A few days back, David Appelman announced the addition of Hard Hit, Medium Hit and Soft Hit data to the batted ball stats on FanGraphs. Since then, I have been playing around with them, and found some interesting things.
But I had no idea what to make of those things. If a guy has an extremely high Hard Hit%, what does that mean? Should we expect regression? Should we expect it to continue? And what does that mean for fantasy value?
Shin-Soo Choo is no longer elite. He used to be, though. Back in 2009-2010, he put up a pair of excellent .300, 20/20 seasons which yielded a 139 wRC+, good enough for 11th in baseball. Just two years ago in a much tougher scoring environment across the league, he hit .285 and went 20/20 yielding a 151 wRC+ (9th-best). The move to Texas wasn’t really an upgrade in park, but it didn’t project to really hurt him, either. After all, his best work came in Cleveland which is hardly known as a hitter-friendly ballpark (these days it plays plus for LHB, though not overwhelmingly so).
Injuries marred his debut season in Texas resulting in an uninspiring effort that saw him hit 13 homers with a .242 AVG, .714 OPS, and just three stolen bases (in seven attempts) in 123 games. Both the ankle and elbow injuries that nagged him throughout the season required surgery and so there was a reasonable expectation of health for Choo coming into the season. His 2014 campaign offered a discount as he sank to the 51st outfielder off the board this draft season. Given the wretched first month to his season, it’s hard not to wonder if either of last year’s injuries or perhaps a new one has cropped up for Choo.
There was a time in fantasy baseball when prospect call-ups barely registered on the radar. I know that sounds crazy given how much we as a community overreact to them now, but when I first started playing there were maybe a handful of call-ups we were waiting for in-season and even then, expectations were tempered. Today, we often have outsized expectations attached to prospects which lead to a lot of disappointment. The newest names almost sure to disappoint the fantasy community are catchers Blake Swihart and Austin Hedges.
Let’s cut straight to the chase. Take a look at the statistical snapshots below:
| Name | PA | HR | R | RBI | SB | CS | K% | BB% | AVG | OBP | SLG | ISO | BABIP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Player 1 | 97 | 0 | 9 | 4 | 6 | 2 | 11.3 % | 11.3 % | .306 | .392 | .376 | .071 | .351 |
| Player 2 | 91 | 1 | 10 | 7 | 6 | 2 | 15.4 % | 5.5 % | .235 | .278 | .318 | .082 | .271 |
Obviously, Player 1 is benefiting from a higher batting average on balls in play while Player 2 is getting burned a bit by his. Still, take away their triple-slash lines (but leave the isolated power) and you have two players with almost identical numbers, down to the six steals on eight attempts and the meager isolated powers (ISOs). Where they differ a bit is in plate discipline: Player 1 has a much healthier walk rate than Player 2 and a couple fewer strikeouts. So while Player 1 is benefiting from the a higher BABIP, he can also reasonably be expected to post a marginally higher batting average and noticeably higher on-base percentage. Most importantly, the two hitters are eligible at the same position and are, thus, substitutable.
Jonathan Lucroy, Yan Gomes, and Travis D’Arnaud all recently hit the disabled list. John Jaso was on the DL after just one plate appearance! Matt Wieters started the season on the DL and it seemed like Devin Mesoraco was headed there soon until a recent PH appearance. He hasn’t started a game since April 11th and he missed seven games in between PH appearances on the 12th and 21st. That is four of the top 10 drafted catchers, another within the top 15, and one of the most popular second catcher options in Jaso as he stood to get a ton of playing time as a non-catching catcher.
The new high-strikeout, pitching-focused environment has changed the game in many ways, but one of the more significant ways is the rising value of middle relievers. For years I used to roster 2-3 middle relievers in an AL- or NL-only league to supplement my staff instead going with the fourth- or fifth-starter from some middling team. The linked article was done in 2011 when it was still kind of a niche strategy that maybe a couple of teams could pull off. There wasn’t the overwhelming volume of viable relievers that we see now with gaudy strikeout rates and microscopic ratios.
This is a tough time of the year for fantasy baseball analysts. We’re not even at the end of week two meaning that in most cases the sample sizes are still too small to really be useful. The downside of this is that the fantasy analysis can become lazy as we wait for more data. Too often you see the vague, unhelpful “sell high” tag attached to any mid-rounder who is off to a high start or “buy low” on the star who has two rotten starts on his ledger thus far. But what does that even really mean? It’s so easy to say and so hard to actually execute.
Nobody who spent a top 20 pick on Stephen Strasburg (6.75 ERA in 10.7 IP) is going to move him for Nick Martinez (0.00 ERA in 14 IP). In fact, they probably aren’t going to move him at all (nor should they). You know what’s easy? Me telling you to go sell high on Chris Heston. But it’s also generic and frankly, shitty advice because it offers no insight into what selling high might be, especially because I know full well that unless Teresa Heston (that Chris’ mom, I looked it up) is in your league, you can’t really cash him in for some great return. The rest of your league is just as skeptical about his dubious 0.69 ERA as you are right now.
If fantasy baseball were a marathon, we’d all have run about a mile and a half. Most races don’t even have their volunteers stand with trays of water cups this early on, so you’d better pace yourself if you’re already tired. I think I forgot to stretch.
Here’s an obligatory sentence reminding you about the caveats about small samples while attributing a shred of validity to them. OK, now that the formalities are out of the way, let’s talk ownership trends. National League outfielders are a promising bunch, especially in regard to the youth movement. I’ve been sold on a handful of them prior to the start of the season, and I’m surprised by their meager ownership numbers. They aren’t completely unowned, as my misleading title alleges, but they’re close enough.
Last week, I identified potential stolen base streamers for daily fantasy leagues and weekly leagues with daily transactions and lineup changes. I used a pitcher’s career caught-stealing and pick-off rates as criteria to determine if a particular matchup was primed for streaming a speedster with the hope of him stealing a base (or two or four).
I like how it turned out, but it felt hastily constructed. A pitcher’s career rate seemed too broad a scope, especially considering the possibility that a pitcher can get better (or, perhaps, worse) at limiting steals and picking off runners over time.
With a little more time and care, I fleshed out everything a bit more and added an additional criterion: catcher effectiveness, which can be most obviously measured by caught-stealing rate. But I think there also is merit to calculating the frequency at which runners attempt to steal on a catcher. In a sense, it measure runners’ perception of a catcher’s skill, especially for those at the tails of the distribution.
I play in a lot of fantasy baseball leagues. Like, a lot. I absolutely love the game, can’t resist a challenge, and have a tough time saying no when invited. All of that adds up to a 16-league portfolio when the dust settles. I’m sure some of you are thinking, “well doesn’t every result help and hurt you at this point?” Not quite. Part of that is because I’m a bulk drafter when it comes to “my guys”. There are certain players on both sides of the ball that I really focus on year-to-year and I end up trying to roster them as often as possible provided the format and pricetag are in line to do so.
I obviously can’t just copy the first team across the remaining 15, though, so I do have a shit-ton of different players. In fact, I have 244 different players at least once. A major part of this is because I’m in two NFBC 50-round draft-and-holds and I’ve got a lot of players on both of those teams that don’t show up anywhere else because of how deep we go in that league. As my league count ballooned, I began keeping track of who I have year-to-year to see how my investments shook out against what I believed coming into the season. We’re going to take a look at my portfolio today from the hitter side of things to see how it all turned out.
