When I analyze a starting pitcher’s strikeout rate spike, I want to see that it’s driven by an increase in swings and misses induced. It could come from other avenues — a higher rate of strikes thrown or more called or foul strikes. But in my eyes, the most believable way to sustain that strikeout rate surge is by generating additional whiffs. So below are the seven American League starting pitchers that have increased their SwStk% marks the most compared to last season.

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Welcome back to another edition of the deep league waiver wire, where there’s another opportunity to take advantage of an injury and a preemptive move for an imminent returnee.

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Three years ago, I introduced the best pitcher expected walk percentage formula yet. The formula uses a pair of strike type rates found at Baseball-Reference.com, including a pitcher’s in-play strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates are per pitch, rather than per inning or per batter. The equation isn’t nearly as strong as my xK% one, as it’s clearly missing sequencing, which may or may not be a consistent skill. But it’s pretty darn good and the best we have at the moment.

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Three years ago, I introduced the best pitcher expected walk percentage formula yet. The formula uses a pair of strike type rates found at Baseball-Reference.com, including a pitcher’s in-play strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates are per pitch, rather than per inning or per batter. The equation isn’t nearly as strong as my xK% one, as it’s clearly missing sequencing, which may or may not be a consistent skill. But it’s pretty darn good and the best we have at the moment.

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It’s that time of year again, American League starting pitcher tier update time! You are no doubt well aware by now that ERA means literally nothing to me this early in the season. Player movement between tiers will only occur when there’s a change in underlying skill, pitch mix, or velocity.

Tiers are named for the best characters on the brilliant FXX show, Man Seeking Woman.

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If you’re hurting for outfield help in your deep mixed or mono league, this week’s deep league waiver wire is to the rescue!

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Two years ago, I modified the first equation I developed to yield an improved expected strikeout rate formula. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates as per pitch, rather than per inning or per batter.

Yesterday, I discussed the starting pitchers whose xK% most exceed their actual strikeout rates. Today, I’ll look at the other side of the list — those starting pitchers whose actual strikeout rates most exceed their xK% marks. These pitchers are at significant risk for regression.

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Two years ago, I modified the first equation I developed to yield an improved expected strikeout rate formula. The formula uses a trio of strike type rates found at Baseball-Reference.com, including a pitcher’s looking, swinging, and foul strike percentages, as well as his overall rate of strikes thrown. The beauty of the equation is that it uses components that stabilize quickly, as the rates as per pitch, rather than per inning or per batter. I calculated the xK% marks for all qualified starting pitchers, compared it to their xK% marks, and sorted. Let’s discuss those with the most significant potential upside, as suggested by xK%.

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Let’s begin our discussion of a foursome of American League starting pitchers saddled with inflated ERAs by presenting two tables first:

Pitching Metric YoY Correlations

Metric	YoY Correlation 2002-2012
WHIP	0.430
ERA	0.373
LOB%	0.238
BABIP	0.235
HR/FB	-0.029

SOURCE: http://www.fangraphs.com/blogs/basic-pitching-metric-correlation-1955-2012-2002-2012/

Pitching Metric Stabilization Points

Metric	Stabilization Point
HR/FB	400 fly balls
BABIP	2,000 balls in play

SOURCE: http://www.fangraphs.com/library/principles/sample-size/

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Let’s go deep down into the depths of your free agent pool. It’s where the risks are great and hidden treasures may emerge. Today’s theme is speculating on two guys that could remain worthless, but will cost you next to nothing to find out.

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