We’re not a month into the season, so it’s finally time to start putting the hitter batted ball distance leaderboard to work. Back in late January, I unveiled my xHR/FB rate equation, which included three components, batted ball distance being one of them. Unfortunately, I don’t have the data for angle and standard deviation, so I cannot calculate xHR/FB rate marks yet and share the biggest discrepancies. However, simply looking at batted ball distance could do a reasonably decent job at identifying those who might be in for a HR/FB rate surge or decline. We’ll start with the potential surgers. These are the hitters whose distance is top notch, but for whatever reason, have posted mediocre or poor HR/FB rates.

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It’s no secret that CC Sabathia has struggled mightily since 2013. It’s also no secret that his troubles have coincided with a swift decline in fastball velocity. Check out his velocity trend since 2011:

Last season he lost over two miles per hour off his fastball to a mark that dipped below the important 90 mph threshold. And yet despite the obvious signs of decline, I remained stubbornly optimistic, thanks to a still respectable SIERA. I (foolishly?) boldly predicted that Sabathia “reminds us of his glory days and earns top 40 starting pitcher value” this year.

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For a change, this week’s pair of recommendations are not the result of injury. One of them could be usable in more than just deep leagues, depending on your specific format, while the other is truly for the deep leaguers.

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Yesterday, I dove into the Baseball-Reference S/Str (swinging strike rate) metric and identified those starting pitchers who have enjoyed the biggest surge compared to last season. So naturally, today I’ll check in on the other side of the coin — those starting pitchers who have suffered through the largest decline in swinging strike rate. Obviously, knowing that a pitcher is inducing fewer swings and misses is worrisome, but we’ll see if there’s any hope for a rebound.

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A week ago, I identified the starting pitchers whose xK% marks were most above their actual strikeout rates. The swinging strike component is the most significant in my xK% equation, so let’s take a look at which pitchers have enjoyed the biggest surge in their S/Str (Baseball-Reference metric) this season. The 2015 numbers do not include yesterday’s starts.

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It’s tier update time! I’m notoriously stubborn when it comes to changing my opinion of a player and three or four starts is far too small a sample to convince me to make any real dramatic adjustments. However, there are some scenarios in which I will reconsider — a change in fastball velocity, a change in pitch mix, an injury, or perhaps the pitcher’s defensive support performing better or worse than expected. That’s really about it. I care little for actual results at this point unless there’s a significant change in results not explained by the aforementioned factors. Like, if Mark Buehrle was suddenly striking out more than a batter per inning. My xK% equation is fantastic in small sample sizes since it’s based on per-pitch metrics and not innings or batters faced. But still, the underlying components themselves could quickly change.

As a reminder, the tiers are named after the best characters from the FXX show, Man Seeking Woman.

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As usual, injuries create opportunity for others. That’s the theme yet again in this week’s edition of the deep league waiver wire.

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Yesterday, I took the first look of the 2015 season at starting pitcher’s xK%. I began with the potential surgers, those pitchers whose expected strikeout rates were most above their actual marks. Today will be the opposite side of the coin. These pitchers have posted strikeout rates significantly above what their mix of strike percentage, swinging, called and foul strike rates suggest. They could be in for strikeout rate collapses if they don’t improve those peripherals.

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As we near the end of the month, our samples remain far too small to perform any serious analysis on. But, that’s really only true when we’re using plate appearances or innings pitched as our denominator. My xK% equation is based on per-pitch metrics, which stabilize much more quickly than anything based on innings. No, I don’t know the actual stabilization point, but since a pitcher has thrown more pitches than he has innings pitched, that’s what’s going to happen.

So let’s take a look at those starting pitchers whose xK% marks are most above their actual strikeout rates. These are the guys with significant upside who should enjoy a strikeout rate surge in the near future.

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The buy low and sell high strategy has been a favorite one of fantasy leaguers for as long as fantasy sports has existed, I would imagine. Unfortunately, it’s nearly dead given the wealth of freely available information and the deeper knowledge we now possess about how to evaluate player performance. Nearly dead, not completely dead. To buy low or sell high on a player, you need a story, a narrative that essentially offers up confirmation bias and makes the owner you’re dealing with feel all fuzzy inside when agreeing to the trade.

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