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, and players returning from injury.

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**Next Project:** Projections for MLB DFS. Look for periodic updates via twitter. I’m shooting for a mid May release.

All double headers are accounted for being 7 innings.

I will post my top model that I use daily (RF), along with the top 2 performing models.

*Not all Lines are posted, I will update projections once lines for all games are posted.

## Summary of Projections

#### Model Record

#### Model Rank

#### Consensus Record

#### Consensus Profitability

#### Model Consensus

## Sharp Report

ML – Some Sharp money has come in on the Orioles (small), Cubs, & Mariners.

O/U – Some Sharp money has come in on the White Sox/Yankees Over 7.5, D-Backs/Rockies Over 11, Cubs/Cardinals Under 8.5, Red Sox/Phillies Under 9, & Mariners/Padres Over 7.5.

## 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 (need a larger sample size than 8 games). 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 1 and 2 runs every night will have very low variance, whereas a team that scores anywhere from 0 to 10 runs on a given night will have very high 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 Mets score (typically) will deviate about 2.15 runs from there average on a given night, where the Reds score (typically) will deviate 4.10 runs 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.

## Starting Pitcher Expected Regression

**Note:** Not all pitchers are in the data source used for xERA resulting in an #Value! error.

This chart shows each of todays starting pitchers ERA along with expected and advanced stats. Basically, a Pitcher who has a higher ERA with lower FIP and Expected ERA (xERA), indicates the pitcher has performed better than what the stats indicate. Conversely, a pitcher with a low ERA but higher FIP & xERA indicate he has benefitted from a few “Lucky Bounces,” but has not been as good as his ERA indicates. ERA+ is a comparison of how their ERA compares to other pitchers in the league adjusted for for ballpark related factors.

FIP (Fielding Independent Pitching) is a way to project the pitchers ERA taking into account only what the pitcher can control. This is the most common way for stat nerds like me to see if a Pitcher is pitching better than or worse than traditional stats suggest. If the FIP is lower than the ERA, I expect the Pitcher to pitch better than past performances, if the FIP is higher than the ERA, I expect the pitcher to be worse than past performances. ERA Value is ERA minus FIP, Positive indicates FIP is better that ERA, negative means FIP is worse than ERA.

In short, ERA+ essentially adjusts for the ballpark and compares the pitcher to the league average. ERA+ of 100 indicates the league average after adjusting for ballpark, above 100 indicates better than average, less than 100 indicates worse than average. To make sense of what’s displayed, the ERA+ Percentage shows how much better (or worse if negative number) the starter’s ERA is compared to the league average ERA, assuming all ballparks are the exact same. The Percentages may seem odd at first when you see a guy is 200% better or worse than the league average, so if you need some additional help understanding Here is a short, quick, easy to understand explanation. This is a good way to factor in something like a Rockies Pitcher who typically pitches in Home Run City (Denver, Colorado), but today is on the road in a more average ballpark. From ERA+ we can also get expected win percentage, which is good to look for expected regression too. for example, If a pitcher that is 4-0, but has an expected win % of 50%, they should probably be 2-2, but may have benefitted from a lot of run support or a bit of luck.

Expected ERA (xERA) is based on expected weighted on base percentage (xwOBA) and is converted to ERA form. It is formulated using exit velocity, launch angle and, on certain types of batted balls, Sprint Speed. It accounts for things like how hard batters are hitting a certain pitcher. The idea is if a pitcher gets hit incredibly hard but right to a fielder for an entire game, he is a bit lucky and his stats will look better than they should. Next game I would expect some of those hard hit balls to find gaps or go over the fence and the pitcher to perform worse. Conversely, a pitcher who gives up a lot of hits in a game from weak contact like bloop shots, dribblers down the line, etc, the xERA will be lower than the actual EAR and I would expect next game the batters don’t get as lucky on the weak contact finding holes to fall into.

# Model Projections

## My Model Choice:

## Random Forest

Undecided Pitchers default to league average stats.

#### Betting Edge

## Top Performing Models:

## 1. Neural Net

Undecided Pitchers default to league average stats.

#### Betting edge

## 2. Ada Boost

Undecided Pitchers default to league average stats.