Building an Analytics Dashboard

Dashboard Design Principles

Effective analytics dashboards transform complex data into actionable insights. In basketball contexts, dashboards serve diverse users: coaches need quick pregame opponent summaries, executives want season-long roster evaluation views, and scouts require prospect comparison tools. This chapter guides you through building dashboards that serve these varied needs.

Choosing Your Stack

Dashboard technology choices depend on your use case. Python's Streamlit and Dash libraries enable rapid prototyping with full customization. R's Shiny framework integrates seamlessly with tidyverse workflows. For production deployments, consider Tableau, Power BI, or custom web applications. Each offers tradeoffs between development speed, customization, and maintenance burden.

# Simple Streamlit dashboard example
import streamlit as st
import pandas as pd
import plotly.express as px

# Page configuration
st.set_page_config(page_title="NBA Analytics Dashboard", layout="wide")

# Load data
@st.cache_data
def load_data():
    return pd.read_csv("player_stats.csv")

players = load_data()

# Sidebar filters
st.sidebar.header("Filters")
teams = st.sidebar.multiselect(
    "Select Teams",
    options=players["team"].unique(),
    default=players["team"].unique()[:5]
)
min_minutes = st.sidebar.slider("Minimum Minutes", 0, 3000, 1000)

# Filter data
filtered = players[
    (players["team"].isin(teams)) &
    (players["minutes"] >= min_minutes)
]

# Main content
st.title("NBA Player Analytics Dashboard")

# Key metrics row
col1, col2, col3 = st.columns(3)
col1.metric("Players", len(filtered))
col2.metric("Avg PPG", f"{filtered['ppg'].mean():.1f}")
col3.metric("Avg TS%", f"{filtered['ts_pct'].mean():.1%}")

# Scatter plot
fig = px.scatter(
    filtered,
    x="usg_pct",
    y="ts_pct",
    size="ppg",
    color="team",
    hover_name="player_name",
    title="Usage vs Efficiency"
)
st.plotly_chart(fig, use_container_width=True)

# Data table
st.dataframe(
    filtered[["player_name", "team", "ppg", "ts_pct", "bpm"]].sort_values("bpm", ascending=False)
)

Building with R Shiny

# R Shiny dashboard example
library(shiny)
library(tidyverse)
library(plotly)

# Load data
players <- read_csv("player_stats.csv")

# UI
ui <- fluidPage(
  titlePanel("NBA Player Analytics Dashboard"),

  sidebarLayout(
    sidebarPanel(
      selectInput("teams", "Select Teams",
                  choices = unique(players$team),
                  selected = unique(players$team)[1:5],
                  multiple = TRUE),
      sliderInput("min_minutes", "Minimum Minutes",
                  min = 0, max = 3000, value = 1000)
    ),

    mainPanel(
      fluidRow(
        valueBoxOutput("player_count"),
        valueBoxOutput("avg_ppg"),
        valueBoxOutput("avg_ts")
      ),
      plotlyOutput("scatter_plot"),
      DT::dataTableOutput("player_table")
    )
  )
)

# Server
server <- function(input, output) {

  filtered <- reactive({
    players %>%
      filter(team %in% input$teams,
             minutes >= input$min_minutes)
  })

  output$scatter_plot <- renderPlotly({
    plot_ly(filtered(), x = ~usg_pct, y = ~ts_pct,
            size = ~ppg, color = ~team,
            text = ~player_name, type = "scatter", mode = "markers")
  })

  output$player_table <- DT::renderDataTable({
    filtered() %>%
      select(player_name, team, ppg, ts_pct, bpm) %>%
      arrange(desc(bpm))
  })
}

shinyApp(ui, server)

Dashboard Components for Basketball

Effective basketball dashboards include several key component types: summary metrics (KPIs like team net rating), comparison visualizations (player scatter plots, team rankings), trend charts (performance over time), and detail tables (sortable player/game data). Layout should guide users from high-level insights to detailed drill-downs.

Performance Optimization

Dashboard responsiveness matters. Strategies include data caching, pre-aggregation of common queries, pagination for large tables, and lazy loading of visualizations. For production dashboards, consider database connections rather than flat files, and implement proper error handling.

Deployment Considerations

Local dashboards work for individual analysis but team sharing requires deployment. Streamlit Cloud, Shinyapps.io, and Heroku offer free tiers for prototypes. Enterprise deployments typically use internal servers with authentication, version control integration, and scheduled data refreshes.

Key Takeaways

• Choose technology based on audience, customization needs, and maintenance capacity
• Design for your users'specific decision-making needs
• Include summary, comparison, trend, and detail components
• Optimize performance through caching and pre-aggregation
• Plan deployment strategy from the beginning