https://github.com/pddiii/mlb-roy

2024 MLB Rookie of the Year Race
https://github.com/pddiii/mlb-roy

baseball baseballr lightgbm lightgbm-classifier mlb pandas python r tidyverse

Last synced: about 1 month ago
JSON representation

2024 MLB Rookie of the Year Race

• Host: GitHub
• URL: https://github.com/pddiii/mlb-roy
• Owner: pddiii
• Created: 2024-08-27T13:36:27.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-09-18T19:57:50.000Z (4 months ago)
• Last Synced: 2024-12-15T11:08:30.243Z (about 1 month ago)
• Topics: baseball, baseballr, lightgbm, lightgbm-classifier, mlb, pandas, python, r, tidyverse
• Language: Jupyter Notebook
• Homepage:
• Size: 55.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# MLB-ROY

Let's talk about the MLB Rookie of the Year Race

## Full Report

[Full Detailed Report found here](/scripts_and_notebooks/report.pdf)

## Data

- 1974 to 2024 rookie data

  - Excluded 1994 and 2020 since they were shortened seasons

  - 1974-2023 were used for Training/Testing Data

  - 2024 was used to make predictions

- Starters: minimum 100 Innings Pitched (IP)

- Relievers: minimum 40 Innings Pitched (IP)

- Batters: minimum 300 Plate Appearances (PA)

## Data Cleaning

[Data Cleaning Notebook](/scripts_and_notebooks/roy_cleaning.ipynb)

### Helper Functions

I utilized several functions repeatedly throughout the modeling process.

In order to reduce the lines of code during the modelling I created a python script (.py file) which contains these various helper functions.

[Helper Functions Script](/scripts_and_notebooks/helper_functions.py)

### Source

There were several sources for the data utilized in this project.

The data in the [Awards Folder](data/awards/) is sourced from either `Lahman` database or `baseballr`.

The data in the [Fielding Folder](data/fielding/) is sourced from the `Lahman` database

The data in the [MLB Folder](data/mlb/) and the data in the [Rookies Folder](data/rookies/) were sourced from FanGraphs utilizing their custom reports feature.

The [Cleaned Player IDs](data/cleaned_player_ids.csv) were sourced from a previous project of mine which combined player ids from the Lahman Database and the more frequently updated [PlayerIDMap](https://docs.google.com/spreadsheets/d/1JgczhD5VDQ1EiXqVG-blttZcVwbZd5_Ne_mefUGwJnk/pubhtml?gid=0&single=true).

## Models

- For both models I fit the model to three different data sets:

  - One for relievers

  - One for starters

  - One for batters

### Vote Recipients

- Output: Probability between 0 and 1 for receiving a Rookie of the Year vote (`vote_getter`)

- Took the top 8 for 2024 from both the AL and NL

- Utilize these predictions in the predictions for the 2024 Rookie of the Year model

  - Rounded the top 8 vote getters to a 1, and the rest to a 0 for proper interpretation in the Rookie of the Year model predictions

**Predictions**

[Vote Getter Predictions](/data/predictions/vote_preds.csv)

### Rookie of the Year

- Output: Probability between 0 and 1 for winning Rookie of the Year (`rookie_of_the_year`)

- Utilize these predictions to discuss the possible Rookie of the Year candidates

**Predictions**

[Rookie of the Year Predictions](/data/predictions/vote_roy_preds.csv)