baseball Vector - Moozonian

GitHub Repo https://github.com/DavidAlmagro/BaseballStrikeZones_SVM

DavidAlmagro/BaseballStrikeZones_SVM

Identification and visualisation of baseball strike zones using Support Vector Machines and player performance data.

GitHub Repo https://github.com/rose620/Baseball_Stats

rose620/Baseball_Stats

MLB's Statcast data to measure exit velocity, launch angle and vectors of baseball trajectory

GitHub Repo https://github.com/ivan-verges/SupportVectorMachinesOnSports

ivan-verges/SupportVectorMachinesOnSports

Support Vector Machine example to predict the Stike Zone Area of a Baseball Player

GitHub Repo https://github.com/twstack/PredictBaseballStrikeZones

twstack/PredictBaseballStrikeZones

Using a Supervised Vector Machine algorithm to predict the true strike zones for specific MLB players.

GitHub Repo https://github.com/rumeysakocc/Analyzing-Salary-Trends-in-Baseball--SVR-and-SVM-Approach

rumeysakocc/Analyzing-Salary-Trends-in-Baseball--SVR-and-SVM-Approach

Salary Prediction using Support Vector Regression (SVR)

GitHub Repo https://github.com/becastil/ml-baseball-strike-zone

becastil/ml-baseball-strike-zone

Using machine learning to predict and visualize MLB player strike zones with Support Vector Machines

GitHub Repo https://github.com/tanercam/Salary-Prediction-Project-of-US-Baseball-Major-League-Players-with-Thirteen-Different-Machine-Learni

tanercam/Salary-Prediction-Project-of-US-Baseball-Major-League-Players-with-Thirteen-Different-Machine-Learni

In this project, thirteen different machine learning models were employed to predict salary of any US Major Baseball League player. These are Linear Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, KNN (K-Nearest Neighbors), SVR (Support Vector Regression), MLP (Multilayer Perceptron), CART (Classification and Regression Trees), Random Forests, GBM (Gradient Boosting Machines), XGBoost (Extreme Gradient Boosting), LightGBM, and CatBoost (Category Boosting) Machine Learning Models.

GitHub Repo https://github.com/data-becki/Predict-Baseball-Strike-Zones-With-Machine-Learning

data-becki/Predict-Baseball-Strike-Zones-With-Machine-Learning

Using an SVM (Support Vector Machine) trained using a baseball dataset to find the decision boundary of the strike zone.

GitHub Repo https://github.com/obour2021/SportsVectorMachine

obour2021/SportsVectorMachine

building support vector machine model to find the decision boundary of the strike zone of baseball player

GitHub Repo https://github.com/akshaykalia/Baseball-feature-extraction

akshaykalia/Baseball-feature-extraction

Tried to make assessment of which feature vectors (i.e. team-based statistics) are more highly correlated to winning. The ultimate actionable goal was to drive greater revenues by helping management leverage existing fixed assets by develop the right mix of variable assets (i.e. cost of players vs benefit of players) in order to create an organization with a greater likelihood of winning.