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, back to backs, COVID Issues, and players returning from injury. All stats pulled are TEAM stats from the season and don’t account for individual players.
Like what you see? Please subscribe or follow me on Twitter (@AnalyticsB2) for the latest news and post info. Want to support the statistical data or have suggestions for improvement, feel free to send me an email (B2SportsStats@gmail.com) or you can donate via the website, Venmo (@B2stats) or Cashapp ($B2stats).
Top Performing Models
Based on Average Win % between ATS, Moneyline, and O/U.
Summary of Projections
Quick look at how teams have performed recently as compared to their season stats. I pulled the stats from NHL.com from the last 2 weeks (anywhere from 6-8 games depending on the team).
Note: Modified the table to pull last 3 weeks due to the layoff over Christmas. Toronto has only played 1 game in the last 3 weeks and won 6-0 so their stats are skewed on this table.
This chart shows teams that are playing better or worse recently compared to there season as whole (in regards to goals). The stats shown are each teams Goals For per Game (GF/G) both season and recently. The “Off Trend” shows if a team has averaged more goals recently than their season average, which typically indicates an improvement in offense recently. The next 2 columns show the Goals Allowed per Game (GA/G) both season and recent. That is how many goals each team is giving up on average. A positive number in the “Def Trend” column indicates a team is allowing less goals per game and playing better defense lately. The overall trend just adds the Off and Def trend to give an overall goal per game difference compared to the season average. Positive numbers mean the team is playing better recently. The trend numbers are color coded to make it easier to read, Green is positive and indicates improvement compared to season average. Red is negative and indicates a team playing worse compared to season average. What the “Total goals Trend” column shows is teams with a higher positive number (Positive, Shaded in green) are scoring more points and/or allowing more points per game recently, indicating their games have been higher scoring and a likelihood that they have been hitting more overs lately. Teams with lower numbers (Negative, Shaded in red), have been scoring less points and/or allowing less points per game recently, indicating their games have been lower scoring and likely trend towards them hitting more under’s lately. Both the overs and unders hitting will be affected by Vegas adjusting the lines based on recent trends.
Last Years results
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 Panthers score (typically) will deviate about 1.12 goals from there average on a given night, where the Blues score (typically) will deviate 2.16 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.
My Model Choice: Random Forrest
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.
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.