English premier league(EPL) is one of the most popular professional soccer leagues around the world. It caught out interest that high-market-value players can achieve astonishing commercial value by increasing TV ratings of soccer game they play. So, we wanted to use the dataset of EPL players to group an optimal soccer team with highest total market value. There are two steps of our analysis. The first one is to predict players’ market values using multiple regression in python. In this process, the noteworthy things are that we split the data into train data and test data, used backward selection to select features that are most correlated to market value and successfully built a regression model to calculate market value accurately. Then comes to optimization and We used Excel Solver as our optimizing tool. Variables are to decide whether to select players or not, constraints comply to the rules of English Fantasy Premier League and the objective is to maximum the total market value of the soccer team. There is one thing notable is that due to the limit of 200 variable in Excel Solver, to achieve the optimal solution, we arranged the players’ market values in descending order and took the first 200 players as our variables. The result of our optimization is that, with the budget of 100 million-euro dollars, the team that we group can achieve market value up to 443.63 and consists of 4 attacker players, 8 defender players and 3 goal keepers.

1. Business Intelligence & Analytics
Fantasy Premier League Soccer
Team OptimizationTeam: Haoran Du, Xiang Yang, Ruiwen Shi
Instructor: Prof. Alkis Vazacopoulos
INTRODUCTION
As we all know, sports data has been a popular topic for data
scientist in the recent years. But Soccer, known to be the most
popular sport on this planet, failed to appear in most of the
analytical studies for it is difficult to collect and organize data
regarding this sport. Unlike basketball or American football,
where the existence of a sole professional league (NBA and
NFL) makes it easy to record everything, soccer is played all
around the world with so many leagues and tournaments
needing considering. Now, we just use the English Premier
League players datasets to do the analysis.
One complicated and interesting feature of players is their
market values, which vary greatly for different players,
different areas and different periods of time. A soccer team
involving top players with high market values never fail to hit
the headlines. It is thus interesting to predict market values of
soccer players in the future using our current market value
data and to optimize a top-valued team.
OBJECTIVES
•Utilizing multiple linear regression to predict market
value of players.
•Utilizing Excel Solver to group a highest total market
value soccer team.
Predicting market values(Multiple Linear
Regression)
Regression Model
Predicted market value = -7.4983 - 0.2698 AP + 0.0024
AW + 3.4635 VF + 22.3183 PS + 0.0398 PA + 6.7612
WT
Notation:
AP: Age of the player
AW: Average daily Wikipedia page views from September 1,
2016 to May 1, 2017
VF: Value in Fantasy Premier League as on July 20th, 2017
PS: Percentage of FPL players who have selected that player in
their team
PA: FPL points accumulated over the previous season
WT: Whether one of the Top 6 clubs
Optimizing soccer team via Excel Solver
Decision: 1 means selecting the player, and 0 means not selecting
the player.
Constraints:
• No more than 3 players from the same club can be selected
• Attackers must be more than 1
• Defenders must be more than 3
• Goalkeeper must be more than 1
• The team must contain 15 players
• Budget is 100 million euro
Objective: Maximizing the total market value of the soccer team.
Optimized Result:
The optimal soccer team players are Romelu Lukaku, Philippe Coutinho, Marcos Alonso, Wayne Rooney, Marcus Rashford, David Luiz, David de
Gea, Kyle Walker, Eric Bailly, Hector Bellerin, Thibaut Courtois, Shakodran Mustafi, Hugo Lloris, Joel Matip, John Stones.
With the budget of 100 million euro dollars, this team can achieve the market value up to 443.63. The team consists of 4 attacker players, 8
defender players and 3 goal keepers .
Multiple Linear Regression Code OLS Regression Results