@ArtSchlichterJr Exactly. A sample size of 100 events over 2 years is considered highly statistically significant. (If the probability, or p-value is equal to or less than 0.05 or 5%, the result is considered statistically significant. A p-value equal to 5% indicates that there is a 95% confidence level in the study. In english, you would need a sample size of 20 or more to acheive this 95% confidence level. 100 events is 5x that.) Not "simply luck." (For another discussion is why the lines are high by 5 points or so, as bookmakers know the public, "the squares money" is 65-35 bet on the Overs (!) There's that ratio again!!!) In dollars, if a better makes 100, $500 wagers, and 65 win and 35 lose, he/she would have won $32,500 and lost $17,500. Add in 10% vig on the winnings and you get a net profit of $32,500 - $3,250 = $29,250 - $17,500 = $11,750. So, while others are waiting for the regression to the mean (alas, the lines vary if greater/fewer points are anticipated, so there goes the coin flip theory) we are making $11,750 on our initial risk of around $1,500 (2/3 win so the buildup happens fast without a huge capital exposure.) Or, in english again, as a sales manager once told my crew, "While you guys were trying to decide who to call, I called them all, made 10 sales, and bought myself a new car."
I tried to let this go yesterday.... But I read it again today and just cant.
This is the biggest bunch or word salad and misused assumptions of statistics.
You are assuming you would know exactly when to start betting this subset, and exactly when to stop. You are also misusing confidence level (or standard deviation). You mix real statistics (65 - 35 real results) with assumed made up statistics (65% of the public bets overs, and the bookmakers know this?). Silly.
Keep doing what you are doing - but leave the math out of it please. Simply put - if you had 100 coin flips - there is a 1.75% chance that 65 or more would be heads. This primetime under run could be just that, variance. It could also be cause and effect, but your attempt to explain it is absurd.
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@ArtSchlichterJr Exactly. A sample size of 100 events over 2 years is considered highly statistically significant. (If the probability, or p-value is equal to or less than 0.05 or 5%, the result is considered statistically significant. A p-value equal to 5% indicates that there is a 95% confidence level in the study. In english, you would need a sample size of 20 or more to acheive this 95% confidence level. 100 events is 5x that.) Not "simply luck." (For another discussion is why the lines are high by 5 points or so, as bookmakers know the public, "the squares money" is 65-35 bet on the Overs (!) There's that ratio again!!!) In dollars, if a better makes 100, $500 wagers, and 65 win and 35 lose, he/she would have won $32,500 and lost $17,500. Add in 10% vig on the winnings and you get a net profit of $32,500 - $3,250 = $29,250 - $17,500 = $11,750. So, while others are waiting for the regression to the mean (alas, the lines vary if greater/fewer points are anticipated, so there goes the coin flip theory) we are making $11,750 on our initial risk of around $1,500 (2/3 win so the buildup happens fast without a huge capital exposure.) Or, in english again, as a sales manager once told my crew, "While you guys were trying to decide who to call, I called them all, made 10 sales, and bought myself a new car."
I tried to let this go yesterday.... But I read it again today and just cant.
This is the biggest bunch or word salad and misused assumptions of statistics.
You are assuming you would know exactly when to start betting this subset, and exactly when to stop. You are also misusing confidence level (or standard deviation). You mix real statistics (65 - 35 real results) with assumed made up statistics (65% of the public bets overs, and the bookmakers know this?). Silly.
Keep doing what you are doing - but leave the math out of it please. Simply put - if you had 100 coin flips - there is a 1.75% chance that 65 or more would be heads. This primetime under run could be just that, variance. It could also be cause and effect, but your attempt to explain it is absurd.
It is well known that it takes 30 variable data points to determine a confidence interval of 5%, however hitting the over/under of a game is not variable - it is attribute. As a result, you need 300 data points (as opposed to 100) to determine a confidence interval of 95%.
It is well known that it takes 30 variable data points to determine a confidence interval of 5%, however hitting the over/under of a game is not variable - it is attribute. As a result, you need 300 data points (as opposed to 100) to determine a confidence interval of 95%.
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