Statistics is a major component of card counting. To be successful with card counting, one must use statistics to determine the probability of winning. This is proven true by the MIT blackjack team. The team counts cards at casinos and uses statistics to increase its odds of beating the house. Statistics correlates to card counting as proven by the MIT blackjack team. In order to understand the correlation, one must first understand statistics. Statistics is a branch of math focused on collecting and interpreting many forms of data (anonymous).
In statistics, the data accumulated is used to draw conclusions (Davidian and Louis). Statistics is used in many real life situations today, including card counting. Card counting is a strategy utilized to determine if a hand will yield a probable advantage to either the player or the dealer (anonymous). It is a complex system that has many levels of difficulty. However, the high-low system is the most rudimentary and simplest used in blackjack (Mezrech, 40).
The high-low system, or basic card counting system, is when a positive, a negative, and a zero point value are assigned to each card value available (anonymous).
Low cards increase the count while high cards do the opposite and decrease the count (anonymous). The reasoning behind this is that as low cards are removed from the deck, the percentage of high cards left in the deck increases and the opposite occurs for the high cards. The effect that each card has is referred to as the effect of removal because it has an effect on the advantage a player has over the house once removed (anonymous). This system of card counting was used by the MIT blackjack team to beat the system.
MIT used basic high-low card counting with its own additions and interpretations to have an edge over the house. The MIT students advanced the basic system by dividing the work. Unlike the basic system where one person did it all, the counting and betting, MIT’s system had three people. The first person was the spotter. The spotter’s job was to play the minimum bet, to minimize money loss during cold decks, while they counted the deck (Mezrich, 45). As the deck became good, for the player, the spotter would signal in either a gorilla or a big player.
A gorilla and a big player are similar in the aspect of big betting. A gorilla is “mindless” and pretends not to know what he is doing and just bets big, and a big player acts professionally, bets big, and knows what he is doing (Mezrich, 45). This system was crucial to the MIT team’s success. The blackjack team is a real life situation that reinforces the correlation between both card counting and statistics. When counting cards, one is collecting and interpreting data such as effect of removal and true count.
This data is then used to draw conclusions to determine the favorable or good cards remaining in the deck or decks to come. When the effect of removal is being calculated, a percentage is also being calculated (anonymous). As the cards shown are in favor of the player, their percentage increases while the dealers’ decreases (Wong). Another situation that proves card counting and statistics are related is calculating the true count. To calculate the true count, one must divide the running count, or the current count, by the number of decks left (Wong).
The true count is a ratio of high cards to low cards remaining that are favorable to the player (anonymous). These examples prove that collecting and interpreting data and drawing conclusions from data, which is statistics, is related to card counting and is, by definition, statistics. MIT’s blackjack team has proven that statistics strongly correlates to card counting. Generally, statistics is the collection and interpretation of data and this data is then used to draw conclusions. Card counting is a strategy used by blackjack players to calculate the advantage they have.
The basic system used gives players the percentage of high cards left so they know the advantage. The MIT blackjack team used the basic system to beat the house when they played. The team was an example of a real life situation that proved statistics and card counting are related. Percentages and ratios are calculated using card counting. The process used is statistics, the collection and interpretation of data. Card counting is taking advantage of the statistical nature of blackjack, which MIT’s blackjack team proved.
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