“A Quick Analysis of 2016 Hitter Projections” by Justin Vibber at Rotographs (@justinvibber)
Vibber looks at how different player projections (Steamer, ZiPS, PECOTA, Pod and Fans) did in 2016 when comparing projections to actual results on a points per game basis broken down by age group (21-26, 27-30, 30+). Some key findings were that PECOTA (Baseball Prospectus) and Pod (projections done by Mike Podhorzer (@mikepodhorzer) did the best at projecting young players, while Steamer (@steamerpro) did the best overall and with older players. While some projections performed better than others, it’s important to note that all of the projections rated out pretty closely with the exception of Fans, which isn’t too surprising since the projections are crowdsourced on Fangraphs (i.e. it relies less on algorithms and math/science). Also not surprisingly, the aggregate of all projections and “machine” projections did the best (“machine” takes the aggregate of all non-Fans projections while aggregate includes Fans), which shows the power of not relying on just one projection system but instead aggregating multiple trusted projections to create a meta-projection of sorts. I always enjoy articles that take a look back to evaluate projections so we can learn from them and identify strengths and weaknesses.
“Can Statcast Help Identify Future Relief Pitcher Success?” (@andrewperpetua & @derpymets)
Perpetua (he’s likely to show up frequently in my batflips articles, since I find his content most relevant and applicable in my own fantasy baseball research) uses a combination of Statcast and xstats.org data to identify relievers who are likely to be successful in the future because of their ability to limit opponents’ batting average, slugging and home runs through low exit velocity and ideal vertical launch angles. The list that he comes up with are good bets to be in the mix for saves next season (many already have a lock-down on closer gigs) and provide help to your team’s ERA and WHIP and boost K/9 rates. Three intriguing names due to their situation and statistical profiles for me are:
- Matt Bush: I’m not convinced Dyson has the stuff to close consistently. Last year he saw a decline in K% (16.2% to 11.2%), increase in BB% (6.8% to 8.1%) and was helped by an unsustainable 85.4% LOB.
- Cam Bedrosian: Some injury concerns, but best pure stuff in a weak bullpen. He will compete with Huston Street and Andrew Bailey for the closer’s role
- Carl Edwards: K%, WHIP and ERA should help any team, regardless of whether he closes. Great pick up for Wade Davis owners who are concerned about last year’s trips to the disabled list.
“The Return of Mike Moustakas” by Randy Holt of Rotographs
Holt’s piece caught me by surprise, since I had pretty much forgotten about Moustakas after a 2016 that was largely lost to injury (I didn’t really notice his absebce). As Holt points out, though, Moustakas’ underlying stats were all headed in the right direction in 2016 with a career low K% (11.0), career high BB% (8.0) and hard hit % (37.4%), and a career low infield fly ball rate of 11.1%, which is huge for someone with a career 16.7% mark. Like many others before him, Moustakas fell victim to a very low BABIP (.219) that did not accurately reflect the underlying performance. Because of the low BABIP that suppressed his other numbers as well as the injury, he’s fallen off the radar of many fantasy baseball managers (myself included) and experts, most of whom have him outside their top 200 players. Holt makes a compelling case for Moustakas as a strong value in 2017 drafts. Take a look at his slash line from xstats.org in the table below and pencil him in to your drafts now:
Mike Moustakas 2016: Career Year Cut Short?
AVG | OBP | SLG | BABIP | |
---|---|---|---|---|
2016 Stats | .240 | .301 | .500 | .219 |
2016 xStats | .294 | .351 | .524 | .294 |
(Source: xstats.org)
Moustakas is firmly on my radar thanks to Holt’s terrific article.
New Player Metric I Love: sOPS+ at BaseballReference.com
This isn’t an article, but I felt like sharing because it’s an exciting discovery. Thanks to Jeff Zimmerman (@jeffwzimmerman) for bringing my attention to the sOPS+ metric at Baseball Reference, which allows you to look at how both pitchers and hitters perform on certain splits compared to the league average. League average is 100 in the metric and anything above 100 sOPS+ is good for a hitter and bad for a pitcher (i.e. they are above average for a hitter or below average for a pitcher) and vice versa. To find sOPS+, just go to a player page at Baseball Reference, click on the “splits” and scroll down (it is the furthest metric to the right on the splits where it is available). Above, I’ve linked to Clayton Kershaw’s splits, so you can get a look at the measurement in action. It’s not surprising, but Kershaw is above average (i.e. below 100 sOPS+) in all but two counts (down 3-0 and 2-0). I’m looking forward to using the tool in future analysis (for instance, I’ve already used it to compare Dallas Keuchel’s 2015 and 2016 seasons. In the former, he was above league average in every single count, whereas in 2016 he struggled on the first pitch and when he got behind in counts).
Anyways, I hope these article bring you some value and get you excited for the 2017 season. Thanks, as always, to the authors of these articles for sharing the content with the world. If you have any other interesting articles, metrics or other fantasy baseball related content, please share with me at @BatFlipCrazy on twitter, batflipcrazy (at) gmail.com or on the contact page at my blog.