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feat(pygal): implement line-win-probability (#5099)
## Implementation: `line-win-probability` - pygal Implements the **pygal** version of `line-win-probability`. **File:** `plots/line-win-probability/implementations/pygal.py` **Parent Issue:** #4418 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23342814683)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/line-win-probability/implementations/pygal.py‎

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""" pyplots.ai
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line-win-probability: Win Probability Chart
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Library: pygal 3.1.0 | Python 3.14.3
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Quality: 84/100 | Created: 2026-03-20
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"""
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import numpy as np
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import pygal
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from pygal.style import Style
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# Data - Simulated NFL game: Eagles vs Cowboys
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np.random.seed(42)
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plays = 120
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# Key waypoints: (play, win_probability)
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waypoints = [
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(0, 0.50),
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(15, 0.38), # DAL FG (3-0)
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(32, 0.62), # PHI TD (7-3)
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(48, 0.35), # DAL TD (10-7)
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(60, 0.40), # Halftime drift
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(72, 0.65), # PHI TD (14-10)
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(85, 0.74), # PHI FG (17-10)
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(95, 0.45), # DAL TD (17-17)
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(112, 0.88), # PHI TD (24-17)
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(120, 0.97), # Final
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]
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scoring_events = {
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15: "DAL FG\n3-0",
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32: "PHI TD\n7-3",
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48: "DAL TD\n10-7",
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72: "PHI TD\n14-10",
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85: "PHI FG\n17-10",
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95: "DAL TD\n17-17",
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112: "PHI TD\n24-17",
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}
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# Generate smooth win probability by interpolating between waypoints with noise
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win_pct = np.zeros(plays + 1)
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for i in range(len(waypoints) - 1):
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p1, v1 = waypoints[i]
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p2, v2 = waypoints[i + 1]
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n = p2 - p1
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t = np.linspace(0, 1, n, endpoint=False)
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# Smooth interpolation with slight S-curve
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interp = v1 + (v2 - v1) * (3 * t**2 - 2 * t**3)
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noise = np.random.normal(0, 0.012, n) * (1 - 0.7 * t) # less noise near events
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win_pct[p1:p2] = np.clip(interp + noise, 0.02, 0.98)
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win_pct[plays] = 0.97
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# Snap scoring event values exactly
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for play, _ in scoring_events.items():
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for p, v in waypoints:
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if p == play:
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win_pct[play] = v
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# Convert to percentage
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win_pct_list = [round(float(p) * 100, 1) for p in win_pct]
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# Split fills: Eagles area above 50%, Cowboys area below 50%
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# This avoids the muddy overlap from both series filling from 0
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eagles_above = [max(pct, 50.0) for pct in win_pct_list] # Clamp at 50 minimum
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cowboys_below = [min(pct, 50.0) for pct in win_pct_list] # Clamp at 50 maximum
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# Custom style - Eagles green (#00843D) vs Cowboys blue (#003594) for high contrast
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custom_style = Style(
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background="white",
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plot_background="white",
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foreground="#2d2d2d",
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foreground_strong="#2d2d2d",
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foreground_subtle="#e0e0e0",
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colors=("#003594", "#00843D", "#003594", "#00843D", "#333333", "#c0392b"),
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font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
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title_font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
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title_font_size=56,
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label_font_size=34,
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major_label_font_size=42,
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value_font_size=28,
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legend_font_size=34,
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legend_font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
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label_font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
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major_label_font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
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value_font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
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opacity=0.50,
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opacity_hover=0.65,
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guide_stroke_color="#e0e0e0",
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guide_stroke_dasharray="3,3",
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major_guide_stroke_color="#cccccc",
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major_guide_stroke_dasharray="6,3",
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stroke_opacity=1.0,
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stroke_opacity_hover=1.0,
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tooltip_font_size=28,
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tooltip_font_family="DejaVu Sans, Helvetica, Arial, sans-serif",
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tooltip_border_radius=8,
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)
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# Chart
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chart = pygal.Line(
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width=4800,
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height=2700,
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title="Eagles vs Cowboys (24-17) \u00b7 line-win-probability \u00b7 pygal \u00b7 pyplots.ai",
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x_title="Game Progression",
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y_title="Win Probability (%)",
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style=custom_style,
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fill=False,
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show_dots=False,
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stroke_style={"width": 4},
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show_y_guides=True,
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show_x_guides=False,
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show_legend=True,
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legend_at_bottom=True,
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legend_box_size=28,
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value_formatter=lambda x: f"{x:.0f}%",
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range=(0, 100),
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min_scale=5,
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max_scale=10,
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margin_bottom=100,
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margin_left=100,
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margin_right=60,
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margin_top=60,
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spacing=12,
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tooltip_border_radius=8,
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tooltip_fancy_mode=True,
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show_minor_x_labels=True,
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x_label_rotation=45,
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)
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# Series 1: Cowboys fill (constant 50% line, fills 0-50 in blue) — legend entry
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chart.add("DAL Cowboys", [50.0] * len(win_pct_list), fill=True, show_dots=False, stroke_style={"width": 0})
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# Series 2: Eagles fill (clamped above 50%, fills green above the 50% line) — legend entry
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chart.add("PHI Eagles", eagles_above, fill=True, show_dots=False, stroke_style={"width": 0})
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# Series 3: Cowboys advantage area (clamped below 50%) — hidden from legend
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cowboys_below_data = [{"value": v, "label": ""} for v in cowboys_below]
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chart.add(None, cowboys_below_data, fill=True, show_dots=False, stroke_style={"width": 0})
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# Series 4: Win probability line (Eagles green, on top) — hidden from legend
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chart.add(None, win_pct_list, fill=False, show_dots=False, stroke_style={"width": 5})
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# Series 5: 50% baseline reference line
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baseline = [50] * len(win_pct_list)
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chart.add("50% Line", baseline, fill=False, show_dots=False, stroke_style={"width": 9, "dasharray": "20, 8"})
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# Series 6: Scoring event markers with tooltip labels
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event_series = [None] * len(win_pct_list)
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for idx, label in scoring_events.items():
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event_series[idx] = {"value": win_pct_list[idx], "label": label}
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chart.add("Scoring Events", event_series, fill=False, show_dots=True, dots_size=24, stroke=False)
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# X-axis labels: quarter markers + scoring event annotations
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label_map = {0: "Kickoff", 30: "Q2", 60: "Halftime", 90: "Q4", 120: "Final"}
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# Add scoring event short labels to x-axis for visible annotations in PNG
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scoring_labels = {
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15: "DAL FG 3-0",
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32: "PHI TD 7-3",
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48: "DAL TD 10-7",
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72: "PHI TD 14-10",
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85: "PHI FG 17-10",
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95: "DAL TD 17-17",
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112: "PHI TD 24-17",
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}
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label_map.update(scoring_labels)
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chart.x_labels = [label_map.get(i, "") for i in range(plays + 1)]
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chart.x_labels_major = list(label_map.values())
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chart.truncate_label = -1
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# Save
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chart.render_to_file("plot.html")
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chart.render_to_png("plot.png")

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