Predictive Models for Game Outcomes in Women's Lacrosse
Michael Scott Brown
Abstract
Open-access reader
Michael Scott Brown
Abstract
Open-access reader
This research presents a predictive model for determining the game outcome of a Women's (Female) Lacrosse game.This is important to coaches regardless of if their team appears to be winning or losing the game.Coaches make decisions throughout the game based upon the belief that they are winning or losing.The model is a Logistic Regression model and can be used with very little data from a game: time remaining and difference between the scores.This could be a valuable tool to coaches that can be used during the game.It is more than 89% accurate.Data used in this research comes from direct matchup games between BigTen Women's Lacrosse teams.The win probability equations, including coefficients, are presented.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This research presents a predictive model for determining the game outcome of a Women's (Female) Lacrosse game.This is important to coaches regardless of if their team appears to be winning or losing the game.Coaches make decisions throughout the game based upon the belief that they are winning or losing.The model is a Logistic Regression model and can be used with very little data from a game: time remaining and difference between the scores.This could be a valuable tool to coaches that can be used during the game.It is more than 89% accurate.Data used in this research comes from direct matchup games between BigTen Women's Lacrosse teams.The win probability equations, including coefficients, are presented.
Key concepts: Psychology, Computer science