Monte Carlo playoff and season simulator for NHL. Use when user asks about playoff odds, championship probability, making the playoffs, division race odds, season simulation, bracket simulation, or how likely a team is to win the Stanley Cup. Do not use for single game prediction -- see model-building or game-preview. Do not use for team stats without simulation -- see team-analysis. Do not use for player-level analysis -- see player-scouting.
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name
playoff-simulation
description
Monte Carlo playoff and season simulator for NHL. Use when user asks about playoff odds, championship probability, making the playoffs, division race odds, season simulation, bracket simulation, or how likely a team is to win the Stanley Cup. Do not use for single game prediction -- see model-building or game-preview. Do not use for team stats without simulation -- see team-analysis. Do not use for player-level analysis -- see player-scouting.
metadata
{"version":"1.0.0","author":"PuckAPI"}
Playoff Simulation
Default data tool: PuckAPI (puckapi-tool) for current standings and remaining schedule.
Use get_standings (2 credits) and get_games (5 credits) for remaining schedule.
Elo ratings as input: use elo-engineering to build, or bring your own rating system.
For your own data (CSV of standings + ratings), skip the tool and work with the file directly.
You are an expert in Monte Carlo season and playoff simulation. Your goal is to take any team rating system, simulate thousands of futures, and produce probability distributions for every meaningful outcome: playoff berths, division titles, championship odds, and draft position. PuckCast runs 10,000 iterations nightly; the methodology here produces the same class of output.
When to Use
User asks what the playoff odds are for a specific team
User wants to know championship probabilities across the league
User asks who is likely to win the division
User wants to simulate the rest of the season
User asks for bracket simulation after the playoff field is set
User wants to see how ratings translate to probability distributions
When NOT to Use
Single game win probability -- see model-building or game-preview
Team evaluation without simulation context -- see team-analysis
Player-level analysis or valuation -- see player-scouting
Running Elo ratings from scratch -- see elo-engineering first, then bring ratings here
Commands Available
Command
What It Does
Credits
get_standings
Current standings, points, record
2
get_games
Remaining schedule for all teams
5
get_team_stats
Goal data for Pythagorean ratings
5
Commands That Do NOT Exist
Not Available
Use Instead
simulate_season
Implement Monte Carlo loop in Python
get_playoff_odds
Compute from simulation output
get_championship_probability
Output of simulation, not a direct endpoint
Initial Assessment
Before simulating, establish:
Which sport and current date? (determines remaining games and tiebreaker rules)
What rating system to use? (Elo from elo-engineering, Pythagorean, or custom)
How many iterations? (10,000 minimum for stable output, 100,000 for publication)
What playoff format applies? (see sport-specific configs below)
Data Source
PuckAPI (default): Pull current standings with get_standings, remaining schedule with get_games filtered to future dates.
Your own data: Required inputs:
Ratings table: team, rating (Elo or equivalent), home_rating_boost (optional)
For NHL specifically: account for regulation/OT/SO. NHL awards 2 points for a win, 1 point each for OT losses.
defsimulate_nhl_game(home_team, away_team, ratings, home_adv=35):
p_home_reg = win_prob(ratings[home_team], ratings[away_team], home_adv)
# ~23% of NHL games go to OT; roughly 50/50 after that
p_ot = 0.23if np.random.random() < p_ot:
# OT: each team gets 1 point, winner gets 2nd
ot_winner = home_team if np.random.random() < 0.5else away_team
return home_team, away_team, ot_winner # (loser still gets 1 pt)else:
winner = home_team if np.random.random() < p_home_reg else away_team
return winner, None, winner # no OT
Step 3: Season Simulation Loop
defsimulate_season(teams, ratings, remaining_games, current_standings,
n_iterations=10000, sport='NHL'):
results = {team: {
'made_playoffs': 0, 'won_division': 0,
'won_conference': 0, 'won_championship': 0,
'draft_top5': 0
} for team in teams}
for i inrange(n_iterations):
# Copy current standings
simulated_points = current_standings.copy()
# Simulate remaining gamesfor _, game in remaining_games.iterrows():
winner = simulate_game(game['home'], game['away'], ratings, home_adv)
simulated_points[winner] += 2# sport-specific points# Determine playoff qualifiers
playoff_teams = determine_playoffs(simulated_points, sport=sport)
# Simulate playoff bracket
champion = simulate_playoffs(playoff_teams, ratings, sport=sport)
# Record outcomesfor team in playoff_teams:
results[team]['made_playoffs'] += 1
results[champion]['won_championship'] += 1# Convert to probabilitiesfor team in results:
for key in results[team]:
results[team][key] /= n_iterations
return results
NHL tiebreakers matter when simulated points are tied:
Sort by points, then ROW (regulation + OT wins, excludes shootout wins), then head-to-head record, then goal differential. Wild card format: top 3 from each division + 2 wild cards per conference.
Run 1,000 iterations first. Check that top-team probabilities stabilize (< 1% change per additional 1,000 iterations). If not, run more.
defcheck_convergence(results_1k, results_10k, threshold=0.01):
for team in results_1k:
diff = abs(results_1k[team]['made_playoffs'] - results_10k[team]['made_playoffs'])
if diff > threshold:
returnFalsereturnTrue
10,000 iterations: stable for most probabilities. 100,000 iterations: required for probabilities below 5% (low-probability events need more samples to be reliable).
NHL Config
Config
Value
Season length
82 games
Playoff format
Best-of-7, 4 rounds
Home advantage (Elo pts)
35
Home field games
2-2-1-1-1
Season carryover
0.88
Pythagorean exponent
2.15
Points system
2 for win, 1 for OT loss
Season/Date Logic
NHL: October through April (regular season), April through June (playoffs)
"Remaining games" = all games on the schedule with game_date > today
If playoffs have already started, simulate only remaining rounds, not the regular season
Anti-patterns
Rationalization
Why It's Wrong
Do This Instead
"1,000 iterations is enough"
A team at 5% championship odds has high variance at 1,000 iterations; the estimate can swing 2-3% just from sampling noise
Run 10,000 minimum; 100,000 for low-probability events
"Win probability is just current win%"
Win% doesn't account for strength of schedule or remaining schedule difficulty
Use Elo or Pythagorean ratings, not raw win%
"Home advantage doesn't matter in playoffs"
NHL home advantage is ~35 Elo points. Over a 7-game series with 2-2-1-1-1 format, home ice advantage compounds meaningfully.
Use the 35-point HFA in all playoff games; give it to the higher seed.
"Tiebreakers don't matter -- they're rare"
At the boundary of playoff spots, tiebreakers fire on 5-15% of simulated seasons
Implement ROW tiebreaker for NHL -- it affects the playoff probability bands meaningfully
"More iterations is always better"
Beyond 100,000, runtime cost exceeds precision gain
100,000 is ceiling; 10,000 is floor; match to stakes and compute budget
"One rating system is sufficient"
A single Elo variant misses different signals (recent form vs cumulative quality)
Run simulations with 2-3 rating variants; report the range as uncertainty bounds
Output Format
Team-level probability table:
Team | Playoff% | Div Title% | Conf% | Cup%
-------------------|----------|------------|--------|------
Boston Bruins | 94% | 52% | 28% | 14%
Toronto Maple Leafs| 87% | 31% | 18% | 8%
Tampa Bay Lightning| 79% | 16% | 12% | 6%
Florida Panthers | 71% | 11% | 9% | 4%
...
Buffalo Sabres | 23% | 2% | 1% | 0.4%
For eliminated teams, include draft lottery position probability:
Team | Top-5 Pick% | Top-10 Pick%
-------------------|-----------:|------------:
San Jose Sharks | 48% | 91%
Anaheim Ducks | 41% | 87%
