Poker Card Values

Poker Hand Rankings - Texas Holdem Starting Hands Chart. At the bottom of this page is a comprehensive listing of Texas Hold'em starting hands based on their EV (expected value). Expected value is the average number of big blinds this hand will make or lose.

Next in the poker hands list is a straight, consisting of a run of five cards of consecutive values, such as 4-5-6-7-8. Aces count as high or low, so you can make a 10-J-Q-K-A straight, the highest, or an A-2-3-4-5 straight, which is the lowest and sometimes called a “wheel”. Our pokemon card value finder is the largest TCG lookup database that is updated on an hourly basis. Our online price guide tool helps users easily search and instantly find the price of any Pokemon cards. You can search by individual card, by Pokemon name, or browse our Pokemon card.

Effective estimation is one of the toughest challenges software developers face in their jobs. Regardless of team size, they need to define, estimate, and distribute work throughout a team. As teams get larger, it becomes even more important to build good habits around planning and estimating work. Lack of planning and estimating reduce confidence in a program, breaks down relationships between the team and the business, and makes development harder on everyone.

Best Scrum Software Every Project Needs

A powerful scrum software that supports scrum project management. It features scrum tools like user story map, product backlog management, sprint backlog management, task management, daily scrum meeting, sprint planning tool, sprint review tool, sprint retrospective tool, burndown, impediment, stakeholder and team management.

The Accuracy of Group vs. Individual Estimation

According to some study on the accuracy of estimation of effort between individual and group in an experiment for a software project. 20 software professionals from the same company individually estimated the work effort required to implement the same software development project. The participants had different background and roles and the software project had previously been implemented. After that, they formed five groups. Each group agreed on one estimation by discussing and combining of the knowledge among them.

Result – The estimates based on group discussions were more accurate than the individual estimates.

What is Planning Poker?

Planning poker (also known as Scrum poker) is a consensus-based, gamified technique for estimating, mostly used to estimate effort or relative size of development goals in software development.

Steps for Planning Poker

1. To start a poker planning session, the product owner or customer reads an agile user story or describes a feature to the estimators.
   For example:
   “Customer logs in to the reservation system”
   “Customer enters search criteria for a hotel reservation”
2. Team members of the group make estimates by playing numbered cards face-down to the table without revealing their estimate (Fibonacci values: 1,2,3,5,8,13,20,40)
3. Cards are simultaneously displayed
4. The estimates are then discussed and high and low estimates are explained
5. Repeat as needed until estimates converge

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.

Poker Card Set Values

Agile Estimation – Relative vs Absolute

An estimate is nothing more than a well educated guess. We use all the knowledge and experience at hand to make a guess about the amount of time it is going to take. So instead of looking at every new work item separately, why not compare it to previously finished work items? It’s easier for humans to relate to similar items than to guess the actual size of things anyway.

For example, is it closer to this really small thing? Or is it more like this normal sized item? Or is it really huge like that one piece of work we finished last month? Doing relative estimates will not only reduce the amount of time spent on estimating work, it will also heavily increase the accuracy of the estimates.

Our brain is not capable of doing absolute estimates; we always put that new thing that we need to estimate in relationship to things we already know.

Fibonacci sequence and Planning Poker

Planning Poker uses of the Fibonacci sequence to assign a point value to a feature or user story. The Fibonacci sequence is a mathematical series of numbers that was introduced in the 13th century and used to explain certain formative aspects of nature, such as the branching of trees. The series is generated by adding the two previous numbers together to get the next value in the sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, and so on.

For agile estimation purposes, some of the numbers have been changed, resulting in the following series: 1, 2, 3, 5, 8, 13, 20, 40, 100 as shown in the Figure below:

The Interpretation of the point assigned to a poker card is listed in the table below:

Point vs Hour Value in Estimation

So why use story points instead of time values? Story pointing allows the team to focus on the complexity and time involved in delivering a piece of work. The team compares the new work against work they’ve already done. They compare the complexity of the new assignment against past challenges and rank the difficulty as well as the time required.

For example, we don’t often account for “the cost of doing business.” Meetings, email, code reviews, etc. with time values. But in reality, all these are necessary practices throughout in our daily life, but don’t actually count as “work.” Story points isolate the software development work from the associated logistic work items, so estimates using point based should more consistent than hour base approach.

Last Updated on January 1, 2018

I recently took a Hackerrank challenge for a job application that involved poker. I'm not a poker player, so I had a brief moment of panic as I read over the problem the description. In this article I want to do some reflection on how I approached the problem.

The hackerrank question asked me to write a program that would determine the best poker hand possible in five-card draw poker. We are given 10 cards, the first 5 are the current hand, and the second 5 are the next five cards in the deck. We assume that we can see the next five cards (they are not hidden). We want to exchange any n number of cards (where n <= 5) in our hand for the next n cards in the deck. For example, we can take out any combination of 2 cards from the hand we are given, but we must replace these two cards with the next two cards from the deck (we can't pick any two cards from the deck).

Suit and value make up the value of playing cards. For example, you can have a 3 of clubs. 3 is the value, clubs is the suit. We can represent this as 3C.

Suits

Clubs CSpades SHeart HDiamonds D

Value (Rank)

2, 3, 4, 5, 6, 7, 8, 9, 10, Jack, Queen, King, Ace

Hands

Here are the hands of poker

1. Royal flush (the problem didn't ask me to consider Royal Flush)

   A, K, Q, J, 10, all the same suit.

2. Straight flush

   Five cards in a sequence, all in the same suit. Ace can either come before 2 or come after King.

3. Four of a kind

   All four cards of the same rank.

4. Full house

   Three of a kind with a pair.

5. Flush

   Any five cards of the same suit, but not in a sequence.

6. Straight

   Five cards in a sequence, but not of the same suit.

7. Three of a kind

   Three cards of the same rank.

8. Two pair

   Two different pairs.

9. Pair

   Two cards of the same rank.

10. High Card

    When you haven't made any of the hands above, the highest card plays.In the example below, the jack plays as the highest card.

Evaluating a hand of cards

A hand is five cards. The first thing I did was write out functions to evaluate if a group of 5 cards satisfies the conditions of one of the ten hands.

Here's a sample hand:

To write functions, I reached for using 2 important python features: set and defaultdict.

Here's an example of a simple function to detect a flush, a hand with cards of all the same suit:

Checking a flush

This function creates a list of the suits in our hand, and then counts the unique elements in that list by making it a set. If the length of the set is 1, then all the cards in the hand must be of the same suit.

But wait, what if we have a straight flush? Also, a hand that satisfies a flush could also be described as a two pair hand. The problem asked me to find the highest possible hand for a given set of cards, so I tried to keep things simple by writing a check_hand() function that checks each hand starting from straight flush down to high card. As soon as a condition for a hand was satisfied, I returned a number that corresponded to the strength of the hand (1 for high card up to 10 for straight flush). The problem didn't include Royal flush, so I will not include that here.

Here's the check_hand function:

This function starts checking the most valuable hands. After it checks the second to lowest hand (pair), it returns a value of 1. This value of 1 corresponds to the 'highest card' hand. Since I'm not comparing the relative value of hands, it doesn't matter what the highest card is, so the number just represents the type of hand that is the strongest.

Other hands

Here are the all of the functions I used to detect hands:

defaultdict is a great built-in that is good to use when you don't know what elements will be in your dictionary, but you know what the initial values of any key that could be added should be. We don't need it here, but the alternative would be to write a very long dictionary where keys are the possible card values and the values of each key is 0.

It would certainly be cleaner and more efficient to write out the above functions into one large function, but I wanted to keep things simple as I was under time constraints.

The next step in the problem is to determine the best possible hand we can get given the hand we are dealt and the 5 cards on top of the deck. I decided to first solve this problem with brute force. Here was my logic for this part: use itertools to get all combinations of groups of 0, 1, 2, 3, 4 and 5 cards from my hand and add the first 5 - n cards from the deck so we get a five card deck. For each combination of cards we can run check_hand() and keep track of the highest rank hand, and then return that hand as the best hand. Here's the code I wrote for this part of the problem:

Lastly, I need to check each hand and print out the best hand possible. Here's the loop I wrote to do this:

This will accept one round of cards per line:

and it will output the following:

This was an interesting problem to deal with as the solution contained several parts that worked together. While solving the problem I aimed worked through to the end leaving some parts to come back to that I felt confident in solving. Instead of writing each function to check differnt hands at the beginning, I filled most of these functions with pass and moved on to write the next part that involves checking each different combination of cards. Recently having worked through python's itertools exercises on Hackerrank, the combinations functions was fresh in my mind.

While I was able to arrive at a solution that satisfied the test cases, I did not have time to think about the efficiency or Big O analysis of the problem.

Suit Card Values Poker

There is obviously some refactoring that I could do to make things cleaner. With more time I would take an object oriented approach by making classes for cards and hands, and adding class methods to evaluate the hands.

Poker Card Color Values

For each round, we have to run check_hand() on each hand combination. Let's think about how many hands we have to evaluate:

We have to consider combinations of cards formed by taking out groups of 0, 1, 2, 3, 4 and 5 cards and adding the next number of cards in the deck that bring the total card count to 5, which means we have to do 5C0 + 5C1 + 5C2 + 5C3 + 5C4 + 5C5 calls to check_hand(). So the sum of total calls is 1 + 5 + 10 + 10 + 5 + 1 = 32.

Poker Card Values

For each of these 32 calls that happen when we run play(), check_hands() runs through each of the check_ functions starting with the highest value hand. As soon as it finds a 'match', check_hands() returns a number value (hand_value) corresponding to straight flush, four of a kind, etc. This value is then compared with the highest value that has been previously found (best_hand) and replaces that value if the current hand's hand rank has a higher value.

Poker Hands Ranked

I'm not sure if there is faster way to find the best hand than the brute force method I implemented.