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.

## Records

#### Top Performing Models

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

Updated after todays games.

- Random Forest – 60%
- kNN -58%
- SVM – 57%
- Neural Network – 56%
- Ada Boost – 55%
- Linear Regression – 52%

## Summary of Projections

#### ATS Projections

(Started tracking 21 Feb) ATS consensus profit/loss: -6.47u [-1.71u yesterday]

#### Moneyline Projections

#### O/U Projections

# Model Projections

## 1. 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.

Hey, love the charts! Just curious how it is that the RF model seems to be so much better than all the others? Curious how the models differ

Many thanks!

Andrew Riley

First Vice President, Investments

Marcus & Millichap,

Brokerage

(416) 585-4667 direct

(416) 206-7550 mobile

The Data the take in is the same, but how they go about projecting future events is very different. Random forest is a decision tree based approach (just like Ada Boost but different), kNN basically groups things together and uses similar historic events to project future events, neural network basically uses a network of computations to try to project, Linear Regression essentially weights each stat based on historical data then uses recent stats to project future scores, so on and so forth. There is a blog called https://towardsdatascience.com/ I highly recommend it, it helped me when I was getting my masters and still reference it from time to time when I’m unfamiliar with something (like when I built an Ada Boost model this year which I have never touched before).