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: plotnine 0.15.7 | Python 3.13.14
Quality: 89/100 | Updated: 2026-06-21
"""
import os
import sys
# Prevent this file from shadowing the plotnine library
sys.path = [p for p in sys.path if os.path.abspath(p or ".") != os.path.dirname(os.path.abspath(__file__))]
import numpy as np
import pandas as pd
from plotnine import (
aes,
annotate,
coord_cartesian,
element_blank,
element_line,
element_rect,
element_text,
geom_hline,
geom_line,
geom_point,
geom_rect,
geom_ribbon,
geom_segment,
geom_text,
ggplot,
labs,
scale_alpha_identity,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
# 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"
ANYPLOT_AMBER = "#DDCC77"
# Data
np.random.seed(42)
n_plays = 130
plays = np.arange(n_plays)
win_prob = np.zeros(n_plays)
win_prob[0] = 0.50
scoring_plays = {
12: ("FG Home", 0.10),
28: ("TD Away", -0.18),
42: ("TD Home", 0.22),
55: ("FG Away", -0.08),
68: ("TD Home", 0.15),
82: ("TD Away", -0.20),
95: ("FG Home", 0.12),
110: ("TD Home", 0.16),
122: ("FG Away", -0.05),
}
events = {}
for i in range(1, n_plays):
drift = np.random.normal(0, 0.012)
if i in scoring_plays:
label, shift = scoring_plays[i]
win_prob[i] = win_prob[i - 1] + shift + drift
events[i] = label
else:
win_prob[i] = win_prob[i - 1] + drift
win_prob = np.clip(win_prob, 0.04, 0.96)
for i in range(n_plays - 8, n_plays):
t = (i - (n_plays - 8)) / 7.0
win_prob[i] = win_prob[n_plays - 9] * (1 - t) + 0.78 * t
home_fill = np.maximum(win_prob, 0.5)
away_fill = np.minimum(win_prob, 0.5)
df = pd.DataFrame({"play": plays, "win_prob": win_prob})
df_home = pd.DataFrame({"play": plays, "ymin": 0.5, "ymax": home_fill, "team": "Eagles (Home)"})
df_away = pd.DataFrame({"play": plays, "ymin": away_fill, "ymax": 0.5, "team": "Cowboys (Away)"})
df_ribbon = pd.concat([df_home, df_away], ignore_index=True)
event_df = pd.DataFrame(
{"play": list(events.keys()), "win_prob": [win_prob[p] for p in events.keys()], "label": list(events.values())}
)
# Annotation positioning: alternate offset direction for closely-spaced events
sorted_plays = sorted(events.keys())
y_offsets = {}
for i, play in enumerate(sorted_plays):
prob = win_prob[play]
base = 0.12 if prob > 0.5 else -0.12
if i > 0:
prev_play = sorted_plays[i - 1]
if abs(play - prev_play) < 22:
prev_y = y_offsets[prev_play]
if (prev_y > win_prob[prev_play]) == (base > 0):
base = -base
y_offsets[play] = float(np.clip(prob + base, 0.07, 0.93))
event_df["label_y"] = [y_offsets[p] for p in event_df["play"]]
highlight_df = pd.DataFrame({"xmin": [104], "xmax": [116], "ymin": [0.50], "ymax": [0.96], "alpha": [0.06]})
quarter_df = pd.DataFrame({"x": [32, 65, 97], "ymin": [0.0] * 3, "ymax": [1.0] * 3})
# Plot
quarter_breaks = [0, 32, 65, 97, 129]
quarter_labels = ["Kickoff", "Q2", "Halftime", "Q4", "Final"]
title = "line-win-probability · python · plotnine · anyplot.ai"
title_fontsize = round(12 * (67 / len(title))) if len(title) > 67 else 12
plot = (
ggplot()
# Decisive moment highlight zone (golden background)
+ geom_rect(
aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax", alpha="alpha"),
data=highlight_df,
fill=ANYPLOT_AMBER,
inherit_aes=False,
)
+ scale_alpha_identity()
# Team-colored area fills above/below 50%
+ geom_ribbon(aes(x="play", ymin="ymin", ymax="ymax", fill="team"), data=df_ribbon, alpha=0.35)
# 50% reference line
+ geom_hline(yintercept=0.5, color=INK_MUTED, size=0.6, linetype="dashed")
# Quarter boundary markers
+ geom_segment(
aes(x="x", xend="x", y="ymin", yend="ymax"),
data=quarter_df,
color=INK_MUTED,
size=0.4,
linetype="dotted",
inherit_aes=False,
)
# Win probability trace
+ geom_line(aes(x="play", y="win_prob"), data=df, color=INK, size=1.2)
# Scoring event markers
+ geom_point(aes(x="play", y="win_prob"), data=event_df, color=INK, fill=ELEVATED_BG, size=4, stroke=0.8, shape="o")
# Event annotation labels (size in mm; 4mm ≈ 11pt — visibly larger than tick labels)
+ geom_text(aes(x="play", y="label_y", label="label"), data=event_df, size=4, fontweight="bold", color=INK_SOFT)
# Scales
+ scale_fill_manual(values={"Eagles (Home)": "#009E73", "Cowboys (Away)": "#AE3030"})
+ scale_x_continuous(breaks=quarter_breaks, labels=quarter_labels, expand=(0.03, 2))
+ scale_y_continuous(
labels=lambda lst: [f"{int(v * 100)}%" for v in lst], limits=(0, 1), breaks=[0, 0.25, 0.5, 0.75, 1.0]
)
+ coord_cartesian(xlim=(-2, 134))
+ labs(x="Game Progression", y="Home Win Probability", title=title, fill="")
# Final score callout box
+ annotate(
"label",
x=10,
y=0.06,
label="Final: Eagles 24 – Cowboys 17",
size=3.5,
fill=ELEVATED_BG,
color=INK,
fontweight="bold",
label_padding=0.5,
)
# Theme — canvas 8×4.5 in @ 400 dpi → 3200×1800 px
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
plot_title=element_text(size=title_fontsize, weight="bold", color=INK),
axis_title_x=element_text(size=10, color=INK),
axis_title_y=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=8, color=INK),
legend_position="top",
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT, size=0.3),
legend_key_size=14,
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.15),
axis_line=element_line(color=INK_SOFT, size=0.3),
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Win Probability Chart on anyplot.ai.