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e0d0e3c | 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 | from __future__ import annotations
import re
from datetime import datetime
from typing import Any
import pandas as pd
import requests
HEADERS = {
"User-Agent": "Mozilla/5.0",
"Accept-Language": "en-US,en;q=0.9",
}
SCORES_URL_TEMPLATE = "https://www.mlb.com/world-baseball-classic/scores/{date_str}"
TEAM_MAP = {
"AUS": "Australia",
"BRA": "Brazil",
"CAN": "Canada",
"CHN": "China",
"TPE": "Chinese Taipei",
"COL": "Colombia",
"CUB": "Cuba",
"CZE": "Czechia",
"DOM": "Dominican Republic",
"GBR": "Great Britain",
"ISR": "Israel",
"ITA": "Italy",
"JPN": "Japan",
"KOR": "Korea",
"MEX": "Mexico",
"NED": "Netherlands",
"NCA": "Nicaragua",
"PAN": "Panama",
"PUR": "Puerto Rico",
"USA": "United States",
"VEN": "Venezuela",
}
FINAL_RE = re.compile(r"^([A-Z]{3})\s+(\d+),\s+([A-Z]{3})\s+(\d+)$")
TEAM_AT_RE = re.compile(r"^([A-Z]{3})\s+@\s+([A-Z]{3})$")
TIME_RE = re.compile(r"^\d{1,2}:\d{2}\s+[AP]M\s+ET$")
TV_MARKERS = {"FS1", "FS2", "FOX", "Tubi"}
def _full_team(code: str) -> str:
return TEAM_MAP.get(code, code)
def _strip_html_to_lines(html: str) -> list[str]:
text = re.sub(r"<script.*?</script>", " ", html, flags=re.DOTALL | re.IGNORECASE)
text = re.sub(r"<style.*?</style>", " ", text, flags=re.DOTALL | re.IGNORECASE)
text = re.sub(r"<[^>]+>", "\n", text)
text = text.replace(" ", " ")
text = re.sub(r"\r", "\n", text)
text = re.sub(r"\n+", "\n", text)
lines = [line.strip() for line in text.split("\n") if line.strip()]
cleaned: list[str] = []
for line in lines:
if line.startswith("Image:"):
continue
if line.startswith("calendar-"):
continue
if line in {"Wrap", "Box", "Story", "Preview", "Tickets"}:
continue
cleaned.append(line)
deduped: list[str] = []
for line in cleaned:
if not deduped or deduped[-1] != line:
deduped.append(line)
return deduped
def fetch_scores_for_date(date_str: str) -> pd.DataFrame:
url = SCORES_URL_TEMPLATE.format(date_str=date_str)
response = requests.get(url, headers=HEADERS, timeout=30)
response.raise_for_status()
lines = _strip_html_to_lines(response.text)
rows: list[dict[str, Any]] = []
i = 0
pending_matchup: tuple[str, str] | None = None
while i < len(lines):
line = lines[i]
final_match = FINAL_RE.match(line)
if final_match:
away_code, away_score, home_code, home_score = final_match.groups()
rows.append(
{
"score_fetch_time": datetime.utcnow(),
"game_date": date_str,
"away_team": _full_team(away_code),
"home_team": _full_team(home_code),
"away_score": int(away_score),
"home_score": int(home_score),
"status": "Final",
"start_time_et": "",
"tv": "",
}
)
i += 1
continue
matchup_match = TEAM_AT_RE.match(line)
if matchup_match:
away_code, home_code = matchup_match.groups()
pending_matchup = (_full_team(away_code), _full_team(home_code))
i += 1
continue
if pending_matchup is not None:
away_team, home_team = pending_matchup
if line == "LIVE":
tv = ""
start_time_et = ""
if i + 1 < len(lines) and lines[i + 1] in TV_MARKERS:
tv = lines[i + 1]
i += 1
rows.append(
{
"score_fetch_time": datetime.utcnow(),
"game_date": date_str,
"away_team": away_team,
"home_team": home_team,
"away_score": None,
"home_score": None,
"status": "Live",
"start_time_et": start_time_et,
"tv": tv,
}
)
pending_matchup = None
i += 1
continue
if TIME_RE.match(line):
tv = ""
if i + 1 < len(lines) and lines[i + 1] in TV_MARKERS:
tv = lines[i + 1]
i += 1
rows.append(
{
"score_fetch_time": datetime.utcnow(),
"game_date": date_str,
"away_team": away_team,
"home_team": home_team,
"away_score": None,
"home_score": None,
"status": "Scheduled",
"start_time_et": line,
"tv": tv,
}
)
pending_matchup = None
i += 1
continue
i += 1
if not rows:
return pd.DataFrame(
columns=[
"score_fetch_time",
"game_date",
"away_team",
"home_team",
"away_score",
"home_score",
"status",
"start_time_et",
"tv",
]
)
df = pd.DataFrame(rows)
df = df.drop_duplicates(subset=["game_date", "away_team", "home_team", "status", "away_score", "home_score"])
return df.reset_index(drop=True) |