Thread: Is this code sufficient for my purposes?

I'm not a programmer. I've been playing poker and I want a tool to simulate some results to better understand variance.

I played a 100 games with each game consisting of 3 players in a winner take all format (so with all else being equal there is a 33.3% chance of winning).

Despite adhering strictly to GTO strategy I found myself under the average of 33.3% with a 28% win rate. In fact at one point I lost 11 games in a row. This is discouraging however I decided I should manage my expectations with context.

I put together this code to simulate playing 100 games x 10, however I am told that this may not produce true random results and I was wondering if there is a better method or piece of code that would be more inline with reality. If it's not true random then this exercise is somewhat pointless.

Since I'm not programmer and a stupid head you might have to spell it out for me. I actually downloaded eclipse today just for this.

Thanks guys!

 
import java.util.Random;
 
public class Random1Class {
 
	public static void main(String[] args) {
 
		int win = 0;	
 
		Random rand = new Random();
		int randomNum;
 
 
for(int v = 0; v < 10; v++) {		
 
		for(int i = 0; i < 100; i++) {
 
			randomNum = rand.nextInt((100 - 1) + 1) + 1;
 
			if( randomNum <= 33.3) {		
				win = win + 1;	
				}	
		}
 
		System.out.println(win);
		win = 0;
}		
 
 
 
	}
 
}

Well it's about finding out what kind of range I can expect to see. For example results above 40 are exceptionally rare but apparently happen.

Well I'm using a Java library function and since I'm not that versed in Java I would like to know whether it's sufficient. The program depends on Math and the Java implementation doesn't it. For example one concern I heard is that calling the random method in short succession doesn't give properly dispersed results. So that's not so much math as what Java does right?

By dispersed I meant on on a implementation level... As is computers find it hard to simulate randomness and having this method repeatedly called close together creates an artificial bias (a tightening of the range as a result of seed bias). Does that make sense? I mean it's just what I have heard when I looked up this function. If you think it's doing it's job and there isn't a bias then I guess it's ok as is. And good catch with the float I'll fix that.