jiayaoqijia Streak Tracker
Momentum matters in sports betting. This skill helps you identify hot/cold streaks, ATS trends, and over/under patterns to find teams due for regression — or continuation.
Basic Streak Analyzer
Input a team's recent results and get a full streak breakdown:
python3 -c "
def analyze_streak(team, results):
'''
results: list of dicts with keys: opponent, margin, ats_result, total_result
ats_result: 'W'=covered, 'L'=didn't cover, 'P'=push
total_result: 'O'=over, 'U'=under, 'P'=push
margin: point differential (positive = win, negative = loss)
'''
wins = [r for r in results if r['margin'] > 0]
losses = [r for r in results if r['margin'] < 0]
ats_w = [r for r in results if r.get('ats_result') == 'W']
ats_l = [r for r in results if r.get('ats_result') == 'L']
overs = [r for r in results if r.get('total_result') == 'O']
unders = [r for r in results if r.get('total_result') == 'U']
# Current straight-up streak
su_streak = 0
su_dir = 'W' if results[0]['margin'] > 0 else 'L'
for r in results:
if (r['margin'] > 0 and su_dir == 'W') or (r['margin'] < 0 and su_dir == 'L'):
su_streak += 1
else:
break
# Current ATS streak
ats_streak = 0
ats_dir = results[0].get('ats_result', 'P')
for r in results:
if r.get('ats_result') == ats_dir and ats_dir != 'P':
ats_streak += 1
else:
break
avg_margin = sum(r['margin'] for r in results) / len(results)
print(f'=== Streak Report: {team} (Last {len(results)} games) ===')
print()
print(f'SU Record: {len(wins)}-{len(losses)} (current streak: {su_streak}{su_dir})')
print(f'ATS Record: {len(ats_w)}-{len(ats_l)} (current streak: {ats_streak}{ats_dir})')
print(f'O/U Split: {len(overs)}O / {len(unders)}U')
print(f'Avg Margin: {avg_margin:+.1f} pts')
print()
# Momentum signal
if su_streak >= 5:
print(f'🔥 HOT STREAK: {su_streak} straight {su_dir}s')
print(' Beware of regression — market may be overvaluing')
elif su_streak <= -4 or (su_dir == 'L' and su_streak >= 4):
print(f'🥶 COLD STREAK: {su_streak} consecutive losses')
print(' Look for value — market may be undervaluing')
if len(ats_w) >= 7:
print(f'📈 ATS HOT: {len(ats_w)}-{len(ats_l)} ATS — public fading becomes attractive')
elif len(ats_l) >= 7:
print(f'📉 ATS COLD: {len(ats_w)}-{len(ats_l)} ATS — sharp money may target them as fade')
if len(overs) >= 7:
print(f'🔓 OVER TREND: {len(overs)} overs in last {len(results)} — total may be inflated')
elif len(unders) >= 7:
print(f'🔒 UNDER TREND: {len(unders)} unders in last {len(results)} — look for unders')
# Example: Lakers last 10 games
lakers = [
{'opponent': 'OKC', 'margin': +8, 'ats_result': 'W', 'total_result': 'O'},
{'opponent': 'PHX', 'margin': +3, 'ats_result': 'L', 'total_result': 'U'},
{'opponent': 'GSW', 'margin': +15, 'ats_result': 'W', 'total_result': 'O'},
{'opponent': 'DEN', 'margin': +6, 'ats_result': 'W', 'total_result': 'O'},
{'opponent': 'MIA', 'margin': -2, 'ats_result': 'L', 'total_result': 'U'},
{'opponent': 'BKN', 'margin': +11, 'ats_result': 'W', 'total_result': 'O'},
{'opponent': 'NYK', 'margin': +4, 'ats_result': 'W', 'total_result': 'U'},
{'opponent': 'TOR', 'margin': +9, 'ats_result': 'W', 'total_result': 'O'},
{'opponent': 'CHI', 'margin': -5, 'ats_result': 'L', 'total_result': 'U'},
{'opponent': 'ATL', 'margin': +7, 'ats_result': 'W', 'total_result': 'O'},
]
analyze_streak('Los Angeles Lakers', lakers)
"
Regression-to-Mean Detector
Find teams statistically due for a reversal:
python3 -c "
def regression_signal(team, win_pct, ats_win_pct, avg_point_diff, games=10):
'''
Identify if a team's recent results look sustainable or due for regression.
'''
print(f'=== Regression Analysis: {team} ===')
print(f'Win %: {win_pct:.1%} | ATS Win %: {ats_win_pct:.1%} | Avg Margin: {avg_point_diff:+.1f}')
print()
signals = []
# Win % check
if win_pct > 0.80:
signals.append(('🔴 SELL', f'Win% ({win_pct:.0%}) is unsustainably high — regression likely'))
elif win_pct < 0.20:
signals.append(('🟢 BUY', f'Win% ({win_pct:.0%}) is depressed — bounce candidate'))
# ATS sustainability
if ats_win_pct > 0.75:
signals.append(('🔴 FADE ATS', f'ATS% ({ats_win_pct:.0%}) over 10 games never holds — market adjusts'))
elif ats_win_pct < 0.25:
signals.append(('🟢 BACK ATS', f'ATS% ({ats_win_pct:.0%}) — oddsmakers likely over-adjusted'))
# Margin sustainability
if avg_point_diff > 18:
signals.append(('⚠️ WARN', f'Avg margin +{avg_point_diff} pts is extraordinary — injury risk or schedule soft spot'))
elif avg_point_diff < -15:
signals.append(('⚠️ WARN', f'Avg margin {avg_point_diff} pts — could be tanking or missing key players'))
# Pythagorean expectation proxy
if avg_point_diff > 0 and win_pct < 0.40:
signals.append(('🟢 VALUE', 'Outgaining opponents but losing — likely due for positive variance'))
elif avg_point_diff < 0 and win_pct > 0.60:
signals.append(('🔴 OVERRATED', 'Win% driven by luck — negative margins say fade'))
if not signals:
signals.append(('⚪ NEUTRAL', 'Results appear sustainable — no strong regression signal'))
for label, msg in signals:
print(f'{label}: {msg}')
# Example
regression_signal('Boston Celtics', win_pct=0.90, ats_win_pct=0.80, avg_point_diff=+14.5, games=10)
print()
regression_signal('Washington Wizards', win_pct=0.20, ats_win_pct=0.30, avg_point_diff=-8.2, games=10)
"
Home/Away Split Tracker
Some teams are completely different animals at home vs. away:
python3 -c "
def home_away_split(team, home_results, away_results):
def stats(results):
wins = sum(1 for r in results if r['margin'] > 0)
ats = sum(1 for r in results if r.get('ats_result') == 'W')
avg = sum(r['margin'] for r in results) / len(results) if results else 0
return wins, len(results) - wins, ats, len(results) - ats, avg
hw, hl, hats_w, hats_l, havg = stats(home_results)
aw, al, aats_w, aats_l, aavg = stats(away_results)
print(f'=== Home/Away Split: {team} ===')
print(f'{'':20} {'HOME':>12} {'AWAY':>12} {'DIFF':>10}')
print(f'{'SU Record':20} {hw}-{hl:>10} {aw}-{al:>10}')
print(f'{'ATS Record':20} {hats_w}-{hats_l:>10} {aats_w}-{aats_l:>10}')
print(f'{'Avg Margin':20} {havg:>+11.1f} {aavg:>+10.1f} {havg-aavg:>+9.1f}')
print()
diff = havg - aavg
if diff > 10:
print(f'⚠️ MAJOR HOME/AWAY SPLIT: +{diff:.1f} pts at home vs away')
print(' Adjust line significantly when they travel')
elif diff > 5:
print(f'Notable split: +{diff:.1f} pts home advantage above league avg')
home = [
{'margin': +12, 'ats_result': 'W'}, {'margin': +8, 'ats_result': 'W'},
{'margin': +3, 'ats_result': 'L'}, {'margin': +20, 'ats_result': 'W'},
{'margin': +7, 'ats_result': 'W'},
]
away = [
{'margin': -3, 'ats_result': 'L'}, {'margin': +2, 'ats_result': 'W'},
{'margin': -8, 'ats_result': 'L'}, {'margin': -1, 'ats_result': 'L'},
{'margin': +5, 'ats_result': 'W'},
]
home_away_split('Denver Nuggets', home, away)
"
Back-to-Back Fatigue Filter
NBA/NHL games on zero rest produce massive ATS edges:
python3 -c "
def b2b_analysis(b2b_results, rested_results):
def pct(results, key, val):
hits = sum(1 for r in results if r.get(key) == val)
return hits / len(results) if results else 0
b2b_ats = pct(b2b_results, 'ats_result', 'W')
rest_ats = pct(rested_results, 'ats_result', 'W')
b2b_margin = sum(r['margin'] for r in b2b_results) / len(b2b_results) if b2b_results else 0
rest_margin = sum(r['margin'] for r in rested_results) / len(rested_results) if rested_results else 0
print('=== Back-to-Back Fatigue Analysis ===')
print(f'On B2B: ATS {b2b_ats:.0%} Avg Margin {b2b_margin:+.1f}')
print(f'Rested: ATS {rest_ats:.0%} Avg Margin {rest_margin:+.1f}')
print(f'Fatigue penalty: {b2b_margin - rest_margin:+.1f} pts per game')
print()
if b2b_ats < 0.35:
print('🔴 SIGNIFICANT B2B FADE SPOT — this team struggles mightily with no rest')
print(' Lean toward fading them on second night of back-to-back')
elif b2b_ats < 0.45:
print('⚠️ Slight B2B disadvantage — worth noting as tiebreaker')
else:
print('✅ Team handles back-to-backs reasonably — no strong fade signal')
# Example
b2b = [
{'margin': -6, 'ats_result': 'L'}, {'margin': -11, 'ats_result': 'L'},
{'margin': +2, 'ats_result': 'L'}, {'margin': -4, 'ats_result': 'L'},
{'margin': -9, 'ats_result': 'L'},
]
rested = [
{'margin': +10, 'ats_result': 'W'}, {'margin': +5, 'ats_result': 'W'},
{'margin': -2, 'ats_result': 'L'}, {'margin': +8, 'ats_result': 'W'},
{'margin': +12, 'ats_result': 'W'},
]
b2b_analysis(b2b, rested)
"
Quick Reference
| Pattern | Betting Implication |
|---|---|
| 5+ SU win streak | Fade — market overvalues, line inflated |
| 0-5 ATS on the road | Automatic home-team ATS lean |
| 7+ overs in a row | Look for sharp under plays |
| B2B on road | One of strongest consistent ATS edges |
| Win% > margin | Regression coming — fade |
| Margin > Win% | Positive variance pending — back |
Author
Created by Ian Alloway — Data Scientist specializing in sports analytics and ML.
License
MIT License