# How Odd(s)

I was recently inspired to make a data visualization that illustrates the concepts discussed in an article I read about the Ralph Vince Experiment, which involved him gathering 40 PhD students and asking them to play a computer betting game.

you can check out my data visualization inspired by the game here: https://codepen.io/lexjacobs/full/xdgMOY/

The rules were simple: each participant was given \$1,000 in virtual currency to start. There would be 100 rounds of betting. Each betting round had a 60% chance of success. They could bet any amount of their remaining funds per bet.

So how many people made money by the end of the game?

Two.

Two?

Two.

Do you think that you would be able to make money given the starting parameters and rules?

Ralph Vince is a financial investor, and so most of the stories analyzing this experiment come from a perspective of financial market trading position sizing. I think that is interesting, and useful from a trading perspective, but also illustrates an interesting phenomena that I think applies to life in general. Small, but regular increases can add up significantly over time. Whereas, large outlays involving risk compound into increasing likelihood of failure, perhaps canceling out the benefit of larger gains. Once you can no longer wager, the game is over. This of course applies to gambling, stock trading, as well as expending energy on any life pursuit that may or may not be regenerative.

#### Regarding my data visualization

I created a simultaneous visualization of 2 risk strategies. The first is where you set a number that sets up your next bet size as a percentage of your total remaining pot. The second is a strategy where you just make the same bet amount, regardless of how much money you have left in your pot.

You can also set the percentage of bets that will be winners, as well as the reward multiplier per win.

It’s pretty amazing to see how well a strategy of small, adjusted bets does over time.

There is also a horizontal line that is rendered each time as a quick reference of how many trials returned a win/loss based on initial pot size.

Feel free to check it out if you want, and I’d be curious about any feedback, or any additional controls that would make this more useful.

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