A win probability chart shows how each team's likelihood of winning evolves over the course of a game. The line starts near 50% and fluctuates based on scoring events, ultimately reaching 100% or 0% at game end. The area above and below the 50% baseline is filled with team colors to convey momentum at a glance. This visualization is widely used across major sports for post-game analysis and live broadcasting.
""" anyplot.ai
line-win-probability: Win Probability Chart
Library: letsplot 4.10.1 | Python 3.13.14
Quality: 87/100 | Updated: 2026-06-21
"""
import os
import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot import ggsave
LetsPlot.setup_html()
THEME = os.getenv("ANYPLOT_THEME", "light")
# Theme-adaptive chrome tokens
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 categorical palette — first series always #009E73
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Team fills — semantic: home team (Eagles) → Imprint green, away (Cowboys) → Imprint blue
HOME_COLOR = IMPRINT_PALETTE[0] # #009E73 — Eagles/home
AWAY_COLOR = IMPRINT_PALETTE[2] # #4467A3 — Cowboys/away
# --- Data: simulated NFL game, Eagles vs Cowboys ---
np.random.seed(42)
total_plays = 120
play_number = np.arange(total_plays)
win_prob = np.zeros(total_plays)
win_prob[0] = 0.50
# Scoring events: (play_index, probability_shift, label)
events = [
(8, 0.12, "Eagles FG 3-0"),
(22, -0.15, "Cowboys TD 3-7"),
(35, 0.18, "Eagles TD 10-7"),
(52, 0.08, "Eagles FG 13-7"),
(68, -0.22, "Cowboys TD 13-14"),
(78, 0.15, "Eagles TD 20-14"),
(92, -0.10, "Cowboys FG 20-17"),
(105, 0.20, "Eagles TD 27-17"),
]
event_plays = {e[0]: e[1] for e in events}
for i in range(1, total_plays):
drift = 0.002 if i > 90 else 0.0
noise = np.random.normal(0, 0.02)
shift = event_plays.get(i, 0.0)
win_prob[i] = np.clip(win_prob[i - 1] + shift + noise + drift, 0.02, 0.98)
# Converge to Eagles win at the end
win_prob[-5:] = np.linspace(win_prob[-6], 0.95, 5)
win_prob[-1] = 0.97
df = pd.DataFrame(
{
"play": play_number,
"win_prob": win_prob,
"baseline": 0.5,
"above_50": np.maximum(win_prob, 0.5),
"below_50": np.minimum(win_prob, 0.5),
}
)
# Key events — alternating label offsets to avoid overlap
key_event_indices = [1, 2, 4, 5, 7]
nudge_directions = [-0.08, 0.07, -0.08, 0.07, -0.08]
key_events = pd.DataFrame(
{
"play": [events[i][0] for i in key_event_indices],
"win_prob": [win_prob[events[i][0]] for i in key_event_indices],
"label": [events[i][2] for i in key_event_indices],
"label_y": [win_prob[events[i][0]] + nudge_directions[j] for j, i in enumerate(key_event_indices)],
}
)
# --- Theme-adaptive chrome ---
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major_y=element_line(color=INK_MUTED, size=0.2),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
axis_title=element_text(color=INK, size=12),
axis_text=element_text(color=INK_SOFT, size=10),
axis_line=element_line(color=INK_SOFT),
panel_border=element_blank(),
plot_title=element_text(color=INK, size=16),
plot_subtitle=element_text(color=INK_SOFT, size=10),
plot_margin=[40, 60, 20, 20],
)
# --- Build plot ---
plot = (
ggplot(df, aes(x="play"))
# Area fills — Imprint palette, theme-constant data colors
+ geom_ribbon(
aes(ymin="baseline", ymax="above_50"),
fill=HOME_COLOR,
alpha=0.28,
)
+ geom_ribbon(
aes(ymin="below_50", ymax="baseline"),
fill=AWAY_COLOR,
alpha=0.28,
)
# Win probability line — theme-adaptive ink
+ geom_line(
aes(y="win_prob"),
color=INK,
size=1.0,
tooltips=layer_tooltips()
.line("Play @play")
.format("win_prob", ".0%")
.line("Win prob: @win_prob"),
)
# 50% reference line
+ geom_hline(yintercept=0.5, color=INK_SOFT, size=0.7, linetype="dashed")
# Quarter dividers
+ geom_vline(xintercept=30, color=INK_MUTED, size=0.5, linetype="dotted")
+ geom_vline(xintercept=60, color=INK_MUTED, size=0.5, linetype="dotted")
+ geom_vline(xintercept=90, color=INK_MUTED, size=0.5, linetype="dotted")
# Key event markers
+ geom_point(
data=key_events,
mapping=aes(x="play", y="win_prob"),
size=3.0,
color=INK,
fill=PAGE_BG,
shape=21,
stroke=1.5,
)
# Key event labels with elevated background
+ geom_label(
data=key_events,
mapping=aes(x="play", y="label_y", label="label"),
size=4,
color=INK,
fill=ELEVATED_BG,
alpha=0.92,
label_padding=0.3,
label_r=0.15,
label_size=0.3,
)
# Scales
+ scale_y_continuous(
breaks=[0.0, 0.25, 0.5, 0.75, 1.0], labels=["0%", "25%", "50%", "75%", "100%"]
)
+ coord_cartesian(ylim=[0.0, 1.05])
+ scale_x_continuous(
breaks=[0, 30, 60, 90, 120], labels=["Q1", "Q2", "Q3", "Q4", "End"]
)
+ labs(
x="Game Progress",
y="Eagles Win Probability",
title="line-win-probability · python · letsplot · anyplot.ai",
subtitle="Eagles 27 – Cowboys 17 · Eagles recover from Q3 deficit for convincing finish",
)
# Canvas: 800×450 × scale=4 → 3200×1800 px (landscape)
+ ggsize(800, 450)
+ theme_minimal()
+ anyplot_theme
)
# --- Save PNG + HTML ---
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Win Probability Chart on anyplot.ai.