For the first time this year, I decided to compare my Pod Projections to Steamer and discuss the players we disagreed on most. Of the hitters we both projected, it was clear I was much more optimistic than Steamer on the whole. However, for Jose Abreu I was actually significantly more pessimistic.

In the review of this series, I will be including my original Pod Projection, and Steamer counting stat projections extrapolated over the same number of plate appearances that I had projected. However, I included Steamer’s actual PA projection in that column. Also included are the player’s 2015 stats, plus the counting stats extrapolated over the number of PA I projected.

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Yes, the 2016 projections are available thanks to Steamer. Well, they are kind of available. The projections’ playing times are all set to 600 PA for hitters, 200 IP for starters and 65 IP for relievers. There is no middle ground right now. The playing time estimates will be included from our depth charts once the postseason is over and the hot stove season is well under way.

I have gone ahead and included the Standard Gain Points value I calculated from the 2014 NFBC leagues from the 2014 season to give the players an overall rank. I will eventually get around to calculating the 2015 SGP values, but these numbers will give owners an initial estimate of the player’s value at the set playing time values.

Here are the hitters and pitchers tables and the values can be download with the link after both tables.

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Well, sorry for no analysis with this final week of starting pitcher values. Eno and I are traveling and I waited as long as I could to try to get this week’s starters. I implemented the changes people recommended last week which is the team(s) the pitcher is facing and the park factor(s). Most of the over-explanation is in last week’s article. Just let us know if there are additional changes you would like to see.

Well, I have had making a two-start evaluator on my to-do list for a while. With the help of Jonah Pemstein, the project is done. A problem is its usefulness with only two weeks left in the season. I am going to roll it out anyway and take comments over the next couple of weeks. It will then be ready for full implementation to start next season.

The setup is pretty simple right now.
•Find the games a pitcher is expected to start this upcoming week.
• Determine the starters projected Steamer stats per start.
• Use my Standing Gain Points formula I calculated to start the season to give each pitcher a weekly value. Then rank them by this value.

This is all the data available for now, but running it for the first time I found a few ways to improve it going forward.
• I ran into a coding error for pitchers who have no more projected starts (end of the list like Hudson or Moore) according to Steamer. I am trying to get the depth charts updated to make sure this doesn’t happen in the future.
• On the same note, the stats for pitchers who are swingman (starter and reliever) may be a bit inflated because of the improved reliever rates.
• Wins are just funky but needed to show the value of the chance to pick up two Wins. Ideas?
• The strength of opponent and park factors are not shown or worked in yet. I am working on this portion now but wanted to make the list available before next week.

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Javier Baez has been getting quite a bit of acclaim since returning to the Cubs with his reduced strikeout rate. In 2014, Baez had a 42% strikeout rate (K%) in the majors and 2015 value of 22% K% looks to be a huge improvement. Don’t get too excited about this small sample of data as his swing strike rate (SwStr%) points to a strikeout rate which will likely increase quickly.

Historically, a hitter’s strikeout rate correlates almost identically to their swinging strike rate. The reason to use SwStr% instead of K%  is because it stabilizes a bit faster. I took all hitters since 2002 who had at least 200 plate appearances in a season and found the linear correlation between their strikeout rate and swinging strike rate. The R-squared between the two values was .947 with the equation working out to:

K% = 2.25 * SwStr%

With this little bit of information, here is a look at Baez’s stats in the majors the past two seasons.

Season: SwStr%, Predicted K%, Actual K%
2014: 19.2%, 43.2%, 41.5%
2015: 18.5%, 41.6%, 22.2%

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If you are looking for analysis here, sorry but there is none. What follows though is the information that might help others do their analysis. At the very least, you’ll have better information on shifted hitters and which teams which employ the shift the most.

Top 30 Players (Basically, any slow left-handed hitter)

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Weekly update:

• First release of 2015 data.
• I combined some of the categories and found some stabilization points with the details in this article.

Pitchers’ Approach Attacking Hitters

Robert Arthur at Baseball Prospectus has shown pitchers will change their approach depending on the hitter’s talent level. Here is a complete list of the number of fastballs (including sinkers) thrown to each hitter of the past two years divided into half seasons. Also the number of pitches in the strike by half season is included along with the fastball percentage in the strike zone.

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Mike Podhozer put out the following question the other day asking if is luck or skill that Brandon McCarthy has such a high HR/FB%.

Prove that Brandon McCarthy‘s HR/FB Rate is Not Just Bad Luck

I started looking at the question several ways and came up with a final conclusion that HR/FB is probably not the perfect stat to use when trying to determine if a pitcher has been lucky or unlucky giving up home runs.

Let me start by going off on a tangent. I am of the camp that players with a huge upswing are the reason groundball pitchers, like McCarthy, have a higher than expected home per fly ball rates. All but the most upward swings will get on top of a sinking ball and drive the ball downward into the ground. The hitter with an upswing will be the ones hitting this sinking pitch. In my opinion, each pitcher will have a subset of players who swing in line with his pitch plain and crush those pitches for home runs.

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Recently, I made an initial push to get a deeper look at hitters. I felt people have enough information on pitchers, especially with the Pitchf/x data available. I finally had some time to dig into the information a little more and have come up with a couple updates.

Nine batted ball categories is too many

Inside Edge makes avaialble nine non-bunt categories for batted balls. Here are the original nine with the xBABIP and wOBAcon:

Batted Ball Type: xBABIP, wOBAcon, % of batted balls
Groundball – Weak: .151, .112, 31.4%
Groundball – Medium: .461, .416, 9.5%
Groundball – Well-Hit: .647, .610, 3.8%
Line Drive – Weak: .622, .579, 2.3%
Line Drive – Medium: .650, .638, 7.3%
Line Drive – Well-Hit: .719, .815, 11.1%
Flyball – Weak: .078, .074, 18.5%
Flyball – Medium: .069, .081, 8.2%
Flyball – Well-Hit: .641, 1.168, 7.8%

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Weekly update:

• Add a bunch of times to first.
• On batted ball, I am trying to correctly combine categories batted ball categories. With categories combined, it will take less time for a hitter’s batted ball profile to stabilize. I hope to have a major update on this area in the next week or two. After that, I can start getting some real values for the stats begin to stabilize.
• Here is a link to the data in an Excel format. For some reason I can only embed OpenOffice files.

Pitchers’ Approach Attacking Hitters

Robert Arthur at Baseball Prospectus has shown pitchers will change their approach depending on the hitter’s talent level. Here is a complete list of the number of fastballs (including sinkers) thrown to each hitter of the past two years divided into half seasons. Also the number of pitches in the strike by half season is included along with the fastball percentage in the strike zone. I will begin adding 2015 information as it becomes available.

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