Spaces:
Sleeping
Sleeping
File size: 12,145 Bytes
080efc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 | """
chart_builder.py
================
Plotly figure factories for the MLB Pregame Dashboard.
All functions return go.Figure objects with a consistent dark theme.
Keep figures compact and information-dense — these live in a tight UI.
"""
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from data_fetcher import LEAGUE_AVG, TEAM_COLORS
# ── Shared theme ──────────────────────────────────────────────────────────────
BG = "#0f1117"
BG_PAPER = "#181c27"
GRID = "#2a2f3e"
TEXT = "#d4d8e8"
ACCENT = "#4a9eff"
LAYOUT_BASE = dict(
paper_bgcolor=BG_PAPER,
plot_bgcolor=BG,
font=dict(color=TEXT, size=11, family="JetBrains Mono, monospace"),
margin=dict(l=36, r=12, t=28, b=28),
showlegend=False,
)
# Color scale for xwOBA: red (bad) → gray (avg) → green (good)
# For pitchers "good" = LOW xwOBA; for hitters "good" = HIGH xwOBA.
def _xwoba_color(val: float, pitcher: bool = False) -> str:
"""Return a hex color relative to league average xwOBA."""
avg = LEAGUE_AVG["xwOBA"]
diff = val - avg
if pitcher:
diff = -diff # for pitchers, lower xwOBA is better
# clamp to ±0.06
diff = max(-0.06, min(0.06, diff))
if diff > 0:
# green spectrum
g = int(180 + diff / 0.06 * 75)
return f"rgb(60,{g},80)"
else:
# red spectrum
r = int(180 + abs(diff) / 0.06 * 75)
return f"rgb({r},60,60)"
def _kpct_color(val: float, pitcher: bool = False) -> str:
avg = LEAGUE_AVG["K_pct"]
diff = val - avg
if pitcher:
diff = -diff
diff = max(-0.08, min(0.08, diff))
return _gradient_color(diff)
def _bbpct_color(val: float, pitcher: bool = False) -> str:
avg = LEAGUE_AVG["BB_pct"]
diff = val - avg
if pitcher:
diff = -diff
diff = max(-0.05, min(0.05, diff))
return _gradient_color(diff)
def _gradient_color(diff: float) -> str:
if diff > 0:
g = int(160 + diff / 0.08 * 75)
return f"rgb(60,{g},80)"
else:
r = int(160 + abs(diff) / 0.08 * 75)
return f"rgb({r},60,60)"
# ══════════════════════════════════════════════════════════════════════════════
# PITCHER — INNING DISTRIBUTION (column chart)
# ══════════════════════════════════════════════════════════════════════════════
def make_innings_chart(game_log_df: pd.DataFrame) -> go.Figure:
"""
Bar chart: last-inning-pitched per start.
X = start date (abbreviated), Y = last full inning pitched.
Color = deeper into game → more blue.
"""
if game_log_df.empty:
return _empty_figure("No inning data")
df = game_log_df.copy()
df["last_inn"] = df["IP"].apply(lambda x: int(x)) # floor innings
# Format dates compactly
df["label"] = pd.to_datetime(df["date"]).dt.strftime("%-m/%-d")
colors = [
f"rgb({max(40, 180 - v * 18)},{max(40, 160 - v * 14)},{min(255, 100 + v * 22)})"
for v in df["last_inn"]
]
fig = go.Figure(go.Bar(
x=df["label"],
y=df["last_inn"],
marker_color=colors,
marker_line_width=0,
hovertemplate="<b>%{x}</b><br>Inning: %{y}<extra></extra>",
))
fig.update_layout(
**LAYOUT_BASE,
title=dict(text="Last Inning Pitched (recent starts)", x=0.02,
font=dict(size=11, color="#8899bb")),
xaxis=dict(gridcolor=GRID, showgrid=False, tickfont=dict(size=9)),
yaxis=dict(gridcolor=GRID, range=[0, 10], dtick=1,
title=dict(text="Inn", font=dict(size=9))),
height=175,
)
return fig
def make_pitch_count_chart(game_log_df: pd.DataFrame) -> go.Figure:
"""
Bar chart of pitch counts per start + horizontal average line.
"""
if game_log_df.empty:
return _empty_figure("No pitch count data")
df = game_log_df.copy()
df["label"] = pd.to_datetime(df["date"]).dt.strftime("%-m/%-d")
avg_pitches = df["pitches"].mean()
fig = go.Figure()
fig.add_trace(go.Bar(
x=df["label"], y=df["pitches"],
marker_color=ACCENT, marker_opacity=0.75, marker_line_width=0,
hovertemplate="<b>%{x}</b><br>Pitches: %{y}<extra></extra>",
))
fig.add_hline(
y=avg_pitches, line_dash="dot", line_color="#ffaa44", line_width=1.5,
annotation_text=f"Avg {avg_pitches:.0f}",
annotation_font=dict(color="#ffaa44", size=9),
annotation_position="top right",
)
fig.update_layout(
**LAYOUT_BASE,
title=dict(text="Pitch Count per Start", x=0.02,
font=dict(size=11, color="#8899bb")),
xaxis=dict(gridcolor=GRID, showgrid=False, tickfont=dict(size=9)),
yaxis=dict(gridcolor=GRID, range=[0, 130],
title=dict(text="Pitches", font=dict(size=9))),
height=175,
)
return fig
# ══════════════════════════════════════════════════════════════════════════════
# PITCHER — FASTBALL VELO BY INNING (box plots)
# ══════════════════════════════════════════════════════════════════════════════
def make_velo_by_inning_chart(velo_by_inning: dict) -> go.Figure:
"""
Box plots of fastball velocity for innings 1–7.
Each box = distribution of all FBs thrown in that inning this season.
"""
if not velo_by_inning:
return _empty_figure("No velocity data")
fig = go.Figure()
for inning in range(1, 8):
velos = velo_by_inning.get(inning, [])
if not velos:
continue
fig.add_trace(go.Box(
y=velos,
x=[f"Inn {inning}"] * len(velos),
name=f"Inn {inning}",
boxpoints=False,
marker_color=ACCENT,
line_color=ACCENT,
fillcolor="rgba(74,158,255,0.18)",
line_width=1.5,
hovertemplate=f"Inn {inning}<br>Median: %{{median:.1f}}<extra></extra>",
))
fig.update_layout(
**LAYOUT_BASE,
title=dict(text="Fastball Velo by Inning (season)", x=0.02,
font=dict(size=11, color="#8899bb")),
xaxis=dict(gridcolor=GRID),
yaxis=dict(gridcolor=GRID, title=dict(text="mph", font=dict(size=9))),
height=210,
boxmode="group",
)
return fig
# ══════════════════════════════════════════════════════════════════════════════
# BULLPEN — ENTRY INNING DISTRIBUTION
# ══════════════════════════════════════════════════════════════════════════════
def make_entry_inning_chart(entry_dist: dict, pitcher_name: str = "") -> go.Figure:
"""
Small horizontal bar chart: % of appearances entering in inn 6/7/8/9+.
Designed to be very compact (height ~90px).
"""
labels = ["6", "7", "8", "9+"]
values = [entry_dist.get(6, 0), entry_dist.get(7, 0),
entry_dist.get(8, 0), entry_dist.get("9+", 0)]
# Colors map to late/high-leverage innings
bar_colors = ["#4a6fa5", "#4a8cbf", "#4aabda", "#4aceff"]
fig = go.Figure(go.Bar(
x=values, y=labels, orientation="h",
marker_color=bar_colors, marker_line_width=0,
hovertemplate="%{y}: %{x:.0%}<extra></extra>",
text=[f"{v:.0%}" if v > 0.05 else "" for v in values],
textfont=dict(size=9, color=TEXT),
textposition="inside",
))
fig.update_layout(
**LAYOUT_BASE,
margin=dict(l=20, r=8, t=20, b=8),
title=dict(text="Entry Inn", x=0.02, font=dict(size=9, color="#8899bb")),
xaxis=dict(showgrid=False, showticklabels=False, range=[0, 1]),
yaxis=dict(showgrid=False, tickfont=dict(size=9)),
height=110,
)
return fig
# ══════════════════════════════════════════════════════════════════════════════
# SPLITS TABLE (styled HTML, not Plotly — for compactness)
# ══════════════════════════════════════════════════════════════════════════════
def make_splits_html(splits: dict, pitcher: bool = False) -> str:
"""
Return a compact HTML table for L/R splits.
Cells are color-coded vs league average.
"""
rows = ""
for hand in ("L", "R"):
key = f"vs_{hand}"
s = splits.get(key, {})
if pitcher:
k_col = _kpct_color(s.get("K_pct", 0), pitcher=True)
bb_col = _bbpct_color(s.get("BB_pct", 0), pitcher=True)
xw_col = _xwoba_color(s.get("xwOBA", 0), pitcher=True)
row = (
f"<tr>"
f"<td class='split-hand'>vs {hand}</td>"
f"<td style='color:{k_col}'>{s.get('K_pct',0):.1%}</td>"
f"<td style='color:{bb_col}'>{s.get('BB_pct',0):.1%}</td>"
f"<td style='color:{xw_col}'>{s.get('xwOBA',0):.3f}</td>"
f"<td class='split-pa'>{s.get('PA',0)}</td>"
f"</tr>"
)
else:
xw_col = _xwoba_color(s.get("xwOBA", 0), pitcher=False)
pa = s.get("PA", 0)
flag = " ⚠" if pa < 100 else ""
row = (
f"<tr>"
f"<td class='split-hand'>vs {hand}</td>"
f"<td style='color:{xw_col}'>{s.get('xwOBA',0):.3f}{flag}</td>"
f"<td class='split-pa'>{pa} PA</td>"
f"</tr>"
)
rows += row
header_cells = (
"<th>Split</th><th>K%</th><th>BB%</th><th>xwOBA</th><th>PA</th>"
if pitcher else
"<th>Split</th><th>xwOBA</th><th>PA</th>"
)
return f"""
<table class="splits-table">
<thead><tr>{header_cells}</tr></thead>
<tbody>{rows}</tbody>
</table>
"""
# ══════════════════════════════════════════════════════════════════════════════
# UTILITY
# ══════════════════════════════════════════════════════════════════════════════
def _empty_figure(msg: str = "No data") -> go.Figure:
fig = go.Figure()
fig.add_annotation(text=msg, x=0.5, y=0.5, xref="paper", yref="paper",
showarrow=False, font=dict(color="#556080", size=12))
fig.update_layout(**LAYOUT_BASE, height=160)
return fig
def team_badge_html(abbr: str, size: int = 28) -> str:
"""Return an inline HTML badge with team colors."""
pri, sec = TEAM_COLORS.get(abbr, ("#444", "#888"))
return (
f'<span style="display:inline-block;padding:2px 8px;border-radius:3px;'
f'background:{pri};color:{sec};font-weight:700;font-size:{size-8}px;'
f'font-family:monospace;letter-spacing:1px;border:1px solid {sec}22;">'
f'{abbr}</span>'
)
|