Not every top prospect immediately plays at an all-star level during their rookie campaign. Not every former top prospect improves each and every year in lockstep until their big breakout. Sometimes prospects take at least a season or two before it all clicks. Sometimes it clicks, but then pitchers or hitters adjust, and they regress before rebounding. Sometimes prospects simply need some additional time in the minor leagues to iron out their kinks before returning with a bang. Every player is different, so it would be silly to write off former top prospects just because they didn’t meet expectations immediately.

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It’s an exciting upcoming week for owners of Rockies hitters. If you don’t count yourself among that group, then this might be the week to join in on the fun. That’s because the team will be embarking on a full seven game homestand! For weekly transaction leagues, that means a full slate of games, all of which come at hitter friendly Coors Field. In these instances, I like to see which Rockies hitters languish in my leagues’ free agent pool so I could potentially take advantage of the strong schedule. So with that in mind, let’s actually review every single Rockies hitter, as any number of them could be available in your league depending on format and size.

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Yesterday, I reviewed the hitters whose FB% marks had changed the most compared to last year. We care about FB% because it directly impacts home runs. All else being equal, the greater the FB%, the higher the home run total. Similarly, pulled fly balls travel over the fence with significantly greater frequency than those hit straightaway or to the opposite field. So it stands to reason that an increased fly ball pull rate would raise a hitter’s HR/FB rate, while the opposite would be true.

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A hitter’s fly ball rate is important to track because the vast majority of home runs are hit via that batted ball type. In fact, according to Statcast’s batted ball type buckets, there have been 10 times more home runs hit on fly balls than on line drives since 2022. So let’s find out which hitters have seen their FB% marks change the most compared to last year, which could explain a jump or dip in home run totals, compared to expectations at this point in the season.

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Yesterday, I reviewed the starting pitchers whose SwStk% has changed most over the last 30 days compared to earlier in the season. Now let’s flip over to the hitters. Today, we’ll review Barrel% changes over the last 30 days, which should explain a power surge or decline over that time period.

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Pitchers change far more frequently than hitters. Their velocity fluctuates from start to start and throughout the season, their pitch mix is often adjusted, and sometimes there are games where the command just ain’t there. So it pays to review a pitcher’s recent skills, as the full season line might not tell the whole story of who that pitcher is right now. With that in mind, let’s find out which pitchers have gained and lost the most SwStk% points over the last 30 days versus the beginning of the season period.

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Yesterday’s dominating non-closing reliever post was pretty popular, with many of you offering additional names. So, I figured I would double up on the theme by posting another group of relievers. This time, I sorted by CSW% to include those who might not be as adept at inducing swinging strikes, but have made up for it by generating a high rate of called strikes. I filtered out anyone that made yesterday’s list or is currently serving as closer.

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Let’s get back to discussing those non-closing middle relievers, the ones you generally ignore in 12-team mixed leagues and shallower, but deliver real value in deep mixed and mono leagues, despite the lack of saves.

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It’s been nearly a month and a half since I last reviewed the starting pitchers who sported the greatest gap between their ERA and xERA marks. So let’s revisit these lists as they could provide you with a quick group of buy low and sell high candidates.

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Statcast has enough toys to keep us nerds busy for months going down rabbit holes and digging into various metrics and numbers galore. One of the expected metrics I’ve rarely used and haven’t reach much about is xHR. It’s exactly what it sounds like, Statcast’s equation that calculates an “expected” home run total.

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