Fantasy Cricket Points Calculator – If you are just a beginner in fantasy cricket, your first step is to understand the basics of fantasy cricket. While learning how to play fantasy cricket, you will need to know how the Fantasy Cricket Points system works for different cricket games. Knowing the points system for ODI, T20, T10, TATA IPL Fantasy Point System will help you to increase your chances to win better.
Knowing the fantasy points system is key and will help you pick the right players for your team. So, check out the detailed fantasy points system for fantasy cricket below.
Fantasy Cricket Points Calculator
All players earn 4 points each just for entering their cricket team’s KSI. The cricketer you choose to captain your TATA IPL Fantasy Cricket team will get 2k points and the vice-captain will get 1.5k points for his performance. For more understanding, read TATA IPL Fantasy Points system above.
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Your Fantasy Cricket points change as the current match progresses. Points can also be changed while the match status is “In Progress” or “Awaiting Review”. Final Fantasy Cricket scores can be viewed after the match is over, when the match status is shown as ‘Completed’.
Yes, the Fantasy Points system for ODI, Test, T20, T10, The Hundred and TATA IPL is different. To know more about fantasy cricket points read it on our page above.
It’s simple – you earn it based on your team’s performance in a real-life LIVE match.
The fantasy points system for cricket has been revised based on the format of the game i.e. it is different for ODI, Test, T20, T10 and other tournaments like One Hundred.
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Did you know? This is because these runs or wickets are not even counted in their official individual records.
Once you’ve joined a game, tap the game tab under My Games on the home screen of your desired sport. Once logged in, you will land on the My Competitions tab where you can view your team’s total score for the individual competitions you have entered for that match. It also tells you which team scored the highest in the case of multiple teams.
Join the world of exciting Fantasy Cricket games and tournaments – Download the Fantasy app to play fantasy cricket or any other fantasy sports game on your Android or iOS device. The Indian Premier League (IPL) is the biggest domestic cricket league in the world and with its thirteenth edition underway, it’s the perfect time to enjoy the game and explore some machine learning techniques along with it.
This year the title sponsors of the IPL are Dream11. Dream11 is a fantasy sports platform based in India. It allows users to play fantasy cricket, hockey, football, kabaddi and basketball.
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On our AI/ML platform, ATH Leaps, we have also created a “Fantasy Team Generator”, where you can play with configurations to build your fantasy team. Read on to learn more about how data science was applied to build this Fantasy Team Generator at LEAPS.
In this article we will apply Prescriptive Analytics to sports where we will focus on picking a fantasy cricket team with one of the matches as an example.
It will be a step-by-step walkthrough of the code using Python. In addition, we will also use the open platform LEAPS Analittica TreasureHunt® to achieve the same without owning the code.
Linear programming in operations research is one of the scientific techniques used to obtain the optimal solution for a given business problem taking into account resource scarcity and constraints.
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Linear programming allows us to model real-life problems in mathematical models represented by linear relationships. It includes an objective function to define what we are trying to achieve and creating constraints as linear inequalities. As the name clearly specifies, the relationship between the objective function and the constraints must be linear in nature (equations of one degree).
In order to create a fantasy team, it is necessary to select the best players based on the objective. The final formed team must follow any rules or restrictions set by the platform.
This makes it a perfect problem that can be solved by linear programming. We need to maximize the team selection metric projection while respecting the given budget (credits) and team composition constraints.
Here we will use two approaches to build the solution, one for users who want to code and solve, and the other is dedicated to users who want to learn the concept and implement it with the tool.
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Analittica TreasureHunt® LEAPS is an open, patented platform for learning, applying and solving all machine learning and artificial intelligence problems of data scientists/analysts. It has more than 650 built-in functions that can be used with a simple point and click. It also provides a coding console for those who want to get their hands dirty.
Select the “Apply” section in the upper left corner of the screen. Select “My cases” — “Create a new case”. Provide a case name, description, dataset, and you’re good to go.
NOTE: Approach 1 can also be done on any Python IDE, but may involve the overhead of setting up dependent packages such as pulp and ipiwidgets.
In addition to the provided rules, additional restrictions can be added for users to play and create the most suitable team.
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Once the optimal solution is identified, it will generate a fantasy team along with some brief details. In addition, we will also use the ipividgets package to build the user interface in the Jupiter notebook so that users can enter additional constraints.
The code snippets below are part of the complete code to help readers understand and try out the flow.
If you are trying this on an ATH LEAPS, select the console option at the top to launch the jupiter kernel
In addition to regular packages like numpi, pandas, sklearn, this code also requires pulp to be installed.
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The program will require two types of input. One will be the player details and the other will be the user configuration to build the linear problem.
Player details are given in the csv file. The content of the sample file will be as below.
Similar variables must be created for each of the constraints. shown above is an example. The same approach can be used to create all the required variables.
This option can be given to the user to be configured based on which the objective function can be established.
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There must be exactly 11 players in the final team. This can be determined by the constraint below.
Each team can have from 4 to 7 players. This can be further configured by the user if required.
Since the 11 teams will only be known during the draw (usually 30 minutes before the match starts), a limit can be added to the players that will be selected.
Above are some examples of how restrictions can be set. In the same way, all necessary restrictions can be established.
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Since the goal is to maximize the goal, set the goal details for the problem accordingly and let the pulp solve it.
A linear program will try to solve the problem by identifying the best option that maximizes the objective function and also satisfies all the constraints that are set.
In case it cannot find any feasible solution with the given constraints, then it returns the status as infeasible. Pulp will discard constraints that cannot be satisfied and return output.
To make the application more interactive, ipiwidgets are used so that the user can select configuration values on the Jupiter notebook.
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We won’t go through the details of creating widgets on the Jupiter notebook because there is already a good amount of documentation for that, and it’s also not our area of focus here.
In this approach, we will see how this problem can be solved without code. Here we will use the point and click features that are readily available on the platform.
To solve this problem, we first need to convert the data into the required format that can be fed into the Linear program
Note that the categorical variables are created twice since two constraints need to be applied to each of the categories for the lower and upper bounds.
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Transpose the columns so that each constraint variable is available as a row in the table. The player name will be the column headers, as the final result should be a binary flag on each player indicating whether they are on the team or not.
The decision variables will be the values that decide the output. It is essentially the right side of the inequality equation.
The above 5 steps will convert the input data into a format that can be fed directly into a linear programming function to give us a fantasy team.
The variable type should be set to Binary in this case because we want to have a binary output of whether the player should be on the fantasy team or not.
Fantasy Cricket Points Calculator
If a solution cannot be found with the given constraints, then the function will return the result as
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