There is just so much fun you can have analyzing the batted ball distance leaderboard. The possibilities are seemingly endless and we haven’t even scratched the surface of exactly how we could use this data. But since we know distance is highly correlated with HR/FB rate, we could look at the leaders and laggards to predict who might be in for a surge or decline in their respective HR/FB rates. The key, of course, is that the player maintains a similar batted ball distance. We’re still dealing with a relatively small sample size here, so these potential surgers will only surge if they keep hitting balls as far as they have on average.

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Collin McHugh is an example of precisely why fantasy owners shy away from spending a whole lot on pitching at their auctions/drafts. There is simply so much more value that comes along during the season from pitchers plucked from free agency that it makes sense to spend the majority of your budget on offense. McHugh has only started three games, but if he did qualify for the leaderboard, he’d rank fourth among all starters in strikeout percentage. Naturally, 99% of fantasy owners are likely asking themselves the same question — Collin McWho?

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Are you desperate for a second baseman or middle infielder in your deep league? Then you’ve come to the right place! I’ve got a pair of possible free agents ripe for the picking just for you.

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Yesterday, I discussed the April batted ball distance surgers. So naturally, today I’ll check in on the decliners. Because regression to the mean is such a powerful force, a distance decline sticks more often than a surge does. In other words, I would be more concerned about a decliner than excited about a surger.

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Now that we’re more than a month into the season, we finally have enough data to start taking batted ball distance numbers seriously. When developing my latest xHR/FB rate equation, I limited the player population to include only those who recorded at least 20 home runs and fly balls. At this point, the majority of the leaderboard sits between 20 and 25, so let’s dig in and start by looking at which hitters have experienced the largest increase since last season.

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It’s updated tier week! As usual, these rankings represent my fantasy value expectations over the rest of the season. While I am not completely ignoring what has happened so far, its effect on my rankings is to merely expand the body of work by a pitcher from which to analyze. Unless there is a dramatic change in underlying skills that looks sustainable or an injury, there shouldn’t be a whole lot of movement after just 30 to 40 innings pitched.

While the preseason tier rankings were technically in descending order of my projected value, most pitchers within a tier are so close to each other that you could basically consider them interchangeable. An extra win, an additional 10 strikeouts, a .290 BABIP versus .295 BABIP are all pretty much random, but can shift a pitcher’s value by a couple of bucks. I didn’t bother moving players around within a tier, which is something I used to do, but provides little incremental value.

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The beauty of playing in a mono league such as AL Tout Wars is that I am able to see first hand who the hot FAAB pickups are. Rather than scour my CBS league’s free agent pool to find players worth considering, I could browse through the players actually bid on in a deep league. Having said that, only one of the two players here were actually added this week. The other was drafted. I’m sure you could guess which is which.

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Yesterday, I used my xBB% equation to identify starting pitchers whose actual walk rates were most above their expected marks. This group should be expected to enjoy a decline in their walk rates moving forward. Today I check in on the guys who may be due for regression. This is your list of fantasy relevant starting pitchers whose xBB% marks are most above their actual walk rates.

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Last week, I used my expected strikeout rate equation to identify the starting pitchers whose actual strikeout rates are most below their expected marks. Today, I’ll look at the other primary skill metric — a pitcher’s walk rate. I developed an expected walk rate equation as well and still use essentially the same one now, unlike the xK% formula which I had since tweaked. The walk rate equation isn’t as good as the strikeout rate one, but it’s the best I have seen out there.

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Given his top prospect pedigree and strong minor league results pre-2013, Tyler Skaggs was already a trendy sleeper heading into spring training with his new American League ball club. Then reports hit that Skaggs’ fastball velocity was way up and suddenly he was no longer just a potential undervalued asset, but a legit breakout candidate. And so far with a 3.21 ERA and 1.11 WHIP, his performance has been everything us owners could have hoped for. Except that the way he has achieved such performance is nothing like we expected. Meet the new Tyler Skaggs.

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