Markov Predictor
Based on Markov Chains
How it Works
Named after Russian mathematician Andrey Markov, Markov Chains are used by Google to rank web pages, by weather services to predict forecasts, and by musicians to compose melodies. The Markov Predictor applies this powerful concept to lottery analysis, asking: "Given what appeared last draw, what's most likely to appear next?"
The Transition Probability Matrix
The probability of Y appearing in the next draw, given X appeared in the current draw.
Markov Chains analyze sequential probability by building a transition matrix from historical data, tracking which numbers tend to "follow" others.
Building the Matrix:
1. For every number X in draw T, record which numbers Y appear in draw T+1 2. Count all transitions: "After 7 appeared, 23 appeared next 15 times out of 50" 3. Calculate probabilities: P(23|7) = 15/50 = 0.30
Applying to Predictions:
1. Look at the most recent draw (e.g., [7, 12, 23, 34, 45]) 2. For each number, check its transition probabilities 3. Aggregate: Which numbers have highest combined probability of following?
The insight:
While individual draws are independent, Markov analysis can reveal subtle patterns in the data, even if those patterns are just statistical noise.
Advantages
- Sophisticated mathematical framework
- Used successfully in many prediction domains
- Captures sequential relationships
- Adapts as new draw data arrives
Considerations
- Lottery draws are theoretically independent
- Requires substantial historical data
- Transition patterns may be coincidental
- Complex to interpret and verify
Visualization: Network Graph
Interactive chart visualization coming soon
Use this Strategy in The Lab
Configure weights and generate predictions with Markov Predictor