NHL Projection Model
Why Poisson?
Hockey scoring fits the Poisson distribution well. Goals are relatively rare events (teams average around 3.05 per game), they arrive roughly independently throughout the game, and the variance-to-mean ratio of NHL goal totals sits close to 1.0, which is exactly what Poisson assumes. That lets the model compute each team's expected scoring rate (lambda) and generate a full 9x9 score matrix with one clean probability for every possible scoreline.
Hockey has a structural quirk: 5-on-5 play, power plays, and the goaltender all contribute to scoring in different ways. The model handles this by computing lambda from a 4-component composite covering 5v5 expected goals (the most predictive team-level metric in hockey), goaltending quality, special teams, and recent form. Flat situational adjustments for home ice, back-to-back fatigue, and travel are applied on top. The starting goaltender is the single highest-leverage variable, capable of swinging win probability by 3-5 percentage points on its own, so the model invests heavily in goalie projection.
Core Formula
x Goalie_Mult + Special_Teams_Net + Situational_Adj
Each team's lambda represents their expected goals for the game. The two lambdas are fed into the Poisson PMF to produce a scoreline probability matrix, then regulation ties are split using a 54% home OT win rate to derive full-game win probabilities.
Composite Weights
The model blends four factors to determine team strength. 5v5 expected goals (xG) gets the largest share because it filters out goaltending and special-teams noise, isolating how well a team generates and suppresses scoring chances at even strength. This is the most stable and predictive signal in hockey analytics.
Goalie Projection (Hockey Marcels)
Goalie save percentage is notoriously noisy, requiring thousands of shots to stabilize. The model uses a "Hockey Marcels" projection system that blends multiple seasons of data with declining weights, then regresses toward the league average by adding 1525 phantom shots at league-average SV%. On game day, the Marcel baseline is blended with the goalie's recent starts to capture current form.
| Parameter | Value |
|---|---|
| Current season | 100% weight |
| Prior season | 60% weight |
| Two seasons ago | 50% weight |
| Three seasons ago | 30% weight |
| Regression shots | 1525 |
| Game-day: recent starts | 35% |
| Game-day: baseline | 65% |
Home Ice & Situational
Home ice advantage in the NHL is modest but real. The home team gets the last change (favorable matchups) plus a crowd effect. Back-to-back games are a significant factor: fatigue affects both skaters and goalies, and the data shows a clear drop in save percentage when a goalie starts both ends of a back-to-back.
| Parameter | Value |
|---|---|
| Home ice goals | +0.175 |
| Home ice win% | +3.5% |
| Colorado altitude | +0.05 extra goals |
| Vegas bonus | +0.03 extra goals |
| B2B goals penalty | -0.175 |
| B2B win% penalty | -4.0% |
| B2B goalie SV% drop | 0.01 |
Special Teams Baselines
Power play and penalty kill performance is measured using a blend of expected goals (xG, which captures shot quality) and actual conversion rates. The xG component gets more weight because actual PP/PK rates are heavily influenced by shooting/save percentage luck and take much longer to stabilize.
| Parameter | Value |
|---|---|
| League PP% | 21% |
| League PK% | 79% |
| xG vs actual blend | 60% xG / 40% actual |
League Baselines
These league-wide averages are the reference point for all team ratings. A team's attack strength, for example, is their 5v5 xG/60 divided by the league average. A ratio above 1.0 means they generate more scoring chances than typical.
| Parameter | Value |
|---|---|
| Goals per game | 3.05 |
| 5v5 xG/60 | 2.485 |
| SV% | .9 |
| Shooting% | 9.5% |
Minimum Edge Thresholds
Hockey's low-scoring nature means small probability differences translate to meaningful edges. Puck line requires the highest threshold because the 1.5-goal spread is essentially a binary bet on whether the game is decided by 2+ goals, which is a notoriously volatile market.
| Parameter | Value |
|---|---|
| Moneyline | 3.0% |
| Puck Line | 3.5% |
| Total | 2.5% |
| Team Total | 2.5% |
| Player Prop | 4.0% |
Markets Explained
The NHL model produces fair odds for the four markets below. Hockey's low-scoring profile makes totals the most efficient market and puck line the most volatile, since a single empty-netter in the final minute can flip a puck line bet.
Moneyline
BOS -140 / TOR +120Straight pick on who wins the game in regulation, overtime, or shootout. The starting goalie is the single highest-leverage input, and a save-percentage swing of 0.010 can shift a moneyline by 10-15 cents. Avoid betting any game where the starting goalie is still listed as TBD.
Puck Line (-1.5)
BOS -1.5 +155 / TOR +1.5 -175A 1.5-goal spread in a sport that averages ~6 goals per game, effectively asking whether the favorite wins by multiple goals. Empty-net situations (down a goal in the final minute, pulling the goalie) make the puck line swingy. The model requires a larger edge here (highest threshold of any NHL market).
Game Total (Over/Under)
O 6.0 -115 / U 6.0 -105Combined regulation + OT goals. The model's strongest edges here come from goalie-vs-goalie matchups the market has underreacted to. A true backup starting against a low-SV% team moves the total noticeably. Half-lines (5.5, 6.5) are preferred over 6.0 to eliminate pushes.
Team Totals
BOS O 3.5 -110 / BOS U 3.5 -110A single team's goal total. Useful for plays that isolate one side's offense (hot power play vs. suppressed PK opponent) without needing a view on the opposing team. Also used when the model has a confident read on one team's scoring while the opposing pace signal is ambiguous.
Model Track Record
A published full-season backtest for NHL is still in progress. Hockey's lower-sample signal (82 games vs 162) combined with the central, high-variance role of goaltending makes walk-forward backtesting a substantial project. Until those numbers are published, the History page is the honest audit trail. It shows every graded bet from live pipeline runs with CLV and P&L attached, and performance there is the real, live track record.
When to Trust This Model (and When Not To)
NHL's reliability depends almost entirely on goalie and rest information being accurate at the time of the projection.
- Games with confirmed starting goalies (morning skate reports)
- Mid-season matchups where both teams have 30+ games of data
- Non-B2B scenarios with normal rest (1-2 days off)
- Games between teams whose special-teams units are stabilized
- Goalie TBD at pipeline run time (a surprise backup swings the number)
- First week of the season (no current-year sample)
- Trade deadline week, when rosters change faster than the model re-rates
- Teams mid-coaching-change, where system shifts can take 5-10 games to show up
- Playoff games (the model is regular-season calibrated)
The parameter values on this page are served live from the model configuration and refresh periodically; when a weight or threshold changes, this page reflects it automatically.