%0 Journal Article %1 brownpredictive %A Brown, Michael Scott %D 2019 %J Applied Mathematics and Sciences: An International Journal (MathSJ) %K End Female Game Lacrosse Logistic Prediction Probability Regression Win Women’s of %N 1 %P 01 - 08 %T PREDICTIVE MODELS FOR GAME OUTCOMES IN WOMEN’S LACROSS %U https://airccse.com/mathsj/papers/6119MathsJ01.pdf %V 6 %X 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.

@article{brownpredictive, abstract = {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. }, added-at = {2020-11-18T13:12:24.000+0100}, author = {Brown, Michael Scott}, biburl = {https://www.bibsonomy.org/bibtex/2874ecc86d207c47efbd7b035f594cd61/journalmathsj}, interhash = {3f4a42d8e66cc1ed7d4f0dc6c500ff3f}, intrahash = {874ecc86d207c47efbd7b035f594cd61}, journal = {Applied Mathematics and Sciences: An International Journal (MathSJ)}, keywords = {End Female Game Lacrosse Logistic Prediction Probability Regression Win Women’s of}, month = {March}, number = 1, pages = {01 - 08}, timestamp = {2020-12-02T12:03:08.000+0100}, title = {PREDICTIVE MODELS FOR GAME OUTCOMES IN WOMEN’S LACROSS}, url = {https://airccse.com/mathsj/papers/6119MathsJ01.pdf}, volume = 6, year = 2019 }