I’ve done this before — compare similar players, one of whom is “name-brand,” the other “generic-brand,” using blind résumés — as have many others. Ben Kaspick carried the torch a while this year, but he credited Joe Douglas with the idea. So let’s say it’s a group effort to which I’ll contribute once again.

In anticipation of 2018 drafts, I wanted to carry out a “buying generic” style of analysis, borrowing in part from too-early mock draft average draft position (ADP) data. I do not intend to construe the following comparisons as rigorous analysis. I do, however, intend to highlight some potential bargains that, if the too-early mock ADP information is concerned, warrant your attention on draft day.

Comparison #1: Outfielders

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Max Kepler wasn’t an incredibly popular sleeper heading into the 2017 season, but I was certainly far from the only analyst who was high on the young German. The 24-year-old was coming off a productive yet unspectacular rookie campaign, and was just one year removed from a breakout year in Double-A that made him a fixture on top prospect lists.

Kepler’s 2016 wasn’t eye-popping, but there were many positive signs for the rookie. His power had just started showing up in games in that breakout Double-A season a year before, and now he was taking the next step and hitting the ball over the fence (17 HR in 447 PA). It certainly wasn’t out of the question to predict another step forward in that department, perhaps to a 20-25 HR season in 2017.

He stole just six bases in the majors in 2016, but the fact that he’d swiped 19 bags in the minors the year before was reason for optimism. Furthermore, his .235 batting average was held down by a .261 BABIP, which seemed far too low for a player with pretty good speed.

In short, it wasn’t hard to envision something like .275/25 HR/15 SB if everything came together in 2017. Despite being an unproven option at a deep position, Kepler was drafted in well over half of Yahoo leagues. Like I said, not a super-popular sleeper, but not flying under the radar either.

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Every year we have a number of players who make their debut towards the end of the season, wildly exceed expectations, and leave us wondering what the future may hold. Last year we had Gary Sanchez. This year, Rhys Hoskins.

Hoskins hit the ground running. I mean, how many guys reach double digit homers before they reach double digit singles? I could probably look it up, I’m not going to. I don’t want to know. Hoskins did it, and that’s good enough for me.

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Before the 2017 season, I decided to switch up my typical “Steamer and I” series posts where I discuss one player in which my Pod Projections differ from Steamer. Instead, I compared my projections in specific fantasy categories to identify upside and downside guys my forecasts hinted at versus Steamer, representing the crowd. Let’s begin our recaps of the new series with the home run upside group, which are those hitters I projected for significantly more home runs than Steamer. Note that I extrapolated the actual Steamer home run projections to match the same number of at-bats I projected, so playing time differences wasn’t a factor.

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Back in February of 2015, I wrote the first edition of this article – I Don’t Get It: Avisail Garcia. Three seasons later, that article has held up really well. Avisail Garcia has posted less than four WAR over his last 1,615 plate appearance. However, since his 2017 campaign checked in at 4.2 WAR with strong four category production, fantasy owners are going to jump back onto the Avisailwagon. I’m here to advise caution.

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Yesterday, I reviewed my list of 2017 HR/FB rate surgers, utilizing my Statcast-fueled xHR/FB rate to identify the guys with significant upside. Today, I’ll recap the list of HR/FB rate decliners. Having not yet looked at my list, I’m nervous the leaguewide spike to record-setting home run numbers is going to make me look silly. Let’s find out!

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Geez, I could have blindly selected a handful of hitters whose HR/FB rates were due to rise in 2017 and probably would have hit on the majority! As I continue to recap my preseason lists, let’s move on to my 2017 HR/FB rate surgers. I compared my 2016 Statcast-fueled xHR/FB rate to the hitter’s actual HR/FB rate and identified six hitters whose xHR/FB rates were significantly above their actual marks, suggesting serious upside. Let’s see how these hitters performed.

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This past weekend, I was in Phoenix for Baseball HQ’s First Pitch forum. It’s an intensive few days of catching up with old friends and focusing on the upcoming fantasy baseball season. There was an underlying theme of the weekend, the fantasy baseball game is being forced to change. Some game facets have experienced some massive adjustments. The following are some of the meta-trends which have quickly popped up over the past few seasons.

Home runs are way up

A few days ago I wrote the following incorrect statement about Carlos Martinez for a 2018 player preview.

His 1.19 HR/9 will likely drop back below the league average.

It was pointed out to me, his home run rate was below league average. I was for sure it was not near 1.20 but I was wrong. Here are the recent league-wide HR/9 values.

2014: 0.86
2015: 1.02
2016: 1.17
2017: 1.27

I remember when the HR/9 hovered around 1.0. Not anymore. Some other pitching stats are feeling the effects of the jump like ERA, but the root cause is more home runs.

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Last Thursday, I discussed the 10 hitters I identified in the preseason as potential 2017 BABIP surgers, due to xBABIP marks well above actual marks. Eight of the nine hitters that actually recorded an at-bat enjoyed a BABIP increase. Let’s see how the nine hitters I identified in the potential BABIP decliners list fared.

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I have spent the past few years creating a family of stats that I’ve called xStats. These stats use Statcast batted ball metrics to analyze each player, which I then manipulate and export in a manner I hope is useful for fans and analysts.

Exit Velocity and launch angle data are good, and I include those, but they aren’t yet intuitive for more baseball fans so I have set forth to display my data in terms of numbers that are more relatable. Namely the standard slash line numbers. I have expected batting average, on base percentage, slugging percentage, batting average on balls in play, and weighted on base average. For pitchers I have bbFIP, which is an ERA scalar. Today, though I’m only going to be looking at batters.

These stats are available, but they don’t help much unless you know how well they are working. To that end, I have created the following table, which compares the regular, standard slash line to the xStats slash line. Read the rest of this entry »