🏁 SliderStats: The Ultimate Tool for Personalized NBA Statistics

🚀 About SliderStats

SliderStats is an all-in-one website that allows users to compare an unlimited number of stats between players. Users can weigh each stat, providing a completely customizable and objective approach to comparing performance metrics. Any basketball fan, ranging from the average casual to a full-time ESPN analyst, can benefit from SliderStats.

Unlike other basketball statistics websites that only show raw stats, SliderStats uses a weighted sum ranking to provide deeper insights into multiple stats between players within a season.

Visit our website: sliderstats.streamlit.app

🌟 Features

Multitude of Statistics

Per Game Stats: Analyze player and team performance on a per-game basis.
Total Stats: View cumulative statistics for players and teams.
Head-to-Head Comparison: Compare stats between two players or teams.
Clutch Stats: Examine performance during crucial moments of the game for specific players or teams.
Playoff Stats: Focus on player performance during the playoffs.

Trademark Sliders

Interactive Sliders: Use sliders to filter and explore data interactively.

Filtering

Year: Filter ranking by year.
Position: Filter ranking by position.
Statistics: Choose which statistics to use.
Range of Stats: Specify the range for each of your stats.

Player Rankings

After filtering and adjusting sliders, see the final rankings of players/teams and their stats.

🖥️ Code

For our data, we use an API from GitHub called nba_api which provides continuously updated data from the NBA's most current games and even games from decades before.

We use a variety of Python libraries, such as Pandas and Streamlit, to format and display the statistics. Below is a snippet of the code used to filter statistics.

for stat in selected_stats_per_game:
    sliders[stat] = st.sidebar.slider(f'Importance of {stat}', -1.0, 1.0, 0.5, 0.01)
st.sidebar.title("Range of Stats")
for stat in selected_stats_per_game:
    if stat in filtered_data_perGame:
        sliders_filter[stat] = st.sidebar.slider(f'Range of {stat}',
                                float(filtered_data_perGame[stat][0]),
                                float(filtered_data_perGame[stat][1]),
                                (float(filtered_data_perGame[stat][0]),
                                float(filtered_data_perGame[stat][1])))
 
# Exclude all of the rows that are not in the player's range
filtered_df = season_perGame_active_players.copy()
for col in season_perGame_active_players.columns[1:]:
    if col in sliders_filter:
        filtered_df = filtered_df[filtered_df[col].between(*sliders_filter[col])]
 
season_perGame_active_players = filtered_df
 
# Filter the DataFrame based on selected stats
filtered_data = season_perGame_active_players[selected_stats_per_game]
 
# Calculate weighted sum based on user-defined importance
normalized_dataPG = (filtered_data - filtered_data.min()) / (filtered_data.max() - filtered_data.min())
# Calculate weighted sum based on user-defined importance
weighted_totalsPG = normalized_dataPG * pd.Series(sliders)
 
# Rounding for all per game stats
season_perGame_active_players[columns_to_keepPG] = season_perGame_active_players[columns_to_keepPG].round(decimals=2)
 
# Calculate the total weighted sum for each player
season_perGame_active_players['Weighted_Sum'] = weighted_totalsPG.sum(axis=1)
# Display sorted players based on weighted sum
sorted_playersPG = season_perGame_active_players.sort_values(by=['Weighted_Sum', 'PLAYER_NAME'], ascending=[False, True])
 
if drop_unused_stats:
    sorted_playersPG = pd.concat([sorted_playersPG['PLAYER_NAME'], sorted_playersPG[selected_stats_per_game], sorted_playersPG['Weighted_Sum']], axis = 1)
 
st.write("Season Per Game Stats (Regular Season) for Active NBA Players:")
st.write(sorted_playersPG)

Technologies

Python
Pandas
Streamlit
nba_api

Contributors

Below are the key roles and team members involved in this project:

Project Managers: Juan Diaz and Jason Yang
Project Members: Alex Qi and Ryan Uyeki
Media Members: Ravinit Chand and Yaelle Kretchmer
Business Member(s): Aashritha Javvaji