Stat 35b: Introduction to Probability with Applications to Poker

Slides:



Advertisements
Similar presentations
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Midterms 2.Flushes 3.Hellmuth vs. Farha 4.Worst possible beat 5.Jackpot.
Advertisements

Stat 35: Introduction to Probability with Applications to Poker Outline for the day: 1.Addiction 2.Syllabus, etc. 3. Wasicka/Gold/Binger Example 4.Meaning.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Zelda, continued. 2.Difficult homework 3 problem. 3.WSOP 2013 hand.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Savage/Tyler, Kaplan/Gazes 2.P(flop a full house) 3.Bernoulli random.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Ly vs Negreanu. 2.Flush draws and straight draws 3.Project B teams.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Straight draws. 2.HW2 clarification. 3.Greenstein vs. Farha AA.
Introduction for Rotarians
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Hand in hw4. 2.Review list 3.Tournament 4.Sample problems * Final.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 0. Collect hw2, return hw1, give out hw3. 1.Project A competition.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Midterm. 2.Review of Bernoulli and binomial random variables. 3.Geometric.
Stat 35: Introduction to Probability with Applications to Poker Outline for the day: 1.Addiction 2.Syllabus, etc. 3. Wasicka/Gold/Binger Example 4.Meaning.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Collect Hw4. 2.Review list. 3.Answers to hw4. 4.Project B tournament.
Suppose someone bets (or raises) you, going all-in. What should your chances of winning be in order for you to correctly call? Let B = the amount bet to.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Hand in hw3. 2.Review of midterm. 3.Project B functions and example.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day, Tue 3/13/12: 1.Collect Hw WSOP main event. 3.Review list.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.hw, terms, etc. 2.WSOP example 3. permutations, and combinations.
The challenge of poker NDHU CSIE AI Lab 羅仲耘. 2004/11/04the challenge of poker2 Outline Introduction Texas Hold’em rules Poki’s architecture Betting Strategy.
Outline for the day: 1.Discuss handout / get new handout. 2.Teams 3.Example projects 4.Expected value 5.Pot odds calculations 6.Hansen / Negreanu 7.P(4.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Addiction 2.Syllabus, etc. 3. Wasicka/Gold/Binger Example 4.Meaning.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.HW3 2.Project B teams 3.Gold vs. Helmuth 4.Farha vs. Gold 5.Flush.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Deal-making and expected value 2.Odds ratios, revisited 3.Variance.
Stat 35b: Introduction to Probability with Applications to Poker Poker Code competition: all-in or fold.   u 
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1. Review list 2.Bayes’ Rule example 3.CLT example 4.Other examples.
Texas Hold’em Playing Styles Team 4 Matt Darryl Alex.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Project B teams 2.Project B example 3.Gold vs Farha 4.Bayes Rule.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day, Tues 2/28/12: 1.Midterms back. 2.Review of midterm. 3.Poisson distribution,
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Expected value and pot odds, continued 2.Violette/Elezra example.
Introduction to Poker Originally created by Albert Wu,
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Project B example, again 2.Booth vs. Ivey 3.Bayes Rule examples.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Odds ratios revisited. 2.Gold/Hellmuth. 3.Deal making. 4.Variance.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Hw, terms, etc. 2.Ly vs. Negreanu (flush draw) example 3. Permutations.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Tournaments 2.Review list 3.Random walk and other examples 4.Evaluations.
1)Hand in HW. 2)No class Tuesday (Veteran’s Day) 3)Midterm Thursday (1 page, double-sided, of notes allowed) 4)Review List 5)Review of Discrete variables.
Outline: 1) Odds ratios, continued. 2) Expected value revisited, Harrington’s strategy 3) Pot odds 4) Examples.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1. Combos, permutations, and A  vs 2  after first ace 2.Conditional.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Expected value. 2.Heads up with AA. 3.Heads up with Gus vs.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Review List 2.Review of Discrete variables 3.Nguyen / Szenkuti.
Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: 1.Odds ratio example again. 2.Random variables. 3.cdf, pmf, and density,
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
Standard undergrad probability course, not a poker strategy guide nor an endorsement of gambling. The usual undergrad topics + random walks, luck and skill,
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
HOW TO PLAY POKER.
Presentation transcript:

Stat 35b: Introduction to Probability with Applications to Poker Outline for the day: P(flop a full house). Pot odds. Multiple callers with KK? Uniform random variables and R. Project teams. More counting problems. Notes: HW2 is due Fri Jan 31. Read up through chapter 4.   u    u 

1. Suppose you’re all in next hand, no matter what cards you get. P(flop a full house)? Key idea: forget order! Consider all combinations of your 2 cards and the flop. Sets of 5 cards. Any such combo is equally likely! choose(52,5) different ones. P(flop full house) = # of different full houses / choose(52,5) How many different full houses are possible? 13 * choose(4,3) different choices for the triple. For each such choice, there are 12 * choose(4,2) choices left for the pair. So, P(flop full house) = 13 * choose(4,3) * 12 * choose(4,2) / choose(52,5) ~ 0.144%, or 1 in 694.

2. POT ODDS CALCULATIONS. Suppose someone bets (or raises) you, going all-in. What should your chances of winning be in order for you to correctly call? Let B = the amount bet to you, i.e. the additional amount you'd need to put in if you want to call. So, if you bet 100 & your opponent with 800 left went all-in, B = 700. Let POT = the amount in the pot right now (including your opponent's bet). Let p = your probability of winning the hand if you call. So prob. of losing = 1-p. Let CHIPS = the number of chips you have right now. If you call, then E[your chips at end] = (CHIPS - B)(1-p) + (CHIPS + POT)(p) = CHIPS(1-p+p) - B(1-p) + POT(p) = CHIPS - B + Bp + POTp If you fold, then E[your chips at end] = CHIPS. You want your expected number of chips to be maximized, so it's worth calling if -B + Bp + POTp > 0, i.e. if p > B / (B+POT).3/39 + 3/39 - C(3,2)/C(39,2) = 15.0%

Example: 2006 World Series of Poker (WSOP).   u  Blinds: 200,000/400,000, + 50,000 ante. Jamie Gold (4 3): 60 million chips. Calls. Paul Wasicka (8 7): 18 million chips. Calls. Michael Binger (A 10): 11 million chips. Raises to $1,500,000. Gold & Wasicka call. (pot = 4,650,000) Flop: 6 10 5. Wasicka checks, Binger bets $3,500,000. (pot = 8,150,000) Gold moves all-in for 16,450,000. (pot = 24,600,000) Wasicka folds. Q: Based on expected value, should he have called? If Binger will fold, then Wasicka’s chances to beat Gold must be at least 16,450,000 / (24,600,000 + 16,450,000) = 40.1%. If Binger calls, it’s a bit complicated, but basically Wasicka’s chances must be at least 16,450,000 / (24,600,000 + 16,450,000 + 5,950,000) = 35.0%.

Want multiple callers with KK? a) You have $100 and KK and are all-in against TT. You're 81% to double up, so your expected number of chips after the hand is 0.81 x $200 = $162. b) You have $100 and KK and are all-in against A9 and TT. You're 58% to have $300, so your expected value is $174. So, if you have KK and an opponent with TT has already called you, and another who has A9 is thinking about whether to call you too, you actually want A9 to call! Given this, one may question Harrington's suggested strategy of raising huge in order to isolate yourself against one player.

4. Uniform Random Variables and R. Continuous random variables are often characterized by their probability density functions (pdf, or density): a function f(x) such that P{X is in B} = ∫B f(x) dx . Uniform: f(x) = c, for x in (a, b). = 0, for all other x. [Note: c must = 1/(b-a), so that ∫ab f(x) dx = P{X is in (a,b)} = 1.] In R, runif(1,min=a,max=b) produces a pseudo-random uniform variable.

5. Project A is problem 8.2, p165. You need to write code to go all in or fold. In R, try: install.packages(holdem) library(holdem) library(help="holdem") timemachine, tommy, ursula, vera, william, and xena are examples. crds1[1,1] is your higher card (2-14). crds1[2,1] is your lower card (2-14). crds1[1,2] and crds1[2,2] are suits of your higher card & lower card. help(tommy) tommy function (numattable1, crds1, board1, round1, currentbet, mychips1, pot1, roundbets, blinds1, chips1, ind1, dealer1, tablesleft) { a1 = 0 if (crds1[1, 1] == crds1[2, 1]) a1 = mychips1 a1 }

help(vera) All in with a pair, any suited cards, or if the smaller card is at least 9. function (numattable1, crds1, board1, round1, currentbet, mychips1, pot1, roundbets, blinds1, chips1, ind1, dealer1, tablesleft) {a1 = 0 if ((crds1[1, 1] == crds1[2, 1]) || (crds1[1, 2] == crds1[2,2]) || (crds1[2, 1] > 8.5)) a1 = mychips1 a1 } You need to email me your function, to frederic@stat.ucla.edu, by Feb 6 at 8:00pm. It should be written (or cut and pasted) simply into the body of the email. If you write it in Word, save as text first, and then paste it into the email. For instance, if your letter is “b”, you might do:

For instance, if your letter is “b”, you might do: bruin = function (numattable1, crds1, board1, round1, currentbet, mychips1, pot1, roundbets, blinds1, chips1, ind1, dealer1, tablesleft) { ## all in with any pair higher than 7s, or if lower card is J or higher a1 = 0 if ((crds1[1, 1] == crds1[2, 1]) && (crds1[1, 1] > 6.5)) a1 = mychips1 if (crds1[1,2] > 10.5) a1 = mychips1 a1 } ## end of bruin

Teams: team a HUANG, RIHAN. ZHANG, HO KWAN. MCELROY, DAVID. team b LI, JESSE. THOMA, TREVOR. DITTMAR, MICHAEL. team c WANG, ERIC. LU, SIAO. GU, JIAQI. team d CHIANG, NICOLE. ZHANG, TONY. ALCORN, KIMBERLY. team e CHEN, ZHAOYUAN. LI, GAN. REITZ, CLAIRE. team f BARBER, THOMAS. MENG, QINGYI. SUEZAKI, PATRICK. team g LAM, SUI YUEN. ZHUANG, YUAN. LAHOZ GONZALEZ, LAURA. team h XIE, ZITONG. GU, YINGQIAN. LUAN, PEIYAO. team i CHEN, YIMING. TUZMEN, MEHMET. WANG, ZHAOXIN. team j POWER, JUSTIN. ALEXANDER, AREN. MAULEON, KIMBERLY. team k KIM, SO YOUNG. CHEN, XINYUAN. DUTTA, ANJALI. team l ZHOU, MENGRUI. YU, BRANDON. SANGHVI, ANAV. team m GAO, YANG. LA, CASEY. TSAI, PEI-CHEN. team n LIU, YIHAO. LI, RUOYU. SHI, BO. team o ZHOU, CAROL. YUN, XINYAO. DUONG, MINDY. team p MAVRODIEV, TEODOR. WONG, LAP KAN. YUZAWA, KATSUHIRO. team q LEE, FIONA. YANG, YIK. HUSSEIN, MARYAM. MOUTOUX, ALEX. team r LI, JIANJUN. MUSTAFA, JAMAL. YANG, YI-LONG. HSUEH, JESSICA. Just submit one email per team.