Blackjack Table-hopping Part2
Most people who watch and understand sports use advanced statistical concepts every day. Let's use baseball as an example. If I say a hitter has a batting average of .312, this is really a number taken from a frequency distribution chart. It tells us the frequency—.312—of the batter's hits, in relation to all of his trips to the plate that resulted in either a hit or an out. Another way of saying this baseball player has a .312 batting average would be to say that if I look at a chart that shows all of his hits and outs for the season, he got a hit 31.2% of the time.
The batting average, .312, or 31.2%, is the "frequency" with which he gets a hit. So, how freaky is it when this batter gets a hit? Not very—his batting average tells us that he gets a hit almost a third of the time.
Most baseball fans know that batting average does not include the times the player walked, or reached first by being hit by a pitch. If we factor in those at-bats, we will end up with his "on-base percentage." But neither batting average nor on-base percentage tells us how many total bases, on average, the player made per trip to the plate. If we just look at the on-base percentage, a guy who hits all singles could look just as good as a guy who hits lots of doubles and triples. So, to distinguish these players from each other we calculate their "slugging percentages." Virtually every spectator sport has mountains of data, based on frequencies, that the fans understand, despite never having taken a course in statistics.
I knew one baseball nut who used the players' batting averages to make a lot of money betting against his buddies. When a player who had a batting average under .333 came to the plate, he would often give 2 to 1 odds to any and all takers that the guy wouldn't get a hit. "My twenty against your ten," he would announce. In fact, he had the best of it, since a batting average below .333 means that the player gets a hit less than one out of three times to the plate. One night, after putting up five twenties against five tens, his spirits were dampened somewhat when the TV sportscaster announced that the batter, who was hitting .295, hit .500 against lefties with runners in scoring position! Whoops! You'd better make sure you're using the right freak chart for the bets you're making!
In any case, a "frequency distribution chart" simply shows us the percentage of times that various things occur. In blackjack, we can analyze a card counting system, and figure out the win/loss rate, by using a frequency distribution. We know that the count is always going up and down, and that sometimes the house has the edge, and sometimes the player has it. But to know exactly how much I expect to win or lose in a specific game, using some specific betting spread based on my count, I need more details. What I need to know is exactly how often the different house/player advantages occur. That is, what are the frequencies of player advantages of 1%, 2%, 3%, and so on? And what are the frequencies of these house advantages? If I know how often these occur in a game, I can figure out how much of a betting spread I need to beat the game. I can also figure out exactly how much I should bet at each specific count in order to get the highest win rate. And I can also use these frequencies to devise a strategy for minimizing my fluctuations.
Drawing up a frequency distribution chart that shows house/player advantages for a blackjack game is not rocket science. There are two ways to do it: You can run a computer simulation, and then study data the computer spits out; or, you can use a mathematical formula for deriving the precise data you seek. Since this is not a math course, I'm not going to explain the process. Think of this chapter as the sports section: In lots of daily papers, you'll find a chart that shows the batting averages, and sometimes the on-base percentages and slugging percentages for all the players, but you're not going to find all the raw data and the formulas used for calculating these totals. You don't need the formulas to understand that a .312 hitter is scary to most pitchers. If you have an interest in the math, get a book on probability and statistics. I just want you to understand what the data we're going to look at means.
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