How to Create Your Own Lineup Optimizer! --Python For Daily Fantasy Sports Episode 1

Introduction

Creating a lineup optimizer can significantly enhance your strategy for daily fantasy sports (DFS). This tutorial walks you through the process of building your own optimizer using Python, focusing on essential programming techniques and the necessary libraries. By the end, you'll have a functional tool to help you develop optimal lineups for various contests.

Step 1: Set Up Your Environment

Before you start coding, ensure you have the required packages installed. You will need:

• pandas
• pulp
• openpyxl
• re

You can install these packages using pip:

pip install pandas pulp openpyxl

Step 2: Import Necessary Libraries

Start your Python script by importing the required libraries:

import pandas as pd
from pulp import *
import openpyxl
import re

Step 3: Prepare Your Excel Workbook

Create an Excel workbook to store the generated lineups. This will be done in the background while your script runs.

workbook = openpyxl.Workbook()
sheet = workbook.active

Step 4: Load Player Data

Load your fantasy player list from a CSV file into a pandas DataFrame for easier manipulation.

player_data = pd.read_csv('your_player_data.csv')

Step 5: Group Data by Position

Regroup the DataFrame by player position and reset the index:

grouped_data = player_data.groupby('Position').apply(lambda x: x.reset_index(drop=True))

Step 6: Create Storage Dictionaries

Define empty dictionaries for storing player salaries and points categorized by position.

salary_dict = {}
points_dict = {}

Step 7: Populate Dictionaries

Loop through the grouped DataFrame to fill in the salary and points dictionaries with player IDs as keys.

for position, group in grouped_data.groupby(level=0):
    salary_dict[position] = {row['PlayerID']: row['Salary'] for _, row in group.iterrows()}
    points_dict[position] = {row['PlayerID']: row['Points'] for _, row in group.iterrows()}

Step 8: Define Positional Constraints

Set up a dictionary that defines how many players you need from each position.

positional_constraints = {
    'PG': 2,
    'SG': 2,
    'SF': 2,
    'PF': 2,
    'C': 1
}

Step 9: Set Salary Cap

Determine your salary cap for lineup creation.

salary_cap = 50000

Step 10: Create the Lineup Optimization Loop

Create a loop to maximize your fantasy points while respecting the positional constraints and salary cap:

# Initialize problem
problem = LpProblem("DFS_Lineup_Optimization", LpMaximize)

# Create binary variables for each player
player_vars = LpVariable.dicts("Player", player_data['PlayerID'], cat='Binary')

# Objective function
problem += lpSum([points_dict[position][player] * player_vars[player] for position in points_dict for player in points_dict[position]]), "Total Points"

# Add constraints for salary cap
problem += lpSum([salary_dict[position][player] * player_vars[player] for position in salary_dict for player in salary_dict[position]]) <= salary_cap, "SalaryCap"

# Add positional constraints
for position, count in positional_constraints.items():
    problem += lpSum([player_vars[player] for player in salary_dict[position]]) == count, f"{position}_constraint"

# Solve the problem
problem.solve()

Step 11: Write Results to Excel

Clean up the variables and write the optimized lineups to your Excel workbook.

for player in player_data['PlayerID']:
    if player_vars[player].value() == 1:
        sheet.append([player])
workbook.save('optimized_lineups.xlsx')

Step 12: Clean Up Format for Upload

Before uploading your results to platforms like FanDuel, ensure the player ID format is correct. You may need to use Excel functions to adjust the IDs.

• Remove the position prefix from player IDs.
• Replace underscores with hyphens in player IDs for compatibility.

Conclusion

You've successfully created a basic lineup optimizer for daily fantasy sports using Python. This tool can help you generate optimal lineups based on player statistics while adhering to positional and salary constraints. For further improvements, consider adding features for real-time data updates, or enhancing the user interface for easier access. Happy optimizing!