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diff --git a/README.md b/README.md
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+---
+pretty_name: "NBA Play by Play Data for 2023 season"
+license: mit
+---
+
+
+
+
+
+# NBA Play-by-Play Data Extraction and Analysissss
+
+## Overview
+This project aims to retrieve play-by-play data for NBA matches in the 2023 season using the Sportradar API. The play-by-play data is fetched from the API, saved into JSON files, and then used to extract relevant features for analysis and other applications. The extracted data is saved in Parquet files for easy access and usage by others.
+
+## Features
+The project provides the following features:
+- Fetching play-by-play data for NBA matches in the 2023 season from the Sportradar API.
+- Saving the fetched data into JSON files for archival and offline use.
+- Extracting relevant features from the JSON files, such as:
+ - Match date and time
+ - Home team and away team information
+ - Play descriptions
+ - Clock time
+ - Event types (e.g., two-pointer, three-pointer, block, foul)
+ - Home team points and away team points
+ - Quarter number
+- Saving the extracted data into Parquet files for easy access and analysis.
+
+## Usage
+1. **Fetching Play-by-Play Data**: To fetch play-by-play data, use the provided `get_game_pbp()` function, which retrieves data from the Sportradar API and saves it into JSON files.
+
+2. **Extracting Features**: Use the `get_game_pbp()` function to extract relevant features from the JSON files and create a DataFrame containing the extracted data.
+
+3. **Saving Data**: The extracted data can be saved into Parquet files using pandas' `to_parquet()` function for future analysis and usage.
+
+## Potential Applications
+- Generating live commentary for NBA matches.
+- Performing in-depth analysis of player performance, team strategies, and game dynamics.
+- Developing predictive models for match outcomes or player performance.
+
+## Contributors
+- [Your Name](https://github.com/yourusername) - Project Lead & Developer
+
+## License
+This project is licensed under the [MIT License](LICENSE).
+
+
+
+
+
diff --git a/Script/.DS_Store b/Script/.DS_Store
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diff --git a/Script/dataset_creation.ipynb b/Script/dataset_creation.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # creating a function which takes in a game id and returns the dataframe of the game's play by play data\n",
+ "\n",
+ "\n",
+ "# def get_game_pbp(game_id):\n",
+ "# # Construct the URL for the play-by-play endpoint\n",
+ "# url = f\"https://api.sportradar.us/nba/trial/v8/en/games/{game_id}/pbp.json?api_key={api_key}\"\n",
+ "\n",
+ "# # Make a GET request to the endpoint\n",
+ "# game_data = requests.get(url)\n",
+ "\n",
+ "# # Check if the request was successful\n",
+ "# if game_data.status_code == 200:\n",
+ "# # Convert the response to JSON\n",
+ "# data = game_data.json()\n",
+ "\n",
+ "# # Extract the relevant information\n",
+ "# scheduled = data[\"scheduled\"] # This is when the game was scheduled\n",
+ "# home_team_name = data[\"home\"][\"market\"] + \" \" + data[\"home\"][\"name\"]\n",
+ "# away_team_name = data[\"away\"][\"market\"] + \" \" + data[\"away\"][\"name\"]\n",
+ "# descriptions = []\n",
+ "# clocks = []\n",
+ "# event_types = []\n",
+ "# home_points = []\n",
+ "# away_points = []\n",
+ "# quarters = []\n",
+ "\n",
+ "# for period in data[\"periods\"]:\n",
+ "# for event in period[\"events\"]:\n",
+ "# descriptions.append(\n",
+ "# event.get(\"description\", None)\n",
+ "# ) # This is what happened\n",
+ "# clocks.append(event.get(\"clock\", None)) # This is when it happened\n",
+ "# event_types.append(\n",
+ "# event.get(\"event_type\", None)\n",
+ "# ) # This is the type of event\n",
+ "# home_points.append(\n",
+ "# event.get(\"home_points\", None)\n",
+ "# ) # This is the home team's points\n",
+ "# away_points.append(\n",
+ "# event.get(\"away_points\", None)\n",
+ "# ) # This is the visitor team's points\n",
+ "# quarters.append(period.get(\"number\", None)) # This is the quarter\n",
+ "\n",
+ "# # Create a DataFrame from the extracted information\n",
+ "# df = pd.DataFrame(\n",
+ "# {\n",
+ "# \"date\": scheduled, # This is when the game was scheduled\n",
+ "# \"home_team_name\": home_team_name,\n",
+ "# \"away_team_name\": away_team_name,\n",
+ "# \"description\": descriptions,\n",
+ "# \"clock\": clocks,\n",
+ "# \"event_type\": event_types,\n",
+ "# \"home_points\": home_points,\n",
+ "# \"away_points\": away_points,\n",
+ "# \"quarter\": quarters,\n",
+ "# }\n",
+ "# )\n",
+ "\n",
+ "# # Return the DataFrame\n",
+ "# return df\n",
+ "\n",
+ "# else:\n",
+ "# # If the request was not successful, return None\n",
+ "# return None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "json_files = []\n",
+ "\n",
+ "# Walk through current directory\n",
+ "for dirpath, dirnames, filenames in os.walk('.'):\n",
+ " # Find JSON files in each directory\n",
+ " for filename in filenames:\n",
+ " if filename.endswith('.json'):\n",
+ " # Append the path to the file\n",
+ " json_files.append(os.path.join(dirpath, filename))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import json\n",
+ "import pandas as pd\n",
+ "\n",
+ "def get_game_pbp_from_file(file_path):\n",
+ " # Load JSON data from the file\n",
+ " with open(file_path, 'r') as file:\n",
+ " data = json.load(file)\n",
+ "\n",
+ " # Extract the relevant information\n",
+ " id = data[\"id\"] # This is the game's ID\n",
+ " scheduled = data[\"scheduled\"] # This is when the game was scheduled\n",
+ " home_team_name = data[\"home\"][\"market\"] + \" \" + data[\"home\"][\"name\"]\n",
+ " away_team_name = data[\"away\"][\"market\"] + \" \" + data[\"away\"][\"name\"]\n",
+ " descriptions = []\n",
+ " clocks = []\n",
+ " event_types = []\n",
+ " home_points = []\n",
+ " away_points = []\n",
+ " quarters = []\n",
+ "\n",
+ " if \"periods\" not in data:\n",
+ " print (f\"File {file_path} does not contain play-by-play data\")\n",
+ " return None\n",
+ " \n",
+ " else:\n",
+ "\n",
+ " for period in data[\"periods\"]:\n",
+ " for event in period[\"events\"]:\n",
+ " descriptions.append(event.get(\"description\", None)) # This is what happened\n",
+ " clocks.append(event.get(\"clock\", None)) # This is when it happened\n",
+ " event_types.append(event.get(\"event_type\", None)) # This is the type of event\n",
+ " home_points.append(event.get(\"home_points\", None)) # This is the home team's points\n",
+ " away_points.append(event.get(\"away_points\", None)) # This is the visitor team's points\n",
+ " quarters.append(period.get(\"number\", None)) # This is the quarter\n",
+ "\n",
+ " # Create a DataFrame from the extracted information\n",
+ " df = pd.DataFrame({\n",
+ " \"id\": id, # This is the game's ID\n",
+ " \"date\": scheduled, # This is when the game was scheduled\n",
+ " \"home_team_name\": home_team_name,\n",
+ " \"away_team_name\": away_team_name,\n",
+ " \"description\": descriptions,\n",
+ " \"clock\": clocks,\n",
+ " \"event_type\": event_types,\n",
+ " \"home_points\": home_points,\n",
+ " \"away_points\": away_points,\n",
+ " \"quarter\": quarters,\n",
+ " })\n",
+ "\n",
+ " # Return the DataFrame\n",
+ " return df\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Just testing the function with one file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
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+ " home_points away_points quarter \n",
+ "0 0 0 1 \n",
+ "1 0 0 1 \n",
+ "2 0 0 1 \n",
+ "3 0 2 1 \n",
+ "4 2 2 1 \n",
+ ".. ... ... ... \n",
+ "458 112 120 4 \n",
+ "459 112 121 4 \n",
+ "460 112 121 4 \n",
+ "461 112 121 4 \n",
+ "462 112 121 4 \n",
+ "\n",
+ "[463 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "file = json_files[0]\n",
+ "\n",
+ "# Get the play by play data from the file\n",
+ "play_by_play_df = get_game_pbp_from_file(file)\n",
+ "\n",
+ "play_by_play_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Doing it on all files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "File ./json_files/play_by_play_sportsradar_69fd5274-20fe-494a-89c2-14988b791d31.json does not contain play-by-play data\n"
+ ]
+ }
+ ],
+ "source": [
+ "all_dfs = []\n",
+ "\n",
+ "# Loop through each JSON file\n",
+ "for file_name in json_files:\n",
+ " # Get the DataFrame for the current file\n",
+ " play_by_play_df = get_game_pbp_from_file(file_name)\n",
+ " # Append the DataFrame to the list\n",
+ " all_dfs.append(play_by_play_df)\n",
+ "\n",
+ "# Concatenate all DataFrames into a single DataFrame\n",
+ "final_df = pd.concat(all_dfs, ignore_index=True)\n",
+ "\n",
+ "# # Print the final DataFrame\n",
+ "# print(final_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "193"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "final_df\n",
+ "\n",
+ "#check for unique dates\n",
+ "\n",
+ "len(final_df[\"id\"].unique())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " Clippers turnover (shot clock violation) | \n",
+ " 00:24 | \n",
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\n",
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+ " 81509f8f-9014-460b-b383-70b65a0efdaf | \n",
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+ " Portland Trail Blazers | \n",
+ " LA Clippers | \n",
+ " Damian Lillard misses three point pullup jump ... | \n",
+ " 00:19 | \n",
+ " threepointmiss | \n",
+ " 102 | \n",
+ " 117 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 88967 | \n",
+ " 81509f8f-9014-460b-b383-70b65a0efdaf | \n",
+ " 2023-03-20T01:00:00+00:00 | \n",
+ " Portland Trail Blazers | \n",
+ " LA Clippers | \n",
+ " Ivica Zubac defensive rebound | \n",
+ " 00:16 | \n",
+ " rebound | \n",
+ " 102 | \n",
+ " 117 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 88968 | \n",
+ " 81509f8f-9014-460b-b383-70b65a0efdaf | \n",
+ " 2023-03-20T01:00:00+00:00 | \n",
+ " Portland Trail Blazers | \n",
+ " LA Clippers | \n",
+ " End of 4th Quarter. | \n",
+ " 00:00 | \n",
+ " endperiod | \n",
+ " 102 | \n",
+ " 117 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
88969 rows × 10 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id date \\\n",
+ "0 6decf9f6-cf39-4bf4-94b1-0acc349bfb69 2023-01-23T02:00:00+00:00 \n",
+ "1 6decf9f6-cf39-4bf4-94b1-0acc349bfb69 2023-01-23T02:00:00+00:00 \n",
+ "2 6decf9f6-cf39-4bf4-94b1-0acc349bfb69 2023-01-23T02:00:00+00:00 \n",
+ "3 6decf9f6-cf39-4bf4-94b1-0acc349bfb69 2023-01-23T02:00:00+00:00 \n",
+ "4 6decf9f6-cf39-4bf4-94b1-0acc349bfb69 2023-01-23T02:00:00+00:00 \n",
+ "... ... ... \n",
+ "88964 81509f8f-9014-460b-b383-70b65a0efdaf 2023-03-20T01:00:00+00:00 \n",
+ "88965 81509f8f-9014-460b-b383-70b65a0efdaf 2023-03-20T01:00:00+00:00 \n",
+ "88966 81509f8f-9014-460b-b383-70b65a0efdaf 2023-03-20T01:00:00+00:00 \n",
+ "88967 81509f8f-9014-460b-b383-70b65a0efdaf 2023-03-20T01:00:00+00:00 \n",
+ "88968 81509f8f-9014-460b-b383-70b65a0efdaf 2023-03-20T01:00:00+00:00 \n",
+ "\n",
+ " home_team_name away_team_name \\\n",
+ "0 Portland Trail Blazers Los Angeles Lakers \n",
+ "1 Portland Trail Blazers Los Angeles Lakers \n",
+ "2 Portland Trail Blazers Los Angeles Lakers \n",
+ "3 Portland Trail Blazers Los Angeles Lakers \n",
+ "4 Portland Trail Blazers Los Angeles Lakers \n",
+ "... ... ... \n",
+ "88964 Portland Trail Blazers LA Clippers \n",
+ "88965 Portland Trail Blazers LA Clippers \n",
+ "88966 Portland Trail Blazers LA Clippers \n",
+ "88967 Portland Trail Blazers LA Clippers \n",
+ "88968 Portland Trail Blazers LA Clippers \n",
+ "\n",
+ " description clock \\\n",
+ "0 Lakers lineup change (Thomas Bryant, LeBron Ja... 12:00 \n",
+ "1 Trail Blazers lineup change (Damian Lillard, J... 12:00 \n",
+ "2 Thomas Bryant vs. Jusuf Nurkic (Dennis Schrode... 12:00 \n",
+ "3 LeBron James makes two point cutting dunk (Den... 11:47 \n",
+ "4 Damian Lillard makes two point jump shot (Josh... 11:31 \n",
+ "... ... ... \n",
+ "88964 Paul George defensive rebound 00:47 \n",
+ "88965 Clippers turnover (shot clock violation) 00:24 \n",
+ "88966 Damian Lillard misses three point pullup jump ... 00:19 \n",
+ "88967 Ivica Zubac defensive rebound 00:16 \n",
+ "88968 End of 4th Quarter. 00:00 \n",
+ "\n",
+ " event_type home_points away_points quarter \n",
+ "0 lineupchange 0 0 1 \n",
+ "1 lineupchange 0 0 1 \n",
+ "2 opentip 0 0 1 \n",
+ "3 twopointmade 0 2 1 \n",
+ "4 twopointmade 2 2 1 \n",
+ "... ... ... ... ... \n",
+ "88964 rebound 102 117 4 \n",
+ "88965 turnover 102 117 4 \n",
+ "88966 threepointmiss 102 117 4 \n",
+ "88967 rebound 102 117 4 \n",
+ "88968 endperiod 102 117 4 \n",
+ "\n",
+ "[88969 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "final_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#save it to parquet\n",
+ "\n",
+ "final_df.to_parquet(\"combined_dataframe.parquet\")\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Separate parquet files for each game\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "File ./json_files/play_by_play_sportsradar_69fd5274-20fe-494a-89c2-14988b791d31.json does not contain play-by-play data\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "\n",
+ "# Loop through each JSON file\n",
+ "for file_name in json_files:\n",
+ " # Get the DataFrame for the current file\n",
+ " play_by_play_df = get_game_pbp_from_file(file_name)\n",
+ " \n",
+ " # Extract home and away team names from the DataFrame\n",
+ " if play_by_play_df is None:\n",
+ " continue\n",
+ "\n",
+ "\n",
+ " home_team = play_by_play_df['home_team_name'].iloc[0]\n",
+ " away_team = play_by_play_df['away_team_name'].iloc[0]\n",
+ " \n",
+ " # Create the file name\n",
+ " parquet_file_name = f\"{home_team}VS{away_team}.parquet\"\n",
+ " \n",
+ " # Save the DataFrame as a Parquet file\n",
+ " play_by_play_df.to_parquet(os.path.join('Data/', parquet_file_name))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "base",
+ "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": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/json_example/play_by_play_sportsradar_00b1424c-c371-4fce-a27b-e0d65fd12552.json b/json_example/play_by_play_sportsradar_00b1424c-c371-4fce-a27b-e0d65fd12552.json
new file mode 100644
index 0000000000000000000000000000000000000000..702dec62c17bc8a3156e95337dbeb9796112c688
--- /dev/null
+++ b/json_example/play_by_play_sportsradar_00b1424c-c371-4fce-a27b-e0d65fd12552.json
@@ -0,0 +1 @@
+{"id": "00b1424c-c371-4fce-a27b-e0d65fd12552", "status": "closed", "coverage": "full", "scheduled": "2023-03-02T00:30:00+00:00", "duration": "2:19", "attendance": 19812, "lead_changes": 3, "times_tied": 3, "clock": "00:00", "quarter": 4, "track_on_court": true, "reference": "0022200937", "entry_mode": "WEBSOCKET", "sr_id": "sr:match:35433453", "clock_decimal": "00:00", "time_zones": {"venue": "US/Eastern", "home": "US/Eastern", "away": "US/Eastern"}, "home": {"name": "Knicks", "alias": "NYK", "market": "New York", "id": "583ec70e-fb46-11e1-82cb-f4ce4684ea4c", "points": 142, "bonus": false, "sr_id": "sr:team:3421", "remaining_timeouts": 3, "reference": "1610612752", "record": {"wins": 37, "losses": 27}}, "away": {"name": "Nets", "alias": "BKN", "market": "Brooklyn", "id": "583ec9d6-fb46-11e1-82cb-f4ce4684ea4c", "points": 118, "bonus": false, "sr_id": "sr:team:3436", "remaining_timeouts": 1, "reference": "1610612751", "record": {"wins": 34, "losses": 28}}, "periods": [{"type": "quarter", "id": "5131c15d-1cc0-4a0b-9c67-cab2ec6da2b9", "number": 1, "sequence": 1, "scoring": {"lead_changes": 3, "home": {"name": "Knicks", "market": "New York", "id": "583ec70e-fb46-11e1-82cb-f4ce4684ea4c", "points": 47, "reference": "1610612752"}, "away": {"name": "Nets", "market": "Brooklyn", "id": "583ec9d6-fb46-11e1-82cb-f4ce4684ea4c", "points": 29, "reference": "1610612751"}}, "events": [{"id": "77c19b32-41b6-463b-a3f5-31ff965ea140", "clock": "12:00", "updated": "2023-03-02T00:41:41Z", "description": "Nets lineup change (Cameron Johnson, Nic Claxton, Spencer Dinwiddie, Dorian Finney-Smith, Mikal Bridges)", "wall_clock": "2023-03-02T00:41:40Z", "sequence": 1677717700893, "home_points": 0, "away_points": 0, "clock_decimal": "12:00", "number": 5002, "event_type": "lineupchange", "attribution": {"name": "Nets", "market": "Brooklyn", "id": "583ec9d6-fb46-11e1-82cb-f4ce4684ea4c", "sr_id": "sr:team:3436", "reference": "1610612751"}, "on_court": {"home": {"name": "Knicks", "market": "New York", "id": "583ec70e-fb46-11e1-82cb-f4ce4684ea4c", "sr_id": "sr:team:3421", "reference": "1610612752", "players": [{"full_name": "RJ Barrett", "jersey_number": "9", "id": "42c5f009-dfd2-4a94-a750-0943791e138e", "sr_id": "sr:player:1770168", "reference": "1629628"}, {"full_name": "Jalen Brunson", "jersey_number": "11", "id": "8d3edba8-9004-4199-9328-cc2208e8b0d3", "sr_id": "sr:player:1159266", "reference": "1628973"}, {"full_name": "Julius Randle", "jersey_number": "30", "id": "24c85a15-686e-4161-934b-40948188fa36", "sr_id": "sr:player:608108", "reference": "203944"}, {"full_name": "Mitchell Robinson", "jersey_number": "23", "id": "97e25d19-7c5c-49f9-91c0-5cd9b9cebdcb", "sr_id": "sr:player:1478984", "reference": "1629011"}, {"full_name": "Quentin Grimes", "jersey_number": "6", "id": "e8486226-53e1-4857-b5f0-8b6fe88bdd8a", "sr_id": "sr:player:1491436", "reference": "1629656"}]}, "away": {"name": "Nets", "market": "Brooklyn", "id": "583ec9d6-fb46-11e1-82cb-f4ce4684ea4c", "sr_id": "sr:team:3436", "reference": "1610612751", "players": [{"full_name": "Cameron Johnson", "jersey_number": "2", "id": "2fe5b749-7bd4-4dbc-acfb-6d675f541e37", "sr_id": "sr:player:1770162", "reference": "1629661"}, {"full_name": "Nic Claxton", "jersey_number": "33", "id": "288519dc-eae8-4daf-afa4-c86322b7460e", "sr_id": "sr:player:1307666", "reference": "1629651"}, {"full_name": "Spencer Dinwiddie", "jersey_number": "26", "id": "8426ab40-888d-4f89-976c-cd9e23717653", "sr_id": "sr:player:607360", "reference": "203915"}, {"full_name": "Dorian Finney-Smith", "jersey_number": "28", "id": "426357dc-340b-4d89-8e3b-7d6b4022b460", "sr_id": "sr:player:1050927", "reference": "1627827"}, {"full_name": "Mikal Bridges", "jersey_number": "1", "id": "83cc5b28-36a7-43ba-ba20-8347bd1de583", "sr_id": "sr:player:1176834", "reference": "1628969"}]}}}, {"id": "1335f54e-1aca-42e7-8b5e-46e14875032a", "clock": "12:00", "updated": "2023-03-02T00:41:41Z", "description": "Knicks lineup change (RJ Barrett, Jalen 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