How to Make Money in Sports Betting with Mathematics and Statistics

The vast majority of bettors — around 97% — bet on teams, players, or outcomes. The remaining 3%, commonly referred to as professional bettors, bet on numbers: data, probabilities, and mathematical discrepancies.

This distinction is fundamental.

Recreational bettors usually place bets based on personal belief, intuition, emotional attachment to a team, or gut feeling. Their decisions are subjective.

Professional bettors, on the other hand, only place a bet when a strict mathematical condition is met:

👉 when the odds offered by the bookmaker are higher than the true probability they assign to an outcome.

In other words, they are not trying to “predict the correct result,” but to buy an undervalued price. This is what is known as a positive expected value bet (EV+).

The core principle: value and probability

Odds are nothing more than the expression of a probability.

• High odds = event considered unlikely
• Low odds = event considered likely

Long-term profitability does not come from picking winners, but from finding pricing errors.

Practical example: Al-Najma vs Al-Ettifaq

Let us take a match from the Saudi Professional League: Al-Najma vs Al-Ettifaq, January 8, 2026.

Our first task is to estimate the true probabilities of the three possible outcomes (1X2), using our algorithm based on statistical data.

Our model produced the following probabilities:

• Al-Najma win (1): 15%
• Draw (X): 18%
• Al-Ettifaq win (2): 67%

Which correspond to the following “fair odds”:

• 1 → 6.66
• X → 5.55
• 2 → 1.49

Analysis of Pinnacle’s odds

The odds offered by Pinnacle for this match were:

• 1 → 5.020
• X → 3.800
• 2 → 1.689

First, we calculate the bookmaker’s margin:

Margin = ((1/5.020)+(1/3.800)+(1/1.689))×100((1 / 5.020) + (1 / 3.800) + (1 / 1.689)) × 100((1/5.020)+(1/3.800)+(1/1.689))×100 – 100

Margin ≈ 5.42%

We then remove this margin to obtain the “true” market odds:

• 1 → 5.292
• X → 4.006
• 2 → 1.781

Which gives Pinnacle’s implied real probabilities:

• 1 → 1 / 5.292 = 18.90%
• X → 1 / 4.006 = 24.96%
• 2 → 1 / 1.781 = 56.14%

Probability comparison

We now compare our probabilities with the bookmaker’s:

• 1: 15% vs 18.90% → no value
• X: 18% vs 24.96% → no value
• 2: 67% vs 56.14% → strong value

Our estimate produced a fair price of 1.49, while Pinnacle was offering 1.689.

This represents a mathematical edge of:

(1.689 / 1.492) × 100 = 113.20%

👉 Which corresponds to a +13.20% expected value bet.

Result and long-term logic

The match ended with a 4–3 win for Al-Ettifaq.

In this specific case, the final result confirmed our position. However, one point is absolutely critical:

✔️ A single match never validates a method.

✔️ Only a large sample size validates a model.

Even with a genuine mathematical edge, many bets will lose. What makes the difference is not the apparent strike rate, but the consistent gap between your probabilities and those of the market.

Over the long term, if your algorithm is accurate and you only bet when value is present, mathematics will do the work.

Conclusion

Being profitable in sports betting is not about instinct, passion for sport, or searching for “sure wins.”

It relies exclusively on three pillars:

• statistical modeling,
• accurate probability estimation,
• systematic identification of value bets.

It is not the easiest approach.

It is not the most exciting.

But it is the only one that works in the long run.