How to tell if a betting strategy is profitable or just lucky
The power of randomness is greater than you think
A p-value tells us how likely it is to observe a result at least this extreme if there were actually no real effect.
If you throw a coin ten times and gets ten heads in a row, it's just an unlikely random event. If you throw a coin ten thousand times and gets 55% heads, you know the coin is unbalanced.
Why? Because of statistical significance. Ten coin flips are just not enough data to draw any conclusions from.
It's the same in sports betting. If you place 100 bets with an average odds of 2.0 and get a 5% yield (profit per dollar invested), there's still a one-in-three chance you got that result just out of pure luck. The p-value is below 0.3.
The number of bets required to reach statistical significance is much higher than what most people think intuitively.
For example, if you after 1100 bets still have 5% yield – you're most likely on to something. The risk of these results being pure chance is now just 5% (p <0.05). So there is a 95% chance that there is something else going on. Like skill, or a profitable value betting product.
What is a p-value?
A p-value is used in statistical hypothesis testing to measure how compatible the observed data is with a null hypothesis. The null hypothesis usually represents no real effect, no difference, or no underlying edge.
The p-value answers the question:
If there were actually no real effect, how surprising would the observed result be?
How to interpret it
A low p-value means the observed result would be relatively unusual if the null hypothesis were true.
For example:
p = 0.03
means that, assuming there is no real effect, there would be about a 3% chance of observing a result this extreme or more extreme due to random variation alone.
A commonly used threshold is:
p < 0.05 = statistically significant
This does not prove that the effect is real. It simply means the data provides relatively strong evidence against the null hypothesis. That being said, a p-value below 0.05 is generally accepted as "statistically significant" – but that is a highly arbitrary limit. Nothing stops you from requiring stricter proof, like a p-value below 0.01.
Why sample size matters
Sample size heavily influences the p-value. With a small sample, variance can have a large impact. A very strong-looking result may therefore still have a relatively high p-value. With a large sample, the estimate becomes more precise. Even a comparatively small effect can then become statistically significant.
A common misunderstanding
A p-value of 0.03 does not mean:
“There is a 97% probability that the effect is real.”
Instead, it means:
“If there were no real effect, results this extreme would occur around 3% of the time.”
That distinction is important.
In betting terms
A useful way to think about it is:
- Yield tells us how large the observed result is.
- Sample size tells us how much data supports that result.
- The p-value helps us assess how likely it is that the result could be explained by random variance alone.
So a high yield with a small sample can be promising, while a smaller positive yield across a very large sample can provide much stronger statistical evidence of a genuine edge.
This is also why value betting is profitable over the long term. The goal is not to predict individual winners, but to repeatedly place bets where the offered odds exceed the estimated true probability. RebelBetting focuses on finding those statistical edges across many markets rather than relying on intuition or favourite teams.
Still have questions?
Can't find the answer you're looking for? Please chat to our friendly team.