The 2017-18 Winter League Campaign consisted of 138 betting days spanning 10 and-a-half months.

Despite a rocky start and other challenging periods throughout 2017-18, a starting bank of 4,000 units was eventually turned into 38,925.

(Bank Development ↔ green profit curve; Profit/Loss ↔ weekly columns)

# Portfolio Chosen

Regarding the selection of systems we will expand a little on our decision-making process in the league-by-league review below. For compiling this portfolio we used our **HDAFU Tables**.

How the systems were originally chosen, you’ll find a detailed explanation using the EPL pick as example here: **Finding a System Using the HO/AO Quotient**

To read about the compilation of the portfolio used for this article here’s a link to our analytical article: **Judging the Risk of a Football Betting Portfolio**.

There you will find explanations on how to calculate the overall probability/expected hit rate of a portfolio and how to judge the risk. You will also learn how to predict winning and losing streaks, and what kind of profit/loss curve to expect when compiling your own portfolio of bets.

Let’s now dive in to the performance report without further ado.

We start with a league-by-league review and then see how they all put together reduced the risk and finally produced a result as expected.

# 2017-18 League-by-League Review

In Image 2 below you can see that hardly any individual system (league) within the portfolio met its expectations (predictions), yet the overall performance of the portfolio fully met the expectations, profit-wise, yield-wise and hit rate-wise.

### (1) Austria Bundesliga – Underdog – Whole of Season

**HIGH RISK**

Hit rate expected: **31.94%** *(associated risk: high)*

Yield expected: **30.36%** *(associated risk: high)*

Despite its low hit rate this system was included because a healthy yield was expected. When judging a system fit for inclusion, healthy yield indicates that a profit can still be made even if something goes wrong, e.g. the hit rate is a little lower than expected.

Hit rate achieved: **30.67%** *(just below expectation)*

Yield achieved: **9.75%** *(far below expectation)*

The hit rate was more or less achieved, but the profit was 65% below budget. Perhaps the odds that were obtained were too low or there were just not enough larger priced winners in this cluster.

### (2) Belgium Jupiler League – Home Win – Whole of Season

**LOW RISK**

Hit rate expected: **86.67%** *(associated risk: low)*

Yield expected: **13.85%** *(associated risk: low)*

Included because of the high hit rate indication to add reliability and stability to the portfolio, despite a low yield expectation.

Hit rate achieved: **80.95%** *(below expectation)*

Yield achieved: **7.90%** *(below expectation)*

The hit rate was below expectation and hence the yield too. However, a profit is a profit albeit small here, but no complaints. In the end, Belgium produced a profit and helped the portfolio to remain balanced over the season.

### (3) Denmark Superligaen – Away Win – Whole of Season

**MEDIUM RISK**

Hit rate expected: **41.67%** *(associated risk: medium)*

Yield expected: **21.18%** *(associated risk: medium)*

Another judgement call asking the anticipated yield of around 20% to buffer the expected hit rate and still turn a profit.

Hit rate achieved: **37.5%** *(below expectation)*

Yield achieved: **8.1%** *(far below expectation)*

The hit rate was below expectation and hence the yield too. But still a profit from a medium risk venture.

### (4) England Premier League – Draw – 1st Half of Season

**HIGH RISK**

Hit rate expected: **39.62%** *(associated risk: medium)*

Yield expected: **38.62%** *(associated risk: high)*

Despite its low hit rate this system was included because a healthy yield was expected. It was felt that the risk was worth the potential reward.

Hit rate achieved: **38.18%** *(just slightly under expectation)*

Yield achieved: **26.73%** *(good, although far below expectation)*

The EPL is probably the most analysed league in the world and it is difficult to spot long-term value for betting systems that are reliable and come out to expectations. The hit rate was in line with expectations but the yield was not. Nevertheless, a healthy profit was achieved.

### (5) France Ligue 1 – Favourite – 2nd Half of Season

**MEDIUM RISK**

Hit rate expected: **50.00%** *(associated risk: medium)*

Yield expected: **24.93%** *(associated risk: medium)*

The yield looked slightly too high for an expected hit rate of 50%. This may have been an arbitrary positive result observed in the inflection point graphs, especially as the expected number of bets was only 36. Nevertheless, this system was included in the portfolio because of the risk-reward balance.

Hit rate achieved: **43.33%** *(below expectation)*

Yield achieved: **10.8%** *(far below expectation)*

Only 30 bets were played (from expected 36) and both hit rate and yield were much lower than expected.

### (6) Germany Bundesliga – Underdog – Whole of Season

**MEDIUM RISK**

Hit rate expected: **31.58%** *(associated risk: high)*

Yield expected: **29.83%** *(associated risk: medium)*

Normally, systems with an expected hit rate of under 35% (classified as ‘high’ risk) and with an expected yield between 15% and 30% (‘medium’ risk) get an overall risk judgement: HIGH RISK.

Although the combined expectations of hit rate and yield here would have labelled this system as ‘high risk’ we downgraded the overall risk judgement to ‘medium risk’ as it was Germany and the Bundesliga is probably the statistically most reliable league we are aware of.

Hit rate achieved: **37.04%** *(above expectation)*

Yield achieved: **55.43%** *(far above expectation)*

German underdogs (not sausage dogs) were a (woman’s) best friend, and proved to be the most successful of all our systems.

If the EPL is just about the most ‘statistically unreliable’ league, then the Bundesliga is at the other end of the scale. There is something typically German about the Bundesliga’s constant conformity to statistics, season in, season out – very correct and very efficient.

### (7) Greece Super League – Home Win – Whole of Season

**LOW RISK**

Hit rate expected: 80.00% *(associated risk: low)*

Yield expected: 9.02% *(associated risk: low)*

The Greek system was chosen for risk diversification in our portfolio. Despite associated lower yields, bets with a high probability of winning break up losing streaks in the portfolio and add stability.

Hit rate achieved: **82.98%** *(slightly above expectation*)

Yield achieved: **8.06%** *(below expectation)*

Not a huge profit but a profit nonetheless. Low priced home wins are naturally odds-on favourites also. Better prices can be achieved on these by placing bets further back in time before the event.

### (8) Italy Serie A – Away Win – Whole of Season

**MEDIUM RISK**

Hit rate expected: **52.59%** *(associated risk: medium)*

Yield expected: **8.49%** *(associated risk: low)*

Like Greece, this one was chosen for risk diversification combining a relatively low yield with a probability over 50%.

Hit rate achieved: **61.11%** *(above expectation)*

Yield achieved: **17.51%** *(far above expectation)*

Italy was our second best performer with its mixture of odds-on and odds-against away teams. However, almost all of these were the favourites and just a 15% swing in hit rate (not unnatural with favourites), was enough to double the yield harvest.

### (9) Netherlands Eredivisie – Favourite – 2nd Half of Season

**MEDIUM RISK**

Hit rate expected: **75.00%** *(associated risk: low)*

Yield expected: **32.23%** *(associated risk: high)*

The yield here stuck out as being too high for a system with such a high probability. This may have therefore been an arbitrary positive result observed in the inflection point graphs, especially as the expected number of bets was as few as 15. Nevertheless, this system was included in the portfolio and judged as medium risk.

Hit rate achieved: **73.33%** *(slightly below expectation)*

Yield achieved: **25.33%** *(below expectation)*

A very tiny system with only 15 bets expected and indeed, 15 bets played in the end. However, not 12 wins as ‘expected’ but only 11. With margins so tight it was no wonder that the yield figure dropped. Otherwise, despite initial indecision whether to include this system, a profit was made.

### (10) Poland Ekstraklasa – Underdog – 1st Half of Season

**HIGH RISK**

Hit rate expected: **25.00%** *(associated risk: high)*

Yield expected: **33.00%** *(associated risk: high)*

With such low probability on the table, unreliability was expected, but the reward (yield) was worth pursuing. Furthermore, only 40 bets were expected. This system therefore had all the hallmarks of ‘high risk’.

Hit rate achieved: **21.62%** *(slightly below expectation)*

Yield achieved: **16.22%** *(far below expectation)*

Betting on underdogs is notoriously volatile and it was lucky that the very last two bets of the season both won to avoid a deficit here. 40 bets were expected, but only 37 played that fitted the selection criteria. 10 wins were expected but only 8 materialised.

This was the only system in the portfolio with such a low hit rate expectation in connection with very few bets expected. It was always going to be a shaky, unreliable ride, and it didin’t disappoint.

## Was there a single system within the portfolio that performed as expected?

There was not a single system that performed *as expected* within the portfolio. Of the 10 chosen systems illustrated here, eight underperformed, and only Italy and Germany overperformed (both ‘medium’ risk systems).

The expected hit rate for Germany was 31.58% and it achieved 37.04%

The expected hit rate for Italy was 52.59% and it achieved 61.11%

Although some of the other eight systems succeeded to surpass their expected hit rates, the profit at the end was less than expected. This indicates that perhaps the odds played across the board were a little below those used in the HDAFU simulation tables.

**Please remember!** Concentrate only on individual leagues whilst compiling your portfolio. Once you start placing bets shift your focus to the overall portfolio performance and bank management. Only after a set period of time, review the individual systems in your portfolio. There will be an article on that topic coming soon…

After you have compiled your portfolio with due care you need to trust your own judgements and follow through fully with your betting endeavour as planned!

# Get the Full Report & Check our Statements

The information contained in the spreadsheet is invaluable and we are sure that you will feel the nominal **£5.00 GBP** charge is a real bargain: It is an ideal template for your own portfolio structuring and monitoring processes, and in addition it provides the opening and closing odds for the 518 bets included in the portfolio.

*The size of this .XLSX Excel file is 568KB:*

**>>> 2017-18 winter league campaign <<< **

**Spreadsheet Features:**

- The spreadsheet details every bet in every system used.
- Includes tabs for the overview of the portfolio, match data of the 10 systems used, as well as their opening and closing odds, and finally the odds used and the staking plan.
- The most important feature is the
**Ratchet**staking tab, which will provide you with the ideas and tools to manage your betting bank professionally.

# Final Performance 2017-18 Winter League Portfolio

On the whole, the portfolio performed as expected:

- A hit rate of 48.32% was expected; 45.37% was achieved
- A profit (flat stakes of 100 units) of 11,253.60 was expected;
**9,909.00**was achieved (if using 100 unit flat stakes during the whole campaign)

You might say that the portfolio underachieved. But this is betting! The only goal is to finish with a profit! Any achieved profit may be below expectations or above, but as long as there is a profit, the portfolio must be considered as having been successful!

Therefore, remember to look at the performance of the whole portfolio for judging if it was successful. Do not concentrate on the performance of individual systems; not even after the season has finished. Potentially fatal if your mindset is affected by worries about individual systems, especially whilst your campaign is in-play.

However, there are exceptions to this rule and I will write about this in another article and explain when a system can be abandoned in-play, or when a new system can (or should) be added during the season.

## 35k in 138 Days?

You certainly noticed that in the above chapter we wrote that the portfolio achieved a profit of 9,909 but stated in the title of this article “35k in 138 Days”.

To predict the most likely performance of a portfolio you only can simulate a profit/loss using flat stakes. The same applies to judging the performance of a portfolio. You can only use flat stakes to appraise the effectiveness of a portfolio.

It doesn’t matter what staking plan has been used; in order to evaluate *(and compare with predictions)* the performance of a portfolio, you will need to see what would have happened when using flat stakes. Otherwise you will be comparing apples with pears.

The sequence of winning and losing bets is pretty random and any staking plan applied would therefore skew the review process. Therefore remember always to use flat stakes for retrospective analysis.

However, the portfolio used to illustrate this article produced a final profit of 34,925 units using ratcheting *(= gradual increase of stakes when winning)*. More details you’ll find in the article: **Winter Leagues 2017-18: Bank Management, Staking and Timing** *>> This article will be published within the next few days.*

## A Portfolio Reduces the Risk

If you have read the article about judging the risk of a portfolio you may remember the following statement about **diversification**:

The rationale behind this technique contends that a portfolio constructed of different kinds of bets will, on average, pose a lower risk than any individual bet (system) found within the portfolio.

Diversification strives to smooth out unsystematic or anomalous risk events (outcome of matches/leagues) in a portfolio so that the positive performance of some bets (winnings) neutralizes the negative performance of others (losers).

Have another look at Image 2 for the losing streak sequences.

The expected individual longest losing streaks within the leagues (systems) were as long as 11 bets in a row.

All three ‘high risk’ systems produced, as expected, very long losing streaks: Austria, EPL, Poland.

Denmark produced an even longer losing streak than expected. It started on the 26/8/2017 and continued until 29/10/17 – more than 9 rounds of games! Nevertheless, they recovered to finish with a positive return, although far lower than expected.

However, the portfolio as a whole only experienced a losing streak of seven bets in a row *(chronologically)*.

But try and look at the portfolio as a whole and consider one round (week) as one ‘single bet’. This perspective reveals a longest streak of four *(out of 47 rounds)* – just about bearable!

*Please remember* that in order to judge the success of any betting portfolio it is necessary to evaluate the performance of all of its systems together as one, not just individual group members (i.e. bets in just one league or system).

*I hope you enjoyed this article and learned how to judge the performance of a portfolio. However, if you are still not clear, then please feel free to ask any questions via the comment section below.*

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