This document discusses optimizing fantasy football teams to maximize points scored over a 17-week season. It develops a nonlinear programming model using an evolutionary solver with 200 binary variables representing each player. The model aims to select players with the highest historical scoring averages who provide consistent weekly points and availability. Future improvements will add constraints to reduce total variance across positions and simulate different draft outcomes based on pick order, as well as calculating optimal bench players for bye-weeks.

1. Fantasy Football Team Optimization
Maria Frolov, Gordon Oxley, Matt Zimmer
Professor : Alkiviadis Vazacopoulos
ObservationsIntroduction
As one of the most prevalent sports in the U.S, billions are
spent betting on fantasy football. The key to a winning
fantasy football team is to draft a team that scores a large
amount of points consistently for every week. To
accomplish this, a fantasy team must consist of players that
score points, that have low variability in points from week to
week, and can be relied on not to miss games (injuries). The
primary goal of our model is to draft players that fit these
descriptions based on the amount of fantasy points scored
using historical weekly data. This will be accomplished by
using the powerful tool of optimization.
Methodology
To model this problem, we developed a nonlinear
programming model using evolutionary solver that has 200
binary decision variables representing each player. Using the
solver tool in Excel to maximize the total points of the
fantasy teams, we found that our model successfully
encapsulates the highest historically scoring fantasy players.
The formula used to optimize is: Σptsj , where ptsj is the
expected points of the player j.
Business Intelligence & Analytics
Problem Statement
Objective: The objective of our model is to maximize the
amount of points that one’s fantasy football team can score
in a 17-week season.
Assumptions: The current assumptions made in our model
are: there must be 10 teams for the draft and we are always
first for the draft picks for each of the positions.
Formulas: The calculation of points for the week are based
on the average weekly return from historical data, if a player
has a bye-week then that player’s return for that week is 0,
and the total return for that season’s portfolio is the sum of
the return from all of the players for every week.
Variables: The variables used in our model are the average
draft position of the players and the actual players. Binary
variables were also used to show whether or not the player is
part of the portfolio.
Constraints: The current constraints of our model are: each
position has a certain number of required players for the
team (1 quarterback, 2 running backs, 3 wide receivers, 1
tight end, 1 kicker, and 1 defense/special team), each
decision variable for each player is binary, and the use of the
evolutionary solver option for non-smooth problems.
Moving Forward
As we move forward with our project, we will be looking to
improve our model by introducing additional constraints.
Similar to that of a portolio of stock returns, we will attempt to
reduce the total portfolio variance by creating covariance
matrices for each position based on weekly expected returns.
Also, we will add constraints to simulate 10 different portoflios
based on the order in which players are chosen since one will
not be first pick for every position as well as calculating the
optimal benched players to be used during bye-weeks.