Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades

11/04/2021
by   Aaron Baughman, et al.
0

Even skilled fantasy football managers can be disappointed by their mid-season rosters as some players inevitably fall short of draft day expectations. Team managers can quickly discover that their team has a low score ceiling even if they start their best active players. A novel and diverse combinatorial optimization system proposes high volume and unique player trades between complementary teams to balance trade fairness. Several algorithms create the valuation of each fantasy football player with an ensemble of computing models: Quantum Support Vector Classifier with Permutation Importance (QSVC-PI), Quantum Support Vector Classifier with Accumulated Local Effects (QSVC-ALE), Variational Quantum Circuit with Permutation Importance (VQC-PI), Hybrid Quantum Neural Network with Permutation Importance (HQNN-PI), eXtreme Gradient Boosting Classifier (XGB), and Subject Matter Expert (SME) rules. The valuation of each player is personalized based on league rules, roster, and selections. The cost of trading away a player is related to a team's roster, such as the depth at a position, slot count, and position importance. Teams are paired together for trading based on a cosine dissimilarity score so that teams can offset their strengths and weaknesses. A knapsack 0-1 algorithm computes outgoing players for each team. Postprocessors apply analytics and deep learning models to measure 6 different objective measures about each trade. Over the 2020 and 2021 National Football League (NFL) seasons, a group of 24 experts from IBM and ESPN evaluated trade quality through 10 Football Error Analysis Tool (FEAT) sessions. Our system started with 76.9 trades and was deployed for the 2021 season with 97.3 To increase trade quantity, our quantum, classical, and rules-based computing have 100 work.

READ FULL TEXT

page 7

page 14

research
02/18/2022

Study of Feature Importance for Quantum Machine Learning Models

Predictor importance is a crucial part of data preprocessing pipelines i...
research
06/15/2014

Soccer League Optimization: A heuristic Algorithm Inspired by the Football System in European Countries

In this paper a new heuristic optimization algorithm has been introduced...
research
07/21/2020

Modeling Player and Team Performance in Basketball

In recent years, analytics has started to revolutionize the game of bask...
research
02/20/2018

A multicriteria selection system based on player performance. Case study: The Spanish ACB Basketball League

In this paper, we describe an approach to rank sport players based on th...
research
12/07/2020

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

The emerging progress of eSports lacks the tools for ensuring high-quali...
research
04/15/2021

Contrastive Learning for Sports Video: Unsupervised Player Classification

We address the problem of unsupervised classification of players in a te...
research
07/05/2023

Improving Algorithms for Fantasy Basketball

Fantasy basketball has a rich underlying mathematical structure which ma...

Please sign up or login with your details

Forgot password? Click here to reset