MAKING A TEAM ON DREAM11: A FANTASY SPORTS PLATFORM
CASE STUDY SOLUTION
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SYNOPSIS
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This case highlights an optimization problem faced by Chhavi Jain, a consultant in a Delhi-based
multinational firm. She is participating in a contest on the Dream11 mobile application (app) against her
college friends. The mobile app allows users to create fantasy teams and participate in live cricket contests.
The final cricket match of the Indian Premier League (IPL) 2020 between the Delhi Capitals and the
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Mumbai Indians is scheduled on November 10. Jain has to create a winning team by selecting 11 players
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using a maximum of 100 credits. The number of credits for each player is different, and the total credit sum
of all players had to be less than 100 credits. She does not know the players and their past performances in
the IPL. Her fantasy team’s winning or losing depends on the total points scored by her team during the
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live match. She tries making a team of 11 players, but she runs out of credits when she picks the 11th player
in her team. She does not want to compromise with a substandard player, as she would lose the opportunity
to win the contest. How shall Jain select a team of 11 players using no more than 100 credits? What shall
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be the criteria for selecting the players in her team?
OBJECTIVES
• Learn the concept of optimization.
• Learn the application of optimization in solving team selection problems.
• Understand the business model of franchised sports leagues and the bidding process.
• Learn to identify the factors of team performance.
• Understand the team selection procedure in the National Football League, National Hockey League,
and Major League Baseball.
The Case Solution Starts From page 6
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ASSIGNMENT QUESTIONS
Question 1: What is Chhavi Jain’s dilemma? How shall she proceed to solve her problem?
Question 2: How is Chhavi Jain’s dilemma analogous to the optimization problem?
Question 3: Formulate an optimization model for Chhavi Jain’s dream team problem.
Question 4: Find the dream team and suggest other alternatives that Chhavi Jain may consider while
selecting a team on Dream11.
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The Case Solution Starts From page 6
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Question 2: How is Chhavi Jain’s dilemma analogous to the optimization problem?
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Jain’s problem is analogous to the optimization problem. She wants to form
a team of 11 out of 45 players to maximize their overall performance. She can select the players in her team
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based on their past performances. The players are the decision variable here. Out of 45 decision variables, Jain
must find 11 decision variables whose total sum of performance will be the maximum. The players’
performance can be evaluated based on points accumulated by each player, strike rate, wickets taken, or selling
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percentage during the IPL. The objective function is the total sum of the performances of all players. Jain
wants to make a team of 11 players using a maximum of 100 credits, considering the rules such as the number
of wicket-keepers, batters, all-rounders, and bowlers. These rules are the constraints of the optimization
model.
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highlight that linear programming is a mathematical technique for optimizing linear
models while adhering to linear constraints. The objective of the mathematical model is to attain maximum
or minimum value with respect to constraints.
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Mathematically, it can be written as follows:
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Maximize 𝐶 𝑇 ∗ 𝑥 + 𝐻𝑇 ∗ 𝑦
subject to
𝐴∗𝑥+𝐵∗𝑦 ≤𝑏
𝑥≥0
𝑦 𝜖 𝑍𝑛 .
Where, 𝐶 𝑇 , and 𝐻𝑇 are cost coefficient, 𝑥 is a continuous positive decision variable, and 𝑦 is positive integer
variable 𝑍 𝑛 .
The general form of an optimization problem can be written as follows:
Maximize or minimize
𝑍 = 𝑐1 𝑥1 + 𝑐2 𝑥2 + ⋯ + 𝑐𝑛 𝑥𝑛
The Case Solution Starts From page 6
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EXHIBIT TN-3: AVAILABLE PLAYERS, CREDITS, SELECTION PERCENTAGE, AND POINTS
Selection Accumulated
Player name Team Player category Credits
percentage points
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R. Pant DC Wicket-Keeper 8.5 24.92 492
A. Carey DC Wicket-Keeper 8.5 0.9 62
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Q. de Kock MI Wicket-Keeper 10 74.4 840
I. Kishan MI Wicket-Keeper 9 68.36 664
A. Tare MI Wicket-Keeper 7.5 0.73 0
S. Dhawan DC Batter 10 69.04 851
S. Iyer DC Batter 9 52.43 640
P. Shaw
The Case Solution Starts From page 6
, EXHIBIT -5: DECISION VARIABLES CORRESPONDING TO EACH PLAYER
Total
Accumulated
Player category Team Player name Variable DV Credit Selection percentage team Total team points
points
credit
All-Rounder DC M. Stoinis X18 9.5 86.68 809 9.5 809
All-Rounder DC A. Patel X19 8.5 22.87 492 8.5 492
All-Rounder DC D. Sams X20 8 0.69 28 0 0
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All-Rounder DC K. Paul X21 8 34.29 0 0 0
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All-Rounder DC L. Yadav X22 7.5 0.55 0 0 0
All-Rounder MI K. Pollard X23 8.5 50.1 536 8.5 536
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All-Rounder MI
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The Case Solution Starts From page 6
CASE STUDY SOLUTION
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pl
SYNOPSIS
m
Sa
This case highlights an optimization problem faced by Chhavi Jain, a consultant in a Delhi-based
multinational firm. She is participating in a contest on the Dream11 mobile application (app) against her
college friends. The mobile app allows users to create fantasy teams and participate in live cricket contests.
The final cricket match of the Indian Premier League (IPL) 2020 between the Delhi Capitals and the
n
Mumbai Indians is scheduled on November 10. Jain has to create a winning team by selecting 11 players
tio
using a maximum of 100 credits. The number of credits for each player is different, and the total credit sum
of all players had to be less than 100 credits. She does not know the players and their past performances in
the IPL. Her fantasy team’s winning or losing depends on the total points scored by her team during the
lu
live match. She tries making a team of 11 players, but she runs out of credits when she picks the 11th player
in her team. She does not want to compromise with a substandard player, as she would lose the opportunity
to win the contest. How shall Jain select a team of 11 players using no more than 100 credits? What shall
So
be the criteria for selecting the players in her team?
OBJECTIVES
• Learn the concept of optimization.
• Learn the application of optimization in solving team selection problems.
• Understand the business model of franchised sports leagues and the bidding process.
• Learn to identify the factors of team performance.
• Understand the team selection procedure in the National Football League, National Hockey League,
and Major League Baseball.
The Case Solution Starts From page 6
, e
pl
m
Sa
n
tio
lu
So
ASSIGNMENT QUESTIONS
Question 1: What is Chhavi Jain’s dilemma? How shall she proceed to solve her problem?
Question 2: How is Chhavi Jain’s dilemma analogous to the optimization problem?
Question 3: Formulate an optimization model for Chhavi Jain’s dream team problem.
Question 4: Find the dream team and suggest other alternatives that Chhavi Jain may consider while
selecting a team on Dream11.
1
The Case Solution Starts From page 6
, e
Question 2: How is Chhavi Jain’s dilemma analogous to the optimization problem?
pl
Jain’s problem is analogous to the optimization problem. She wants to form
a team of 11 out of 45 players to maximize their overall performance. She can select the players in her team
m
based on their past performances. The players are the decision variable here. Out of 45 decision variables, Jain
must find 11 decision variables whose total sum of performance will be the maximum. The players’
performance can be evaluated based on points accumulated by each player, strike rate, wickets taken, or selling
Sa
percentage during the IPL. The objective function is the total sum of the performances of all players. Jain
wants to make a team of 11 players using a maximum of 100 credits, considering the rules such as the number
of wicket-keepers, batters, all-rounders, and bowlers. These rules are the constraints of the optimization
model.
n
tio
highlight that linear programming is a mathematical technique for optimizing linear
models while adhering to linear constraints. The objective of the mathematical model is to attain maximum
or minimum value with respect to constraints.
lu
Mathematically, it can be written as follows:
So
Maximize 𝐶 𝑇 ∗ 𝑥 + 𝐻𝑇 ∗ 𝑦
subject to
𝐴∗𝑥+𝐵∗𝑦 ≤𝑏
𝑥≥0
𝑦 𝜖 𝑍𝑛 .
Where, 𝐶 𝑇 , and 𝐻𝑇 are cost coefficient, 𝑥 is a continuous positive decision variable, and 𝑦 is positive integer
variable 𝑍 𝑛 .
The general form of an optimization problem can be written as follows:
Maximize or minimize
𝑍 = 𝑐1 𝑥1 + 𝑐2 𝑥2 + ⋯ + 𝑐𝑛 𝑥𝑛
The Case Solution Starts From page 6
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EXHIBIT TN-3: AVAILABLE PLAYERS, CREDITS, SELECTION PERCENTAGE, AND POINTS
Selection Accumulated
Player name Team Player category Credits
percentage points
lu
R. Pant DC Wicket-Keeper 8.5 24.92 492
A. Carey DC Wicket-Keeper 8.5 0.9 62
So
Q. de Kock MI Wicket-Keeper 10 74.4 840
I. Kishan MI Wicket-Keeper 9 68.36 664
A. Tare MI Wicket-Keeper 7.5 0.73 0
S. Dhawan DC Batter 10 69.04 851
S. Iyer DC Batter 9 52.43 640
P. Shaw
The Case Solution Starts From page 6
, EXHIBIT -5: DECISION VARIABLES CORRESPONDING TO EACH PLAYER
Total
Accumulated
Player category Team Player name Variable DV Credit Selection percentage team Total team points
points
credit
All-Rounder DC M. Stoinis X18 9.5 86.68 809 9.5 809
All-Rounder DC A. Patel X19 8.5 22.87 492 8.5 492
All-Rounder DC D. Sams X20 8 0.69 28 0 0
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All-Rounder DC K. Paul X21 8 34.29 0 0 0
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All-Rounder DC L. Yadav X22 7.5 0.55 0 0 0
All-Rounder MI K. Pollard X23 8.5 50.1 536 8.5 536
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All-Rounder MI
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The Case Solution Starts From page 6
