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In a recent magazine article, poker guru Ed Miller discussed preflop play for $1-$2 no-limit hold’em. He offers a checklist of six “principles.” Great! But, he makes one statement that should be corrected – at least for low-limit games:
“If you think like everyone else, you will play like everyone else, and you will lose the table rake – about $10 an hour.”
Playing poker with fewer than 52 cards is not a new idea. In the first half of the 19th century, the earliest form of poker was played with just 20 cards - the ace, king, queen, jack and ten of each suit - with five cards dealt to each of four players.
All things being equal, in the long run, an average low-limit player will lose much more than $10 an hour with a rake of $5 per hand.
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Explanation: Thirty to 40 hands are dealt each hour of play – averaging about 35 hands per hour. (Check it out yourself.) Hence, the rake-per-hour is $5 x 35 = $175. With nine players at the table that’s almost $20 an hour for each. With fewer players, that $5 rake will cost even more.
In addition to the rake, there’s also the Bad Beat Jackpot drop – $1 per hand – another $35 an hour for the table, or about $4 per hour for each player. Then there’s the tip to the dealer. In a low-limit game, that adds up to almost $30 an hour per player if the table is full – more if the table is not full.
Buy-in for $100, and it will have cost the average low-limit player all of that after 3 to 4 hours.
But, your goal is to go home a winner. Poker is a game of skill. Being highly skilled, you play much better than the average player.
Luck – chance – is also a key factor. You cannot influence which card in the deck might next be dealt. But, you can control how you play your hand and influence your opponents’ play – making luck less significant. Basically, that’s what Miller’s checklist is all about.
Starting-hand selection is important. “If you’re out of position, play about 15 percent of your hands. If you are on the button, play about 35 percent.”
Using the Hold’em Algorithm those percentages are correct. More important, the algorithm also provides the criteria for selecting those hands. Avoiding poor starting-hands – playing moderately tight – you are less likely to lose. And, you rise above the average player.
Avoid strength. Tight players rarely raise. If one does, assume he has a strong hand; play accordingly. Observing their tells can also help. That gains you a big edge. And, you rise above the average player.
Attack weakness. Some opponents play too many hands preflop; most will be weak. “When you suspect your opponents are in with weak hands, you should attack them with raises… Be aggressive when your opponents don’t tag themselves with strength preflop.”
Don’t try to make Hands. Don’t think/play your hands like everyone else; if so, “you will lose the table rake.” Example: “If your opponents are limping, they’re probably weak… Attack them with a raise – even if your hand isn’t so great either.”
Good advice for no-limit games where you can size the bet to force out those opponents. In limit games, use the Esther Bluff.
Take this principle one step further: Don’t chase post-flop when you have fewer than six outs, especially if there is a raise. Consider if the pot is big enough to give you a Positive Expectation.
Choose hands that have equity when called. “If you think you will be able to play aggressively on a wide range of flops… you have a hand worth playing.”
Miller prefers hands like 8-7 suited over A-4 offsuit as 8-7 suited “hits a wide range of flops, ensuring that you have equity.”
Defend blinds against steals, not strong raises. This is essentially a restatement of Miller’s principles (2) and (3), applied to hands when you are in the blinds. “When your opponent makes a raise that is likely to be a steal, defend with… re-raises and calls.”
Miller closes: “Avoid strength. Attack weakness.” Good advice.
Planning poker, also called Scrum poker, is a consensus-based, gamified technique for estimating, mostly used to estimate effort or relative size of development goals in software development. In planning poker, members of the group make estimates by playing numbered cards face-down to the table, instead of speaking them aloud. The cards are revealed, and the estimates are then discussed. By hiding the figures in this way, the group can avoid the cognitive bias of anchoring, where the first number spoken aloud sets a precedent for subsequent estimates.
Planning poker is a variation of the Wideband delphi method. It is most commonly used in agile software development, in particular in Scrum and Extreme Programming.
The method was first defined and named by James Grenning in 2002[1] and later popularized by Mike Cohn in the book Agile Estimating and Planning,[2] whose company trade marked the term [3] and a digital online tool.[4]
Process[edit]
Rationale[edit]
The reason to use planning poker is to avoid the influence of the other participants. If a number is spoken, it can sound like a suggestion and influence the other participants' sizing. Planning poker should force people to think independently and propose their numbers simultaneously. This is accomplished by requiring that all participants show their card at the same time.
Equipment[edit]
Planning poker is based on a list of features to be delivered, several copies of a deck of cards and optionally, an egg timer that can be used to limit time spent in discussion of each item.
The feature list, often a list of user stories, describes some software that needs to be developed.
The cards in the deck have numbers on them. A typical deck has cards showing the Fibonacci sequence including a zero: 0, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89; other decks use similar progressions with a fixed ratio between each value such as 1, 2, 4, 8, etc.
The reason for using the Fibonacci sequence instead of simply doubling each subsequent value is because estimating a task as exactly double the effort as another task is misleadingly precise. A task which is about twice as much effort as a 5, has to be evaluated as either a bit less than double (8) or a bit more than double (13).
Several commercially available decks use the sequence: 0, ½, 1, 2, 3, 5, 8, 13, 20, 40, 100, and optionally a ? (unsure), an infinity symbol (this task cannot be completed) and a coffee cup (I need a break, and I will make the rest of the team coffee). The reason for not exactly following the Fibonacci sequence after 13 is because someone once said to Mike Cohn 'You must be very certain to have estimated that task as 21 instead of 20.' Using numbers with only a single digit of precision (except for 13) indicates the uncertainty in the estimation. Some organizations[which?] use standard playing cards of Ace, 2, 3, 5, 8 and king. Where king means: 'this item is too big or too complicated to estimate'. 'Throwing a king' ends discussion of the item for the current sprint.
Smartphones allow developers to use mobile apps instead of physical card decks. When teams are not in the same geographical locations, collaborative software can be used as replacement for physical cards.
Procedure[edit]
At the estimation meeting, each estimator is given one deck of the cards. All decks have identical sets of cards in them.
The meeting proceeds as follows:
- A Moderator, who will not play, chairs the meeting.
- The Product Owner provides a short overview of one user story to be estimated. The team is given an opportunity to ask questions and discuss to clarify assumptions and risks. A summary of the discussion is recorded, e.g. by the Moderator.
- Each individual lays a card face down representing their estimate for the story. Units used vary - they can be days duration, ideal days or story points. During discussion, numbers must not be mentioned at all in relation to feature size to avoid anchoring.
- Everyone calls their cards simultaneously by turning them over.
- People with high estimates and low estimates are given a soap box to offer their justification for their estimate and then discussion continues.
- Repeat the estimation process until a consensus is reached. The developer who was likely to own the deliverable has a large portion of the 'consensus vote', although the Moderator can negotiate the consensus.
- To ensure that discussion is structured; the Moderator or the Product Owner may at any point turn over the egg timer and when it runs out all discussion must cease and another round of poker is played. The structure in the conversation is re-introduced by the soap boxes.
The cards are numbered as they are to account for the fact that the longer an estimate is, the more uncertainty it contains. Thus, if a developer wants to play a 6 he is forced to reconsider and either work through that some of the perceived uncertainty does not exist and play a 5, or accept a conservative estimate accounting for the uncertainty and play an 8.
Benefits[edit]
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A study by Moløkken-Østvold and Haugen[5] reported that planning poker provided accurate estimates of programming task completion time, although estimates by any individual developer who entered a task into the task tracker was just as accurate. Tasks discussed during planning poker rounds took longer to complete than those not discussed and included more code deletions, suggesting that planning poker caused more attention to code quality. Planning poker was considered by the study participants to be effective at facilitating team coordination and discussion of implementation strategies.
See also[edit]
South Point Poker Tournaments
- Comparison of Scrum software, which generally has support for planning poker, either included or as an optional add-on.
South Point Poker
References[edit]
Pokerstars Point System
- ^'Wingman Software Planning Poker - The Original Paper'. wingman-sw.com. Retrieved 5 July 2017.
- ^Mike Cohn (November 2005). 'Agile Estimating and Planning'. Mountain Goat Software. Retrieved 1 February 2008.
- ^'Planning poker - Trademark, Service Mark #3473287'. Trademark Status & Document Retrieval (TSDR). 15 January 2008. Retrieved 26 May 2014.
- ^Cohn, Mike. 'Planning Poker Cards: Effective Agile Planning and Estimation'. Mountain Goat Software. Mountain Goat Software. Retrieved 30 March 2016.
- ^K Moløkken-Østvold, NC Haugen (10–13 April 2007). 'Combining Estimates with Planning Poker—An Empirical Study'. 18th Australian Software Engineering Conference. IEEE: 349–58. doi:10.1109/ASWEC.2007.15. ISBN978-0-7695-2778-9. S2CID11429738.
- Mike Cohn (2005). Agile Estimating and Planning (1 ed.). Prentice Hall PTR. ISBN978-0-13-147941-8.