Profitable Betting: Control Your Wins with Decimal Odds and Winning Rate

Ways to profitable betting

One solid fact is if such a thing (system!) exists its power isn’t in accurately predicting!
Remember that!

If you search the web about this topic it is very probable you can bump up to next advice:
Profitable betting requires a combination of knowledge, analysis, and disciplined decision-making.

ChatGPT provides a more specific answer:
By understanding decimal odds, considering real probabilities derived from statistical data, and calculating Expected Value, you can gain an edge in the world of betting

When I asked the AI about long-run betting on low decimal odds, artificial intelligence applied:
In the world of betting, one fundamental rule stands tall:
if the probability of winning a game is high, then the possibility of winning definitely exists.
Conversely, as the probability decreases, the chances of winning diminish

Let’s dwell briefly on the answers offered by AI

a) ”By understanding decimal odds, considering real probabilities..”
ChatGPT ‘understand’ difference among decimal odds, d.o, of some event and ‘real’ probability of the same event!
We know that 1/d.o give the bookie’s probability.
If d.o(event)=2 then p_bookie= 1/2 =50% that event will be realized

On the other hand, if we follow the statistical data related to that d.o. value, we can obtain the statistical probability of the realization of that same event

Here we are only interested in the win, 1, or the loss of the bet, 0
Therefore, the random variable x of some event has the next form:

x=

Through E.V, we find out how much we will win or lose on average per bet for that variable x, in a long series of bets
And based on that we accept or reject the bet

c) “..one fundamental rule stands tall: if the probability of winning a game is high, then the possibility of winning definitely exists….”
This conclusion can be applied to a single bet or to a homogeneous series of bets
What it means for one bet is self-evident:
lower d.o. -> greater chance of winning->smaller profit et vice versa

Profitable Betting through long-run betting winning rate and appropriate decimal odds

Some successive series(arrays!) of bets become homogenous if muffling is applied (on the same series!)
For example, let d.o represent decimal odds for the event with random variable x (that for e.v!)
The probability that the event doesn’t occur in the first bet is denoted by q, 0<q-1

Next, we observe 2. bet:
The probability that the event doesn’t occur in the first and second bet is q*q in the 3. bet probability that the same event doesn’t occur in the first, second, and third bet:
q*q*q
And so on and so forth

Finally, we have got strictly decreasing sequence :
q,q²,q³,…,qⁿ
And that is the heart of muffling-decreasing of loss probability

For example, let q=70%
So win-probability is only: 1- 0.7=30%
Suppose we adjust our payments for 5 bets
If we fail in all 5 bets, our investment failed!
What is the probability of such an outcome?
q⁵=0.7⁵ = 0.16807~17%
So with such an investment adjustment, the probability that we will win a bet within a cycle of 5 bets is ~83%

Decimal odds and the winning rate

A very simple rule ruled over betting:
If the probability of winning a game is high(expressed in decimal odds), then such a possibility definitely exists!
On the other hand, if the same probability is decreasing then also the same possibility(of a win event) is diminishing

Orientation in all of this is given by European, decimal odds, d_o(event)

Sweden ‐ Allsvenskan
Jul 3,2023

X H D A
Elfsborg‐Hammarby IF 1.69 4.1 5

p_bookie isn’t ‘real’ probability, moreover, strictly speaking, it is not a probability in the mathematical sense!
Why?
Here is why:
p_bookie(Home)+ p_bookie(D)+ p_bookie(A)=64%+25%+19%=102.6%
Difference:102.6%-100%=2.6% is called-Overround,over

And because ovr, p_bookie is called Implied probability or ‘probabilities’ that don’t add up to 1
In truth, a fact does not add up to 1 opens a wide range of betting models and strategies!!

Decimal odds and winning rate from statistics

To obtain more relevant probabilities, it is essential to gather relevant statistical data.
This website provides rough data necessary for calculating “real” probabilities.
Let’s refer to a downloaded workbook for basic but useful insights:

By applying a joint filter, we can focus on specific ranges of odds and their corresponding probabilities:

Home Win: 1.65 < 1.69 < 1.70
&
Away Win: 4.94 < 5.00 < 5.05
……………………………………………..
Result-distribution of ‘real’ probabilities:
85% Home Win, 0% Draw, 15% Away Win

Calculating Expected Value as Criterium for Profitable Betting

The calculation of EV involves multiplying the probability of winning by the potential return and subtracting the probability of losing multiplied by the amount wagered.
Let’s examine the calculation of Expected Value in its binomial form, denoted as E(x):
Ev(x) = p(x) * b – q * s

Here, p(x) represents the probability of the chosen outcome, b represents the potential return, q represents the probability of losing, and s represents the stake.
It’s important to note that the nature of coefficient b depends on whether the stake is considered a unit or an actual monetary value, such as € 1.
In the case of fractional odds (Anglo-Saxon format), b is fractional when the stake is a unit bet.

When E(x) > 0, the circumstances of the game are in favor of the bettor.
On the other hand, when E(x) < 0, the circumstances are unfavorable.
Achieving positive E(x) values is the ultimate goal for long-term profitable betting.

E(Elfsborg home win) = 0.85 * (1.69 – 1) – 0.15 = 0.85 * 0.69 – 0.15 ≈ 0.44

This means that, on average, for every 1€ staked in such a market, you can expect to gain approximately 44 cents.

‘approximately’ here mean that sometimes you will experience loss, but in the majority of cases, your bets will be winning
‘approximately’ also point out toward optimizing the punter’s bankroll
Bankroll optimization is in strong correlation with rationality(reasoning) in betting