I filled out the proposal.md file.

I have just started the project so there is nothing to reflect on yet.  I am excited for this proejct as NBA data is very interesting to me.
This commit is contained in:
jwberent
2025-10-18 12:47:07 -04:00
parent 0e3396811b
commit 4b4b74d964
6 changed files with 10223 additions and 18 deletions

View File

@@ -0,0 +1,576 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "worldwide-blood",
"metadata": {},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"id": "understanding-numbers",
"metadata": {},
"source": [
"*✏️ Write 2-3 sentences describing your research.*"
]
},
{
"cell_type": "markdown",
"id": "greater-circular",
"metadata": {},
"source": [
"## Overarching Question: [✏️ PUT YOUR QUESTION HERE ✏️]"
]
},
{
"cell_type": "markdown",
"id": "appreciated-testimony",
"metadata": {},
"source": [
"*✏️ Write 2-3 sentences explaining why this question.*"
]
},
{
"cell_type": "markdown",
"id": "permanent-pollution",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "technical-evans",
"metadata": {},
"outputs": [],
"source": [
"#Include any import statements you will need\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "overhead-sigma",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"### 💻 FILL IN YOUR DATASET FILE NAME BELOW 💻 ###\n",
"\n",
"file_name = \"modern_RAPTOR_by_player.csv\"\n",
"dataset_path = \"data/\" + file_name\n",
"\n",
"df = pd.read_csv(dataset_path)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "heated-blade",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>player_name</th>\n",
" <th>player_id</th>\n",
" <th>season</th>\n",
" <th>poss</th>\n",
" <th>mp</th>\n",
" <th>raptor_box_offense</th>\n",
" <th>raptor_box_defense</th>\n",
" <th>raptor_box_total</th>\n",
" <th>raptor_onoff_offense</th>\n",
" <th>raptor_onoff_defense</th>\n",
" <th>...</th>\n",
" <th>raptor_offense</th>\n",
" <th>raptor_defense</th>\n",
" <th>raptor_total</th>\n",
" <th>war_total</th>\n",
" <th>war_reg_season</th>\n",
" <th>war_playoffs</th>\n",
" <th>predator_offense</th>\n",
" <th>predator_defense</th>\n",
" <th>predator_total</th>\n",
" <th>pace_impact</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alex Abrines</td>\n",
" <td>abrinal01</td>\n",
" <td>2017</td>\n",
" <td>2387</td>\n",
" <td>1135</td>\n",
" <td>0.745505</td>\n",
" <td>-0.372938</td>\n",
" <td>0.372567</td>\n",
" <td>-0.418553</td>\n",
" <td>-3.857011</td>\n",
" <td>...</td>\n",
" <td>0.543421</td>\n",
" <td>-1.144832</td>\n",
" <td>-0.601411</td>\n",
" <td>1.249008</td>\n",
" <td>1.447708</td>\n",
" <td>-0.198700</td>\n",
" <td>0.077102</td>\n",
" <td>-1.038677</td>\n",
" <td>-0.961575</td>\n",
" <td>0.326413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alex Abrines</td>\n",
" <td>abrinal01</td>\n",
" <td>2018</td>\n",
" <td>2546</td>\n",
" <td>1244</td>\n",
" <td>0.317549</td>\n",
" <td>-1.725325</td>\n",
" <td>-1.407776</td>\n",
" <td>-1.291727</td>\n",
" <td>-0.049694</td>\n",
" <td>...</td>\n",
" <td>-0.020826</td>\n",
" <td>-1.502642</td>\n",
" <td>-1.523468</td>\n",
" <td>0.777304</td>\n",
" <td>0.465912</td>\n",
" <td>0.311392</td>\n",
" <td>-0.174621</td>\n",
" <td>-1.112625</td>\n",
" <td>-1.287247</td>\n",
" <td>-0.456141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alex Abrines</td>\n",
" <td>abrinal01</td>\n",
" <td>2019</td>\n",
" <td>1279</td>\n",
" <td>588</td>\n",
" <td>-3.215683</td>\n",
" <td>1.078399</td>\n",
" <td>-2.137285</td>\n",
" <td>-6.158856</td>\n",
" <td>4.901168</td>\n",
" <td>...</td>\n",
" <td>-4.040157</td>\n",
" <td>1.885618</td>\n",
" <td>-2.154538</td>\n",
" <td>0.178167</td>\n",
" <td>0.178167</td>\n",
" <td>0.000000</td>\n",
" <td>-4.577678</td>\n",
" <td>1.543282</td>\n",
" <td>-3.034396</td>\n",
" <td>-0.268013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Precious Achiuwa</td>\n",
" <td>achiupr01</td>\n",
" <td>2021</td>\n",
" <td>1581</td>\n",
" <td>749</td>\n",
" <td>-4.122966</td>\n",
" <td>1.359278</td>\n",
" <td>-2.763688</td>\n",
" <td>-4.050779</td>\n",
" <td>-0.919712</td>\n",
" <td>...</td>\n",
" <td>-4.347596</td>\n",
" <td>0.954821</td>\n",
" <td>-3.392775</td>\n",
" <td>-0.246055</td>\n",
" <td>-0.246776</td>\n",
" <td>0.000721</td>\n",
" <td>-3.817713</td>\n",
" <td>0.474828</td>\n",
" <td>-3.342885</td>\n",
" <td>0.329157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Precious Achiuwa</td>\n",
" <td>achiupr01</td>\n",
" <td>2022</td>\n",
" <td>3802</td>\n",
" <td>1892</td>\n",
" <td>-2.521510</td>\n",
" <td>1.763502</td>\n",
" <td>-0.758008</td>\n",
" <td>-1.687893</td>\n",
" <td>3.103441</td>\n",
" <td>...</td>\n",
" <td>-2.517372</td>\n",
" <td>2.144151</td>\n",
" <td>-0.373221</td>\n",
" <td>2.262658</td>\n",
" <td>2.309611</td>\n",
" <td>-0.046953</td>\n",
" <td>-2.483956</td>\n",
" <td>2.024360</td>\n",
" <td>-0.459596</td>\n",
" <td>-0.728609</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" player_name player_id season poss mp raptor_box_offense \\\n",
"0 Alex Abrines abrinal01 2017 2387 1135 0.745505 \n",
"1 Alex Abrines abrinal01 2018 2546 1244 0.317549 \n",
"2 Alex Abrines abrinal01 2019 1279 588 -3.215683 \n",
"3 Precious Achiuwa achiupr01 2021 1581 749 -4.122966 \n",
"4 Precious Achiuwa achiupr01 2022 3802 1892 -2.521510 \n",
"\n",
" raptor_box_defense raptor_box_total raptor_onoff_offense \\\n",
"0 -0.372938 0.372567 -0.418553 \n",
"1 -1.725325 -1.407776 -1.291727 \n",
"2 1.078399 -2.137285 -6.158856 \n",
"3 1.359278 -2.763688 -4.050779 \n",
"4 1.763502 -0.758008 -1.687893 \n",
"\n",
" raptor_onoff_defense ... raptor_offense raptor_defense raptor_total \\\n",
"0 -3.857011 ... 0.543421 -1.144832 -0.601411 \n",
"1 -0.049694 ... -0.020826 -1.502642 -1.523468 \n",
"2 4.901168 ... -4.040157 1.885618 -2.154538 \n",
"3 -0.919712 ... -4.347596 0.954821 -3.392775 \n",
"4 3.103441 ... -2.517372 2.144151 -0.373221 \n",
"\n",
" war_total war_reg_season war_playoffs predator_offense \\\n",
"0 1.249008 1.447708 -0.198700 0.077102 \n",
"1 0.777304 0.465912 0.311392 -0.174621 \n",
"2 0.178167 0.178167 0.000000 -4.577678 \n",
"3 -0.246055 -0.246776 0.000721 -3.817713 \n",
"4 2.262658 2.309611 -0.046953 -2.483956 \n",
"\n",
" predator_defense predator_total pace_impact \n",
"0 -1.038677 -0.961575 0.326413 \n",
"1 -1.112625 -1.287247 -0.456141 \n",
"2 1.543282 -3.034396 -0.268013 \n",
"3 0.474828 -3.342885 0.329157 \n",
"4 2.024360 -0.459596 -0.728609 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "continental-franklin",
"metadata": {},
"source": [
"**Data Overview**\n",
"\n",
"*✏️ Write 2-3 sentences describing this dataset. Be sure to include where the data comes from and what it contains.*"
]
},
{
"cell_type": "markdown",
"id": "infinite-instrument",
"metadata": {},
"source": [
"# Methods and Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "basic-canadian",
"metadata": {},
"outputs": [],
"source": [
"#Import any helper files you need here"
]
},
{
"cell_type": "markdown",
"id": "recognized-positive",
"metadata": {},
"source": [
"## First Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
]
},
{
"cell_type": "markdown",
"id": "graduate-palmer",
"metadata": {},
"source": [
"### Methods"
]
},
{
"cell_type": "markdown",
"id": "endless-variation",
"metadata": {},
"source": [
"*Explain how you will approach this research question below. Consider the following:* \n",
" - *Which aspects of the dataset will you use?* \n",
" - *How will you reorganize/store the data?* \n",
" - *What data science tools/functions will you use and why?* \n",
" \n",
"✏️ *Write your answer below:*\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "portuguese-japan",
"metadata": {},
"source": [
"### Results "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "negative-highlight",
"metadata": {},
"outputs": [],
"source": [
"#######################################################################\n",
"### 💻 YOUR WORK GOES HERE TO ANSWER THE FIRST RESEARCH QUESTION 💻 \n",
"### \n",
"### Your data analysis may include a statistic and/or a data visualization\n",
"#######################################################################"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "victorian-burning",
"metadata": {},
"outputs": [],
"source": [
"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
]
},
{
"cell_type": "markdown",
"id": "collectible-puppy",
"metadata": {},
"source": [
"## Second Research Question: [✏️ PUT YOUR QUESTION HERE ✏️]\n"
]
},
{
"cell_type": "markdown",
"id": "demographic-future",
"metadata": {},
"source": [
"### Methods"
]
},
{
"cell_type": "markdown",
"id": "incorporate-roller",
"metadata": {},
"source": [
"*Explain how you will approach this research question below. Consider the following:* \n",
" - *Which aspects of the dataset will you use?* \n",
" - *How will you reorganize/store the data?* \n",
" - *What data science tools/functions will you use and why?* \n",
"\n",
"✏️ *Write your answer below:*\n"
]
},
{
"cell_type": "markdown",
"id": "juvenile-creation",
"metadata": {},
"source": [
"### Results "
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "pursuant-surrey",
"metadata": {},
"outputs": [],
"source": [
"#######################################################################\n",
"### 💻 YOUR WORK GOES HERE TO ANSWER THE SECOND RESEARCH QUESTION 💻 \n",
"###\n",
"### Your data analysis may include a statistic and/or a data visualization\n",
"#######################################################################"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "located-night",
"metadata": {},
"outputs": [],
"source": [
"# 💻 YOU CAN ADD NEW CELLS WITH THE \"+\" BUTTON "
]
},
{
"cell_type": "markdown",
"id": "infectious-symbol",
"metadata": {},
"source": [
"# Discussion"
]
},
{
"cell_type": "markdown",
"id": "furnished-camping",
"metadata": {
"code_folding": []
},
"source": [
"## Considerations"
]
},
{
"cell_type": "markdown",
"id": "bearing-stadium",
"metadata": {},
"source": [
"*It's important to recognize the limitations of our research.\n",
"Consider the following:*\n",
"\n",
"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
"- *What were limitations of your datset?*\n",
"- *Are there any known biases in the data?*\n",
"\n",
"✏️ *Write your answer below:*"
]
},
{
"cell_type": "markdown",
"id": "beneficial-invasion",
"metadata": {},
"source": [
"## Summary"
]
},
{
"cell_type": "markdown",
"id": "about-raise",
"metadata": {},
"source": [
"*Summarize what you discovered through the research. Consider the following:*\n",
"\n",
"- *What did you learn about your media consumption/digital habits?*\n",
"- *Did the results make sense?*\n",
"- *What was most surprising?*\n",
"- *How will this project impact you going forward?*\n",
"\n",
"✏️ *Write your answer below:*"
]
}
],
"metadata": {
"jupytext": {
"cell_metadata_json": true,
"text_representation": {
"extension": ".Rmd",
"format_name": "rmarkdown",
"format_version": "1.2",
"jupytext_version": "1.9.1"
}
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}