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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An unambiguously true fact: Matt Holliday is an old man in baseball years and in dog years. Another unambiguously true fact: Holliday has been remarkably consistent throughout his career. Hear ye:

• He has hit at least 20 home runs in nine straight years, a streak currently matched only by David Ortiz and Miguel Cabrera;
• His walk rate has hovered between 10 and 12 percent for the better part of a decade, and his strikeout rate has always resided in the mid-teens;
• He has averaged 647 plate appearances per season dating back to 2006;
• And honestly, I don’t know if I’ve ever seen a batted ball profile so absurdly consistent.

Yet a slight dip in power and a career-worst batting average (.272, aka what some players can only dream of hitting) in 2014 couldn’t have come at a worse age, and it all has fans and projection systems alike running for the hills, as shown below.
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The Steamer and I series concludes as I finish with the last starting pitcher comparisons. Today, I check in on two pitchers of whom the Steamer projections are significantly more optimistic than my Pod Projections.

As per SIERA, Zack Greinke is coming off the second best skills of his career, driven in part by a rebound in his slider usage. He’s managed to post sub-3.00 ERAs for two straight seasons now, which has made him the 10th pitcher off the board on average in NFBC drafts. James Shields hit the free agent jackpot by signing with a National League team and remaining in a pitcher friendly home park. He, too, is loved by drafters, who are nabbing him 22nd among starters in NFBC. Let’s see why Steamer and I disagree on these two fine lads.

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The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X, and the process continues to evolve and improve.

After winning both the American League MVP and Cy Young awards back in 2011, Justin Verlander followed up strongly the next season, finishing second in the Cy Young voting. But in 2013, his fastball velocity slipped for a third straight year, which drove a decline in his peripherals and resulted in his highest SIERA mark since 2008. It got even worse last season, as his velocity dipped another mile per hour, his strikeout rate fell to its lowest mark since 2006, and he couldn’t strand runners at the rate he used to, while poor defense behind him added more fuel to the fire. All told, his ERA jumped above 4.00 for just the second time of his career.

At age 32 and with a seemingly clear explanation for his recent struggles in the form of the velocity loss, it’s no surprise that fantasy owners aren’t sure what to expect from Verlander this season. On average, he’s just the 46th starting pitcher off the board in NFBC leagues at pick 186, and his pick range sits between 93 and 253. That’s quite the discount for someone with his track record who may have had just one poor season. So let’s get to the projecting.

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The Steamer and I series continues this week as I move onto starting pitcher comparisons. Today, I check in on two pitchers of whom the Steamer projections are significantly more bearish than my Pod Projections.

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