https://github.com/texasbe2trill/hooplyticsr
hooplyticsR is a basketball analytics project that uses machine learning to predict player performance metrics. It applies k-Nearest Neighbors (kNN) regression models to forecast key basketball statistics like points, rebounds, assists, and fantasy scores using the caret package in R.
https://github.com/texasbe2trill/hooplyticsr
basketball basketball-stats caret fantasy-basketball ggplot2 histogram knn machine-learning nba nbastatr nbastats r r-programming
Last synced: 8 months ago
JSON representation
hooplyticsR is a basketball analytics project that uses machine learning to predict player performance metrics. It applies k-Nearest Neighbors (kNN) regression models to forecast key basketball statistics like points, rebounds, assists, and fantasy scores using the caret package in R.
- Host: GitHub
- URL: https://github.com/texasbe2trill/hooplyticsr
- Owner: texasbe2trill
- License: mit
- Created: 2025-02-11T19:59:05.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-19T18:53:46.000Z (8 months ago)
- Last Synced: 2025-02-19T19:39:10.228Z (8 months ago)
- Topics: basketball, basketball-stats, caret, fantasy-basketball, ggplot2, histogram, knn, machine-learning, nba, nbastatr, nbastats, r, r-programming
- Language: R
- Homepage: https://texasbe2trill.github.io/hooplyticsR/
- Size: 5.24 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# hooplyticsR: Basketball Metrics Analysis Using kNN
## Overview
**hooplyticsR** is a basketball analytics project focused on predicting various player performance metrics using machine learning techniques. The project utilizes the `caret` package in R, applying k-Nearest Neighbors (kNN) regression models to forecast key basketball statistics such as points, rebounds, assists, and fantasy scores.
## Key Features
- **Multiple Metric Predictions**: Predicts points, rebounds, assists, total PRA (points, rebounds, assists), 3-pointers made, steals + blocks, turnovers, and fantasy scores.
- **Machine Learning with kNN**: Utilizes kNN regression models for accurate performance prediction.
- **Cross-Validation**: Employs 5-fold repeated cross-validation to ensure robust model evaluation.
- **Data Pre-processing**: Filters and transforms player data for consistent training and evaluation.
## Dataset
The project uses the nbastatR library containing player-level basketball statistics with columns for points (`pts`), rebounds (`treb`), assists (`ast`), steals (`stl`), blocks (`blk`), turnovers (`tov`), 3-pointers made (`fg3m`), and fantasy scores (`fpts`).
## Model Training and Evaluation
### Model Training
- Models are trained for each metric using kNN from the `caret` package.
- Training data is split into 80% training and 20% testing sets.
- Features include points, rebounds, assists, steals, blocks, and turnovers.
### Model Evaluation
- Predictions are generated for each model using the test data.
## Usage
1. Install `renv` (if not already installed)
Before activating the lock file, ensure you have the `renv` package installed:
``` r
install.packages("renv")
library(renv) # Loads the renv package
renv::restore() # Install exact packages used during development
```
2. **Data Input**: nbastatR data is loaded and filtered to include player data for training and evaluation.
3. **Training**: Run the training functions to generate models for each basketball metric.
4. **Prediction**: Use the evaluation function to generate predictions for new player data.
## Example Usage
``` r
# Define the NBA players you want to analyze
players <- c("Anthony Edwards", "Austin Reaves", "Klay Thompson")
# Retrieve player data from the past 10 seasons
player_data <- game_logs(seasons = 2015:2025, result_types = "player",
Sys.setenv("VROOM_CONNECTION_SIZE" = 131072 \* 2))
# Train KNN models for the selected players
model_data <- train_knn_models(player_data, players)
models <- model_data$models
train_data <- model_data$train_data
test_data <- model_data$test_data
# Evaluate the trained models
predictions <- evaluate_knn_models(models, test_data)
# Define player-specific projections (e.g., points per game)
player_projections <- list(
"Austin Reaves" = list(points_model = 19.5),
"Anthony Edwards" = list(points_model = 30.5),
"Klay Thompson" = list(points_model = 15)
)
# Define player 5-game averages for the model input
player_5_game_averages <- list(
"Austin Reaves" = list(points_model = 27),
"Anthony Edwards" = list(points_model = 32),
"Klay Thompson" = list(points_model = 20)
)
# Make fantasy decisions based on predictions, test data, projections, and 5-game averages
results <- make_fantasy_decisions_by_model(predictions, test_data,
player_projections, player_5_game_averages)
```