NHL Round 1 Playoff Projections – 2021

These are statistical projections and shouldn’t be the only thing that factors in to betting on a team, the stats only tell part of the story. Keep an eye on injuries & COVID Issues keeping key players out. All stats pulled are TEAM stats and don’t account for individual players.

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*28 May Projections Posted

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Records

Top Performing Models

Based on Average Win % between ATS, Moneyline, and O/U.

Summary of Projections

Consensus Projections

(Started tracking 21 Feb) ATS consensus profit/loss: -17.90 [-0u yesterday]

(Started tracking 25 Mar) ML consensus profit/loss: +8.79 [+0u yesterday]

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Team Variance

Updated every Sunday.

This image has an empty alt attribute; its file name is nhl_var-1.png

What this chart is showing is each teams Variance & Standard Deviation (Std Dev). Variance and Std Dev are calculated from the season stats. The list is ordered from lowest team variance to most team variance. The variance is how wide spread the data is, a team that scores between 0 to 10 goals every night will have very high variance, whereas a team that scores anywhere from 2 to 3 on a given night will have very low variance. The Std Dev is the square root of the variance and is a good measure for how consistent a team is… NOT how good or bad a team is, but how consistent they are. Std Dev shows the amount a teams score typically deviates from the average on a given night. The Jets score (typically) will deviate about 1.33 goals from there average on a given night, where the Capitals score (typically) will deviate 1.81 goals from their average on a given night. Obviously the lower the Std Dev the easier it is for my models to project the score and provide higher probabilities.

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Today’s Projections – Updated Daily

1. Support Vector Machine (SVM)

Model Projections

How to read the projections: The model type is at the very top. Matchups are denoted by the alternating white/green pairings, away teams are on top, home teams on bottom. The model analyzes the matchup and projects the home and away teams score (Proj Score). The difference between the home teams score and the away teams score give us what the model says the line should be (Proj Line). The “Line” column is what the Vegas line is at the time I run the model. The “Line Diff” is the difference in the projected line and the Vegas line. A positive line diff means the projected line is in the away teams favor compared to the Vegas line, negative means the projected line is in the home teams favor. The “Cover Prob” uses a normal distribution and the teams variance to project each teams probability to cover the listed Vegas line. Same thing for totals, “Proj Total” is the sum of the projected scores, “Line Total” is the listed Vegas total for the game, the difference between the two, and the probability to go over or stay under denoted by “O” and “U”. The “ML prob” is the probability of a team to cover the moneyline, or the outright winner.

Betting Edge

If you don’t understand what you are looking at, I recommend reading my post about betting tips. The percentages show the betting Edge, which is the cover prob (from above) minus the implied probability (-110 odds implied prob is 52.4%). If a team has a 92.4% chance to win and a 52.4% implied probability (or -110 odds), the Edge is 40%. The Edge alone doesn’t mean you should blindly bet it. To quantify, >35% is great value, 20-35% is really good, 10-20% is decent, <10 is ok value, blanks are negative value.

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2. Random Forest (R.F.)

Model Projections

How to read the projections: The model type is at the very top. Matchups are denoted by the alternating white/green pairings, away teams are on top, home teams on bottom. The model analyzes the matchup and projects the home and away teams score (Proj Score). The difference between the home teams score and the away teams score give us what the model says the line should be (Proj Line). The “Line” column is what the Vegas line is at the time I run the model. The “Line Diff” is the difference in the projected line and the Vegas line. A positive line diff means the projected line is in the away teams favor compared to the Vegas line, negative means the projected line is in the home teams favor. The “Cover Prob” uses a normal distribution and the teams variance to project each teams probability to cover the listed Vegas line. Same thing for totals, “Proj Total” is the sum of the projected scores, “Line Total” is the listed Vegas total for the game, the difference between the two, and the probability to go over or stay under denoted by “O” and “U”. The “ML prob” is the probability of a team to cover the moneyline, or the outright winner.

Betting Edge

If you don’t understand what you are looking at, I recommend reading my post about betting tips. The percentages show the betting Edge, which is the cover prob (from above) minus the implied probability (-110 odds implied prob is 52.4%). If a team has a 92.4% chance to win and a 52.4% implied probability (or -110 odds), the Edge is 40%. The Edge alone doesn’t mean you should blindly bet it. To quantify, >35% is great value, 20-35% is really good, 10-20% is decent, <10 is ok value, blanks are negative value.

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Series Projections – Round 1

Each series was run through the machine learning algorithms to project the score. Rather than using the last 8 games for team stats like I used all season for daily projections, these projections leveraged stats from 1 Mar through the end of the season (Wanted to throw out teams that started poorly but have been clicking on all cylinders through the middle/late part of the season).

I ran 500 Monte Carlo simulations to project how the series would go. If you don’t know what a Monte Carlo sim is, it’s basically just running the matchups 500 times using a teams Variance to come up with 500 different game results. Using the 500 Games, I counted the first team to 4 wins to get the expected series winner and counted the number of times a team won the series and the series score when a given team won.

The number of wins and win percentage is fairly easy to understand, its just the number of games out of the 500 that a particular team won and its corresponding win %.

The average game margin can tell you how close or how tight the games were. A larger average scoring margin says there were more blowout in favor of that team. a value close to 0 indicates a lot of closer games or an equal number of blowouts form each side.

The average margin of victory in games won tells you how close the games were in games that a team won. A large number typically means that when they did win, they usually crushed them. a small number means that when they did win, it was typically a close game.

Average score is just the average score by each team across all Monte Carlo Sims and the Game total is the average amount of goals scored by both teams across all simulations.

[LR = Linear Regression, kNN = k-Nearest Neighbor, RF = Random Forest, SVM = Support Vector Machine, NN = Neural Network, Ada Boost = Adaptive Boosting]

Bruins vs. Capitals

Using stats from 1 Mar – end of season:

This is a pretty evenly split series with 3 models projecting the Bruins and 3 projecting the Capitals and the 2 top models on opposite sides. I don’t have a whole lot to say about this data, it should be an exciting and fairly even series.

Using stats from the Entire Season:

Including earlier months, the numbers all favor the Bruins a little more than the Capitals.

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Islanders vs Penguins

Using stats from 1 Mar – end of season:

This simulation heavily favors the Penguins to handle the Islanders. Penguins win a majority of the games and the series across all models, with a high likelihood the series goes 5 or 6 games.

Using stats from the Entire Season:

Incorporating the full season data makes this series pretty evenly split with half the models on either side for both the number of games won and the series outcome. The average points scored per game also dropped across all models.

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Wild vs Golden Knights

Using stats from 1 Mar – end of season:

Most models have VGK winning a majority of the games and as a result winning the series in 5-7 games.

Using stats from the Entire Season:

Incorporating the full season data gives VGK a majority of the wins, but the series scores are more evenly split, which to me means the games are closer than using more recent data. This is supported by the decrease in the margin of victory numbers being smaller in the full season data.

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Lightning vs Panthers

Using stats from 1 Mar – end of season:

All of the models pretty heavily favor the Panthers to beat the Lightning. The number of goals scored is decreased from the full season projections below indicating better goalie play for these 2 teams as the season progressed.

Using stats from the Entire Season:

Including the full season the numbers still favor the Panthers slightly, but it is definitely a tighter series between these two.

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Predators vs Hurricanes

Using stats from 1 Mar – end of season:

Most of the models favor the Predators to win this series, although it is projected to be fairly close. I don’t know much about Hockey, but looking at just the numbers and stats, this appears to be a good upset candidate.

Using stats from the Entire Season:

Including earlier months, the numbers are pretty evenly split although the series numbers appear to slightly favor the Hurricanes. I don’t know much about Hockey, but looking at just the numbers and stats, this appears to be a good upset candidate.

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Blues vs Avalanche

Using stats from 1 Mar – end of season:

Most of the models favor the Avalanche in 5-6 games.

Using stats from the Entire Season:

Full season stats still favor the Avalanche although the numbers are a little bit closer than recent stats. The total goals scored average for the full season is also reduced which means the goalie play was worse as the season progressed.

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Canadiens vs Maple Leafs

Using stats from 1 Mar – end of season:

All of the models heavily favor the Maple Leafs to handle the Canadiens

Using stats from the Entire Season:

Looking at the full season, the Maple Leafs are still the favorite.

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Jets vs. Oilers

Using stats from 1 Mar – end of season:

All of the models heavily favor the Oilers to beat the Jets.

Using stats from the Entire Season:

Including earlier months, makes the numbers fairly even, but still slightly favoring the Oilers.

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