Lineups.py

# Required imports
import numpy as np
import pandas as pd
import os
import streamlit as st
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Rectangle, Arc
from nba_api.stats.endpoints import shotchartdetail
import json
from pathlib import Path
import requests

mavs_pbp_season = pd.read_parquet(str(Path.cwd()) + "/data/mavs_pbp_season.parquet")
all_players_data = pd.read_parquet(str(Path.cwd()) + "/data/player_data.parquet")

mavs_players_names = all_players_data[all_players_data['team'] == 'DAL']

teams_json = json.loads(requests.get('https://raw.githubusercontent.com/bttmly/nba/master/data/teams.json').text)
all_teams = {}
for team in teams_json:
    all_teams[team['teamName']] = team['teamId']
del all_teams['Dallas Mavericks']

lineups = pd.read_parquet(str(Path.cwd()) + "/data/lineups.parquet")

# Get the position of a given player
def get_position(player_name):
    positions = all_players_data[all_players_data['name'] == player_name].reset_index()
    unique_pos = list(positions['startPos'].unique())
    counts = {}
    for pos in unique_pos:
        counts[pos] = len(all_players_data[all_players_data['startPos'] == pos])
    if "" in counts:
        del counts[""]
    max_count = 0
    max_pos = ""
    for pos in counts:
        if max_count < counts[pos]:
            max_count = counts[pos]
            max_pos = pos
    
    return max_pos

def get_team_id(team_name):
    return all_teams[team_name]

def get_team_abbrev(team_id):
    all_player_data = all_players_data[all_players_data['nbaTeamId'] == str(team_id)].reset_index()
    return all_player_data.at[0, 'team']

# Define function for the first tab
def Lineups():
    st.header("Lineups")

    teams = list(all_teams.keys())
    selected_team = st.selectbox("Select an opponent to create possible roster against", teams, index=0)
    st.markdown("Optimal lineups against: **{0}** (click to change)".format(selected_team))

    team_abbrev = get_team_abbrev(int(get_team_id(selected_team)))

    team_lineups = lineups[lineups['team'] == team_abbrev].reset_index()
    filtered_lineups = pd.DataFrame(columns=['C', 'PF', 'SG', 'PG', 'SF', 'play_type', 'offense_strength', 'defense_strength'])
    for i in range(len(team_lineups.index)):
        lineup = team_lineups.at[i,'lineup']
        lineup_pos = {'C': [], 'PF': [], 'SG': [], 'PG': [], 'SF': []}
        for player in lineup:
            lineup_pos[get_position(player)].append(player)
        check = True
        updated_lineup = {}
        for pos in lineup_pos:
            if len(lineup_pos[pos]) == 0:
                check = False
        #st.write(updated_lineup)
        if check:
            for pos in lineup_pos:
                if len(lineup_pos[pos]) == 1:
                    updated_lineup[pos] = lineup_pos[pos][0]
                elif "Luka Doncic" in lineup_pos[pos]:
                    updated_lineup[pos] = "Luka Doncic"
                elif "Kyrie Irving" in lineup_pos[pos]:
                    updated_lineup[pos] = "Kyrie Irving"
                else:
                    updated_lineup[pos] = lineup_pos[pos][0]
            new_lineup = [updated_lineup['C'], updated_lineup['PF'], updated_lineup['SG'], updated_lineup['PG'], updated_lineup['SF']]
            new_lineup.append(team_lineups.at[i,'play_type'])
            new_lineup.append(team_lineups.at[i,'offense_strength'])
            new_lineup.append(team_lineups.at[i,'defense_strength'])
            
            filtered_lineups.loc[len(filtered_lineups.index)] = new_lineup
    filtered_lineups.drop_duplicates(inplace=True)
    filtered_lineups.set_index('play_type', inplace=True)
    st.dataframe(filtered_lineups)

    # Metric information
    play_col, offense_col, defense_col = st.columns(3)

    if len(filtered_lineups.index) > 0:
        play_col.metric(":green[**Play Type**]", list(filtered_lineups.index)[0])
    offense_col.metric(":green[**Offensive Strength Average**]", "{:.3f}".format(filtered_lineups['offense_strength'].mean()))
    defense_col.metric(":green[**Defensive Strength Average**]", "{:.3f}".format(lineups['defense_strength'].mean()))