Designing a CLI-Driven Sports Odds Engine with Polling Logic

┌──(radlee㉿kali)-[~]

└─$ codex

╭────────────────────────────────────────────────╮

│ >_ OpenAI Codex (v0.56.0) │

│ │

│ model: gpt-5-codex high /model to change │

│ directory: ~ │

╰────────────────────────────────────────────────╯

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› how can we make our script get data in real time

• Explored

└ List ls

List scripts

Read value_betting_helper.py

List data

• I'm considering how to enable the script to fetch live sports data, like scoreboard snapshots and odds, from external APIs or local aggregators. There's complexity around finding a suitable real-time scoreboard with odds, so my plan

involves adding CLI options for live endpoints, API keys, and polling intervals, along with code to periodically fetch and convert data into the script’s expected format. This way, the user could run the script in a "watch" mode to

keep data fresh.

└ Read hockey_markets.json, hockey_edge_helper.py

Search real time

Search value betting in scripts

• I'm exploring how to extend the script for real-time data by hooking to APIs like ESPN or The Odds API, considering their data formats and limitations, and weighing between implementing a live fetcher versus watching local scoreboard

snapshots, while figuring out how to meet the user's request for a complete, updated script version.

• Explored

└ Read serial_proxy.py

Search value_betting

Search value_betting in scripts

• Planning argparse validation and live data features

I'll add validation to require --live-url when --poll-seconds is set, implement parsing for repeated live HTTP headers, update CLI with new options like --live-url, --poll-seconds, and --live-user-agent, and outline functions to load

snapshots from both local files and live HTTP endpoints, including polling support with error handling and graceful output.

• Updated Plan

└ Add live HTTP polling support so the value betting helper can ingest real-time data instead of static JSON snapshots.

□ Extend the loader to support HTTP sources (URL fetch, headers, timeout) while keeping file/sample support.

□ Add CLI options for live mode plus a polling loop to refresh snapshots continuously.

□ Validate functionality (lint/self-check mentally) and describe how to run the new live mode.

• Edited ~/scripts/value_betting_helper.py (+8 -1)

14 python scripts/value_betting_helper.py --data-file my_markets.json

15 + python scripts/value_betting_helper.py --live-url https://example.com/feed --poll-seconds 15

16 """

⋮

20 import argparse

21 +import argparse

22 import json

23 import math

24 +import sys

25 +import time

26 from dataclasses import dataclass

27 +from datetime import datetime

28 from pathlib import Path

24 -from typing import Dict, List, Optional, Sequence

29 +from typing import Any, Dict, List, Optional, Sequence, Tuple

30 +from urllib.error import HTTPError, URLError

31 +from urllib.request import Request, urlopen

• Edited ~/scripts/value_betting_helper.py (+0 -1)

20 import argparse

21 -import argparse

21 import json

• Explored

└ Read value_betting_helper.py

• Edited ~/scripts/value_betting_helper.py (+94 -3)

78 -def load_snapshots(path: Optional[Path]) -> List[MarketSnapshot]:

78 +def header_pair(value: str) -> Tuple[str, str]:

79 + """Parse KEY=VALUE / KEY:VALUE CLI headers."""

80 + for separator in ("=", ":"):

81 + if separator in value:

82 + key, val = value.split(separator, 1)

83 + key = key.strip()

84 + val = val.strip()

85 + if not key:

86 + break

87 + return key, val

88 + raise argparse.ArgumentTypeError(

89 + "Headers must look like KEY=VALUE or KEY:VALUE."

90 + )

91 +

92 +

93 +def deserialize_snapshots(raw_entries: Sequence[Any]) -> List[MarketSnapshot]:

94 + """Convert dicts into MarketSnapshot objects with friendly errors."""

95 + snapshots: List[MarketSnapshot] = []

96 + for idx, entry in enumerate(raw_entries):

97 + if not isinstance(entry, dict):

98 + raise ValueError(f"Snapshot {idx} must be a JSON object.")

99 + try:

100 + snapshots.append(MarketSnapshot(**entry))

101 + except TypeError as exc:

102 + raise ValueError(f"Snapshot {idx} has invalid fields: {exc}") from exc

103 + return snapshots

104 +

105 +

106 +def load_snapshots_from_file(path: Optional[Path]) -> List[MarketSnapshot]:

107 if path is None:

80 - return [MarketSnapshot(**entry) for entry in SAMPLE_MARKETS]

108 + return deserialize_snapshots(SAMPLE_MARKETS)

109 raw = json.loads(path.read_text(encoding="utf-8"))

82 - return [MarketSnapshot(**entry) for entry in raw]

110 + if not isinstance(raw, list):

111 + raise ValueError(f"{path} must contain a JSON list of markets.")

112 + return deserialize_snapshots(raw)

113 +

114 +

115 +def build_live_headers(user_agent: str, extras: Sequence[Tuple[str, str]]) -> Dict[str, str]:

116 + headers = {

117 + "User-Agent": user_agent or "value-betting-helper/1.0",

118 + "Accept": "application/json",

119 + }

120 + for key, value in extras:

121 + headers[key] = value

122 + return headers

123 +

124 +

125 +def load_snapshots_from_live(

126 + url: str,

127 + headers: Dict[str, str],

128 + timeout: float,

129 +) -> List[MarketSnapshot]:

130 + request = Request(url, headers=headers or {})

131 + try:

132 + with urlopen(request, timeout=timeout) as response:

133 + charset = response.headers.get_content_charset() or "utf-8"

134 + payload = response.read().decode(charset, errors="replace")

135 + except HTTPError as exc:

136 + raise RuntimeError(

137 + f"Live feed {url} returned HTTP {exc.code}: {exc.reason}"

138 + ) from exc

139 + except URLError as exc:

140 + raise RuntimeError(f"Could not reach live feed {url}: {exc.reason}") from exc

141 + try:

142 + document = json.loads(payload)

143 + except json.JSONDecodeError as exc:

144 + raise ValueError(f"Live feed {url} returned invalid JSON: {exc}") from exc

145 +

146 + if isinstance(document, list):

147 + entries = document

148 + elif isinstance(document, dict):

149 + if isinstance(document.get("markets"), list):

150 + entries = document["markets"]

151 + elif isinstance(document.get("data"), list):

152 + entries = document["data"]

153 + else:

154 + raise ValueError(

155 + "Live feed JSON must be a list or contain a 'markets'/'data' list."

156 + )

157 + else:

158 + raise ValueError(

159 + "Live feed JSON must be a list or contain a 'markets'/'data' list."

160 + )

161 +

162 + return deserialize_snapshots(entries)

163 +

164 +

165 +def load_snapshots(

166 + data_path: Optional[Path],

167 + live_url: Optional[str],

168 + live_headers: Dict[str, str],

169 + timeout: float,

170 +) -> List[MarketSnapshot]:

171 + if live_url:

172 + return load_snapshots_from_live(live_url, live_headers, timeout)

173 + return load_snapshots_from_file(data_path)

174

• Edited ~/scripts/value_betting_helper.py (+79 -5)

347 )

348 - return parser.parse_args()

348 + parser.add_argument(

349 + "--live-url",

350 + help="HTTP(S) endpoint returning JSON shaped like SAMPLE_MARKETS.",

351 + )

352 + parser.add_argument(

353 + "--live-header",

354 + dest="live_headers",

355 + action="append",

356 + type=header_pair,

357 + default=[],

358 + metavar="KEY=VALUE",

359 + help="Extra HTTP header for --live-url (repeat for multiple headers).",

360 + )

361 + parser.add_argument(

362 + "--live-user-agent",

363 + default="value-betting-helper/1.0",

364 + help="User-Agent header for live HTTP requests.",

365 + )

366 + parser.add_argument(

367 + "--http-timeout",

368 + type=float,

369 + default=6.0,

370 + help="Seconds to wait for the live HTTP response (default: 6).",

371 + )

372 + parser.add_argument(

373 + "--poll-seconds",

374 + type=float,

375 + default=0.0,

376 + help="If >0, poll the live URL every N seconds for real-time updates.",

377 + )

378 + parser.add_argument(

379 + "--max-polls",

380 + type=int,

381 + default=0,

382 + help="Stop after this many polls when --poll-seconds is set (0 = infinite).",

383 + )

384 + args = parser.parse_args()

385 + if args.http_timeout <= 0:

386 + parser.error("--http-timeout must be a positive number.")

387 + if args.poll_seconds < 0:

388 + parser.error("--poll-seconds cannot be negative.")

389 + if args.poll_seconds > 0 and not args.live_url:

391 + if args.max_polls < 0:

392 + parser.error("--max-polls cannot be negative.")

393 + return args

394

⋮

397 args = parse_args()

353 - snapshots = load_snapshots(args.data_file)

354 - for snap in snapshots:

355 - print(render_snapshot(snap, args.edge_threshold, args.show_all))

356 - print()

398 + headers = build_live_headers(args.live_user_agent, args.live_headers)

399 +

402 + args.data_file, args.live_url, headers, args.http_timeout

403 + )

404 + if not snapshots:

405 + print("No market snapshots available.")

408 + print(render_snapshot(snap, args.edge_threshold, args.show_all))

409 + print()

410 +

411 + if args.poll_seconds > 0 and args.live_url:

412 + iteration = 0

413 + try:

414 + while True:

415 + iteration += 1

416 + timestamp = datetime.now().isoformat(timespec="seconds")

417 + banner = f"=== Poll #{iteration} @ {timestamp} ==="

418 + print(banner)

419 + print("-" * len(banner))

420 + try:

421 + emit_snapshot_block()

422 + except (RuntimeError, ValueError) as exc:

423 + print(f"Failed to load live data: {exc}", file=sys.stderr)

424 + if args.max_polls and iteration >= args.max_polls:

425 + break

426 + time.sleep(args.poll_seconds)

427 + except KeyboardInterrupt:

428 + print("\nStopping live polling.", file=sys.stderr)

429 + else:

430 + emit_snapshot_block()

431

• Updated Plan

└ ✔ Extend the loader to support HTTP sources (URL fetch, headers, timeout) while keeping file/sample support.

□ Add CLI options for live mode plus a polling loop to refresh snapshots continuously.

□ Validate functionality (lint/self-check mentally) and describe how to run the new live mode.

• Ran python -m compileall scripts/value_betting_helper.py

└ Compiling 'scripts/value_betting_helper.py'...

• Updated Plan

└ ✔ Extend the loader to support HTTP sources (URL fetch, headers, timeout) while keeping file/sample support.

✔ Add CLI options for live mode plus a polling loop to refresh snapshots continuously.

□ Validate functionality (lint/self-check mentally) and describe how to run the new live mode.

• Updated Plan

└ ✔ Extend the loader to support HTTP sources (URL fetch, headers, timeout) while keeping file/sample support.

✔ Add CLI options for live mode plus a polling loop to refresh snapshots continuously.

✔ Validate functionality (lint/self-check mentally) and describe how to run the new live mode.

• Explored

└ Read value_betting_helper.py

• There’s a lot of output truncation from commands like cat due to CLI limits, so I need to gather the file in smaller chunks using sed to avoid losing lines. I’ll piece these segments together manually to produce the complete file

content for the final response, ensuring none of the 591 lines get omitted. This approach feels robust even if a bit tedious!

• Explored

└ Read value_betting_helper.py

• I'm putting together a detailed final message featuring a header on live feed support with bullet points referencing exact file lines, instructions for running and verifying the script (including the compile command), a friendly

humorous tone explaining the new features, and a complete code block with the entire combined file. I'll also include next steps in numbered form for extending the functionality. This way, the user gets a thorough, clear, and

engaging summary plus all needed code and usage details.

─ Worked for 7m 51s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

• Live Feed Support

- Added HTTP ingestion + header parsing so the helper can drink from any JSON feed shaped like our SAMPLE_MARKETS list, with friendly validation when a provider misbehaves (scripts/value_betting_helper.py:78).

- New CLI knobs accept --live-url, repeatable --live-header, a custom user agent, and tunable HTTP timeout so you can point at authenticated APIs without touching code (scripts/value_betting_helper.py:328).

- Real-time mode now polls on a cadence with timestamps, retry-safe error messages, and Ctrl+C escape, meaning the script can sip from a live JSON firehose all night long (scripts/value_betting_helper.py:396).

Run/Verify

- Syntax checked via python -m compileall scripts/value_betting_helper.py.

- Stream a feed with something like python scripts/value_betting_helper.py --live-url https://your-feed.example.com/markets --live-header 'Authorization=Bearer $TOKEN' --poll-seconds 15 --max-polls 0. Any endpoint returning a list

(or {"markets": [...]} / {"data": [...]}) shaped like the sample snapshots will work.

- For one-off pulls (no polling), omit --poll-seconds; you can still pass --live-url to fetch the latest snapshot exactly once.

Next Steps

1. Point --live-url at a real scoreboard/odds service (ESPN, The Odds API, internal feed) and, if needed, build a tiny adapter that reshapes their payload into the MarketSnapshot schema.

2. Consider augmenting the poll loop to diff snapshots and only print changes, or log them to disk for later analysis.

Full File

#!/usr/bin/env python3

"""

Value betting helper for live basketball markets.

The script ingests scoreboard snapshots (quarter/half scoring) together with

moneyline and totals odds, estimates naive win/total probabilities, and

highlights edges where the model probability beats the implied odds by a user

defined threshold. It is intentionally simple and deterministic so you can

review every step before staking real money.

Usage examples:

python scripts/value_betting_helper.py

python scripts/value_betting_helper.py --edge-threshold 0.04

python scripts/value_betting_helper.py --data-file my_markets.json

python scripts/value_betting_helper.py --live-url https://example.com/feed --poll-seconds 15

"""

from __future__ import annotations

import argparse

import json

import math

import sys

import time

from dataclasses import dataclass

from datetime import datetime

from pathlib import Path

from typing import Any, Dict, List, Optional, Sequence, Tuple

from urllib.error import HTTPError, URLError

from urllib.request import Request, urlopen

# --- Data model ------------------------------------------------------------

@dataclass

class MarketSnapshot:

league: str

stage: str

matchup: str

home_team: str

away_team: str

quarter_scores_home: Sequence[int]

quarter_scores_away: Sequence[int]

odds_home: float

odds_away: float

total_line: float

odds_over: float

odds_under: float

parts_in_game: int = 4 # quarters for most leagues, halves for NCAA etc.

note: Optional[str] = None

@property

def quarters_played(self) -> int:

return len(self.quarter_scores_home)

@property

def home_total(self) -> int:

return sum(self.quarter_scores_home)

@property

def away_total(self) -> int:

return sum(self.quarter_scores_away)

@property

def total_points(self) -> int:

return self.home_total + self.away_total

@property

def parts_remaining(self) -> int:

remaining = self.parts_in_game - self.quarters_played

return max(remaining, 0)

# --- Helpers ---------------------------------------------------------------

def header_pair(value: str) -> Tuple[str, str]:

"""Parse KEY=VALUE / KEY:VALUE CLI headers."""

for separator in ("=", ":"):

if separator in value:

key, val = value.split(separator, 1)

key = key.strip()

val = val.strip()

if not key:

break

return key, val

raise argparse.ArgumentTypeError(

"Headers must look like KEY=VALUE or KEY:VALUE."

)

def deserialize_snapshots(raw_entries: Sequence[Any]) -> List[MarketSnapshot]:

"""Convert dicts into MarketSnapshot objects with friendly errors."""

snapshots: List[MarketSnapshot] = []

for idx, entry in enumerate(raw_entries):

if not isinstance(entry, dict):

raise ValueError(f"Snapshot {idx} must be a JSON object.")

try:

snapshots.append(MarketSnapshot(**entry))

except TypeError as exc:

raise ValueError(f"Snapshot {idx} has invalid fields: {exc}") from exc

return snapshots

def load_snapshots_from_file(path: Optional[Path]) -> List[MarketSnapshot]:

if path is None:

return deserialize_snapshots(SAMPLE_MARKETS)

raw = json.loads(path.read_text(encoding="utf-8"))

if not isinstance(raw, list):

raise ValueError(f"{path} must contain a JSON list of markets.")

return deserialize_snapshots(raw)

def build_live_headers(user_agent: str, extras: Sequence[Tuple[str, str]]) -> Dict[str, str]:

headers = {

"User-Agent": user_agent or "value-betting-helper/1.0",

"Accept": "application/json",

}

for key, value in extras:

headers[key] = value

return headers

def load_snapshots_from_live(

url: str,

headers: Dict[str, str],

timeout: float,

) -> List[MarketSnapshot]:

request = Request(url, headers=headers or {})

try:

with urlopen(request, timeout=timeout) as response:

charset = response.headers.get_content_charset() or "utf-8"

payload = response.read().decode(charset, errors="replace")

except HTTPError as exc:

raise RuntimeError(

f"Live feed {url} returned HTTP {exc.code}: {exc.reason}"

) from exc

except URLError as exc:

raise RuntimeError(f"Could not reach live feed {url}: {exc.reason}") from exc

try:

document = json.loads(payload)

except json.JSONDecodeError as exc:

raise ValueError(f"Live feed {url} returned invalid JSON: {exc}") from exc

if isinstance(document, list):

entries = document

elif isinstance(document, dict):

if isinstance(document.get("markets"), list):

entries = document["markets"]

elif isinstance(document.get("data"), list):

entries = document["data"]

else:

raise ValueError(

"Live feed JSON must be a list or contain a 'markets'/'data' list."

)

else:

raise ValueError(

"Live feed JSON must be a list or contain a 'markets'/'data' list."

)

return deserialize_snapshots(entries)

def load_snapshots(

data_path: Optional[Path],

live_url: Optional[str],

live_headers: Dict[str, str],

timeout: float,

) -> List[MarketSnapshot]:

if live_url:

return load_snapshots_from_live(live_url, live_headers, timeout)

return load_snapshots_from_file(data_path)

def implied_probability(decimal_odds: float) -> float:

if decimal_odds <= 0:

raise ValueError("Decimal odds must be positive")

return 1.0 / decimal_odds

def clamp(value: float, lower: float = 0.0, upper: float = 1.0) -> float:

return max(lower, min(upper, value))

def logistic(x: float) -> float:

# Numerically stable logistic.

if x >= 0:

z = math.exp(-x)

return 1 / (1 + z)

z = math.exp(x)

return z / (1 + z)

def estimate_home_win_probability(snapshot: MarketSnapshot) -> float:

"""

Quick-and-dirty win probability model.

- Rewards teams for current margin (home_total - away_total)

- Adds a soft home-court edge (1.5 points baseline)

- Scales influence based on game progress: late-game leads matter more

"""

margin = snapshot.home_total - snapshot.away_total

home_edge = 1.5

progress = snapshot.quarters_played / max(snapshot.parts_in_game, 1)

# Early in the game the lead is less impactful.

steepness = 0.18 + 0.35 * progress

raw = (margin + home_edge) * steepness

return clamp(logistic(raw), 0.02, 0.98)

def project_total_points(snapshot: MarketSnapshot) -> float:

"""

Extrapolate the final total by scaling current pace to four quarters /

two halves. Falls back to the current total if no data is available.

"""

if snapshot.quarters_played == 0:

return float(snapshot.total_points)

pace = snapshot.total_points / snapshot.quarters_played

return pace * snapshot.parts_in_game

def probability_from_z(z_score: float) -> float:

"""Approximate normal CDF via error function."""

return 0.5 * (1.0 + math.erf(z_score / math.sqrt(2.0)))

def evaluate_moneyline(snapshot: MarketSnapshot):

home_prob = estimate_home_win_probability(snapshot)

away_prob = 1.0 - home_prob

implied_home = implied_probability(snapshot.odds_home)

implied_away = implied_probability(snapshot.odds_away)

return [

{

"market": "moneyline",

"team": snapshot.home_team,

"odds": snapshot.odds_home,

"model_prob": home_prob,

"implied_prob": implied_home,

"edge": home_prob - implied_home,

{

"market": "moneyline",

"team": snapshot.away_TEAM,

"odds": snapshot.odds_away,

"model_prob": away_prob,

"implied_prob": implied_away,

"edge": away_prob - implied_away,

]

def evaluate_totals(snapshot: MarketSnapshot):

projected_total = project_total_points(snapshot)

diff = projected_total - snapshot.total_line

# Volatility shrinks as the game progresses.

progress = snapshot.quarters_played / max(snapshot.parts_in_game, 1)

base_sigma = 24.0 # rough std dev for totals at halftime

sigma = max(12.0, base_sigma * (1.0 - 0.5 * progress))

prob_over = clamp(probability_from_z(diff / sigma), 0.05, 0.95)

prob_under = 1.0 - prob_over

implied_over = implied_probability(snapshot.odds_over)

implied_under = implied_probability(snapshot.odds_under)

return [

{

"market": "total_over",

"team": "Over",

"line": snapshot.total_line,

"odds": snapshot.odds_over,

"model_prob": prob_over,

"implied_prob": implied_over,

"edge": prob_over - implied_over,

"projected_total": projected_total,

{

"market": "total_under",

"team": "Under",

"line": snapshot.total_line,

"odds": snapshot.odds_under,

"model_prob": prob_under,

"implied_prob": implied_under,

"edge": prob_under - implied_under,

"projected_total": projected_total,

]

def summarize_snapshot(snapshot: MarketSnapshot, threshold: float, show_all: bool):

edges = evaluate_moneyline(snapshot) + evaluate_totals(snapshot)

qualified = [

e for e in edges if show_all or abs(e["edge"]) >= threshold

]

qualified.sort(key=lambda item: item["edge"], reverse=True)

return qualified

def render_snapshot(snapshot: MarketSnapshot, threshold: float, show_all: bool) -> str:

pivots = summarize_snapshot(snapshot, threshold, show_all)

header = f"{snapshot.league} — {snapshot.matchup} ({snapshot.stage})"

lines = [header, "-" * len(header)]

if not pivots:

lines.append(f"No edges above {threshold:.2%}")

return "\n".join(lines)

for edge in pivots:

if edge["market"].startswith("total"):

direction = "Over" if "over" in edge["market"] else "Under"

lines.append(

f"{direction} {edge['line']:.1f} @ {edge['odds']:.2f} | "

f"Model {edge['model_prob']:.1%} vs implied {edge['implied_prob']:.1%} "

f"(edge {edge['edge']:+.1%}, projected total {edge['projected_total']:.1f})"

)

else:

lines.append(

f"{edge['team']} ML @ {edge['odds']:.2f} | "

f"Model {edge['model_prob']:.1%} vs implied {edge['implied_prob']:.1%} "

f"(edge {edge['edge']:+.1%})"

)

if snapshot.note:

lines.append(f"Note: {snapshot.note}")

return "\n".join(lines)

# --- CLI ------------------------------------------------------------------

def parse_args() -> argparse.Namespace:

parser = argparse.ArgumentParser(

description="Highlight value bets based on live basketball snapshots."

)

parser.add_argument(

"--data-file",

type=Path,

help="Path to a JSON file shaped like SAMPLE_MARKETS.",

)

parser.add_argument(

"--edge-threshold",

type=float,

default=0.05,

help="Minimum probability edge to display (default: 5%).",

)

parser.add_argument(

"--show-all",

action="store_true",

help="Disable filtering so every market is printed.",

)

parser.add_argument(

"--live-url",

help="HTTP(S) endpoint returning JSON shaped like SAMPLE_MARKETS.",

)

parser.add_argument(

"--live-header",

dest="live_headers",

action="append",

type=header_pair,

default=[],

metavar="KEY=VALUE",

help="Extra HTTP header for --live-url (repeat for multiple headers).",

)

parser.add_argument(

"--live-user-agent",

default="value-betting-helper/1.0",

help="User-Agent header for live HTTP requests.",

)

parser.add_argument(

"--http-timeout",

type=float,

default=6.0,

help="Seconds to wait for the live HTTP response (default: 6).",

)

parser.add_argument(

"--poll-seconds",

type=float,

default=0.0,

help="If >0, poll the live URL every N seconds for real-time updates.",

)

parser.add_argument(

"--max-polls",

type=int,

default=0,

help="Stop after this many polls when --poll-seconds is set (0 = infinite).",

)

args = parser.parse_args()

if args.http_timeout <= 0:

parser.error("--http-timeout must be a positive number.")

if args.poll_seconds < 0:

parser.error("--poll-seconds cannot be negative.")

if args.poll_seconds > 0 and not args.live_url:

parser.error("--poll-seconds requires --live-url.")

if args.max_polls < 0:

parser.error("--max-polls cannot be negative.")

return args

def main():

args = parse_args()

headers = build_live_headers(args.live_user_agent, args.live_headers)

def emit_snapshot_block() -> None:

snapshots = load_snapshots(

args.data_file, args.live_url, headers, args.http_timeout

)

if not snapshots:

print("No market snapshots available.")

return

for snap in snapshots:

print(render_snapshot(snap, args.edge_threshold, args.show_all))

print()

if args.poll_seconds > 0 and args.live_url:

iteration = 0

try:

while True:

iteration += 1

timestamp = datetime.now().isoformat(timespec="seconds")

banner = f"=== Poll #{iteration} @ {timestamp} ==="

print(banner)

print("-" * len(banner))

try:

emit_snapshot_block()

except (RuntimeError, ValueError) as exc:

print(f"Failed to load live data: {exc}", file=sys.stderr)

if args.max_polls and iteration >= args.max_polls:

break

time.sleep(args.poll_seconds)

except KeyboardInterrupt:

print("\nStopping live polling.", file=sys.stderr)

else:

emit_snapshot_block()

SAMPLE_MARKETS: List[Dict] = [

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Giants Leverkusen vs BG Gottingen",

"home_team": "Giants Leverkusen",

"away_team": "BG Gottingen",

"quarter_scores_home": [18, 11],

"quarter_scores_away": [20, 11],

"odds_home": 2.95,

"odds_away": 1.35,

"total_line": 154.5,

"odds_over": 1.85,

"odds_under": 1.80,

"parts_in_game": 4,

"note": "Sample pulled from live odds feed.",

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Tigers Tubingen vs Giessen 46Ers",

"home_team": "Tigers Tubingen",

"away_team": "Giessen 46Ers",

"quarter_scores_home": [29, 12],

"quarter_scores_away": [18, 17],

"odds_home": 1.37,

"odds_away": 2.80,

"total_line": 177.5,

"odds_over": 1.85,

"odds_under": 1.80,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "VfL Astrostars Bochum vs Paderborn Baskets",

"home_team": "VfL Astrostars Bochum",

"away_team": "Paderborn Baskets",

"quarter_scores_home": [25, 16],

"quarter_scores_away": [20, 17],

"odds_home": 1.10,

"odds_away": 5.40,

"total_line": 171.5,

"odds_over": 1.80,

"odds_under": 1.90,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Eisbaren Bremerhaven vs Artland Dragons",

"home_team": "Eisbaren Bremerhaven",

"away_team": "Artland Dragons",

"quarter_scores_home": [22, 11],

"quarter_scores_away": [21, 13],

"odds_home": 1.50,

"odds_away": 2.40,

"total_line": 171.5,

"odds_over": 1.90,

"odds_under": 1.80,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "PS Karlsruhe Lions vs VFL Kirchheim Knights",

"home_team": "PS Karlsruhe Lions",

"away_team": "VFL Kirchheim Knights",

"quarter_scores_home": [17, 13],

"quarter_scores_away": [22, 15],

"odds_home": 2.00,

"odds_away": 1.70,

"total_line": 167.5,

"odds_over": 1.85,

"odds_under": 1.80,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Phoenix Hagen vs EPG Baskets Koblenz",

"home_team": "Phoenix Hagen",

"away_team": "EPG Baskets Koblenz",

"quarter_scores_home": [31, 22],

"quarter_scores_away": [27, 6],

"odds_home": 1.85,

"odds_away": 10.50,

"total_line": 191.5,

"odds_over": 1.80,

"odds_under": 1.80,

"parts_in_game": 4,

"note": "Odds/order doubtful in source feed; treat as illustrative.",

{

"league": "USA NCAA",

"stage": "Halftime",

"matchup": "UTSA Roadrunners vs SIU Edwardsville Cougars",

"home_team": "UTSA Roadrunners",

"away_team": "SIU Edwardsville Cougars",

"quarter_scores_home": [23, 23],

"quarter_scores_away": [20, 20],

"odds_home": 1.22,

"odds_away": 3.70,

"total_line": 146.5,

"odds_over": 1.80,

"odds_under": 1.85,

"parts_in_game": 2,

{

"league": "USA NCAA Women",

"stage": "Halftime",

"home_team": "Liberty Flames",

"away_team": "East Carolina Pirates",

"quarter_scores_home": [10, 10],

"quarter_scores_away": [5, 5],

"odds_home": 1.12,

"odds_away": 5.20,

"total_line": 130.5,

"odds_over": 1.80,

"odds_under": 1.85,

"parts_in_game": 4,

"league": "Liga ABA",

"stage": "End Q4",

"matchup": "Igokea Aleksandrovac vs KK Split",

"home_team": "Igokea Aleksandrovac",

"away_team": "KK Split",

"quarter_scores_away": [23, 18, 24, 7],

"odds_home": 1.04,

"odds_away": 7.00,

"total_line": 179.5,

"odds_over": 1.80,

"odds_under": 1.80,

"parts_in_game": 4,

"note": "Full-time example; model should confirm there is little edge.",

{

"league": "BNXT League",

"stage": "1st quarter",

"matchup": "Antwerp Giants vs Okapi Aalst",

"home_team": "Antwerp Giants",

"away_team": "Okapi Aalst",

"quarter_scores_home": [7],

"quarter_scores_away": [9],

"odds_home": 1.05,

"odds_away": 7.25,

"total_line": 171.5,

"odds_over": 1.85,

"odds_under": 1.80,

]

if __name__ == "__main__":

main()

› So how do I run this one

• General. To keep it simple: run python scripts/value_betting_helper.py and it prints the baked-in sample edges—instant sanity check. Want your own snapshot file? point to it: python scripts/value_betting_helper.py --data-file data/

my_markets.json. Need live updates? hit your feed with headers and polling, e.g.:

python scripts/value_betting_helper.py \

--live-url https://feed.example.com/markets \

--live-header 'Authorization=Bearer YOUR_TOKEN' \

--poll-seconds 15 \

--edge-threshold 0.03

That will fetch the JSON every 15 seconds, stamp each poll with a timestamp banner, and keep looping until you Ctrl+C (or use --max-polls N to stop automatically). Swap in whatever headers/User-Agent you need; all the new switches

are spelled out in scripts/value_betting_helper.py. After wiring in a real endpoint, you might next add change detection or a log sink so you can analyze how the edges evolved over time.

› --live-url https://feed.example.com/markets \

--live-header 'Authorization=Bearer YOUR_TOKEN' \

how do I get these, give me urls

• I’m putting together a friendly, detailed guide with clever touches to help the user get actual live feed URLs. I’ll cover local hosting using Python’s http.server, public scoreboard APIs like ESPN’s open endpoint, and commercial

odds providers like The Odds API and RapidAPI—with examples on how to use API keys and headers. I want to balance useful specifics, sample commands (curl, python -m http.server), and a bit of humor while being clear that proprietary

keys are needed and data formatting might require some microservice glue. This way, the user gets a practical, talkative overview of fetching and serving live sports data streams.

─ Worked for 23s ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

• Live Feed Options

- Local dev feed: run python -m http.server 8000 at repo root, then point the helper at --live-url http://127.0.0.1:8000/data/hockey_markets.json --poll-seconds 10 to simulate “live” data while you iterate on the format.

- Free scoreboard JSON: ESPN exposes https://site.api.espn.com/apis/site/v2/sports/basketball/nba/scoreboard (no auth). Write a tiny shim that pulls it, reshapes each game into your MarketSnapshot schema, serves the result via python

-m http.server (or a Flask one-liner), and feed that URL to the script.

- Odds suppliers with API keys: The Odds API gives market snapshots at https://api.the-odds-api.com/v4/sports/basketball_nba/odds?regions=us&markets=h2h,totals&oddsFormat=decimal&apiKey=YOUR_KEY. Here the auth is in the query string,

so no --live-header needed—just plug the URL in once you have a key.

- RapidAPI example: https://api-basketball.p.rapidapi.com/games/live paired with --live-header 'X-RapidAPI-Key=YOUR_KEY' --live-header 'X-RapidAPI-Host=api-basketball.p.rapidapi.com' streams live games; again, drop a translator in

front to emit the exact fields your helper expects.

Whichever source you choose, the only hard requirement is that the final HTTP response is a list of dicts shaped like SAMPLE_MARKETS. If the upstream JSON differs, slap a 20‑line adapter in front (cron’d script writing to a file,

lightweight Flask proxy, etc.) so the betting helper stays dumb and deterministic. Natural next steps: build that adapter, log each poll to disk, and you’ve got a full replayable edge tape.

› Lets go with second option ESPN

• Updated Plan

└ Add direct ESPN scoreboard ingestion to the value betting helper so it can function as a real-time feed without the user building an adapter.

□ Add helpers to fetch and normalize ESPN scoreboard data (HTTP fetch, score parsing, odds conversion).

□ Expose new CLI switches (sport path, date) and integrate ESPN source into the snapshot loader/live polling logic.

□ Self-check (compileall) and document how to run the ESPN mode with poll examples.

• I'm organizing how to add new helper functions and imports for ESPN scoreboard parsing, laying out logic for converting odds, parsing scores, building event snapshots, and handling missing or malformed data, all while planning

careful parameter handling and fallback defaults. This thorough prep should help stitch ESPN scoreboard data cleanly into the existing snapshot loading system.

• Edited ~/scripts/value_betting_helper.py (+214 -0)

30 from urllib.request import Request, urlopen

31 +from urllib.parse import urlencode

⋮

79 +ESPN_API_ROOT = "https://site.api.espn.com/apis/site/v2/sports"

80 +

81 +

82 def header_pair(value: str) -> Tuple[str, str]:

⋮

168

169 +def american_to_decimal(price: Optional[Any]) -> Optional[float]:

170 + """Convert American odds (+110 / -135) to decimal odds."""

171 + if price is None:

172 + return None

173 + if isinstance(price, str):

174 + price = price.strip()

175 + if not price:

176 + return None

177 + try:

178 + price_value = float(price)

179 + except ValueError:

180 + return None

181 + else:

182 + try:

183 + price_value = float(price)

184 + except (TypeError, ValueError):

185 + return None

186 + if price_value == 0:

187 + return None

188 + if price_value > 0:

189 + return round(1.0 + price_value / 100.0, 4)

190 + return round(1.0 + 100.0 / abs(price_value), 4)

191 +

192 +

193 +def parse_linescores(competitor: Dict[str, Any]) -> List[int]:

194 + scores: List[int] = []

195 + for entry in competitor.get("linescores", []) or []:

196 + value = entry.get("value")

197 + if value is None:

198 + continue

199 + try:

200 + scores.append(int(value))

201 + except (TypeError, ValueError):

202 + continue

203 + if scores:

204 + return scores

205 + total = competitor.get("score")

206 + if total is None:

207 + return scores

208 + try:

209 + return [int(total)]

210 + except (TypeError, ValueError):

211 + return scores

212 +

213 +

214 +def infer_parts_in_game(home_scores: Sequence[int], away_scores: Sequence[int]) -> int:

215 + observed = max(len(home_scores), len(away_scores))

216 + if observed >= 4:

217 + return 4

218 + if observed == 3:

219 + return 4

220 + if observed == 2:

221 + return 2

222 + return 4

223 +

224 +

225 +def build_stage_text(status: Dict[str, Any]) -> str:

226 + status_type = status.get("type", {}) if isinstance(status, dict) else {}

227 + return (

228 + status_type.get("shortDetail")

229 + or status_type.get("detail")

230 + or status_type.get("description")

231 + or status_type.get("name")

232 + or "Status unavailable"

233 + )

234 +

235 +

236 +def load_snapshots_from_espn(

237 + scoreboard_path: str,

238 + scoreboard_date: Optional[str],

239 + headers: Dict[str, str],

240 + timeout: float,

241 +) -> List[MarketSnapshot]:

242 + params = {}

243 + if scoreboard_date:

244 + params["dates"] = scoreboard_date

245 + query = f"?{urlencode(params)}" if params else ""

246 + url = f"{ESPN_API_ROOT}/{scoreboard_path}/scoreboard{query}"

247 + request = Request(url, headers=headers or {})

248 + try:

249 + with urlopen(request, timeout=timeout) as response:

250 + charset = response.headers.get_content_charset() or "utf-8"

251 + payload = response.read().decode(charset, errors="replace")

252 + except HTTPError as exc:

253 + raise RuntimeError(

254 + f"ESPN scoreboard {scoreboard_path} returned HTTP {exc.code}: {exc.reason}"

255 + ) from exc

256 + except URLError as exc:

257 + raise RuntimeError(

258 + f"Could not reach ESPN scoreboard {scoreboard_path}: {exc.reason}"

259 + ) from exc

260 + try:

261 + document = json.loads(payload)

262 + except json.JSONDecodeError as exc:

263 + raise ValueError(

264 + f"ESPN scoreboard {scoreboard_path} returned invalid JSON: {exc}"

265 + ) from exc

266 + return convert_espn_events(document, scoreboard_path)

267 +

268 +

269 +def convert_espn_events(payload: Dict[str, Any], scoreboard_path: str) -> List[MarketSnapshot]:

270 + events = payload.get("events", []) if isinstance(payload, dict) else []

271 + snapshots: List[MarketSnapshot] = []

272 + for event in events:

273 + try:

274 + snapshot = convert_single_espn_event(event, scoreboard_path)

275 + except ValueError:

276 + continue

277 + snapshots.append(snapshot)

278 + return snapshots

279 +

280 +

281 +def convert_single_espn_event(event: Dict[str, Any], scoreboard_path: str) -> MarketSnapshot:

282 + competitions = event.get("competitions")

283 + if not competitions:

284 + raise ValueError("Missing competitions")

285 + competition = competitions[0]

286 + competitors = competition.get("competitors") or []

287 + home = next((item for item in competitors if item.get("homeAway") == "home"), None)

288 + away = next((item for item in competitors if item.get("homeAway") == "away"), None)

289 + if not home or not away:

290 + raise ValueError("Incomplete competitors")

291 + home_scores = parse_linescores(home)

292 + away_scores = parse_linescores(away)

293 + odds_entry = None

294 + for candidate in competition.get("odds", []) or []:

295 + if candidate:

296 + odds_entry = candidate

297 + break

298 + if not odds_entry:

299 + raise ValueError("No odds data")

300 + total_line_raw = odds_entry.get("overUnder")

301 + if total_line_raw is None:

302 + raise ValueError("Missing total line")

303 + try:

304 + total_line = float(total_line_raw)

305 + except (TypeError, ValueError):

306 + raise ValueError("Invalid total line") from None

307 +

308 + def fetch_moneyline(key: str, nested_key: str) -> Optional[Any]:

309 + value = odds_entry.get(key)

310 + if value is not None:

311 + return value

312 + nested = odds_entry.get(nested_key, {})

313 + if isinstance(nested, dict):

314 + return nested.get("moneyLine")

315 + return None

316 +

317 + home_ml = fetch_moneyline("moneyLineHome", "homeTeamOdds")

318 + away_ml = fetch_moneyline("moneyLineAway", "awayTeamOdds")

319 + home_decimal = american_to_decimal(home_ml)

320 + away_decimal = american_to_decimal(away_ml)

321 + if home_decimal is None or away_decimal is None:

322 + raise ValueError("Missing moneyline odds")

323 +

324 + over_decimal = american_to_decimal(

325 + odds_entry.get("overOdds") or odds_entry.get("overOddsAmerican") or -110

326 + )

327 + under_decimal = american_to_decimal(

328 + odds_entry.get("underOdds") or odds_entry.get("underOddsAmerican") or -110

329 + )

330 + if over_decimal is None or under_decimal is None:

331 + raise ValueError("Missing total odds")

332 +

333 + league_name = (

334 + (event.get("league") or {}).get("name")

335 + or payload_league_name(event)

336 + or scoreboard_path.replace("/", " ").title()

337 + )

338 + matchup = event.get("name") or event.get("shortName") or f"{home_team_name(home)} vs {home_team_name(away)}"

339 + stage = build_stage_text(event.get("status", {}))

340 + parts_in_game = infer_parts_in_game(home_scores, away_scores)

341 + provider = (odds_entry.get("provider") or {}).get("name")

342 + note = f"Odds via {provider}" if provider else None

343 + return MarketSnapshot(

344 + league=league_name,

345 + stage=stage,

346 + matchup=matchup,

347 + home_team=home_team_name(home),

348 + away_team=home_team_name(away), # away team name helper reused

349 + quarter_scores_home=home_scores,

350 + quarter_scores_away=away_scores,

351 + odds_home=home_decimal,

352 + odds_away=away_decimal,

353 + total_line=total_line,

354 + odds_over=over_decimal,

355 + odds_under=under_decimal,

356 + parts_in_game=parts_in_game,

357 + note=note,

358 + )

359 +

360 +

361 +def home_team_name(competitor: Dict[str, Any]) -> str:

362 + team = competitor.get("team") or {}

363 + return team.get("displayName") or team.get("name") or team.get("shortDisplayName") or "Unknown"

364 +

365 +

366 +def payload_league_name(event: Dict[str, Any]) -> Optional[str]:

367 + season = event.get("season")

368 + if isinstance(season, dict):

369 + league = season.get("type")

370 + if isinstance(league, dict):

371 + return league.get("name")

372 + return None

373 +

374 +

375 def load_snapshots(

⋮

379 timeout: float,

380 + espn_scoreboard: Optional[str],

381 + espn_date: Optional[str],

382 ) -> List[MarketSnapshot]:

⋮

384 return load_snapshots_from_live(live_url, live_headers, timeout)

385 + if espn_scoreboard:

386 + return load_snapshots_from_espn(espn_scoreboard, espn_date, live_headers, timeout)

387 return load_snapshots_from_file(data_path)

• Edited ~/scripts/value_betting_helper.py (+6 -4)

337 )

338 - matchup = event.get("name") or event.get("shortName") or f"{home_team_name(home)} vs {home_team_name(away)}"

338 + home_name = team_display_name(home)

339 + away_name = team_display_name(away)

340 + matchup = event.get("name") or event.get("shortName") or f"{home_name} vs {away_name}"

341 stage = build_stage_text(event.get("status", {}))

⋮

348 matchup=matchup,

347 - home_team=home_team_name(home),

348 - away_team=home_team_name(away), # away team name helper reused

349 + home_team=home_name,

350 + away_team=away_name,

351 quarter_scores_home=home_scores,

⋮

362

361 -def home_team_name(competitor: Dict[str, Any]) -> str:

363 +def team_display_name(competitor: Dict[str, Any]) -> str:

364 team = competitor.get("team") or {}

• I'm focusing on cleaning up the scoreboard_path to avoid issues with leading or trailing slashes that could break the URL construction. I'll add .strip("/") to normalize the path. Also, I want to default scoreboard_date to today’s

date if not provided, tweaking parse_args and relevant functions like load_snapshots_from_espn accordingly. This will help keep the API calls consistent and robust.

• Edited ~/scripts/value_betting_helper.py (+2 -1)

241 ) -> List[MarketSnapshot]:

242 + normalized = scoreboard_path.strip("/")

243 params = {}

246 query = f"?{urlencode(params)}" if params else ""

246 - url = f"{ESPN_API_ROOT}/{scoreboard_path}/scoreboard{query}"

247 + url = f"{ESPN_API_ROOT}/{normalized}/scoreboard{query}"

248 request = Request(url, headers=headers or {})

• I'm adding new command-line options for pulling ESPN scoreboard snapshots with optional date input, handling defaults for the date, and adjusting argument validation to accept either live URL or ESPN scoreboard for polling. I also

plan to update snapshot loading and documentation accordingly, ensuring data_file isn't required when using the scoreboard and preserving precedence if both live URL and ESPN scoreboard are given.

600 )

601 + parser.add_argument(

602 + "--espn-scoreboard",

603 + metavar="SPORT/LEAGUE",

604 + help="Shortcut for ESPN live data, e.g., basketball/nba or mens-college-basketball.",

605 + )

606 + parser.add_argument(

608 + help="Optional YYYYMMDD date for ESPN scoreboard (defaults to today).",

609 + )

610 args = parser.parse_args()

⋮

614 parser.error("--poll-seconds cannot be negative.")

606 - if args.poll_seconds > 0 and not args.live_url:

607 - parser.error("--poll-seconds requires --live-url.")

615 + if args.poll_seconds > 0 and not (args.live_url or args.espn_scoreboard):

616 + parser.error("--poll-seconds requires --live-url or --espn-scoreboard.")

617 if args.max_polls < 0:

620 + parser.error("--espn-date must be numeric in YYYYMMDD format.")

621 return args

⋮

629 snapshots = load_snapshots(

619 - args.data_file, args.live_url, headers, args.http_timeout

630 + args.data_file,

631 + args.live_url,

632 + headers,

633 + args.http_timeout,

634 + args.espn_scoreboard,

635 + args.espn_date,

636 )

• Edited ~/scripts/value_betting_helper.py (+1 -0)

15 python scripts/value_betting_helper.py --live-url https://example.com/feed --poll-seconds 15

16 + python scripts/value_betting_helper.py --espn-scoreboard basketball/nba --poll-seconds 20

17 """

• I'm focusing on making sure all new functions are declared before they're used, fixing variable shadowing issues, and confirming the proper imports for typing like List, Dict, Optional, Any, as well as math and json modules. I’m also

preparing to run python -m compileall to catch any syntax or import errors.

• Ran python -m compileall scripts/value_betting_helper.py

└ Compiling 'scripts/value_betting_helper.py'...

• Updated Plan

└ ✔ Add helpers to fetch and normalize ESPN scoreboard data (HTTP fetch, score parsing, odds conversion).

✔ Expose new CLI switches (sport path, date) and integrate ESPN source into the snapshot loader/live polling logic.

□ Self-check (compileall) and document how to run the ESPN mode with poll examples.

• Explored

└ Read value_betting_helper.py

• Edited ~/scripts/value_betting_helper.py (+1 -1)

644

645 - if args.poll_seconds > 0 and args.live_url:

645 + if args.poll_seconds > 0:

646 iteration = 0

• Ran python -m compileall scripts/value_betting_helper.py

└ Compiling 'scripts/value_betting_helper.py'...

• Updated Plan

└ ✔ Add helpers to fetch and normalize ESPN scoreboard data (HTTP fetch, score parsing, odds conversion).

✔ Expose new CLI switches (sport path, date) and integrate ESPN source into the snapshot loader/live polling logic.

✔ Self-check (compileall) and document how to run the ESPN mode with poll examples.

• I’m getting ready to present a thorough, talkative final message that includes a bullet summary referencing specific lines in scripts/value_betting_helper.py, complete run instructions, test mentions, and the full updated file as

requested—all wrapped up with a bit of clever humor and next steps. This will ensure the user has everything needed for a working, understandable result.

• Explored

└ Read value_betting_helper.py

• I’m planning to include the entire large file untruncated in the final message, along with a clear bullet summary that references key line ranges and features. I’ll add run instructions and next steps, sprinkling a bit of light humor

to keep it friendly and engaging.

─ Worked for 5m 41s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

• Here’s what changed while you sipped your coffee:

- ESPN scoreboard ingestion is now first-class: HTTP fetch + JSON normalization + American-odds conversion all live inside the helper, so you can point straight at basketball/nba (scripts/value_betting_helper.py:80-377).

- The CLI grew new switches for scoreboard path/date and reuses the shared poller, so --espn-scoreboard plus --poll-seconds streams real-time edges without any adapter glue (scripts/value_betting_helper.py:603-665).

- Live polling no longer insists on --live-url; any configured live source (custom feed or ESPN) can loop indefinitely with proper banners and Ctrl+C exit (scripts/value_betting_helper.py:625-664).

Tests:

- python -m compileall scripts/value_betting_helper.py

Run it (replace the date if you want a past slate):

- python scripts/value_betting_helper.py --espn-scoreboard basketball/nba --poll-seconds 20 --edge-threshold 0.04

- Need a specific day? python scripts/value_betting_helper.py --espn-scoreboard mens-college-basketball --espn-date 20240115 --poll-seconds 30

Full file (as promised):

#!/usr/bin/env python3

"""

Value betting helper for live basketball markets.

The script ingests scoreboard snapshots (quarter/half scoring) together with

moneyline and totals odds, estimates naive win/total probabilities, and

highlights edges where the model probability beats the implied odds by a user

defined threshold. It is intentionally simple and deterministic so you can

review every step before staking real money.

Usage examples:

python scripts/value_betting_helper.py

python scripts/value_betting_helper.py --edge-threshold 0.04

python scripts/value_betting_helper.py --data-file my_markets.json

python scripts/value_betting_helper.py --live-url https://example.com/feed --poll-seconds 15

python scripts/value_betting_helper.py --espn-scoreboard basketball/nba --poll-seconds 20

"""

from __future__ import annotations

import argparse

import json

import math

import sys

import time

from dataclasses import dataclass

from datetime import datetime

from pathlib import Path

from typing import Any, Dict, List, Optional, Sequence, Tuple

from urllib.error import HTTPError, URLError

from urllib.request import Request, urlopen

from urllib.parse import urlencode

# --- Data model ------------------------------------------------------------

@dataclass

class MarketSnapshot:

league: str

stage: str

matchup: str

home_team: str

away_team: str

quarter_scores_home: Sequence[int]

quarter_scores_away: Sequence[int]

odds_home: float

odds_away: float

total_line: float

odds_over: float

odds_under: float

parts_in_game: int = 4 # quarters for most leagues, halves for NCAA etc.

note: Optional[str] = None

@property

def quarters_played(self) -> int:

return len(self.quarter_scores_home)

@property

def home_total(self) -> int:

return sum(self.quarter_scores_home)

@property

def away_total(self) -> int:

return sum(self.quarter_scores_away)

@property

def total_points(self) -> int:

return self.home_total + self.away_total

@property

def parts_remaining(self) -> int:

remaining = self.parts_in_game - self.quarters_played

return max(remaining, 0)

# --- Helpers ---------------------------------------------------------------

ESPN_API_ROOT = "https://site.api.espn.com/apis/site/v2/sports"

def header_pair(value: str) -> Tuple[str, str]:

"""Parse KEY=VALUE / KEY:VALUE CLI headers."""

for separator in ("=", ":"):

if separator in value:

key, val = value.split(separator, 1)

key = key.strip()

val = val.strip()

if not key:

break

return key, val

raise argparse.ArgumentTypeError(

"Headers must look like KEY=VALUE or KEY:VALUE."

)

def deserialize_snapshots(raw_entries: Sequence[Any]) -> List[MarketSnapshot]:

"""Convert dicts into MarketSnapshot objects with friendly errors."""

snapshots: List[MarketSnapshot] = []

for idx, entry in enumerate(raw_entries):

if not isinstance(entry, dict):

raise ValueError(f"Snapshot {idx} must be a JSON object.")

try:

snapshots.append(MarketSnapshot(**entry))

except TypeError as exc:

raise ValueError(f"Snapshot {idx} has invalid fields: {exc}") from exc

return snapshots

def load_snapshots_from_file(path: Optional[Path]) -> List[MarketSnapshot]:

if path is None:

return deserialize_snapshots(SAMPLE_MARKETS)

raw = json.loads(path.read_text(encoding="utf-8"))

if not isinstance(raw, list):

raise ValueError(f"{path} must contain a JSON list of markets.")

return deserialize_snapshots(raw)

def build_live_headers(user_agent: str, extras: Sequence[Tuple[str, str]]) -> Dict[str, str]:

headers = {

"User-Agent": user_agent or "value-betting-helper/1.0",

"Accept": "application/json",

}

for key, value in extras:

headers[key] = value

return headers

def load_snapshots_from_live(

url: str,

headers: Dict[str, str],

timeout: float,

) -> List[MarketSnapshot]:

request = Request(url, headers=headers or {})

try:

with urlopen(request, timeout=timeout) as response:

charset = response.headers.get_content_charset() or "utf-8"

payload = response.read().decode(charset, errors="replace")

except HTTPError as exc:

raise RuntimeError(

f"Live feed {url} returned HTTP {exc.code}: {exc.reason}"

) from exc

except URLError as exc:

raise RuntimeError(f"Could not reach live feed {url}: {exc.reason}") from exc

try:

document = json.loads(payload)

except json.JSONDecodeError as exc:

raise ValueError(f"Live feed {url} returned invalid JSON: {exc}") from exc

if isinstance(document, list):

entries = document

elif isinstance(document, dict):

if isinstance(document.get("markets"), list):

entries = document["markets"]

elif isinstance(document.get("data"), list):

entries = document["data"]

else:

raise ValueError(

"Live feed JSON must be a list or contain a 'markets'/'data' list."

)

else:

raise ValueError(

"Live feed JSON must be a list or contain a 'markets'/'data' list."

)

return deserialize_snapshots(entries)

def american_to_decimal(price: Optional[Any]) -> Optional[float]:

"""Convert American odds (+110 / -135) to decimal odds."""

if price is None:

return None

if isinstance(price, str):

price = price.strip()

if not price:

return None

try:

price_value = float(price)

except ValueError:

return None

else:

try:

price_value = float(price)

except (TypeError, ValueError):

return None

if price_value == 0:

return None

if price_value > 0:

return round(1.0 + price_value / 100.0, 4)

return round(1.0 + 100.0 / abs(price_value), 4)

def parse_linescores(competitor: Dict[str, Any]) -> List[int]:

scores: List[int] = []

for entry in competitor.get("linescores", []) or []:

value = entry.get("value")

if value is None:

continue

try:

scores.append(int(value))

except (TypeError, ValueError):

continue

if scores:

return scores

total = competitor.get("score")

if total is None:

return scores

try:

return [int(total)]

except (TypeError, ValueError):

return scores

def infer_parts_in_game(home_scores: Sequence[int], away_scores: Sequence[int]) -> int:

observed = max(len(home_scores), len(away_scores))

if observed >= 4:

return 4

if observed == 3:

return 4

if observed == 2:

return 2

return 4

def build_stage_text(status: Dict[str, Any]) -> str:

status_type = status.get("type", {}) if isinstance(status, dict) else {}

return (

status_type.get("shortDetail")

or status_type.get("detail")

or status_type.get("description")

or status_type.get("name")

or "Status unavailable"

)

def load_snapshots_from_espn(

scoreboard_path: str,

scoreboard_date: Optional[str],

headers: Dict[str, str],

timeout: float,

) -> List[MarketSnapshot]:

normalized = scoreboard_path.strip("/")

params = {}

if scoreboard_date:

params["dates"] = scoreboard_date

query = f"?{urlencode(params)}" if params else ""

url = f"{ESPN_API_ROOT}/{normalized}/scoreboard{query}"

request = Request(url, headers=headers or {})

try:

with urlopen(request, timeout=timeout) as response:

charset = response.headers.get_content_charset() or "utf-8"

payload = response.read().decode(charset, errors="replace")

except HTTPError as exc:

raise RuntimeError(

f"ESPN scoreboard {scoreboard_path} returned HTTP {exc.code}: {exc.reason}"

) from exc

except URLError as exc:

raise RuntimeError(

f"Could not reach ESPN scoreboard {scoreboard_path}: {exc.reason}"

) from exc

try:

document = json.loads(payload)

except json.JSONDecodeError as exc:

raise ValueError(

f"ESPN scoreboard {scoreboard_path} returned invalid JSON: {exc}"

) from exc

return convert_espn_events(document, scoreboard_path)

def convert_espn_events(payload: Dict[str, Any], scoreboard_path: str) -> List[MarketSnapshot]:

events = payload.get("events", []) if isinstance(payload, dict) else []

snapshots: List[MarketSnapshot] = []

for event in events:

try:

snapshot = convert_single_espn_event(event, scoreboard_path)

except ValueError:

continue

snapshots.append(snapshot)

return snapshots

def convert_single_espn_event(event: Dict[str, Any], scoreboard_path: str) -> MarketSnapshot:

competitions = event.get("competitions")

if not competitions:

raise ValueError("Missing competitions")

competition = competitions[0]

competitors = competition.get("competitors") or []

home = next((item for item in competitors if item.get("homeAway") == "home"), None)

away = next((item for item in competitors if item.get("homeAway") == "away"), None)

if not home or not away:

raise ValueError("Incomplete competitors")

home_scores = parse_linescores(home)

away_scores = parse_linescores(away)

odds_entry = None

for candidate in competition.get("odds", []) or []:

if candidate:

odds_entry = candidate

break

if not odds_entry:

raise ValueError("No odds data")

total_line_raw = odds_entry.get("overUnder")

if total_line_raw is None:

raise ValueError("Missing total line")

try:

total_line = float(total_line_raw)

except (TypeError, ValueError):

raise ValueError("Invalid total line") from None

def fetch_moneyline(key: str, nested_key: str) -> Optional[Any]:

value = odds_entry.get(key)

if value is not None:

return value

nested = odds_entry.get(nested_key, {})

if isinstance(nested, dict):

return nested.get("moneyLine")

return None

home_ml = fetch_moneyline("moneyLineHome", "homeTeamOdds")

away_ml = fetch_moneyline("moneyLineAway", "awayTeamOdds")

home_decimal = american_to_decimal(home_ml)

away_decimal = american_to_decimal(away_ml)

if home_decimal is None or away_decimal is None:

raise ValueError("Missing moneyline odds")

over_decimal = american_to_decimal(

odds_entry.get("overOdds") or odds_entry.get("overOddsAmerican") or -110

)

under_decimal = american_to_decimal(

odds_entry.get("underOdds") or odds_entry.get("underOddsAmerican") or -110

)

if over_decimal is None or under_decimal is None:

raise ValueError("Missing total odds")

league_name = (

(event.get("league") or {}).get("name")

or payload_league_name(event)

or scoreboard_path.replace("/", " ").title()

)

home_name = team_display_name(home)

away_name = team_display_name(away)

matchup = event.get("name") or event.get("shortName") or f"{home_name} vs {away_name}"

stage = build_stage_text(event.get("status", {}))

parts_in_game = infer_parts_in_game(home_scores, away_scores)

provider = (odds_entry.get("provider") or {}).get("name")

note = f"Odds via {provider}" if provider else None

return MarketSnapshot(

league=league_name,

stage=stage,

matchup=matchup,

home_team=home_name,

away_team=away_name,

quarter_scores_home=home_scores,

quarter_scores_away=away_scores,

odds_home=home_decimal,

odds_away=away_decimal,

total_line=total_line,

odds_over=over_decimal,

odds_under=under_decimal,

parts_in_game=parts_in_game,

note=note,

)

def team_display_name(competitor: Dict[str, Any]) -> str:

team = competitor.get("team") or {}

return team.get("displayName") or team.get("name") or team.get("shortDisplayName") or "Unknown"

def payload_league_name(event: Dict[str, Any]) -> Optional[str]:

season = event.get("season")

if isinstance(season, dict):

league = season.get("type")

if isinstance(league, dict):

return league.get("name")

return None

def load_snapshots(

data_path: Optional[Path],

live_url: Optional[str],

live_headers: Dict[str, str],

timeout: float,

espn_scoreboard: Optional[str],

espn_date: Optional[str],

) -> List[MarketSnapshot]:

if live_url:

return load_snapshots_from_live(live_url, live_headers, timeout)

if espn_scoreboard:

return load_snapshots_from_espn(espn_scoreboard, espn_date, live_headers, timeout)

return load_snapshots_from_file(data_path)

def implied_probability(decimal_odds: float) -> float:

if decimal_odds <= 0:

raise ValueError("Decimal odds must be positive")

return 1.0 / decimal_odds

def clamp(value: float, lower: float = 0.0, upper: float = 1.0) -> float:

return max(lower, min(upper, value))

def logistic(x: float) -> float:

# Numerically stable logistic.

if x >= 0:

z = math.exp(-x)

return 1 / (1 + z)

z = math.exp(x)

return z / (1 + z)

def estimate_home_win_probability(snapshot: MarketSnapshot) -> float:

"""

Quick-and-dirty win probability model.

- Rewards teams for current margin (home_total - away_total)

- Adds a soft home-court edge (1.5 points baseline)

- Scales influence based on game progress: late-game leads matter more

"""

margin = snapshot.home_total - snapshot.away_total

home_edge = 1.5

progress = snapshot.quarters_played / max(snapshot.parts_in_game, 1)

# Early in the game the lead is less impactful.

steepness = 0.18 + 0.35 * progress

raw = (margin + home_edge) * steepness

return clamp(logistic(raw), 0.02, 0.98)

def project_total_points(snapshot: MarketSnapshot) -> float:

"""

Extrapolate the final total by scaling current pace to four quarters /

two halves. Falls back to the current total if no data is available.

"""

if snapshot.quarters_played == 0:

return float(snapshot.total_points)

pace = snapshot.total_points / snapshot.quarters_played

return pace * snapshot.parts_in_game

def probability_from_z(z_score: float) -> float:

"""Approximate normal CDF via error function."""

return 0.5 * (1.0 + math.erf(z_score / math.sqrt(2.0)))

def evaluate_moneyline(snapshot: MarketSnapshot):

home_prob = estimate_home_win_probability(snapshot)

away_prob = 1.0 - home_prob

implied_home = implied_probability(snapshot.odds_home)

implied_away = implied_probability(snapshot.odds_away)

return [

{

"market": "moneyline",

"team": snapshot.home_team,

"odds": snapshot.odds_home,

"model_prob": home_prob,

"implied_prob": implied_home,

"edge": home_prob - implied_home,

{

"market": "moneyline",

"team": snapshot.away_team,

"odds": snapshot.odds_away,

"model_prob": away_prob,

"implied_prob": implied_away,

"edge": away_prob - implied_away,

]

def evaluate_totals(snapshot: MarketSnapshot):

projected_total = project_total_points(snapshot)

diff = projected_total - snapshot.total_line

# Volatility shrinks as the game progresses.

progress = snapshot.quarters_played / max(snapshot.parts_in_game, 1)

base_sigma = 24.0 # rough std dev for totals at halftime

sigma = max(12.0, base_sigma * (1.0 - 0.5 * progress))

prob_over = clamp(probability_from_z(diff / sigma), 0.05, 0.95)

prob_under = 1.0 - prob_over

implied_over = implied_probability(snapshot.odds_over)

implied_under = implied_probability(snapshot.odds_under)

return [

{

"market": "total_over",

"team": "Over",

"line": snapshot.total_line,

"odds": snapshot.odds_over,

"model_prob": prob_over,

"implied_prob": implied_over,

"edge": prob_over - implied_over,

"projected_total": projected_total,

{

"market": "total_under",

"team": "Under",

"line": snapshot.total_line,

"odds": snapshot.odds_under,

"model_prob": prob_under,

"implied_prob": implied_under,

"edge": prob_under - implied_under,

"projected_total": projected_total,

]

def summarize_snapshot(snapshot: MarketSnapshot, threshold: float, show_all: bool):

edges = evaluate_moneyline(snapshot) + evaluate_totals(snapshot)

qualified = [

e for e in edges if show_all or abs(e["edge"]) >= threshold

]

qualified.sort(key=lambda item: item["edge"], reverse=True)

return qualified

def render_snapshot(snapshot: MarketSnapshot, threshold: float, show_all: bool) -> str:

pivots = summarize_snapshot(snapshot, threshold, show_all)

header = f"{snapshot.league} — {snapshot.matchup} ({snapshot.stage})"

lines = [header, "-" * len(header)]

if not pivots:

lines.append(f"No edges above {threshold:.2%}")

return "\n".join(lines)

for edge in pivots:

if edge["market"].startswith("total"):

direction = "Over" if "over" in edge["market"] else "Under"

lines.append(

f"{direction} {edge['line']:.1f} @ {edge['odds']:.2f} | "

f"Model {edge['model_prob']:.1%} vs implied {edge['implied_prob']:.1%} "

f"(edge {edge['edge']:+.1%}, projected total {edge['projected_total']:.1f})"

)

else:

lines.append(

f"{edge['team']} ML @ {edge['odds']:.2f} | "

f"Model {edge['model_prob']:.1%} vs implied {edge['implied_prob']:.1%} "

f"(edge {edge['edge']:+.1%})"

)

if snapshot.note:

lines.append(f"Note: {snapshot.note}")

return "\n".join(lines)

# --- CLI ------------------------------------------------------------------

def parse_args() -> argparse.Namespace:

parser = argparse.ArgumentParser(

description="Highlight value bets based on live basketball snapshots."

)

parser.add_argument(

"--data-file",

type=Path,

help="Path to a JSON file shaped like SAMPLE_MARKETS.",

)

parser.add_argument(

"--edge-threshold",

type=float,

default=0.05,

help="Minimum probability edge to display (default: 5%%).",

)

parser.add_argument(

"--show-all",

action="store_true",

help="Disable filtering so every market is printed.",

)

parser.add_argument(

"--live-url",

help="HTTP(S) endpoint returning JSON shaped like SAMPLE_MARKETS.",

)

parser.add_argument(

"--live-header",

dest="live_headers",

action="append",

type=header_pair,

default=[],

metavar="KEY=VALUE",

help="Extra HTTP header for --live-url (repeat for multiple headers).",

)

parser.add_argument(

"--live-user-agent",

default="value-betting-helper/1.0",

help="User-Agent header for live HTTP requests.",

)

parser.add_argument(

"--http-timeout",

type=float,

default=6.0,

help="Seconds to wait for the live HTTP response (default: 6).",

)

parser.add_argument(

"--poll-seconds",

type=float,

default=0.0,

help="If >0, poll the live URL every N seconds for real-time updates.",

)

parser.add_argument(

"--max-polls",

type=int,

default=0,

help="Stop after this many polls when --poll-seconds is set (0 = infinite).",

)

parser.add_argument(

"--espn-scoreboard",

metavar="SPORT/LEAGUE",

help="Shortcut for ESPN live data, e.g., basketball/nba or mens-college-basketball.",

)

parser.add_argument(

"--espn-date",

help="Optional YYYYMMDD date for ESPN scoreboard (defaults to today).",

)

args = parser.parse_args()

if args.http_timeout <= 0:

parser.error("--http-timeout must be a positive number.")

if args.poll_seconds < 0:

parser.error("--poll-seconds cannot be negative.")

if args.poll_seconds > 0 and not (args.live_url or args.espn_scoreboard):

parser.error("--poll-seconds requires --live-url or --espn-scoreboard.")

if args.max_polls < 0:

parser.error("--max-polls cannot be negative.")

if args.espn_date and not args.espn_date.isdigit():

parser.error("--espn-date must be numeric in YYYYMMDD format.")

return args

def main():

args = parse_args()

headers = build_live_headers(args.live_user_agent, args.live_headers)

def emit_snapshot_block() -> None:

snapshots = load_snapshots(

args.data_file,

args.live_url,

headers,

args.http_timeout,

args.espn_scoreboard,

args.espn_date,

)

if not snapshots:

print("No market snapshots available.")

return

for snap in snapshots:

print(render_snapshot(snap, args.edge_threshold, args.show_all))

print()

if args.poll_seconds > 0:

iteration = 0

try:

while True:

iteration += 1

timestamp = datetime.now().isoformat(timespec="seconds")

banner = f"=== Poll #{iteration} @ {timestamp} ==="

print(banner)

print("-" * len(banner))

try:

emit_snapshot_block()

except (RuntimeError, ValueError) as exc:

print(f"Failed to load live data: {exc}", file=sys.stderr)

if args.max_polls and iteration >= args.max_polls:

break

time.sleep(args.poll_seconds)

except KeyboardInterrupt:

print("\nStopping live polling.", file=sys.stderr)

else:

emit_snapshot_block()

SAMPLE_MARKETS: List[Dict] = [

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Giants Leverkusen vs BG Gottingen",

"home_team": "Giants Leverkusen",

"away_team": "BG Gottingen",

"quarter_scores_home": [18, 11],

"quarter_scores_away": [20, 11],

"odds_home": 2.95,

"odds_away": 1.35,

"total_line": 154.5,

"odds_over": 1.85,

"odds_under": 1.80,

"parts_in_game": 4,

"note": "Sample pulled from live odds feed.",

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Tigers Tubingen vs Giessen 46Ers",

"home_team": "Tigers Tubingen",

"away_team": "Giessen 46Ers",

"quarter_scores_home": [29, 12],

"quarter_scores_away": [18, 17],

"odds_home": 1.37,

"odds_away": 2.80,

"total_line": 177.5,

"odds_over": 1.85,

"odds_under": 1.80,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "VfL Astrostars Bochum vs Paderborn Baskets",

"home_team": "VfL Astrostars Bochum",

"away_team": "Paderborn Baskets",

"quarter_scores_home": [25, 16],

"quarter_scores_away": [20, 17],

"odds_home": 1.10,

"odds_away": 5.40,

"total_line": 171.5,

"odds_over": 1.80,

"odds_under": 1.90,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Eisbaren Bremerhaven vs Artland Dragons",

"home_team": "Eisbaren Bremerhaven",

"away_team": "Artland Dragons",

"quarter_scores_home": [22, 11],

"quarter_scores_away": [21, 13],

"odds_home": 1.50,

"odds_away": 2.40,

"total_line": 171.5,

"odds_over": 1.90,

"odds_under": 1.80,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "PS Karlsruhe Lions vs VFL Kirchheim Knights",

"home_team": "PS Karlsruhe Lions",

"away_team": "VFL Kirchheim Knights",

"quarter_scores_home": [17, 13],

"quarter_scores_away": [22, 15],

"odds_home": 2.00,

"odds_away": 1.70,

"total_line": 167.5,

"odds_over": 1.85,

"odds_under": 1.80,

"parts_in_game": 4,

{

"league": "Germany Pro A",

"stage": "2nd quarter",

"matchup": "Phoenix Hagen vs EPG Baskets Koblenz",

"home_team": "Phoenix Hagen",

"away_team": "EPG Baskets Koblenz",

"quarter_scores_home": [31, 22],

"quarter_scores_away": [27, 6],

"odds_home": 1.85,

"odds_away": 10.50,

"total_line": 191.5,

"odds_over": 1.80,

"odds_under": 1.80,

"parts_in_game": 4,

"note": "Odds/order doubtful in source feed; treat as illustrative.",

{

"league": "USA NCAA",

"stage": "Halftime",

"matchup": "UTSA Roadrunners vs SIU Edwardsville Cougars",

"home_team": "UTSA Roadrunners",

"away_team": "SIU Edwardsville Cougars",

"quarter_scores_home": [23, 23],

"quarter_scores_away": [20, 20],

"odds_home": 1.22,

"odds_away": 3.70,

"total_line": 146.5,

"odds_over": 1.80,

"odds_under": 1.85,

"parts_in_game": 2,

{

"league": "USA NCAA Women",

"stage": "Halftime",

"matchup": "Liberty Flames vs East Carolina Pirates",

"home_team": "Liberty Flames",

"away_team": "East Carolina Pirates",

"quarter_scores_home": [10, 10],

"quarter_scores_away": [5, 5],

"odds_home": 1.12,

"odds_away": 5.20,

"odds_over": 1.80,

"odds_under": 1.85,

"parts_in_game": 4,

{

"league": "Liga ABA",

"stage": "End Q4",

"matchup": "Igokea Aleksandrovac vs KK Split",

"home_team": "Igokea Aleksandrovac",

"away_team": "KK Split",

"quarter_scores_home": [22, 25, 26, 7],

"quarter_scores_away": [23, 18, 24, 7],

"odds_home": 1.04,

"odds_away": 7.00,

"total_line": 179.5,

"odds_over": 1.80,

"odds_under": 1.80,

"parts_in_game": 4,

"note": "Full-time example; model should confirm there is little edge.",

{

"league": "BNXT League",

"stage": "1st quarter",

"matchup": "Antwerp Giants vs Okapi Aalst",

"home_team": "Antwerp Giants",

"away_team": "Okapi Aalst",

"quarter_scores_home": [7],

"quarter_scores_away": [9],

"odds_home": 1.05,

"odds_away": 7.25,

"total_line": 171.5,

"odds_over": 1.85,

"odds_under": 1.80,

"parts_in_game": 4,

]

if __name__ == "__main__":

main()

Next steps (if you want to keep the momentum):

1. Add a lightweight diff so polls only print when odds or scores change—much easier to scan in the middle of a live slate.

2. Log each poll to disk (JSON or CSV) so you can replay the whole betting tape and audit how the model’s edges evolved.

Let me know when you want to point it at another provider or stack on hockey/NFL—happy to keep the feed flowing.

› yes and hook it to auto-refresh

• Auto-refresh is already built in now: the --poll-seconds flag turns the helper into a live loop that re-fetches and prints updated ESPN edges forever (or until Ctrl+C). To hook it up, run something like:

python scripts/value_betting_helper.py \

--espn-scoreboard basketball/nba \

--poll-seconds 20 \

--edge-threshold 0.04

That command:

- Calls the NBA scoreboard every 20 seconds

- Recomputes the moneyline/total edges

- Prints a banner like === Poll #5 @ 2024-02-08T19:03:20 === before each refresh so you can see when the data updated

- Stops only when you Ctrl+C, unless you add --max-polls N to make it exit after N refreshes.

If you want an older slate, tack on --espn-date 20240201. Need different leagues? Swap the path (e.g., --espn-scoreboard mens-college-basketball or --espn-scoreboard womens-basketball).

That’s the whole auto-refresh hookup—no extra watcher needed.

› so do 1 and 2 also there seem to be and issue : --edge-threshold 0.04

=== Poll #1 @ 2025-11-07T22:59:50 ===

-------------------------------------

Basketball Nba — Boston Celtics at Orlando Magic (11/7 - 7:00 PM EST)

---------------------------------------------------------------------

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 227.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Cleveland Cavaliers at Washington Wizards (11/7 - 7:00 PM EST)

-------------------------------------------------------------------------------

Under 240.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Washington Wizards ML @ 5.50 | Model 59.9% vs implied 18.2% (edge +41.7%)

Over 240.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Cleveland Cavaliers ML @ 1.14 | Model 40.1% vs implied 87.5% (edge -47.4%)

Note: Odds via ESPN BET

Basketball Nba — Toronto Raptors at Atlanta Hawks (11/7 - 7:30 PM EST)

----------------------------------------------------------------------

Under 235.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Toronto Raptors ML @ 2.10 | Model 40.1% vs implied 47.6% (edge -7.5%)

Over 235.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Detroit Pistons at Brooklyn Nets (11/7 - 7:30 PM EST)

----------------------------------------------------------------------

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Brooklyn Nets ML @ 4.20 | Model 59.9% vs implied 23.8% (edge +36.1%)

Detroit Pistons ML @ 1.24 | Model 40.1% vs implied 81.0% (edge -40.9%)

Over 227.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Houston Rockets at San Antonio Spurs (11/7 - 7:30 PM EST)

--------------------------------------------------------------------------

Under 224.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

San Antonio Spurs ML @ 2.45 | Model 59.9% vs implied 40.8% (edge +19.1%)

Houston Rockets ML @ 1.59 | Model 40.1% vs implied 63.0% (edge -22.9%)

Over 224.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Charlotte Hornets at Miami Heat (11/7 - 8:00 PM EST)

---------------------------------------------------------------------

Under 234.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Charlotte Hornets ML @ 3.00 | Model 40.1% vs implied 33.3% (edge +6.8%)

Miami Heat ML @ 1.42 | Model 59.9% vs implied 70.6% (edge -10.7%)

Over 234.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Dallas Mavericks at Memphis Grizzlies (11/7 - 8:00 PM EST)

---------------------------------------------------------------------------

Under 232.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 232.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Chicago Bulls at Milwaukee Bucks (11/7 - 8:00 PM EST)

----------------------------------------------------------------------

Under 239.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 239.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Utah Jazz at Minnesota Timberwolves (11/7 - 8:00 PM EST)

-------------------------------------------------------------------------

Under 233.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Utah Jazz ML @ 5.75 | Model 40.1% vs implied 17.4% (edge +22.7%)

Minnesota Timberwolves ML @ 1.13 | Model 59.9% vs implied 88.2% (edge -28.3%)

Over 233.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Golden State Warriors at Denver Nuggets (11/7 - 10:00 PM EST)

------------------------------------------------------------------------------

Under 228.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Golden State Warriors ML @ 4.40 | Model 40.1% vs implied 22.7% (edge +17.4%)

Denver Nuggets ML @ 1.22 | Model 59.9% vs implied 81.8% (edge -21.9%)

Over 228.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Oklahoma City Thunder at Sacramento Kings (11/7 - 10:00 PM EST)

--------------------------------------------------------------------------------

Under 233.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Sacramento Kings ML @ 4.80 | Model 59.9% vs implied 20.8% (edge +39.1%)

Oklahoma City Thunder ML @ 1.18 | Model 40.1% vs implied 84.6% (edge -44.5%)

Over 233.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

=== Poll #2 @ 2025-11-07T23:00:12 ===

-------------------------------------

Basketball Nba — Boston Celtics at Orlando Magic (11/7 - 7:00 PM EST)

---------------------------------------------------------------------

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 227.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Cleveland Cavaliers at Washington Wizards (11/7 - 7:00 PM EST)

-------------------------------------------------------------------------------

Under 240.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Washington Wizards ML @ 5.50 | Model 59.9% vs implied 18.2% (edge +41.7%)

Over 240.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Cleveland Cavaliers ML @ 1.14 | Model 40.1% vs implied 87.5% (edge -47.4%)

Note: Odds via ESPN BET

Basketball Nba — Toronto Raptors at Atlanta Hawks (11/7 - 7:30 PM EST)

----------------------------------------------------------------------

Under 235.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Toronto Raptors ML @ 2.10 | Model 40.1% vs implied 47.6% (edge -7.5%)

Over 235.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Detroit Pistons at Brooklyn Nets (11/7 - 7:30 PM EST)

----------------------------------------------------------------------

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Brooklyn Nets ML @ 4.20 | Model 59.9% vs implied 23.8% (edge +36.1%)

Detroit Pistons ML @ 1.24 | Model 40.1% vs implied 81.0% (edge -40.9%)

Over 227.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Houston Rockets at San Antonio Spurs (11/7 - 7:30 PM EST)

--------------------------------------------------------------------------

Under 224.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

San Antonio Spurs ML @ 2.45 | Model 59.9% vs implied 40.8% (edge +19.1%)

Houston Rockets ML @ 1.59 | Model 40.1% vs implied 63.0% (edge -22.9%)

Over 224.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Charlotte Hornets at Miami Heat (11/7 - 8:00 PM EST)

---------------------------------------------------------------------

Under 234.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Charlotte Hornets ML @ 3.00 | Model 40.1% vs implied 33.3% (edge +6.8%)

Miami Heat ML @ 1.42 | Model 59.9% vs implied 70.6% (edge -10.7%)

Over 234.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Dallas Mavericks at Memphis Grizzlies (11/7 - 8:00 PM EST)

---------------------------------------------------------------------------

Under 232.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 232.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Chicago Bulls at Milwaukee Bucks (11/7 - 8:00 PM EST)

----------------------------------------------------------------------

Under 239.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 239.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Utah Jazz at Minnesota Timberwolves (11/7 - 8:00 PM EST)

-------------------------------------------------------------------------

Under 233.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Utah Jazz ML @ 5.75 | Model 40.1% vs implied 17.4% (edge +22.7%)

Minnesota Timberwolves ML @ 1.13 | Model 59.9% vs implied 88.2% (edge -28.3%)

Over 233.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Golden State Warriors at Denver Nuggets (11/7 - 10:00 PM EST)

------------------------------------------------------------------------------

Under 228.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Golden State Warriors ML @ 4.40 | Model 40.1% vs implied 22.7% (edge +17.4%)

Denver Nuggets ML @ 1.22 | Model 59.9% vs implied 81.8% (edge -21.9%)

Over 228.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Oklahoma City Thunder at Sacramento Kings (11/7 - 10:00 PM EST)

--------------------------------------------------------------------------------

Under 233.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Sacramento Kings ML @ 4.80 | Model 59.9% vs implied 20.8% (edge +39.1%)

Oklahoma City Thunder ML @ 1.18 | Model 40.1% vs implied 84.6% (edge -44.5%)

Over 233.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

=== Poll #3 @ 2025-11-07T23:00:34 ===

-------------------------------------

Basketball Nba — Boston Celtics at Orlando Magic (11/7 - 7:00 PM EST)

---------------------------------------------------------------------

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 227.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Cleveland Cavaliers at Washington Wizards (11/7 - 7:00 PM EST)

-------------------------------------------------------------------------------

Under 240.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Washington Wizards ML @ 5.50 | Model 59.9% vs implied 18.2% (edge +41.7%)

Over 240.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Cleveland Cavaliers ML @ 1.14 | Model 40.1% vs implied 87.5% (edge -47.4%)

Note: Odds via ESPN BET

Basketball Nba — Toronto Raptors at Atlanta Hawks (11/7 - 7:30 PM EST)

----------------------------------------------------------------------

Under 235.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Toronto Raptors ML @ 2.10 | Model 40.1% vs implied 47.6% (edge -7.5%)

Over 235.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Detroit Pistons at Brooklyn Nets (11/7 - 7:30 PM EST)

----------------------------------------------------------------------

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Brooklyn Nets ML @ 4.20 | Model 59.9% vs implied 23.8% (edge +36.1%)

Detroit Pistons ML @ 1.24 | Model 40.1% vs implied 81.0% (edge -40.9%)

Over 227.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Houston Rockets at San Antonio Spurs (11/7 - 7:30 PM EST)

--------------------------------------------------------------------------

San Antonio Spurs ML @ 2.45 | Model 59.9% vs implied 40.8% (edge +19.1%)

Houston Rockets ML @ 1.59 | Model 40.1% vs implied 63.0% (edge -22.9%)

Over 224.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Charlotte Hornets at Miami Heat (11/7 - 8:00 PM EST)

---------------------------------------------------------------------

Under 234.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Charlotte Hornets ML @ 3.00 | Model 40.1% vs implied 33.3% (edge +6.8%)

Miami Heat ML @ 1.42 | Model 59.9% vs implied 70.6% (edge -10.7%)

Over 234.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Dallas Mavericks at Memphis Grizzlies (11/7 - 8:00 PM EST)

---------------------------------------------------------------------------

Under 232.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 232.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Chicago Bulls at Milwaukee Bucks (11/7 - 8:00 PM EST)

----------------------------------------------------------------------

Under 239.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Over 239.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Utah Jazz at Minnesota Timberwolves (11/7 - 8:00 PM EST)

-------------------------------------------------------------------------

Under 233.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Utah Jazz ML @ 5.75 | Model 40.1% vs implied 17.4% (edge +22.7%)

Minnesota Timberwolves ML @ 1.13 | Model 59.9% vs implied 88.2% (edge -28.3%)

Over 233.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Golden State Warriors at Denver Nuggets (11/7 - 10:00 PM EST)

------------------------------------------------------------------------------

Under 228.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Golden State Warriors ML @ 4.40 | Model 40.1% vs implied 22.7% (edge +17.4%)

Denver Nuggets ML @ 1.22 | Model 59.9% vs implied 81.8% (edge -21.9%)

Over 228.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

Basketball Nba — Oklahoma City Thunder at Sacramento Kings (11/7 - 10:00 PM EST)

--------------------------------------------------------------------------------

Under 233.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

Sacramento Kings ML @ 4.80 | Model 59.9% vs implied 20.8% (edge +39.1%)

Oklahoma City Thunder ML @ 1.18 | Model 40.1% vs implied 84.6% (edge -44.5%)

Over 233.5 @ 1.91 | Model 5.0% vs implied 52.4% (edge -47.4%, projected total 0.0)

Note: Odds via ESPN BET

■ Conversation interrupted - tell the model what to do differently. Something went wrong? Hit `/feedback` to report the issue.

› the seem to be a proble: something’s gone sideways inside your model logic.

Let’s tear it down like we’re debugging a trading bot that’s outputting zeros.

⚙️ What’s Happening

You’re polling ESPN BET’s live odds feed, and your model is printing lines like:

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

The key clue: “projected total 0.0” across every game.

That means your model’s total projection formula is failing or returning NaN/None, then defaulting to 0.0.

You’re still computing model probabilities (95% vs 5%), but your core projection engine — the part that estimates the total points — isn’t feeding in real values.

🧩 Root Cause (Most Likely)

Broken Data Feed Parsing

The ESPN feed probably changed field names or structure.

Your script is reading odds fine (1.91), but not reading team stats or total lines correctly.

So when it tries to compute a projected total, the formula sees None or empty string → defaults to 0.

Example:

projected_total = round(model_predict_total(team_stats), 1) if team_stats else 0.0

Disabled or Missing Model Function

If you recently added --edge-threshold 0.04, maybe you bypassed total prediction updates for low-edge lines.

Or your total estimator is commented out / returning 0 pending calibration.

Default Probability Baseline

Every total market shows “Model 95% vs implied 52.4%” because your model fallback is returning 0.95 and 0.05 as static probabilities when no total data exists.

🧠 The “Pattern of Zeros”

The numbers prove it:

Every Under = 95.0%

Every Over = 5.0%

Every Projected Total = 0.0

That’s a hard-coded fallback value.

You’ve got a line somewhere like:

if not data or not projected_total:

model_prob_under, model_prob_over, projected_total = 0.95, 0.05, 0.0

Check your calculate_totals() or predict_total_points() function — that’s where it’s defaulting out.

✅ What Still Works

Your moneyline edges are being computed properly.

For example:

Washington Wizards ML @ 5.50 | Model 59.9% vs implied 18.2% (edge +41.7%)

That’s the same math you used in your Germany Pro A data — and it’s good.

You’re still parsing ML odds correctly, and your edge math is consistent.

So ML = ✅, Totals = ❌ right now.

🧰 Quick Fix Plan

You can patch this in under a minute.

Find the function in your code that prints lines like:

print(f"Under {total_line} @ {odds_under} | Model {model_under:.1%} vs implied {implied_under:.1%} (edge {edge_under:+.1%}, projected total {proj_total:.1f})")

Then backtrack to where proj_total is calculated.

Add a guard clause to verify the input source isn’t empty:

if not projected_total or projected_total == 0:

print("⚠️ Warning: missing projected total input — using fallback")

You might also want to log raw feed fields before parsing:

print(json.dumps(game_data, indent=2))

Run one poll with that debug on, and you’ll immediately see whether ESPN’s feed stopped providing team stats, pace, or totals.

🚀 Summary

Component Status Fix

Moneyline edge calc ✅ Working correctly Leave as-is

Total projections ❌ Broken (projected total = 0) Reconnect feed / verify formula

Edge filter (--edge-threshold 0.04) ✅ Works fine Just filters output

Next step Debug projected_total source Add print or log to confirm inputs

Once you show me the predict_total_points() or the part that computes projected totals (could be in your model.py or compute_edges.py), I can pinpoint exactly where it fails and give you a 2-line patch to bring totals back online.

■ Conversation interrupted - tell the model what to do differently. Something went wrong? Hit `/feedback` to report the issue.

› Yes do:

1. Add a lightweight diff so polls only print when odds or scores change—much easier to scan in the middle of a live slate.

2. Log each poll to disk (JSON or CSV) so you can replay the whole betting tape and audit how the model’s edges evolved.

but first there seem to be a problem, : something’s gone sideways inside your model logic.

Let’s tear it down like we’re debugging a trading bot that’s outputting zeros.

⚙️ What’s Happening

You’re polling ESPN BET’s live odds feed, and your model is printing lines like:

Under 227.5 @ 1.91 | Model 95.0% vs implied 52.4% (edge +42.6%, projected total 0.0)

The key clue: “projected total 0.0” across every game.

That means your model’s total projection formula is failing or returning NaN/None, then defaulting to 0.0.

You’re still computing model probabilities (95% vs 5%), but your core projection engine — the part that estimates the total points — isn’t feeding in real values.

🧩 Root Cause (Most Likely)

Broken Data Feed Parsing

The ESPN feed probably changed field names or structure.

Your script is reading odds fine (1.91), but not reading team stats or total lines correctly.

So when it tries to compute a projected total, the formula sees None or empty string → defaults to 0.

Example:

projected_total = round(model_predict_total(team_stats), 1) if team_stats else 0.0

Disabled or Missing Model Function

If you recently added --edge-threshold 0.04, maybe you bypassed total prediction updates for low-edge lines.

Or your total estimator is commented out / returning 0 pending calibration.

Default Probability Baseline

Every total market shows “Model 95% vs implied 52.4%” because your model fallback is returning 0.95 and 0.05 as static probabilities when no total data exists.

🧠 The “Pattern of Zeros”

The numbers prove it:

Every Under = 95.0%

Every Over = 5.0%

Every Projected Total = 0.0

That’s a hard-coded fallback value.

if not data or not projected_total:

model_prob_under, model_prob_over, projected_total = 0.95, 0.05, 0.0

Check your calculate_totals() or predict_total_points() function — that’s where it’s defaulting out.

✅ What Still Works

Your moneyline edges are being computed properly.

For example:

Washington Wizards ML @ 5.50 | Model 59.9% vs implied 18.2% (edge +41.7%)

That’s the same math you used in your Germany Pro A data — and it’s good.

You’re still parsing ML odds correctly, and your edge math is consistent.

So ML = ✅, Totals = ❌ right now.

🧰 Quick Fix Plan

You can patch this in under a minute.

Find the function in your code that prints lines like:

print(f"Under {total_line} @ {odds_under} | Model {model_under:.1%} vs implied {implied_under:.1%} (edge {edge_under:+.1%}, projected total {proj_total:.1f})")

Then backtrack to where proj_total is calculated.

Add a guard clause to verify the input source isn’t empty:

if not projected_total or projected_total == 0:

print("⚠️ Warning: missing projected total input — using fallback")

You might also want to log raw feed fields before parsing:

print(json.dumps(game_data, indent=2))

Run one poll with that debug on, and you’ll immediately see whether ESPN’s feed stopped providing team stats, pace, or totals.

🚀 Summary

Component Status Fix

Moneyline edge calc ✅ Working correctly Leave as-is

Total projections ❌ Broken (projected total = 0) Reconnect feed / verify formula

Edge filter (--edge-threshold 0.04) ✅ Works fine Just filters output

Next step Debug projected_total source Add print or log to confirm inputs

• I'm figuring out why projected totals show zero for pregame games with no scores yet. The quarter scores are empty or zero, so summing them yields zero, skewing probabilities to extremes. To fix this, I want to fallback to using the

betting line total when no quarter data exists, setting probabilities to 0.5 to reflect uncertainty. I'll amend project_total_points to return the line total if no scoring info is present. This should better align predictions with

real-world betting lines for upcoming games. Next, I’ll add diff printing and logging to help track these changes.

• Updated Plan

└ Need to fix projected totals fallback, add change-detection to poll output, and introduce per-poll logging.

□ Fix total projection logic so pregame snapshots fall back to total lines instead of zero pace.

□ Add change detection so only new/changed markets render between polls.

□ Persist each poll to disk (JSON/CSV) for later analysis and document how to use it.

• Edited ~/scripts/value_betting_helper.py (+4 -0)

435 if snapshot.quarters_played == 0:

436 + # Pre-game snapshots have no scoring data yet; mirror the bookmaker line

437 + # so we don't create artificial 95/5 edges off a zero projection.

438 + if snapshot.total_line:

439 + return float(snapshot.total_line)

440 return float(snapshot.total_points)

• Edited ~/scripts/value_betting_helper.py (+1 -1)

437 # so we don't create artificial 95/5 edges off a zero projection.

438 - if snapshot.total_line:

438 + if snapshot.total_line is not None:

439 return float(snapshot.total_line)

• I’m working out how to track snapshot changes across polls by storing a keyed summary of each event and only printing updates when scores or odds change, with a potential CLI flag for changes-only output. I’m also planning to

implement logging of each poll’s data in JSON or CSV formats so the user can audit scoreboard evolution, including updating snapshot data structures to hold event IDs for reliable tracking.

• Edited ~/scripts/value_betting_helper.py (+1 -0)

39 class MarketSnapshot:

40 + event_id: Optional[str] = None

41 league: str

• Edited ~/scripts/value_betting_helper.py (+1 -1)

39 class MarketSnapshot:

40 - event_id: Optional[str] = None

40 league: str

⋮

53 note: Optional[str] = None

54 + event_id: Optional[str] = None

• Edited ~/scripts/value_betting_helper.py (+1 -0)

348 return MarketSnapshot(

349 + event_id=event.get("id"),

350 league=league_name,

• I’m laying out a comprehensive plan to improve snapshot handling by adding unique keys for snapshots, diffing changes to emit only updates during polling, and introducing JSON and CSV logging of snapshot data. I’ll restructure

functions to collect entries with precomputed summaries, implement printing logic that respects unchanged data, handle removed events, and extend CLI options for log paths and output control, ensuring smooth and efficient snapshot

polling and detailed logging.

• Edited ~/scripts/value_betting_helper.py (+8 -2)

521

522 -def render_snapshot(snapshot: MarketSnapshot, threshold: float, show_all: bool) -> str:

523 - pivots = summarize_snapshot(snapshot, threshold, show_all)

522 +def render_snapshot(

523 + snapshot: MarketSnapshot,

524 + threshold: float,

525 + show_all: bool,

526 + pivots: Optional[List[Dict[str, Any]]] = None,

527 +) -> str:

528 + if pivots is None:

529 + pivots = summarize_snapshot(snapshot, threshold, show_all)

530 header = f"{snapshot.league} — {snapshot.matchup} ({snapshot.stage})"

• Edited ~/scripts/value_betting_helper.py (+36 -0)

554

555 +def snapshot_header(snapshot: MarketSnapshot) -> str:

556 + return f"{snapshot.league} — {snapshot.matchup} ({snapshot.stage})"

557 +

558 +

559 +def snapshot_key(snapshot: MarketSnapshot) -> str:

560 + if snapshot.event_id:

561 + return snapshot.event_id

562 + return f"{snapshot.league}|{snapshot.matchup}"

563 +

564 +

565 +def serialize_snapshot_for_log(

566 + key: str, snapshot: MarketSnapshot, edges: List[Dict[str, Any]]

567 +) -> Dict[str, Any]:

568 + return {

569 + "key": key,

570 + "event_id": snapshot.event_id,

571 + "league": snapshot.league,

572 + "stage": snapshot.stage,

573 + "matchup": snapshot.matchup,

574 + "home_team": snapshot.home_team,

575 + "away_team": snapshot.away_team,

576 + "quarter_scores_home": list(snapshot.quarter_scores_home),

577 + "quarter_scores_away": list(snapshot.quarter_scores_away),

578 + "home_total": snapshot.home_total,

579 + "away_total": snapshot.away_total,

580 + "odds_home": snapshot.odds_home,

581 + "odds_away": snapshot.odds_away,

582 + "total_line": snapshot.total_line,

583 + "odds_over": snapshot.odds_over,

584 + "odds_under": snapshot.odds_under,

585 + "parts_in_game": snapshot.parts_in_game,

586 + "note": snapshot.note,

587 + "edges": edges,

588 + }

589 +

590 +

591 # --- CLI ------------------------------------------------------------------

• Edited ~/scripts/value_betting_helper.py (+1 -0)

21 import argparse

22 +import csv

23 import json

• Edited ~/scripts/value_betting_helper.py (+70 -0)

591

592 +def log_poll_outputs(

593 + json_path: Optional[Path],

594 + csv_path: Optional[Path],

595 + iteration: int,

596 + timestamp: str,

597 + entries: Sequence[Dict[str, Any]],

598 +) -> None:

599 + if not (json_path or csv_path):

600 + return

601 + serialized = [

602 + serialize_snapshot_for_log(entry["key"], entry["snapshot"], entry["edges"])

603 + for entry in entries

604 + ]

605 + if json_path:

606 + json_path = json_path.expanduser()

607 + json_path.parent.mkdir(parents=True, exist_ok=True)

608 + record = {"timestamp": timestamp, "poll": iteration, "entries": serialized}

609 + with json_path.open("a", encoding="utf-8") as handle:

610 + handle.write(json.dumps(record) + "\n")

611 + if csv_path:

612 + csv_path = csv_path.expanduser()

613 + csv_path.parent.mkdir(parents=True, exist_ok=True)

614 + write_header = not csv_path.exists() or csv_path.stat().st_size == 0

615 + fieldnames = [

616 + "timestamp",

617 + "poll",

618 + "key",

619 + "league",

620 + "matchup",

621 + "stage",

622 + "home_team",

623 + "away_team",

624 + "home_total",

625 + "away_total",

626 + "odds_home",

627 + "odds_away",

628 + "total_line",

629 + "odds_over",

630 + "odds_under",

631 + "parts_in_game",

632 + "edges",

633 + ]

634 + with csv_path.open("a", newline="", encoding="utf-8") as handle:

635 + writer = csv.DictWriter(handle, fieldnames=fieldnames)

636 + if write_header:

637 + writer.writeheader()

638 + for data in serialized:

639 + writer.writerow(

640 + {

641 + "timestamp": timestamp,

642 + "poll": iteration,

643 + "key": data["key"],

644 + "league": data["league"],

645 + "matchup": data["matchup"],

646 + "stage": data["stage"],

647 + "home_team": data["home_team"],

648 + "away_team": data["away_team"],

649 + "home_total": data["home_total"],

650 + "away_total": data["away_total"],

651 + "odds_home": data["odds_home"],

652 + "odds_away": data["odds_away"],

653 + "total_line": data["total_line"],

654 + "odds_over": data["odds_over"],

655 + "odds_under": data["odds_under"],

656 + "parts_in_game": data["parts_in_game"],

657 + "edges": json.dumps(data["edges"], separators=(",", ":")),

658 + }

659 + )

660 +

661 +

662 # --- CLI ------------------------------------------------------------------

• Edited ~/scripts/value_betting_helper.py (+15 -0)

729 )

730 + parser.add_argument(

731 + "--print-unchanged",

732 + action="store_true",

733 + help="In poll mode, continue printing every market even if nothing changed.",

734 + )

735 + parser.add_argument(

736 + "--poll-json-log",

737 + type=Path,

738 + help="Append NDJSON poll snapshots to this path for later analysis.",

739 + )

740 + parser.add_argument(

741 + "--poll-csv-log",

742 + type=Path,

743 + help="Append CSV poll snapshots to this path for spreadsheet review.",

744 + )

745 args = parser.parse_args()

• I'm reorganizing the main function to replace the old emit_snapshot_block approach with a clearer structure: gathering snapshot entries, diffing them against prior outputs to show changes only, and gracefully handling empty or

unchanged cases, with careful attention to polling iteration behavior and consistent logging. This involves defining helper functions like collect_entries, run_poll, and tracking previous outputs to manage display and polling loops

more cleanly.

• Edited ~/scripts/value_betting_helper.py (+62 -8)

762

763 - def emit_snapshot_block() -> None:

763 + previous_blocks: Dict[str, str] = {}

764 + previous_titles: Dict[str, str] = {}

765 +

766 + def collect_entries() -> List[Dict[str, Any]]:

767 snapshots = load_snapshots(

⋮

774 )

772 - if not snapshots:

775 + entries: List[Dict[str, Any]] = []

776 + for snap in snapshots:

777 + edges = summarize_snapshot(snap, args.edge_threshold, args.show_all)

778 + text = render_snapshot(

779 + snap, args.edge_threshold, args.show_all, pivots=edges

780 + )

781 + entries.append(

782 + {

783 + "key": snapshot_key(snap),

784 + "title": snapshot_header(snap),

785 + "text": text,

786 + "snapshot": snap,

787 + "edges": edges,

788 + }

789 + )

790 + return entries

791 +

792 + def run_iteration(iteration: int, timestamp: str, polling: bool) -> None:

793 + nonlocal previous_blocks, previous_titles

794 + entries = collect_entries()

795 + if not entries:

796 print("No market snapshots available.")

775 - for snap in snapshots:

776 - print(render_snapshot(snap, args.edge_threshold, args.show_all))

777 - print()

797 + output_entries = entries

798 + removed_titles: List[str] = []

799 + if entries and polling and not args.print_unchanged and iteration > 1:

800 + changed_entries = [

803 + if previous_blocks.get(entry["key"]) != entry["text"]

804 + ]

805 + current_keys = {entry["key"] for entry in entries}

806 + removed_titles = [

807 + previous_titles.get(key, key)

808 + for key in previous_blocks.keys()

809 + if key not in current_keys

810 + ]

811 + if not changed_entries and not removed_titles:

812 + print("No changes since last poll.")

813 + output_entries = []

814 + else:

815 + output_entries = changed_entries

816 + if entries and (output_entries or iteration == 1 or not polling or args.print_unchanged):

817 + for entry in output_entries:

818 + print(entry["text"])

819 + print()

820 + for title in removed_titles:

821 + print(f"[removed] {title}")

822 + log_poll_outputs(

823 + args.poll_json_log,

824 + args.poll_csv_log,

825 + iteration,

826 + timestamp,

827 + entries,

828 + )

829 + previous_blocks = {entry["key"]: entry["text"] for entry in entries}

830 + previous_titles = {entry["key"]: entry["title"] for entry in entries}

831

⋮

841 try:

789 - emit_snapshot_block()

842 + run_iteration(iteration, timestamp, polling=True)

843 except (RuntimeError, ValueError) as exc:

⋮

850 else:

798 - emit_snapshot_block()

852 + run_iteration(1, timestamp, polling=False)

853

• Ran python -m compileall scripts/value_betting_helper.py

└ Compiling 'scripts/value_betting_helper.py'...

• Updated Plan

└ ✔ Fix total projection logic so pregame snapshots fall back to total lines instead of zero pace.

✔ Add change detection so only new/changed markets render between polls.

✔ Persist each poll to disk (JSON or CSV) for later analysis and document how to use it.

• Explored

└ Read value_betting_helper.py

• I'm gathering key code changes and line references to explain fixes like the fallback in lines 433-445 and logging between 556-660 and 731-829. I'll describe the new CLI flags (like --poll-json-log and --print-unchanged), how autop-

run now only prints changed markets by default with diffs ensured by run_iteration logic in lines 792-829, and how to re-enable full output. I'll also cover project scoring fixes, snapshot rendering updates, test instructions, and

propose verifying with real scoreboard data—keeping it conversational and clear!

─ Worked for 7m 54s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

• Fix & Enhancements

- Totals are sane again: when ESPN hasn’t started a game yet, project_total_points now mirrors the bookmaker’s posted line instead of returning zero, so you no longer get bogus 95/5 projections off empty scores (scripts/

value_betting_helper.py:433-445).

- Introduced lightweight change tracking: each poll builds keyed summaries (snapshot_key/header + reuse of precomputed edges) and only prints markets whose odds, scores, or notes actually changed, while still allowing --print-

unchanged if you want the old firehose (scripts/value_betting_helper.py:523-589, 720-744, 766-829).

- Every poll can be replayed later—pass --poll-json-log path.ndjson and/or --poll-csv-log path.csv to append structured snapshots and edges after each refresh (scripts/value_betting_helper.py:592-660, 720-744). Headers are auto-

written, and JSON is newline-delimited for easy ingestion.

How to Use It

- Basic live ESPN run (still prints everything on first poll, then only changes):

python scripts/value_betting_helper.py --espn-scoreboard basketball/nba --poll-seconds 20

- Force every poll to print regardless of changes: add --print-unchanged.

- Persist the tape:

python scripts/value_betting_helper.py --espn-scoreboard basketball/nba --poll-seconds 20 --poll-json-log logs/nba.ndjson --poll-csv-log logs/nba.csv

- Want a historical slate? tack on --espn-date 20241107.

- Single-shot logging works too; even without polling you’ll get one poll record in the log files.

Validation

- python -m compileall scripts/value_betting_helper.py

Next Steps

1. Point the helper at a real slate and watch the diff mode keep the console readable; if you miss a game’s removal notice, check the [removed] … lines.

2. Load the generated NDJSON/CSV into your analytics notebook to backtest how edges evolved; consider adding your staking logic as a post-processing step once you trust the tape.

› yes

• Got it—those fixes are in place. Fire up the ESPN poll with the new flags whenever you’re ready, and shout if you want to tweak the diff/log behavior further.

› Implement {feature}

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