A basketball shot chart overlays shooting data on a half-court diagram, plotting each shot attempt as a point colored by outcome (made or missed). The court drawing includes the three-point arc, free-throw line, paint/key area, and basket, providing spatial context for analyzing shooting patterns and efficiency. Essential for basketball analytics and player evaluation.
""" anyplot.ai
scatter-shot-chart: Basketball Shot Chart
Library: altair 6.2.1 | Python 3.13.14
Quality: 90/100 | Updated: 2026-06-21
"""
import os
import altair as alt
import numpy as np
import pandas as pd
from PIL import Image
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint palette — semantic exception: Made=green (#009E73), Missed=matte-red (#AE3030)
MADE_COLOR = "#009E73"
MISSED_COLOR = "#AE3030"
# Data — realistic NBA half-court shot attempts
np.random.seed(42)
close_angles = np.random.uniform(0.15, np.pi - 0.15, 100)
close_dist = np.random.uniform(1.5, 8, 100)
mid_angles = np.random.uniform(0.2, np.pi - 0.2, 100)
mid_dist = np.random.uniform(8, 22, 100)
three_angles = np.random.uniform(0.35, np.pi - 0.35, 80)
three_dist = np.random.uniform(23.5, 27, 80)
ft_angles = np.random.uniform(np.pi / 2 - 0.08, np.pi / 2 + 0.08, 20)
ft_dist = np.full(20, 13.75) + np.random.normal(0, 0.3, 20)
shot_x = np.concatenate(
[
close_dist * np.cos(close_angles),
mid_dist * np.cos(mid_angles),
three_dist * np.cos(three_angles),
ft_dist * np.cos(ft_angles),
]
)
shot_y = np.concatenate(
[
close_dist * np.sin(close_angles),
mid_dist * np.sin(mid_angles),
three_dist * np.sin(three_angles),
ft_dist * np.sin(ft_angles),
]
)
shot_type = ["2-pointer"] * 200 + ["3-pointer"] * 80 + ["free-throw"] * 20
make_probs = np.concatenate([np.full(100, 0.55), np.full(100, 0.40), np.full(80, 0.35), np.full(20, 0.80)])
made = np.random.binomial(1, make_probs).astype(bool)
shots_df = pd.DataFrame(
{
"shot_x": np.clip(shot_x, -24.5, 24.5),
"shot_y": np.clip(shot_y, -4, 40),
"result": np.where(made, "Made", "Missed"),
"shot_type": shot_type,
}
)
# Court geometry (NBA half-court, basket at origin) — flat rows for Altair line mark
theta_ft = np.linspace(0, np.pi, 60)
theta_3 = np.linspace(np.arccos(22 / 23.75), np.pi - np.arccos(22 / 23.75), 100)
theta_ra = np.linspace(0, np.pi, 40)
theta_b = np.linspace(0, 2 * np.pi + 0.1, 40)
theta_cc = np.linspace(np.pi, 2 * np.pi, 40)
corner_y = np.sqrt(23.75**2 - 22**2)
segments = [
([-25, -25], [-5.25, 41.75], "sideline_l"),
([25, 25], [-5.25, 41.75], "sideline_r"),
([-25, 25], [-5.25, -5.25], "baseline"),
([-25, 25], [41.75, 41.75], "halfcourt"),
([-8, -8], [-5.25, 13.75], "paint_l"),
([8, 8], [-5.25, 13.75], "paint_r"),
([-8, 8], [13.75, 13.75], "ft_line"),
(6 * np.cos(theta_ft), 13.75 + 6 * np.sin(theta_ft), "ft_circle"),
([-22, -22], [-5.25, corner_y], "corner3_l"),
([22, 22], [-5.25, corner_y], "corner3_r"),
(23.75 * np.cos(theta_3), 23.75 * np.sin(theta_3), "three_arc"),
(4 * np.cos(theta_ra), 4 * np.sin(theta_ra), "restricted"),
(0.75 * np.cos(theta_b), 0.75 * np.sin(theta_b), "basket"),
([-3, 3], [-1.0, -1.0], "backboard"),
(6 * np.cos(theta_cc), 41.75 + 6 * np.sin(theta_cc), "center_circle"),
]
court_rows = []
for xs, ys, seg_name in segments:
for i, (xi, yi) in enumerate(zip(xs, ys, strict=True)):
court_rows.append({"cx": float(xi), "cy": float(yi), "seg": seg_name, "ord": i})
court_df = pd.DataFrame(court_rows)
# Zone shooting percentages
paint_mask = (shots_df["shot_y"] < 13.75) & (shots_df["shot_x"].abs() < 8) & (shots_df["shot_type"] != "free-throw")
mid_mask = (shots_df["shot_type"] == "2-pointer") & ~((shots_df["shot_y"] < 13.75) & (shots_df["shot_x"].abs() < 8))
three_mask = shots_df["shot_type"] == "3-pointer"
paint_pct = int(100 * shots_df.loc[paint_mask, "result"].eq("Made").mean())
mid_pct = int(100 * shots_df.loc[mid_mask, "result"].eq("Made").mean())
three_pct = int(100 * shots_df.loc[three_mask, "result"].eq("Made").mean())
total_fg = int(100 * shots_df["result"].eq("Made").mean())
zone_df = pd.DataFrame(
[
{"label": f"Paint: {paint_pct}%", "zx": 0.0, "zy": 6.0},
{"label": f"Mid-Range: {mid_pct}%", "zx": 0.0, "zy": 20.0},
{"label": f"3PT: {three_pct}%", "zx": 0.0, "zy": 30.0},
]
)
# Shared scales — equal 52-unit domain on both axes for undistorted 1:1 court
x_scale = alt.Scale(domain=[-26, 26], nice=False)
y_scale = alt.Scale(domain=[-7, 45], nice=False)
TITLE = "scatter-shot-chart · python · altair · anyplot.ai"
# Court lines layer
court = (
alt.Chart(court_df)
.mark_line(strokeWidth=1.5, color=INK_SOFT)
.encode(
x=alt.X("cx:Q", scale=x_scale, axis=None),
y=alt.Y("cy:Q", scale=y_scale, axis=None),
detail="seg:N",
order="ord:Q",
)
)
# Shot markers layer
shots = (
alt.Chart(shots_df)
.mark_point(filled=True, size=55, opacity=0.6, strokeWidth=0.5, stroke=PAGE_BG)
.encode(
x=alt.X("shot_x:Q", scale=x_scale, axis=None),
y=alt.Y("shot_y:Q", scale=y_scale, axis=None),
color=alt.Color(
"result:N",
scale=alt.Scale(domain=["Made", "Missed"], range=[MADE_COLOR, MISSED_COLOR]),
legend=alt.Legend(
title="Shot Result", titleFontSize=14, labelFontSize=12, symbolSize=120, orient="top-right", offset=8
),
),
shape=alt.Shape("result:N", scale=alt.Scale(domain=["Made", "Missed"], range=["circle", "cross"]), legend=None),
tooltip=[
alt.Tooltip("shot_type:N", title="Shot Type"),
alt.Tooltip("result:N", title="Result"),
alt.Tooltip("shot_x:Q", title="X (ft)", format=".1f"),
alt.Tooltip("shot_y:Q", title="Y (ft)", format=".1f"),
],
)
)
# Zone percentage labels
zones = (
alt.Chart(zone_df)
.mark_text(fontSize=13, fontWeight="bold", color=INK_MUTED, opacity=0.85)
.encode(x=alt.X("zx:Q", scale=x_scale, axis=None), y=alt.Y("zy:Q", scale=y_scale, axis=None), text="label:N")
)
# Compose with theme-adaptive chrome
chart = (
(court + shots + zones)
.properties(
width=500,
height=500,
background=PAGE_BG,
title=alt.Title(
TITLE,
fontSize=16,
color=INK,
subtitle=f"NBA Player Shot Chart — 300 Attempts (FG {total_fg}%)",
subtitleFontSize=12,
subtitleColor=INK_SOFT,
subtitlePadding=6,
),
)
.interactive()
.configure_view(fill=PAGE_BG, strokeWidth=0)
.configure_title(color=INK)
.configure_legend(fillColor=ELEVATED_BG, strokeColor=INK_SOFT, labelColor=INK_SOFT, titleColor=INK)
)
# Save — pad to exact 2400×2400 (square, 1:1 court aspect ratio)
TW, TH = 2400, 2400
chart.save(f"plot-{THEME}.png", scale_factor=4.0)
_img = Image.open(f"plot-{THEME}.png").convert("RGB")
_w, _h = _img.size
if _w > TW or _h > TH:
raise SystemExit(
f"altair vl-convert produced {_w}×{_h}, exceeds target {TW}×{TH}. "
f"Shrink chart .properties(width=, height=) values and re-render."
)
if _w < TW or _h < TH:
_canvas = Image.new("RGB", (TW, TH), PAGE_BG)
_canvas.paste(_img, ((TW - _w) // 2, (TH - _h) // 2))
_canvas.save(f"plot-{THEME}.png")
chart.save(f"plot-{THEME}.html")
Part of Basketball Shot Chart on anyplot.ai.