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Day: MondayĀ Ā Date: 19.09.2022

League: *NORWAY Division 2 – Group 2*

Match: *Ull/Kisa – Valerenga 2*

Tip:* Over 2.5 Goals*

Odds:* 1.50*Ā Ā Result: *3:0 Won*

* robert7weldon@gmail.com*

WhatsApp support:Ā *+43 681 10831491*

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In February 2019, FixedMatch.Bet Betting Resources published an article * Halftime Fulltime Fixed Matches Big Odds* modeling a bettorās range of possible

*returns. Around an expect performance there is a distribution of possible outcomes influence by good and bad luck, define by the mathematics of the normal distribution. To help bettors visualise this, we made available a simple performance distribution*

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**calculator fixed matches**Flashscore place for watching live results and old results. This analysis only consider stakes of the same size (level stakes). Whilst Iām very much an advocate of this money management strategy, others quite reasonably prefer a different one. The most common one is to bet a percentage stake based on the current size of oneās bankroll.

Unsurprisingly, the aforementioned method is known as percentage staking. Itās a strategy Iāve discussed before on FixedMatch.Bet in comparison to level staking. The simplest version is to bet the same percentage for every bet, regardless of the odds. More sophisticated versions, like Kelly staking, advocate taking both the odds and the size of oneās expected value into account when defining the percentage size.

**How does percentage staking work?**

Suppose a bettor starts with a bankroll of 100 units. They decide they want to bet 1% of their bankroll on their bets. The first bet will therefore be 1 unit. If it wins at odds of 2.00, the bankroll will now stand at 101. Hence, their next bet will have a stake of 1.01 units, which is 1% of 101. If the first bet had lost, the bankroll would stand at 99 units and the next bet would have a stake of 0.99 units.

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Kelly staking specifically defines the percentage figure that should be apply by dividing the expect value by the decimal odds minus 1. For example, a bet at odds of 3.00 with an expect value of 10% or 0.1 would be assigned a percentage stake of 0.1 / 3 ā 1 = 5%. There are those who argue Kelly staking is too risky to be considered a realistic money management strategy, since it can sometimes advise very large percentage figures. To moderate this risk, fractional Kelly is often * COMBO FIXED MATCHES* considered.

**The skewed distribution of possible returns from percentage staking**

The chart below (reproduced from my earlier article on FixedMatch.Bet) compares the distribution of possible returns for level stakes versus percentage staking for one * fixed odds tips betting* scenario produced via a Monte Carlo simulation. In comparison to level staking, percentage staking, with some good fortune, can see some very big bankrolls.

The distribution has what we would term positive skew. In this scenario, some profits were considerable larger than 7,000 units but for clarity I have omitted * Halftime Fulltime Fixed Matches Big Odds* them.

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In fact, for the simplest of scenarios where the odds and stake percentage of every bet are the same, we donāt need to resort to a Monte Carlo simulation; itās possible to produce the distribution mathematically.

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Consider the following example. A bettor places their first bet at evens with a 10% stake. If it wins, their bankroll is now 110% (or 1.1) times the original bankroll. If it loses, it will be only 90% (or 0.9) times the original bankroll. The same is true after each sequential bet. Consequently, if the bettor bets 10 times and has six winners, we can easily calculate the growth in their bankroll as follows:

Bankroll growth = 1.16Ā x 0.94Ā = 1.162 or 116.2%

**The bettor could start with six winners**

It doesnāt matter what order the wins and losses come in. The bettor could start with six winners and finish with four losers; or they could start with four winners and finish with six losers; or any other of the 210 total possible ways of arranging this combination of winners and losers. They will still finish with 116.2% of what they started with.

Thus, for n bets with stakes S% and w winners:

Bankroll growth = (1 + S)w(1 ā S)n-w

The biggest bankroll growth in my * FIXED MATCHES PREDICTIONS* above was 948.8. I havenāt kept the actual win/loss figures but knowing there were 1,000 bets with odds of 2.0 and stakes of 5%, I can use this formula to determine that the actual number of winners was 581.

Furthermore, if we know the expected value (EV) for our bets, we can calculate the expected rate of bankroll growth as follows:

Expected bankroll growth = {(EV x S) +1}n

For example, if this bettorās EV is 20% or 0.2, their expect (or mean) bankroll growth will be given by {(0.2*0.1)+1}10 = 1.0210 = 1.219 or 121.9%. Readers might observe that this is greater than the bankroll growth associate with winning six out of 10 even-money bets, which is what is implied by a 20% EV.

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This is because the bankroll growth for more wins contributes disproportionately more to the average than those for fewer wins – remember the distribution of possible returns is positively skew. Thus, whilst the most typical (median) bankroll growth in this example will be 116.2%, the expect (or mean) value will be 121.9%.

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Obviously, this assumes that EV is the same for every bet, a huge oversimplification but necessary to define the mathematics.

If we rewrite (EV x S) + 1 as the expected bank growth factor, F, then we have:

Expected bankroll growth = Fn

,and thus:

n = LogF(Expected bankroll growth)

,where F is the base of the logarithm.

For * fixed matches free bets* with the same stake percentage and EV, the logarithm of the expected bankroll growth will be proportional to the number of bets. Similarly, the logarithm of the actual bankroll growth will also be proportional to the number wins. This is visually demonstrate for our example bettor here. The second chart is the same as the first but with a logarithmic y-axis.

You may have noticed that five wins and five losses, which for a level staker would result in a break-even return from even-money bets, results in a slight loss with percentage staking (bankroll growth = 0.951). It takes a bigger percentage growth to recover a previous loss, but if percentages for stakes stay the same, one win following one loss wonāt quite recover the initial lost stake.

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Similarly, one loss following one win will lose you more than you initially won on your first bet. The same is true over 10 bets (or any number of bets). If the bankroll growth for one win and one loss is 0.99 (1.1 x 0.9), then for five wins and five losses it is 0.995Ā = 0.951.

The skewed distribution of returns from percentage staking is log-normal.

If the number of wins in a series of bets is proportional to the logarithm of the bankroll growth, we should expect to see a log-normal distribution of possible bankroll growth. Our * PAID DAILY TIPS FIXED MATCHES 1X2* can help bettors get a good return on their investment.

A log-normal distribution is one where the logarithm of the data isĀ normally distributedĀ (the familiar bell-shaped curve). Below I have plotted the frequency distribution of the natural logarithm (Ln) of the 10,000 observed bankroll growths from the same Monte Carlo simulation I referred to earlier.

Instead of transforming the bankroll growth figures logarithmically, I can instead display the original figures using a logarithmic scale. The results are visually equivalent.

The average or expected bankroll growth for this Monte Carlo sample was 12.2. How does that compare to the figure calculated from first principles using the equation above? With an EV of 5% (0.05) for the 1,000 bets and the stake size 5% (or 0.05), the answer is 1.00251000Ā = 12.1, an excellent match. Unsurprisingly, the median bankroll growth (the centre of the distribution) was considerably lower at 3.49, with only 21.7% of bankroll growth figures higher than the expected figure of 12.2. Remember, a few very large bankrolls positively skew the mean.

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Is there a way to calculate the probability of achieving a specific bankroll growth? One can look at the chart above and make visual estimates, although given the logarithmic scale, that is no easy task. Alternatively, we can just count the number of times a bankroll finished higher than a certain threshold. In this Monte Carlo sample, for example, a bankroll finished with more than it started with (bankroll growth = 1) 78.5% of the time, and at least doubled 63.5% of the time.

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However, using Excel there is an easier method. Having calculated the natural logarithm (using the =Ln function) for all simulated bankroll growth figures, it is then possible to use the follow function:

LOGNORM.DIST(x,mean,SD,true)

where x is your chosen bankroll growth * Halftime Fulltime Fixed Matches Big Odds* (for example 2 for a doubling), āmeanā and āSDā are the average and standard deviation respectively of your natural logarithm values, and ātrueā ensures a cumulative probability.

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Using this formula, the probability of finishing with more than you had start with (x = 1) was estimated to be 78.2%, the probability of doubling your bankroll (x = 2) was 63.6% and the probability of exceeding expectation (x = 12.2) was 21.7%, almost the same figures as * Halftime Fulltime Fixed Matches Big Odds* from counting.

On the other hand, if your goal is truly to maximise your profits, then you will need to consider how much you are willing to risk too. To do that, we must set aside the simple EV calculation and use my Theoretical Kelly Optimization (TKO). Analysis method to find the optimal strategy ā the one that maximises your percentage of expected growth (EG) for your bankroll.

This optimal strategy is the goal of the Kelly Criterion, but since that well-known formula only applies to independent bets. You have to use more complex math when analysing situations like this when you bet on both sides of a market. So how do you take this +EV opportunity and use it to optimise your EG? That is what we will figure out here.