Stat 35b: Introduction to Probability with Applications to Poker

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Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Stat 35b: Introduction to Probability with Applications to Poker
Presentation transcript:

Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: Collect HW2. Luck and skill in poker. Yang / Kravchenko. Testing your R function. Violette / Elezra example. Odds ratios revisited, and Gold vs. Hellmuth on HSP. Project A is due by email by Feb 6, 8pm. You may email me for your teammates’ email addresses. Homework 3 is due Fri Feb 28, 10am. 4.7, 4.8, 4.12, 4.16, 5.2, 6.2.   u    u 

1. Collect HW2. 2. Luck vs. skill. pp 71-79. Any thoughts? 3. Yang / Kravchenko. Yang A 10u. Pot is 19million. Bet is 8.55 million. Needs P(win) > 8.55 ÷ (8.55 + 19) = 31%. vs. AA: 8.5%. AJ-AK: 25-27%. KK-TT: 29%. 99-22: 44-48%. KQs: 56%. Bayesian method: average these probabilities, weighting each by its likelihood. See p49-53.

3. Yang / Kravchenko. Yang A 10u. Pot is 19.0 million. Bet is 8.55 million. Suppose that, averaging the different probabilities, P(Yang wins) = 30%. And say Yang calls. Let X = the number of chips Kravchenko has after the hand. What is E(X)? [Note, if Yang folds, then X = 19.0 million for sure.] E(X) = ∑ [k * P(X=k)] = [0 * 30%] + [27.55 million * 70%] = 19.285 million. 4. Testing your R function.

5. Example: Poker Superstars Invitational Tournament, FSN, October 2005. Ted Forrest: 1 million chips Freddy Deeb: 825,000 Blinds: 15,000 / 30,000 Cindy Violette: 650,000 Eli Elezra: 575,000 * Elezra raises to 100,000  * Forrest folds. * Deeb, the small blind, folds. * Violette, the big blind with Ku Ju, calls. * The flop is: 2u 7 Au * Violette bets 100,000. * Elezra raises all-in to 475,000. (pot = 790,000) So, it's 375,000 more to Violette. She folds. Q: Based on expected value, should she have called? Her chances must be at least 375,000 / (790,000 + 375,000) = 32%.3

Violette has Ku Ju. The flop is: 2u 7 Au. Q: Based on expected value, should she have called? Her chances must be at least 375,000 / (790,000 + 375,000) = 32%. vs. AQ: 38%. AK: 37% AA: 26% 77: 26% A7: 31% A2: 34% 72: 34% TT: 54% T9: 87% 73: 50% Harrington's principle: always assume at least a 10% chance that opponent is bluffing. Bayesian approach: average all possibilities, weighting them by their likelihood. Maybe she's conservative.... but then why play the hand at all?

Violette has Ku Ju. The flop is: 2u 7 Au. Q: Based on expected value, should she have called? Her chances must be at least 375,000 / (790,000 + 375,000) = 32%. vs. AQ: 38%. AK: 37% AA: 26% 77: 26% A7: 31% A2: 34% 72: 34% TT: 54% T9: 87% 73: 50% Harrington's principle: always assume at least a 10% chance that opponent is bluffing. Bayesian approach: average all possibilities, weighting them by their likelihood. Maybe she's conservative.... but then why play the hand at all? Reality: Elezra had 7u 3. Her chances were 51%. Bad fold. What was her prob. of winning (given just her cards and Elezra’s, and the flop)? Of choose(45,2) = 990 combinations for the turn & river, how many give her the win? First, how many outs did she have? eight us + 3 kings + 3 jacks = 14. She wins with (out, out) or (out, nonout) or (non-u Q, non-u T) choose(14,2) + 14 x 31 + 3 * 3 = 534 but not (k or j, 7 or non-u 3) and not (3u , 7 or non-u 3) - 6 * 4 - 1 * 4 = 506. So the answer is 506 / 990 = 51.1%.