Webbie

PHI @ NYM Under 7
DeGrom at home has allowed 38 runs in 25 games started over two years! That is a 1.87 ERA with average against of .209. Impressive for sure. Eickhoff of PHI came over from TEX. He already has 3 starts against the Mets. Only allowing 7 runs in those 3 games and an average against of .189. These teams rank in the lower half of the league on offense currently. PHI 21st and NYM 23rd. There is expected to be a wind of 13 mph from the west which is blowing in or across from left field at Citi Field. I would expect there to be limited hits and no power keeping the score down.

TEX Holland ML Pick: Santiago is the starter for LAA tonight. He is 2-6 9GS in April. He is 1-4 L5 home vs Texas. He does not have terrible personal ERA or average against numbers but there is more to this story. Holland is 8-1 L9 vs LAA. Add to that, The LAA are the 2^nd worst offense in MLB right now (only SD worse) as they have scored a grand total of 1 run this season! TEX is 11-4 L15 vs LHP

FREE PICK LAD Maeda ML
SD has the softest lineup in MLB. Cashner has good stats against LAD but not wins (3-7). He also struggles versus key LAD hitters. Gonzalez, Crawford, and Turner hit him! I don’t believe SD hitters can get it done against someone they have not seen. They have 6 hits in the whole series.

Both starting pitchers Liriano (Pit) and Wainwright (STL) have been around the MLB block. They have each started Opening Day games in the past. Wainwright is coming back from season-long injury in 2015.

The official Wikipedia definition of regression to the mean is this. In statistics, regression toward (or to) the mean is the phenomenon that if a variable is extreme on its first measurement, it will tend to be closer to the average on its second measurement—and if it is extreme on its second measurement, it will tend to have been closer to the average on its first. This information can be used to aid us in baseball. Fantasy league analysis sites use this to help decide which hitters will stay hot. We can certainly use this information to our benefit when handicapping baseball games too. 

Charting GameScore for a pitcher the best way I have found to use regression. GameScore is a score earned by the starting pitcher based on how they pitched regardless of wins and losses. It is my preferred way to gauge starting pitcher performance. GameScore is a formula Bill James created. It rewards pitchers for outs and strikeouts but downgrades them for hits and walks, and runs. Anyway, the idea is to compare the scores. Pitchers are human and will have peaks with valleys in their performance, we are charting those scores to  predict when those peaks and valleys are! It is not as difficult to do as it sounds.

In general, you'll need to find the running history of GameScore for the pitcher. ESPN stats is good for this. The pitcher will have an average for the season and month. I use those as the "mean". Look at  the last start, the combined average of the last three starts and a combined average for the last seven starts for the pitcher. We are comparing the recent performance to the mean. We are looking to see if he is over the mean with a possible regression back to it, or he is undervalued and going to perform up to the mean. Always look to see how many starts since his last poor outing or bomb. Good pitchers have 3-5 starts between bombs. Ideally, you want your pitcher to be performing without being in the regression zone. However, when they are susceptible to a stinker, do an in-depth research of his hitter matchups! If he does poorly versus the hitters he faces today, it just might be his regression day and our winning wager day. 

I have included the proper scoring for GameScore. It is listed below. The GameScore is listed in each box score summary as well.

Game Score is a metric devised by Bill James to determine the strength of a pitcher in any particular baseball game. To determine a starting pitcher's game score:

Start with 50 points.
Add one point for each out recorded, so three points for every complete inning pitched.
Add two points for each inning completed after the fourth.
Add one point for each strikeout.
Subtract two points for each hit allowed.
Subtract four points for each earned run allowed.
Subtract two points for each unearned run allowed.
Subtract one point for each walk.

Think of this. We can to some degree predict when a pitcher will perform below his normal mean. We can also predict the opposite, so a pitcher will perform above his mean. Getting good at these scenarios throughout the season will provide untold opportunities we can capitalize on.