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.

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).

*Updated with 24-25 June projections

## Records

#### Top Performing Models

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

## Summary of Projections

#### Consensus Projections

## Team Variance

Updated every Sunday.

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.

# 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.

## 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.

## 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]*