Is the Lottery Really Random?

Every week, thousands of people try their luck in the lottery. Some choose birthdays, others have their lucky numbers, and many simply let a computer generate a random combination.

But what if we looked at the lottery from a different perspective?

As someone interested in statistics, data analysis, and Python, I wanted to explore a simple question: Can historical lottery data tell us anything useful about future draws?

Theoretically, the answer should be no. In a fair lottery, every valid combination has exactly the same probability of being drawn. Previous results should not influence future outcomes.

Still, I was curious about what would happen if I tried to build a predictive model and tested it against real lottery results.

And that is how my Sportka Prediction Lab started.

Turning Lottery Results Into Data

Sportka is a Czech lottery where players select six numbers from 1 to 49.

For this project, I collected historical drawing results dating back to 1994 and built a data analysis workflow in Python.

Rather than simply looking for the most frequently drawn numbers, I decided to approach the problem more systematically.

My analysis includes:

• Historical frequency analysis: Identifying how often individual numbers have appeared.
• Rolling time windows: Comparing recent drawing patterns with long-term historical data.
• Statistical testing: Checking whether observed differences could reasonably occur by chance.
• Monte Carlo simulations: Simulating random outcomes to establish realistic expectations.
• Predictive strategies: Testing different ways of selecting combinations based on historical information.

The project currently includes 14 model configurations, covering different historical periods and selection methods.

However, finding patterns in historical data is not the same as predicting the future. Random sequences naturally produce clusters, streaks, and apparent trends.

The real challenge is determining whether any strategy performs better than chance on genuinely new data.

The Five-Week Experiment

Instead of drawing conclusions from historical simulations alone, I decided to run a prospective experiment.

From October 25 to November 29, 2026, I am testing the model against actual Sportka draws.

The process is straightforward. Predictions are generated before each drawing, recorded, and then compared with the official results.

Alongside the statistical models, I am also testing a random selection strategy as a baseline.

Throughout the experiment, I will track the number of correctly predicted numbers, winning combinations, ticket costs, payouts, and overall return on investment (ROI).

The key question is not simply whether the model produces a winning ticket. Even completely random selections can win.

What matters is whether the results provide credible evidence that the model performs better than random selection.

And with a relatively short testing period, even an unusually good outcome may simply be luck.

What Do I Expect?

I am not expecting Python to magically discover the winning lottery numbers.

In fact, my starting assumption is that no strategy will demonstrate a reliable predictive advantage over a properly constructed random baseline.

But that does not make the experiment pointless.

This project is an opportunity to explore statistical inference, simulation, model evaluation, and the challenges of working with inherently random processes.

It is also a reminder that a model can identify patterns without those patterns having any predictive value.

What’s Next?

The experiment is already underway, and I will publish the complete results after November 29, 2026.

In the follow-up article, I will compare the strategies, evaluate their financial performance, and examine whether the observed outcomes are statistically meaningful.

Will the models outperform random selection, or will randomness prove impossible to beat?

That is exactly what I want to find out.

Follow Analyst’s Diary for the results of the Sportka Prediction Lab.