Risk Analysis, Optimization, and the Special Sauce

Okay, let’s get back to baseball betting investor Clay Graham and his words to live by: "picking a winner is not the same thing as making a smart bet."

Ah, could you say a little more about that?
"To pick a winner you need to choose the team with the highest probability of winning."
Right.  The probability of winning.  Simple. 
"Ah,  not really—but let’s get back to that.  To make a smart bet, you put your money where the expected return on investment–your ROI– is greater than zero."
Well, obviously.  But how do you know when you’re doing that?
"You are doing that when the probability of winning times the expected payoff (for the payoff just consult the "lines" that appear in the newspapers sport section every day) minus the probability of losing times its cost is greater than zero.  In other words:
[probability of winning x payoff] – [probability of losing x cost]  > 0.
I get it.  So let’s work on the details of this probability of winning/probability of losing.  How do I calculate that?
"Well, of course, my method is proprietary."
Oh, so that’s where the risk analysis and the Monte Carlo Excel spreadsheet  come in.
"My model is also proprietary."
I’ll bet that’s where you get to work with the genetic algorithm optimization stuff.
"Proprietary is proprietary."
What is this, Clay, the special sauce?
"Have I told you about the optimization model I built for a major-league club’s batting order–wish I could give you the name of the club."
Wish you could too.

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