2018, the Year of the Pitching Wave

In 2015 the Cubs saw their primary core all come up at around the same time.  Bryant, Russel, and Schwarber all came onto the scene to join the 2014 prospects of Baez and Soler.  This same kind of tidal wave of talent can happen with pitching in 2018 and 2019.

I provided some information on them below and these are the guys that are ranked in the Cubs top 25.  These guys all have 1-2 starter type potential, and if they flunk out of starting pitching they could become prime bullpen arms.

So when Arrieta and Lackey are no longer here, we will have a few of these guys to try out and see if they could stick in the 2018 rotation.  If all things hit, like they did with position players, we could see a staff similar to the Mets and the Indians, ripe with young arms that throw fast.

The below analysis can be found at MLB.com:

Dylan Cease, age 20, expected 2018

As one of the best high school power arms in the 2014 Draft, Cease projected as a possible first-rounder before he hurt his elbow that March. The injury ultimately required Tommy John surgery, though that didn’t dissuade the Cubs from paying him $1.5 million in the sixth round. That gamble could pay off big, as he has more upside than any pitcher in Chicago’s farm system.

Cease reached 97 mph with his fastball before he got hurt and hit 100 shortly after he returned to the mound last summer. He sits in the mid-90s with his heater, which also features life that makes it even tougher to barrel. He has turned what was a three-quarters breaking ball into a true power curveball that one club official likened to Dwight Gooden’s.

Like most young pitchers with rocket arms, Cease needs to refine his changeup and use it more. Though he’s not very physical, he’s able to generate premium stuff with athleticism and arm speed rather than excessive effort in his delivery. The Cubs have helped him clean up his mechanics some and he should be able to repeat them efficiently enough to fill the strike zone.

Duane Underwood, age 22, expected 2018

Though he flashed a 98-mph fastball and overpowering curveball as a high school senior, Underwood fell to the second round of the 2012 Draft because he lacked consistency. He had only sporadic success in his first two pro seasons after signing for $1.05 million, but began to take off in 2014 after dedicating himself to improved conditioning. He sat out two months in 2015 with elbow inflammation and has been less sharp this season, when he missed time with forearm inflammation.

Underwood’s fastball is notable for both its 92-96 mph velocity and its late life, which makes it difficult to square up for hitters. Both his curveball and changeup show signs of becoming plus pitches but neither is fully reliable yet. Getting in better shape has helped his control, though his walk rate spiked when he got to Double-A in 2016.

Underwood doesn’t miss as many bats as his stuff indicates he should, demonstrating his need to get more consistent with his secondary pitches and his command. If he can do that, he’ll reach his ceiling as a No. 2 starter. If not, he might find more success as a late-inning reliever.

Oscar De La Cruz, age 21, expected 2018

De La Cruz originally tried out for teams as a 6-foot-4 shortstop in the Dominican Republic before signing with the Cubs as a pitcher for $85,000 in October 2012. He spent his first two pro seasons in the Rookie-level Dominican Summer League before making a huge leap forward in 2015, performing so well in extended spring training that he skipped a level to the Short Season Northwest League. He got off to a late start in 2016 after coming down with a sore elbow during Spring Training.

The most physical pitcher in Chicago’s system, De La Cruz is bigger than his listed 6-foot-4 and 200 pounds and still has projection remaining. His present stuff already is enticing, starting with a 92-95 mph fastball that can reach 97 and plays up because of its movement, angle and plane. His curveball lacks consistency but features power and shows signs of becoming a plus offering.

De La Cruz’s changeup is less refined but he exhibits some feel for the pitch. He has good athleticism and body control for a big pitcher, allowing him to repeat his delivery and pound the strike zone.

Trevor Clifton, age 21, expected 2018

Though he was extremely raw as a Tennessee high schooler in 2013, Clifton still earned a $375,000 bonus in the 12th round because he had a live arm and projectable body. Minor League pitching coordinator Derek Johnson (now the Brewers’ pitching coach) and low Class A South Bend pitching coach Brian Lawrence helped him simplify his delivery and make the transition from thrower to pitcher, and Clifton made significant strides in 2015.

Clifton has gotten stronger since turning pro, adding about 40 pounds to his 6-foot-4 frame, and his quick arm delivers consistent 92-94 mph fastballs that have reached as high as 97. He’s doing a better job of staying on top of his curveball, which features tight spin and could become a plus pitch. His changeup has improved into at least an average offering that helps him keep left-handers at bay.

Clifton is still developing his control and command but is making steady progress in that regard. He has the upside of a mid-rotation starter with the fallback of becoming a power-armed reliever. He might operate in the mid 90s in shorter stints, and there may be more velocity in his tank.


Another New Stat, Contact WAM

I have felt that Wins Above Replacement has a very low starting bar for measuring how valuable a player is (starting at around 47 wins for a team of 0 WAR players) so I have been thinking of a way to improve on that.   With Contact WAM (wins above the mean) I have used what is called a z-score to determine how well a person performs with a contact instance at the plate.

The technical explanation of what it does is taking any contact instance resulting in a safe hit (single, double, triple, and a home run) and using how often a player gets that per at bat.  I then take the league mean (average) of the same per at bat number and get a number for each player of how well they performed above or below the league mean by using z-scores for each contact instance.

Here are the results for the Cubs hitters over 200 at bats this year.

Name Contact WAM
Kris Bryant 3.65
Chris Coghlan 3.35
Anthony Rizzo 3.34
Dexter Fowler 3.00
Chris Denorfia 2.03
Starlin Castro 1.47
Jorge Soler 1.44
Kyle Schwarber 0.92
Addison Russell 0.73
Miguel Montero -0.003

I think this determines how well a hitter performs when they make contact compared to the league average.  This is not adjusted for position or for the league they are in.

In the next few days I plan on messing with this more and coming up with a way to reduce theses numbers to include non-contact instances such as walks and strikeouts, and contact instances resulting in an out.  The goal is to try to start with an 81 win starting point for a team and seeing if this stat can approach a teams actual win result by using z-scores away from the mean.

Right now the total Contact WAM is just under 20, which would put the Cubs at 101 wins (81 +20), which is obviously too high so a correction is in order to include the outs, which I will have to figure out how to do soon.

Correlated Run Contribution

I do believe I have settled on a name for what I want to call this correlation stat for an at bat instance.  It comes with finding how the leagues batting average, on base percentage, and slugging percentages correlates with average earned runs per nine innings (ERA).  In the previous post I had shown how little batting average correlates with earned run average and am going to cover how on base, and slugging percentages correlate with ERA.

OBP graphIn the above graph it is hard to determine how much on base percentage correlates to earned runs but that is why Excel has the handy correlation coefficient.  So for the stat that I created I found the correlation to be at .83, which is much higher than what batting average was, which is not surprising in the least.

Now let’s take a look at slugging percentage:

SluggingSlugging percentage when combined with ERA on a graph makes it almost look like an exact correlation, and it almost is with a correlation coefficient of .94, and if I were to graph OPS it would show a correlation of .97, which is statistically significant.  So what to do with all these correlations?

I ended up just taking a batting average, an on base percentage and a slugging percentage and multiplying them by it’s correlation coefficient, thus reducing them to it’s true effect on creating a run, and then I added all those percentages up to get what I call the correlated run contribution.

So in the national league the league leaders in 2014 looked like this:

Andrew McCutchen                    1.04
Giancarlo Stanton                       1.03
Anthony Rizzo*                            .99
Justin Morneau*                          .97
Buster Posey                                  .96
Yasiel Puig                                     .95
Matt Kemp                                    .94
Josh Harrison                              .943
Jayson Werth                               .935
Jonathan Lucroy                         .933

If you were to rank the top ten hitters by OPS, a few guys would shift around here, Puig would be ahead of Posey and Morneau, and Freddie Freeman would have knocked out Lucroy of the top ten here.  So what is the difference?  The slight advantage a hitter has in batting average, so if a hitter had a higher batting average but lower OPS there were times where the .62 correlated run contribution made a large enough difference to be more valuable than getting on base.

This is the kind of result I had intended to see when creating this stat, as I thought that although OPS had an incredibly high correlation to runs being created, it did leave out the anomalous hitters who hit for high contact and thus have higher batting averages.  So in some cases, some hitters that hit for higher average, but draw fewer walks can indeed contribute more to a run scored than a guy who hits for a lower average but walks more, of course they would have to be very close to each other in OPS for the contact hitter to jump ahead.  Thus if you are GM and you had two similar OPS hitters in free agency and needed a 3-5 hitter you would probably want the guy who had a higher CRC, and if you were looking for a 1-2 hitter a guy with higher OPS.

My New Correlation Stat with no Name

So I am trying to find out which batting instances contribute the most to creating runs.  Right now I am playing around with it just to see what kind of correlation coefficient I get.  Which tries to show how much an instance contributes to a higher ERA, which I am using ERA because it is the best way for me to measure runs minus any errors.  Maybe I will change that method as well as time goes on and I play with this more.

But here is my very first correlation using batting average per year and earned run average per year, league wide.  Surprisingly batting average only has a correlation of .621, which typically a strong correlation wants to be near .90 or higher to be significant.

Now when you plot batting average and earned runs on a graph, with the data normalized they do appear to have a connection, just not significant enough.

The orange line is batting average and blue line is ERA.  Because I am new with Excel graphs and with MSPaint features, this graph looks pretty anemic, and I promise as I do this more often my skills will improve in that regard, but you can get the general idea here.

BAERA My intent after running this through several different statistical categories like batting average, OBA, OPS, Slugging, etc…  Is to find out which instances have the highest correlations to earned runs and to add a weighted number to each instance and come up with a new stat!  We will see how this goes.   Also I am taking ideas on a name for this stat, please leave any suggestions via comments or email.