In our campaign from 06/07 to 27/11/2019, there was a total of 26 bets satisfying the HO/AO criteria. 18 of these bets lost and eight won. The odds of the bets placed ranged from 3.32 to 4.29.

Profit: £355.00

League 03) Finland: Veikkausliiga

System 5 – 1st Half: Draw
High-risk

Finland Veikkausliiga: 1st Half & Whole-Season Draw Combined Graphs

It may seem paradoxical that this part of the curve appears below the profit line in the graph (i.e. in the ‘red’) but as you can see the curve begins around -5,000 units and climbs to around -2,000 units, meaning the results in this sector are combining to create a profit of around +3,000 units (i.e. an improvement from -5,000 to -2,000).

The 2nd half-season graph was again very different.

Finland Veikkausliiga: 2nd Half-Season Draw Graph

But, as you can see from the odds label at the top of these graphs, a system in this zone would involve backing draws at considerably longer odds (i.e. a far higher risk system), and with only 2,000 units profit made in the previous five seasons.




RESULT

In our campaign from 03/04 to 19/06/2019, there was a total of five bets satisfying the HO/AO criteria. Four of these bets lost and only one won. The odds of the bets placed ranged from 3.33 to 3.63.

Loss: -£137.00

System 6 – 2nd Half: Favourite
High-risk

Finland Veikkausliiga: 2nd Half & Whole-Season Favourite Win Combined Graphs

The 1st half-season graph showed once again what huge differences exist between the 1st and 2nd halves of league seasons as far as match results and odds are concerned.

Finland Veikkausliiga: 1st Half-Season Favourite Win Graph

Dividing this by five to give a seasonal expectation meant that nothing spectacular could be expected from this system.

However, the favourites alluded to would all be home favourites between odds of 2.30 and 2.45 meaning that a profit would be secured even if there were an equal number of winning and losing bets.


RESULT

In our campaign from 27/07 to 19/10/2019, there was a total of four bets satisfying the HO/AO criteria. Two of these bets lost and two won. The odds of the bets placed ranged from 2.33 to 2.43.

Profit: £68.00

League 04) Iceland: Úrvalsdeild

System 7 – 1st Half: Home Win
Medium-risk

Iceland Úrvalsdeild: 1st Half & Whole-Season Home Win Combined Graphs

Iceland Úrvalsdeild: 2nd Half-Season Home Win Graph

The home odds for these bets were likely to be between around 2.00 and 2.60.

Dividing the five-season profit figure of the proposed system produced an expectation of just 400 units profit per season.



RESULT

In our campaign from 27/04 to 06/07/2019, there was a total of 18 bets satisfying the HO/AO criteria. Seven of these bets lost and 11 won. The odds of the bets placed ranged from 2.09 to 2.56.

Profit: £668.00

System 8 – 2nd Half: Draw
High-risk

Iceland Úrvalsdeild: 2nd Half & Whole-Season Draw Combined Graphs

Once again, the 1st half-season graph bears little resemblance.

Iceland Úrvalsdeild: 1st Half-Season Draw Graph

In all cases, the away team will be the favourite to win the match.

Expectations are higher than the 1st half-season home win system (here, around 700 units per season on average) but, with higher rewards on offer, the risks are also greater.



RESULT

In our campaign from 13/07 to 22/09/2019, there was a total of 20 bets satisfying the HO/AO criteria. 14 of these bets lost and six won. The odds of the bets placed ranged from 3.44 to 4.21.

Profit: £188.00

League 05) Ireland: Premier League

System 9 – 1st Half: Home Win
High-risk

Ireland Premier League: 1st Half & Whole-Season Home Win Combined Graphs

Ireland Premier League: 2nd Half-Season Home Win Graph

This places the home win bets in a zone of odds between around 3.40 and 5.00: In other words, every bet we make will be on a home underdog to win.

The rewards are not great (around 500 units per season on average), but there is little else to aim in the 1st half of the season in this league.

It may be high-risk, but the consolation is that it is also a home-win-based system.

RESULT

In our campaign from 22/02 to 18/05/2019, there was a total of 15 bets satisfying the HO/AO criteria. 11 of these bets lost and four won. The odds of the bets placed ranged from 3.42 to 4.81.

Profit: £158.00

System 10 – 2nd Half: Away Win
High-risk

Ireland Premier League: 2nd Half & Whole-Season Away Win Combined Graphs

The 1st half-season picture contains a similar shallow rise towards the right-hand side of the profit curve but, like the other two graphs, this is in a zone where odds-on favourites are winning very small increments to the bottom line (amounts which are barely worth the work involved).

Ireland Premier League: 1st Half-Season Away Win Graph

All of our bets will, therefore, be backing away underdogs to win.

Remembering the more vertical spike from earlier, you will see the odds there are far higher (between around 6.80 and 7.50). At these odds, it would only take one or two anomalous results to produce a misleading peak of this nature.


RESULT

In our campaign from 31/05 to 22/10/2019, there was a total of 12 bets satisfying the HO/AO criteria. Eight of these bets lost and four won. The odds of the bets placed ranged from 3.25 to 4.54.

Profit: £434.00

League 06) Japan: J-League 1

System 11 – 1st Half: Away Win
Medium-risk

Japan J-League 1: 1st Half & Whole-Season Away Win Combined Graphs

The 2nd half-season image suggests a reversal of fortunes between these marks.

Japan J-League 1: 2nd Half-Season Away Win Graph

However, those over 1.000 (before 1.015) will be slight favourites to win.

Looking at the 1st half-season image, the odds range of our bet placements is likely to be between around 2.60 and 3.40.

It is worth noting that Japan always seems to contain an away win system in one half of the season or the other.


RESULT

In our campaign from 23/02 to 30/06/2019, there was a total of 42 bets satisfying the HO/AO criteria. 25 of these bets lost and 17 won. The odds of the bets placed ranged from 2.60 to 3.41.

Profit: £705.00

System 12 – 2nd Half: Draw
High-risk

Japan J-League 1: 2nd Half & Whole-Season Draw Combined Graphs

Japan J-League 1: 1st Half-Season Draw Graph

And, at this end of the scale, the matches included were likely to be between odds-on home favourites and longer-priced away underdogs.

But the fact the peak in the profit curve showed in both the 1st and 2nd half-season graphs was indicative of a good chance of repeated profits from this zone.


RESULT

In our campaign from 07/07 to 07/12/2019, there was a total of 12 bets satisfying the HO/AO criteria. Eight of these bets lost and four won. The odds of the bets placed ranged from 3.72 to 5.36.

Profit: £445.00

League 07) Norway: Eliteserien

System 13 – 1st Half: Home Win
Medium-risk

Norway Eliteserien: 1st Half & Whole-Season Home Win Combined Graphs

The 2nd half-season graph suggested half the size of reward between the same quotient points.

Norway Eliteserien: 2nd Half-Season Home Win Graph

In reality, every bet would be backing a home underdog, although not a great distance apart from the away favourite odds.

The profit curve for the 1st half-season contains many jagged peaks and troughs and, at these odds, it was expected that more bets would lose than would win.


RESULT

In our campaign from 31/03 to 15/07/2019, there was a total of 29 bets satisfying the HO/AO criteria. 18 of these bets lost and 11 won. The odds of the bets placed ranged from 2.74 to 4.11.

Profit: £778.00

System 14 – 2nd Half: Away Win
High-risk

Norway Eliteserien: 2nd Half & Whole-Season Away Win Combined Graphs

In the 1st half-season graph, there was a similar rise in the profit curve but the five-season haul was around 2,000 units.

Norway Eliteserien: 1st Half-Season Away Win Graph

The part of the curve containing our system existed within a fairly narrow margin, so not many bets were expected.

But, at the odds highlighted in the graph, not many bets would need to win to turn a profit here.



RESULT

In our campaign from 04/08 to 01/12/2019, there was a total of 11 bets satisfying the HO/AO criteria. Eight of these bets lost and three won. The odds of the bets placed ranged from 4.55 to 5.56.

Profit: £348.00

League 08) South Korea: K League 1

System 15 – 1st Half: Draw
Medium-risk

South Korea K League 1: 1st Half & Whole-Season Draw Combined Graphs

The 2nd half-season graph is once again very different in appearance.

South Korea K League 1: 2nd Half-Season Draw Graph

The potential rewards are large – around 6,000 units profit from the five-season historical trend or 1,200 per season on average.

The season is 228 matches in length. Our expectations were around 75 bets (66% of 114).


RESULT

In our campaign from 01/03 to 07/07/2019, there was a total of 79 bets satisfying the HO/AO criteria. 54 of these bets lost and 25 won. The odds of the bets placed ranged from 2.87 to 4.50.

Profit: £684.00

System 16 – 2nd Half: Draw
Medium-risk

South Korea K League 1: 2nd Half & Whole-Season Draw Combined Graphs

A reminder of the 1st half graph in isolation is shown below.

South Korea K League 1: 1st Half-Season Draw Graph

Here, we are looking at draws between well-matched teams, with the home team being the slight favourite in each case.

The quotients represent a small margin of the entire graph, so we are not expecting anywhere near as many bets as in the 1st half-season.


RESULT

In our campaign from 20/07 to 30/11/2019, there was a total of 14 bets satisfying the HO/AO criteria. Nine of these bets lost and five won. The odds of the bets placed ranged from 3.03 to 3.52.

Profit: £265.00

League 09) Sweden: Allsvenskan

System 17 – 1st Half: Draw
Medium-risk

Sweden Allsvenskan: 1st Half & Whole-Season Draw Combined Graphs

Sweden Allsvenskan: 2nd Half-Season Draw Graph

The feel from the odds range is that every match selected ‘could go either way’, which is always prime territory for backing the draw.

The five-season historical profit is over 3,750 units or around 750 on average per season.



RESULT

In our campaign from 01/04 to 15/07/2019, there was a total of 36 bets satisfying the HO/AO criteria. 19 of these bets lost and 17 won. The odds of the bets placed ranged from 2.99 to 3.80.

Profit: £2,100.00

System 18 – 2nd Half: Underdog
High-risk

Sweden Allsvenskan: 2nd Half & Whole-Season Underdog Combined Graphs

Sweden Allsvenskan: 1st Half-Season Underdog Graph

The ‘could go either way’ factor might be beneficial if enough underdogs prevail at the higher odds they ultimately must carry (i.e. more ‘value’) to offset the weight of money placed on the favourites.

The margins are once again narrow between 0.666 and 0.943, so not a huge number of games are expected to qualify for betting upon.

And underdog betting usually suits the second half of a season:

• Teams towards the lower echelons of a league tend to fight harder for survival
• Perhaps a team near the bottom has already faced most of its toughest matches in the first half of the programme and goes into the second half at misleadingly longer odds to win its games

RESULT

In our campaign from 20/07 to 28/10/2019, there was a total of 15 bets satisfying the HO/AO criteria. 10 of these bets lost and five won. The odds of the bets placed ranged from 2.74 to 3.45.

Profit: £68.00

League 10) U.S.A.: Major League Soccer

System 19 – 1st Half: Favourite
Medium-risk

U.S.A. Major League Soccer: 1st Half & Whole-Season Favourite Combined Graphs

U.S.A. Major League Soccer: 2nd Half Favourite Graph

But from the historical perspective, this small zone has accrued over 2,000 units of profit in the previous five seasons.

All teams backed will be slight home favourites in very evenly matched games.

Neither many bets nor huge profits were expected.

With several hundred games in this league during a season, and with the format regularly changing (i.e. increasing numbers of teams in recent seasons) we felt that choosing systems in the M.L.S. with fewer potential selections would be better for the overall health of our portfolio.

RESULT

In our campaign from 31/03 to 02/06/2019, there was a total of five bets satisfying the HO/AO criteria. Three of these bets lost and two won. The odds of the bets placed ranged from 2.55 to 2.66.

Profit: £14.00

System 20 – 2nd Half: Home Win
Medium-risk

U.S.A. Major League Soccer: 2nd Half & Whole-Season Home Win Combined Graphs

U.S.A. Major League Soccer: 1st Half Home Win Graph

All of the bets expected will be home favourites at odds of 2.00 or below and above 1.60.

At these odds, the hit rate must remain high to make a profit knowing that approximately one-quarter of all games in the 2nd half-season will qualify for betting.

This is a medium-risk system with the potential for low gains. But, in keeping with our philosophy about concentrating upon more stable leagues, it also had the security of potentially low losses.

RESULT

In our campaign from 04/07 to 06/10/2019, there was a total of 40 bets satisfying the HO/AO criteria. 16 of these bets lost and 24 won. The odds of the bets placed ranged from 1.69 to 2.00.

Profit: £327.00

Conclusion

The overall profit total was pleasing and, to reiterate, the profit curve graph at the start of this article shows what would have been achieved with flat 100 unit stakes.

However, by using a ratcheting system to incrementally increase stakes in tandem with stop-loss stake reduction at 85% of the bank each round, the profit figure would have been almost 10k higher. Ratcheting without stop-loss in place, in this case, would have netted a figure almost 14k higher.

We will shortly be publishing an article showing an articulate staking plan in action on this portfolio of bets.

The hit rate achieved was just over 38% using predominantly medium and high-risk systems, with just one low-risk system in use.

But, it should be noted that 64% of the bets placed were low-medium risk (269 bets), whilst 36% were in high-risk systems (150 bets).

The longest losing streak was only nine bets (once). The longest winning streak was eight bets (twice).

Of course, the stark reality of the campaign sticks out like a sore thumb in the profit curve graph. Using flat stakes, betting could have been stopped in mid-September having achieved the level of profit registered by mid-December. In effect, the last three months of the portfolio were a zero-sum game. Hindsight always offers up a wonderful reality check.

Emphasis on Strategy

In a word, the strategy is diversification.

Using all 10 of the Summer League HDAFU tables available we decided this term to maximise the number of systems gleaned from them by choosing a 1st half and a 2nd half-season system from each league. In other words, using the 10 tables to come up with a total of 20 different systems.

(See 6) Example and Summary in this link to illustrate why we split the profit and loss curves into the halves of each season.

The other important reason for splitting each Summer League into two systems (and ignoring whole season systems in all Summer Leagues), is the fact that the leagues themselves are not as popular with punters as the main European leagues.

Because of the lack of demand in some areas of the Summer Leagues (i.e. less money wagered makes it harder for bookmakers to balance their book), odds are sometimes adversely affected with higher over-rounds: In other words, there are less ‘value’ opportunities available for punters.

In addition, a tendency exists for bookmakers to react to historical results trends at the beginning of a new season by lowering odds across the board to counter the efficacy of punters trying to take advantage of them.

Therefore, without the ability to second-guess what is likely to happen with the odds trends in games not yet played, by concentrating on one bet type in any league for no longer than half a season, the chances of picking a system discriminated against by the bookmakers are minimised as much as is possible.

Also, if a system is going to go wrong, then better to limit the damage to half a season, rather then endure the misery of continuing losses (and hoping in vain that they improve) throughout the full term.

Final Words

Soccerwidow uses the term ‘Summer Leagues’ to describe those where the season begins and ends during the same calendar year.

In other words, leagues outside the usual format of those beginning in one calendar year and completing in the next (what we call ‘Winter Leagues’).

Although our own HDAFU campaigns are traditionally a mixture of ‘Summer’ and ‘Winter’ leagues it is difficult to provide a summary of the whole due to its overlapping and ever-running nature. In effect, the Soccerwidow campaign is one long, never-ending portfolio: The betting equivalent of perpetual motion.

In this way, it is as close to the bookmakers’ own business model as possible but on a micro-level by comparison.

Bear in mind that with the Winter League portfolio running concurrently, this article represents an isolated snapshot of what happened to our Summer Leagues during the time frame represented by 419 bets placed in 10 leagues (20 systems) over a period of 288 days from the 22nd of February to the 7th of December 2019, both dates inclusive.

Here again, is that free download link – Click on the button below to get your 2019 Summer League Campaign spreadsheet.

>>> 2019 summer league campaign <<<

(Then click on the green Checkout button, fill in your name/email address details and press Submit Order. A download link will then be sent to your email address – and it won’t cost you a penny! Check your junk mail if it doesn’t arrive in your inbox immediately).


When buying this spreadsheet you will also receive a coupon code offering a discount of £7.00 GBP, redeemable against the purchase of any individual HDAFU Table. This allows you the opportunity to experiment and explore your first HDAFU Table without paying the full price for it and before you commit to buying more – formulate strategies for any current season ‘on the cheap’!

Buy your tables in the Soccerwidow HDAFU Store (heavily discounted bundles of 5, 10, 15, 25 leagues also now available!)

Coronavirus Thoughts

The effects of coronavirus on the various leagues around the world are still unknown.

After the March 2011 Tsunami in Japan, the J1-League was halted for seven weeks. When the league restarted it assumed exactly the same statistical pattern as expected from previous seasons: the elongated break between rounds had no effects on the trends of the observed results (although only one round of matches had been played in the 2011 season up to that moment).

Coronavirus has suspended leagues for far longer and at a different point in each, and the possibility of many leagues resuming against a backdrop of empty stadia is another factor to consider. Will home advantage be to some extent neutralized by the lack of fans?

All in all, differences like this may affect individual results but, from a statistical perspective, previous trends will in all likelihood remain evident. But don’t hold us to that.

Whilst things stabilize around the world it is probably better to pause betting portfolios for a while and monitor your systems on paper. However, Value Calculation and Over/Under goal betting are probably still valid as the concepts are applied to and rely on calculating each game individually.

Stay safe.

We cannot say in advance with accuracy how many fish there are to catch or, after the event, how many fish were not caught…

Image: tobkatrina (Shutterstock)

The Mindset Required for Successful System Betting

System betting is similar to fishing with a net.

Of course, it is easy to say how many bets were won over the course of a full season (i.e. how many fish were caught).

But it is also easy to evaluate how many opportunities to win were missed (i.e. bets not placed; fish not caught).

And calculating how many winning opportunities there were in total (i.e. how many fish there were to catch) is also straightforward.

With this much control over the available information we assure you that it is possible to identify, formulate, refine, test, and finally deploy a successful betting system.

The ultimate goal of system betting is to create a strategy that becomes just a numbers exercise, and where obtaining the highest price for the desired outcome is the primary objective for achieving ‘value’ in order to gain maximum reward.

Betting is an Approximate Science

The equatorial circumference of the Earth is widely accepted as 40,075 km. However, the circumference from pole to pole is slightly less as the Earth is not exactly spherical.

Furthermore, if you were to bring into the equation all the geographical features in the path of the measuring tape and calculate the circumference including every peak and trough of every mountain and valley, then this would provide yet another different value.

But which is the correct measurement?

Let’s look at another example: Motor car producers test their vehicles and provide guarantees but occasionally they get it wrong and mass returns are ordered to rectify a common fault.

Of course, a vehicle’s longevity also depends where in the world and on which terrain it is used, how it is driven and maintained, plus a host of other immeasurable factors.

If you were to ask a vehicle manufacturer how long a particular model will last it will be impossible for him to say with any degree of accuracy, although he can predict that a rough percentage of vehicles will still be on the road in one year from now, less in two-years’ time, fewer in three-years’ time, and so on.

What we are trying to say is that too many extraneous factors prevent total accuracy with any mathematical calculation and scholars of math will readily agree that theirs is not an exact science; some allowance for error has to be accepted in any equation or calculation. (For example, the formula for Pi (Π) produces an infinitely long number, not an exact one).

Likewise, all betting strategies based on historical figures can never be 100% accurate.

Acceptable Accuracy

It is impossible to predict for sure the outcome of any one single event but it is much easier to say, for example, that between 40 and 50% of the 380 matches in league ‘X’ will be home wins next season based on the historical results in that league.

This is where system betting or ‘blanket betting’ becomes interesting.

But how do we create a strategy to catch as many of the 40-50% home wins as possible using the fewest number of wagers?

Finding a Betting System

The statistics we keep reveal that just three scorelines account for around 70% of all half-time scores. It does not matter which top-flight league you look at, the same three scores repeat themselves in more or less the same quantities per league, every season.

Can you guess what these three scorelines are before checking out this article?

Patterns such as this are ideal for planning a betting system; in fact anything you can find which happens often enough and regularly enough to warrant continual betting on the same desired outcome for consistent profits.

It goes without saying that bookmakers are not stupid and it is rare to find a result over the course of time uniformly priced in your favour.

In other words, it is hard to find an event which will be profitable when staking or risking the same amount on the same desired outcome in every match in a league during the course of an entire season. But whilst it is difficult, it is not impossible.

The Key to Unlocking the Potential of any System

When looking at system betting, we always prefer to look at the last five seasons’ results as this usually provides a sufficiently large pool of statistics to be significant. Working with too few data is more of an accuracy issue than working with too many, but five seasons’ worth is plenty/ideal.

The key to unlocking the potential of any system is your ability to filter-out matches which are statistically less likely to bring the desired outcome and, when such games are analysed collectively as a cluster, do not bring enough winning bets to make a profit.

It is clear that excluding unprofitable betting clusters such as this will increase the efficiency/profitability of any strategy.

In other words, narrow down the field to find where the majority of winning bets occur and then concentrate your strategy within this area, dispensing with the other groups of matches that are historically unprofitable.

When sorting data and filtering it, cut-off points often appear at both ends or either end of the odds spectrum. You will find that bets may become unprofitable at a certain price and lower or, at a certain price and higher, or both (and vice versa).

Filtering for Success

A good initial filter is to sort your collection of results into ascending or descending order according to the size of the home team’s odds to win.

Ultimately, you will sort the entire list of matches by as many criteria as you can think of, such as: home, away, or draw odds (or these odds converted into probability percentages – remember, this is 1 divided by the decimal odds, expressed as a percentage); desired event odds; home odds multiplied by the away odds; in fact any numerical factor you can think of to find one or more filters to discard the unprofitable matches.

In this way, filtering out undesirable matches turns system betting into a form of value betting.

Once you have filtered the entire list, it is worth looking at each individual season to see if the same elimination process works in a micro format. Paper test everything until you are happy that the system can be profitable.

The next step is to test with live events without committing money to the outcomes. Does the elimination filter or set of filters still work?

Keep observing and recording, refining and correcting, until you are happy that your system really does have a future.

Never be delusional about system or blanket betting. What works for one league several seasons in a row may fail due to an abnormal season ahead.

It is therefore important never to count on one league or one system at any one time. It is far better to spread your risk and have several systems in play at the same time. Successful system betting relies on the law of large numbers to spread risk.

The final fundamental ingredient of any betting strategy is a flexible and effective staking plan to act as the glue which holds everything together. A stop-loss (to prevent total loss of betting bank) and ratchet provision (increasing stakes incrementally) is the gloss on any staking plan.

Thus, building a solid betting system is just a series of ingredients and interconnected steps. Rather like baking a cake without a recipe; you may have to keep experimenting with the ingredients of your betting system until the end result becomes palatable.

Go Fish!

To conclude this article, we return to our earlier metaphor. (Oh my cod!).

Dip your net (i.e. your betting system) into the pool of chosen (filtered) matches.

How closely knitted the mesh of your net is will determine how many big fish (winning bets) your filter(s) will allow you to catch (win), and how many pass through the mesh of the net (the minnows, which are unprofitable to catch).

Through trial and error, adjust the size of the mesh to catch only what you want in order to achieve as many winning bets with as few wagers as possible.

Thinking along these lines will increase your chances of developing a successful strategy.

Or, why not look into our HDAFU Tables where the whole job is done and presented on a plate for you to pick systems from the leagues of your choice.

Happy fishing!

From a football perspective, although we personally limit ourselves to analysing only the previous five seasons’ data with the Soccerwidow HDAFU Tables, or the previous 10 calendar years’ match results with the Soccerwidow Value Calculator, data on Oddsportal is in fact available for the last 20 seasons or more in leagues with enough popularity and longevity, such as the English Premier League.

Registering with Oddsportal

Signing up is simple and places no obligation on you whatsoever. Our own account was set up many years ago and in all that time we have received no emails of any kind from Oddsportal, its partners, or any associated spam, a rarity following sign-ups of any nature these days.

Oddsportal’s priority is to achieve sign-ups via their site to the bookmakers they feature in order to earn affiliate commission from those customers. You don’t have to sign-up with any bookmakers, but you can continue to use all of Oddsportal’s features as a registered member.

However, if you choose not to register for an account, you will be restricted to seeing and exploring the odds of just a small selection (usually 14-16 in number) of the 80+ bookmakers Oddsportal features at any one time.

As an aside, Soccerwidow is not affiliated in any way with Oddsportal but owing to their importance in the grand scheme of things, we are always happy to recommend them.

Oddsportal Settings

Once you have registered and signed-in to your account, you will need to customise your Oddsportal layout. In the top right-hand corner of the home page you will see the settings button illustrated with a small cog symbol (next to the “logout” button).

You will then see the various settings options. Here is a screenshot showing Soccerwidow’s settings, which we use to facilitate our own data scraping and odds checking:

Oddsportal Settings Screenshot

Notes:

• The “Primary Type of Odds” setting refers to what you see when you open any individual game. If you choose the AH (Asian Handicap) O/U (Over/Under), or any of the other options, then that bet type tab will be the first you see. However, it will always be the 1X2 odds displayed when initially opening a league.
• It is usually quicker to see which bookmaker is offering the best odds if you “Sort bookmakers by” bookmaker payout. The bookmaker list of the matches you open will then appear roughly in descending odds order. The highest 1X2 odds time-stamped most recently will usually be in the top half of the list. (But of course, usually no one bookmaker will be offering best price on all three outcomes, so you will have to hunt for them). Here’s a quick example of bookmaker payout order:

Oddsportal Bookmaker Payout Order Screenshot

Next Page: My Bookmakers Tab; Problem Bookmakers; Manage My Leagues

2016-17 Winter League Profit Curve

We will try to avoid repeating what was said in the summer league article so that it is all new information for you here.

For the sake of completeness, you should still read and digest both reports for a full idea of our strategies and thought processes.

And you will find the current stock of available HDAFU tables via this link.

2016-17 Campaign Report

Measures of Risk

Before beginning to plan any portfolio or placing bets, you will need to review your analyses and rank the systems you have found according to risk exposure based on the values of the upper inflection point odds.

This will allow you to compile a portfolio with a healthy balance of risk, which is essential to the success of any investment plan.

Here is our rough guide:

• Low Risk (Probability 45.00% or more; upper inflection point maximum odds of 2.22)
• Low-medium Risk (Probability 44.99%-35.00%; upper inflection point maximum odds of 2.85)
• Medium Risk (Probability 34.99%-22.50%; upper inflection point maximum odds of 4.44)
• Medium-high Risk (Probability 22.99%-16.00%; upper inflection point maximum odds of 6.25)
• High Risk (Probability 15.99% or less; upper inflection point odds above 6.25)

Discretion is used if a system does not fit these parameters or crosses two or more classifications – In these cases, the harmonic mean odds of all the games in the set is used as the benchmark to guage risk. The Excel formula for a range of odds in cells A1 to A100 would be: =HARMEAN(A1:A100)

Measures of Success

You will also need a definitive framework to be able to judge the final results.

For us, the final results of any league fall into four distinct categories:

• Systems that achieve a six-season-high (i.e. profits larger than any of the previous five seasons). (Over-Achievers).
• Those that make a profit over and above the size of the initial stake (£100 in our case), but fall short of six-season-high results. (Achievers).
• Strategies that break-even or, record a tiny profit or loss up to the size of the initial stake (£100 in our case). (Zero-Sum).
• Leagues that make a loss over and above the size of the initial stake. (Losers).

You can already see that two of these outcomes are favourable, one is neutral, and only one is detrimental.

2016-17 League-by-League Review

Let’s have a brief look at each league to see how our 22 systems fared. (Alphabetically according to the tab codes in the workbook):

1. AUS1 – Austria Bundesliga – Whole Season System

2. BEL1 – Belgium Jupiler League – Whole Season System
Up to, but not including Europa League and Championship Group splits

3. CZE1A – Czech Republic 1. Liga – First Half Season System
Up to the 04/12/2016 winter break

4. CZE1B – Czech Republic 1. Liga – Second Half Season System
From 18/02/2017 start of the second half of the season

5. DEN1A – Denmark Superligaen – First Half Season System

6. ENG1 – England Premier League – Whole Season System

7. FRA1 – France Ligue 1 – Whole Season System

8. FRA2 – France Ligue 2 – Whole Season System

9. GER1A – Germany Bundesliga 1 – First Half Season System
Up to 21/12/2016 winter break

10. GER1B – Germany Bundesliga 1 – Second Half Season System
From 20/01/2017 start of the second half of the season

11. GRE1 – Greece Super League – Whole Season System

Towards the end of the ante post market many smaller bookmakers will already have reached their financial liability threshold for a particular match outcome and drop out of the race to be best price.

Others will not have time to re-balance their books if they were to take ‘new money’ on a particular outcome.

The larger, more sophisticated bookmakers will attempt to achieve arbitrage during the in-play market if their book is unbalanced at kick-off, and every firm will consider striking bets with other bookmakers or betting exchanges to ensure a profit whatever the match result.

Ante Post: Definition

‘Ante post’ is a term borrowed from horseracing and means ‘before the event’.

In the online football betting arena, the ante post market for any match exists between the time of the very first postings of odds up until the moment of kick-off, when the ante post odds are suspended and replaced by live, in-play odds.

Unreliability of Average Odds

Because of the time factor and the necessity for financial parity, the average odds across the board for each outcome fall as the market gets closer and closer to kick-off.

Sometimes, the beginning of this fall is noticeable even further back than 24-hours prior to kick-off.

This is especially the case in less popular events where the stream of bets placed is much smaller and more intermittent. With less money arriving at unpredictable intervals it is a harder task to balance the book. This is the main reason why prices are mostly under-valued across the board outside the more popular top-flight leagues.

Therefore, with average odds, the picture at the close of the ante post is usually false. It bears little or no resemblance to what has been happening throughout the entire ante post period.

Stability of Highest Odds

In contrast, the ‘highest’ market odds generally experience much smaller fluctuations throughout the ante post period; there is always a bookmaker willing to offer best price until they can no longer afford to do so or, are replaced at the top by a competitor looking to increase its own market share on a particular outcome.

And of course, there is always at least one bookmaker offering best price on an outcome at any given time. (You will never see every bookmaker offering the same price at the same time).

In the huge majority of games, even higher prices are available at various points during ante post than the highest bookmaker odds that settle as the market closes.

It sounds like a paradox but using highest bookmaker odds to work out a strategy provides a more reliable and accurate benchmark of ‘average’ than using the actual average odds, and also imparts a more realistic picture of what is achievable when seeking to place bets which satisfy your selection criteria.

If you source your historical data from www.oddsportal.com (free), www.football-data.co.uk (free), or www.football-bet-data.com (at a small cost), then always use the highest bookmaker odds in any of your analyses.

NB. With third party data it is essential to know when it has been captured. Football-Bet-Data and OddsPortal are the only ones to our knowledge providing historical odds from a range of bookmakers taken consistently at the point the ante post market closes.

FREE Download!
Average Vs. Highest Odds: Excel .xlsx Comparison Spreadsheet

View an annotated 14-day ante post analysis of one full round of matches in the English Premier League showing the differences between average and highest bookmaker odds.

The spreadsheet also concentrates on the final 48-hours of the ante post market, providing perfect examples to support the hypotheses outlined in this article:

Click on the above button – in the new tab click on the ‘Continue Checkout’ button. Enter your name and email address to allow our automatic shopping cart to deliver the file by email to you, free of charge. The .xlsx file size is 112 KB. When you receive your confirmation email, just click on ‘View Purchase Online’ (in the email text) to download the file.

Compile Your Football Betting Systems Using Highest Odds!

Ultimately, even the least discerning of online punters will never look to place bets at average odds. Every punter worth his salt will always look to place bets at the best prices possible for maximum returns.

If you are playing the highest odds you can find, then your strategy analyses should also be based on highest market odds.

We go one step further with our strategies and utilise an ‘Odds Toggle Mechanism’ so that we can compare actual results with the data set used to compile the strategy. Read why here.

Today’s article discusses the question what would have happened when backing the underdog playing away from home in the German Bundesliga?

Such a match was played in this league on 23/05/2015 between Moenchengladbach and Augsburg. The best bookmaker odds for the full-time 1×2 market at kick-off were: 1.57 Home; 5.00 Draw; 7.30 Away.

Moenchengladbach were the clear favourites at 1.57; Augsburg the rank outsiders. However, the men of Augsburg won the game, 1-3, defying their long odds.

How regular do such things occur? Is it profitable to bet on outsiders?

Here’s a screenshot from the ‘Backing by Odds’ tab in the simulation table for this league:

German Bundesliga – ‘Backing by Odds’ tab – Five Seasons 2010-15

In the table above you can see that from a total of 306 matches during 2014-15, the away team won 79 times. (Click on the table to enlarge it in a new browser tab).

79 of 306 is 25.8%, and this percentage shows that the away team won, on average, slightly better than once every four matches.

Profit and Loss Sectors when Betting on the Away Team

Looking at the profit/loss (P/L) summaries in the ‘Totals’ column, adding together the first six rows of odds clusters produces a loss of -2,564 units, based on a flat stake of 100 units per bet.

Essentially this means if the away team was priced as a clear favourite or close to the home team’s prices, they won less frequently than the probabilities indicated by their odds. The last of these first six cluster groups closes at away odds of 2.90.

Look at the second row of the table. The odds cluster between 1.66 (implied probability 60.2%) and 2.00 (implied probability 50%) contains 83 matches and, if the odds had been ‘fair’, 55.1% (60.2% + 50% / 2) of the away teams priced in this group should have won.

As you can see, this was not the case! Of 83 games in five seasons only 43 were away wins (51.8%).

Therefore, punters who regularly backed away favourites in the Bundesliga during 2010-15 surrendered ‘value’ in their bets to the bookmakers. When this happens, only one side of the deal wins in the long-run; invariably it isn’t the bettors!

Okay, let’s take a look at the away underdogs…

German Bundesliga – ‘Inflection Points’ tab – Five Seasons 2010-15

This screenshot shows a steep rising curve starting at odds of 4.40 and continuing until odds of 17.0.

Over five seasons, 462 matches fell into this group (Moenchengladbach vs. Augsburg being one of them). The away underdog won 88 times = 19% hit rate!

In these odds clusters the away team won, on average, once in every five matches. The average betting odds were 6.40, representing a probability of 15.6%.

The curve shows, as well as the calculations (19%/15.6% = 121.7%), that the mathematical advantage was on the side of the gambler!

The P/L curve registered 653 units profit at the start of our selected segment and finished at 13,727 units. This is a difference of 13,074 units of profit located solely within the away odds cluster group from 4.40 to 17.0.

Why does this advantage exist? How does it happen?

Backing Low Odds Favourites – Downfall of any Betting System

Most bettors prefer betting on the more popular and ‘emotionally safer’ shorter-priced favourites, but please ask yourself the following two questions:

• How does a profit-oriented company (i.e. bookmaker) set its prices?
• Should the prices (odds) for favourites rise or drop?

Both common sense and business acumen prevail in this situation:

The market dynamics are the following: The more bets expected to be placed on a particular outcome, the more bookmakers reduce their odds. Reducing odds mean that the bettor must risk more money (stake more) to achieve the same financial outcome. The punter therefore pays a ‘higher price’ (gets lower odds) for the same product:

Odds 2.0 → stake 50 = win 50
Odds 1.5 → stake 100 = win 50
Odds 1.25 → stake 200 = win 50

Falling odds means:

⇒ Rising stakes
⇒ Potential to lose more money
⇒ Lower percentage returns should the bet win!

Although this relationship may seem paradoxical, falling odds means rising prices!

Bookies adjust Favourite & Underdog Odds to Public Expectations

To reiterate: Falling odds for an outcome is a clear indicator that this is a favourite. Warning! Dropping odds do not indicate that the statistical probability for the favourite winning the game is improving; purely the fact that the outcome is becoming more and more favoured by bettors. This is a betting fundamental, which many gamblers are totally unaware of.

Falling odds mean bookmakers are effectively raising the price for the product! The product itself does not change in the slightest (i.e. betting on the favourite), but it becomes more expensive to buy. The bettor has to risk more money in order to win the same amount. In this case, you do not get ‘more for your money’, but considerably less!

Let’s use a different example. A confectionery company launches a new chocolate bar, which becomes an instant success. Demand increases; the company naturally takes advantage of the situation by raising the price. You can certainly make the statement that if the price of the chocolate increases it is a ‘favourite’, but the product itself never changes – it’s still a 100g chocolate bar!

The last word here is that since the books have to be ‘balanced’ (i.e. the payout of all three 1×2 bets combined needs to add up to around 100%), whilst the ‘prices’ for favourites are lowered to take advantage of the demand, on the opposite side, the odds for the underdogs rise.

Logically speaking, any full-time (FT) football betting strategy must be riskier than betting on the half-time (HT) market as there is more time for a result to go against you.

On the contrary, you can of course say there is more time for the result to go in your direction, but the old cliché, ‘It’s a game of two halves’, is never more appropriate than when looking at the stark differences between HT and FT betting.

Major Differences between HT and FT Statistics

1. Goal Distribution

Compared to FT goals totals there are naturally fewer goals in the first halves of games in every league you can possibly analyse.

The HT goals data set produces higher concentrations of correct score frequencies within a tighter range of outcomes than the FT market. Fewer goals also mean higher and tighter under/over ‘X’ goals frequencies, and indeed, every other HT goal related market.

FT scores are far less predictable due to a wider variety of actual outcomes and the significance of the FT data set is more diluted as even the most populated correct score clusters contain fewer results than the most populated HT score clusters.

Thus, a larger pool of statistics is needed for FT analyses before the cluster groups can be considered as being significantly populated.

2. HT and FT 1×2 Results

First Half Mentality:

It is a hard fact that no game can be won in the first half. For many teams the first half is all about containment and reserving energy for the more decisive second period of play.

The first halves of so many matches see teams cancelling each other – underlying fitness is more evenly matched between the teams at the beginning of a game.

It is undeniable that players are physiologically fresher at the start of the first half than they are at the beginning of the second period.

Mentality may also be affected knowing that there is always the second half in which to make up for mistakes and/or missed opportunities in the first.

Whatever the reasons for first half deadlock it is a fact that the draw is the most common HT 1×2 result in every league and in every season.

Second Half Mentality:

The second half will always be played under different circumstances to the first.

Rarely do you see teams ‘throwing the kitchen sink’ in the first half. Typically, this is a second half ploy seen towards the end of games when, if it reaps rewards, there is little time for the other team to hit back.

The bigger, richer clubs can afford better training and medical facilities, which means fitter players. Player fitness levels tend to be more of a factor the longer the game goes on.

Tactical substitutions are normally performed in the second halves of matches when fresh legs versus tiring ones can make all the difference.

Fitness can have even more of an effect on the FT outcome if a team is playing within three days of their last match (as it is medically acknowledged that players need at least three days to recover between games).

An example of this fatigue syndrome is the ‘European hangover’ seemingly suffered by teams playing midweek European competition games. No matter how large their squads are, there are always a noticeable number of below par domestic performances from these teams the following weekend.

And of course, successful teams tend to play more games per season than not so successful ones.

The most common FT 1×2 result is the home win but not in every league in every season (i.e. more volatile).

3. Favourites and Underdogs

The majority of bettors are usually driven by a desire to back the nailed-on favourites, especially when looking at less popular or more obscure leagues.

But greater value is more often found in the underdog market, predominantly so in less popular leagues where favourites tend to be perennially under-priced (appearing to be heavier favourites than they really are). In response, underdog prices are over-priced (indicating a smaller chance of winning the game than they actually have).

Underdogs tend to perform better in the first halves of games. Usually their mentality is to ‘have a go’ from the start rather than sit back and wait to be beaten by a better team on paper. They have nothing to lose.

Unfortunately for them, underdogs don’t often see the job through to the end. The FT underdog market does perform well in some leagues but often not as well as it does at HT in those same leagues.

The draw is usually the shortest priced event at HT except when overwhelming favourites play rank outsiders; the draw then becomes the second shortest price. The 0-0 draw is the most common HT result in every league and in every season.

In the FT market, the favourite is always either the home team or the away team, never the draw, although we will see from our example on the next page that the 1-1 draw is the most common FT score in almost every league.

Firstly, I will say that it is certainly possible to find a winning Lay The Draw system. However, it requires not only an excellent feel for the 1×2 betting odds market but also, knowledge of exactly what to look for and how to filter out a promising football betting system from a jumble of data.

In this article you will learn the most common approaches bookmakers use to set their 1×2 betting odds, focusing on the draw odds. I will explore examples from the EPL and compare market prices with the ‘true’ odds. Using this knowledge together with findings from the HDA Simulation Tables, I will identify one team which looks very promising to lay the draw in each of its home league matches for the forthcoming season.

Lay the Draw – English Premier League 2008-09 to 2012-13

Here is a screenshot from our HDAFU simulation table for the English Premier League (just one of 20 tables in the fixed win – fixed risk staking plan tab):

Lay The Draw Betting Simulation Results – EPL 2008-09 to 2012-13

In the last five seasons, if a bettor decided to lay the draw in every EPL match for a fixed risk of 10 units (fixed risk = limiting each bet to lose the same set amount; profit therefore fluctuates with each bet), a loss of 380.42 units would have been observed. The many red numbers in this table are difficult to ignore.

Only by specialising in laying the draw consistently for the two Manchester clubs (City and United) when both (or either) were playing at home, would the bettor have seen a profit after five seasons (118.04 units for City; 143.85 units for United).

A profit would have been rather unlikely if he had chosen a combination of any of the other teams. Including teams such as Fulham, West Brom, and West Ham when playing at home may have improved the chances of a profit but, their positive results after five seasons are not as promising as the two Manchester teams.

For all the other teams scheduled to be playing in the 2013-14 EPL season (all those with a yellow background to their name in the table will be playing in the new season), there are only red numbers including Arsenal, Chelsea, and Tottenham, although their negative results are not quite so bad as the others.

Why do Some Teams Bring Lay The Draw Profits and Others Not?

To understand why some home teams bring a profit when laying the draw and others not requires a deeper understanding of football betting odds calculation.

It is a hard fact, but true: There is no universal formula for successful football or sports betting in general. There is no rule that applies to every team and every market without exception which is always profitable to use. It is not possible to automate sports betting picks. You must look at each team individually and attempt to understand the bookmakers’ approach when they are setting odds.

It is crucial to remember and bear in mind that bookmakers live from offering sports bets. They need to ensure long term solvency despite advanced computer technology, the availability of more and more raw statistical data, and the increasing mathematical awareness of their customers.

The main business objective of bookmakers is ensuring profit, paying the bills and employees’ wages, and attempting to keep gamblers happy and entertained.

Understand Betting Odds Calculation

Bookmakers’ football betting odds are mainly based on historical statistics and also on public opinion. Bookmakers need to balance their books so they are not exposed to losing considerable sums on any particular outcome.

However, some teams neither obey their previous seasons’ statistical trends, nor comply to opposing teams’ statistics, or even to league averages. Some teams have a huge and unique home-field advantage; others just do not travel well. Whatever the set of factors involved in individual games the formulas which govern calculations of draw odds sometimes don’t reflect satisfactory market expectations.

Of course, you cannot fully eliminate the chances that now and again a match finishes in a draw, but this is observed far less frequently than mathematical formulas predict or as indicated by the bookmakers’ odds. Sometimes, betting odds offered in the market are repetitively too low for some teams and therefore great value can be gained if one succeeds to identify them and choose to lay the draw whenever they play at home.

The following table is a comparison of the average distribution of the draw in the English Premier League with the observed distribution of three selected teams: Arsenal, Manchester United, Tottenham:

EPL Draw Distribution 2008-09 to 2012-13

You can see that Arsenal are a good example of a team that “more or less” sticks to the average distribution season after season. There were no major deviations except in the 2009-2010 season. This means that positive betting results for Arsenal draw lays are fairly unlikely. If you can remember the table on the previous page, Arsenal’s red figures now seem justified.

Bookmakers therefore succeed quite well in pricing Arsenal’s home games and they are able to set the odds in a manner which, at the end of a season, show no great variations and the expected distribution matches quite reasonably the observed results.

This is contrary to other forms of gambling loaded in favour of the house such as lottery and roulette, where success tends to rely more on luck than strategy.

Image: PhotoSky (Shutterstock)

With football betting, there are only three possible half-time and full-time outcomes (home/draw/away) for example, and sometimes just two sides to a football bet (e.g. over/under ‘X’ goals).

In contrast, the UK National Lottery has 45 numbers providing over 14 million combinations of the six needed for a jackpot, meaning the chances of winning are drastically low.

Similarities of Professional Football Betting and Stock Market

Indeed, football betting is a little like stock market shares where it is possible to select profitable investments, and traders can forecast with a deal of accuracy whether prices are more likely to rise or fall.

Whereas in stock markets prices sometimes rise or fall unexpectedly, freak results also happen in football as every fan will have observed.

I was fortunate enough to receive tutoring on financial and quantitative analysis of stock markets at university but of course, football betting is not on the syllabus. However, the more I study betting and its underlying statistics, the more parallels I can draw with stock market analysis.

The secret of long-term financial success in the stock market is not necessarily always about buying the right shares or options.

Obviously, a good selection of instruments naturally influences the profit margin in a positive manner but, long-term financial success is not guaranteed if the portfolio is wrongly structured. The same applies to football betting.

A professional stock broker would not dream of investing all of his clients’ money in just one share or option. Indices such as DAX, MDAX and TecDAX all include at least 30 companies in their portfolios. The DAX 100, as its name suggests, contains 100 companies.

Why, therefore, does the stock broker look to invest in a wide range of markets? The answer is obvious: This is done to reduce or spread the risk if one or another share price develops in the wrong direction. And, even in the very unlikely event that the whole stock market collapses, the indices will never totally drop to zero.

Translated into gambling, these principles indicate that you should never ever put all your bank on one individual bet, however ‘sure’ it appears. You must always bet on (invest in) a portfolio of events in order to spread the risk/investment for long-term returns.

What Exactly is a Portfolio?

One interpretation reads: …”A bundle of investments in the possession of an institution or an individual… Usually, an extensive analysis precedes the structuring of a portfolio… A portfolio is typically part of the strategy to reduce the risks of financial investments by diversifying”…

Translated into betting language:
…”A portfolio is a package of bets where extensive analysis has determined the choices (picks)…This is an essential part of the whole betting strategy in order to reduce the risks of losing by diversifying”…

Portfolio Betting Example

This is an example of my portfolio of bets for 26.2.2011:

Laying a considerably weaker away team (in terms of odds):

Backing at odds between 3.35 and 3.5:

Laying the Draw:

Laying the home team in games between equally matched teams (in terms of odds):

I carried out an extensive statistical analysis before picking these games as my ‘value bet portfolio’ for the weekend and happily, seven out of eight bets (87.5%) won.

My calculations also revealed that had only five (62.5%) won, the profit would still have been in excess of 10%. If only 50% of the bets had been successful, the financial loss would have been limited to 10% of the bank.

Probabilities

The calculated probability that all eight of the selected picks lost was minimal at 0.0026% (38,428 to 1). The probability that all eight picks won was 3.53% (28 to 1).

The probability that seven or eight of the eight picks won was 20.77% (4.8 to 1).

In layman’s terms this means that assuming my trend analyses and calculations of the probabilities were correct (of course, there is always the possibility of calculation errors), statistically speaking with this type of portfolio structure, every 27th weekend is likely to produce eight out of eight winning picks, and on average, every 5th weekend is likely to finish with seven out of eight correct picks.


Based on my own statistical calculations for each event, the individual probabilities equated to:

• Aston Villa vs. Blackburn Rovers – Lay Away (84.69% probability of Blackburn not winning)
• Wolverhampton Wanderers vs. Blackpool – Lay Away (84.69% probability of Blackpool not winning)
• Wigan Athletic vs. Manchester United – Back Over 3.5 Goals (38.24% chance of happening)
• Hull City vs. Cardiff City – Back Draw (37.27% chance of happening)
• Barnsley vs. Norwich City – Lay Draw (81.07% chance the match is not drawn)
• Doncaster Rovers vs. Watford – Lay Home (75.28% probability of Doncaster not winning)
• Middlesbrough vs. QPR – Lay Home (75.28% probability of Middlesbrough not winning)
• Millwall vs. Nottingham Forest – Lay Home (75.28% probability of Millwall not winning)

If you multiply all the probabilities, 84.69% x 82.69% x 38.24%, et cetera, the result is 3.53%. This was the calculated probability that all eight of the chosen independent events would occur (i.e. all eight bets win).

The opposite is of course to multiply the probabilities of the selected bets not winning: 15.31% (1 minus 84.69%) x 15.31% (1 minus 84.69%) x 61.76% (1 minus 38.24%), et cetera, for a result of 0.0026%. This was the probability of all eight bets losing.

Roughly speaking, the chance of winning all eight selected bets was 1,350 times higher than losing all eight of them (38,428 divided by 28 or alternatively, 3.53% divided by 0.0026%).

In this fashion, you can compute all the other probabilities for each permutation of events to structure the portfolio and organise its staking. However, an exact explanation of probability calculation and portfolio diversification would complicate this article just now.

If you would like further depth on this topic, then please read my articles on combinatorics and probability and also on probability and deviation, both of which offer a more advanced approach to the thinking needed before composing structured betting portfolios.

Please post any questions or comments in the Leave a Reply box below.

We will also investigate the proposed stop-loss point at the end of the calendar year, and try to project what practical uses the theory behind this system may have in a different arena.

2012-13 Paper Test

As we have seen, this is a progressive system with each stake individually calculated to claw-back all previous losses and to collect a net £100 win when the desired correct score line arrives.

In this way, the staking plan becomes exponential as we saw in the first of our three articles.

Taking the same staking system into its second season and, still assuming that each bet can be placed at odds of 11.0, the table for match rounds 24-46 is as follows:

Correct Score Roulette Staking Plan Table

NB. The new season begins on the above staking plan table for non premier league teams, but for the top-flight teams, match 20 on the original staking plan table represents the first game of the 2012-13 season (as Premier League teams play only 19 home games in a season).


You can see on the table above how quickly the stakes grow from game to game for teams continually failing to register the elusive 2-0 home win score line.

At some stage it will become difficult to get stakes of this size placed with any one market and therefore the system may have to rely on split stakes placed with more than one bookmaker or betting exchange to achieve full coverage of each match.

Looking ahead, staking may become more and more tricky in order to force this football betting system to its final conclusion; pursuing this course is dependent on having a very large betting bank and having the desired result arrive before bankruptcy.