Machado is still hitting the ball really, really hard, but is that enough?
Do you think Manny Machado and the Orioles are happy there isn’t a #narrative to go with the worst hitting season of his career? On one hand, no one is propagating wild theories that Joe Maddon broke him with some intentional walks. No one is whispering about mysterious maladies or off-field distractions. No, all they have is a .230/.296/.445 first-half line and assurances that Machado is hitting the ball hard. Really, really hard.
While that makes him a slam dunk buy-low option for your fantasy team, the uplifting outlook probably isn’t raising many spirits in the Orioles' front office—or in Machado’s camp either, for that matter. The team that could contend without starting pitching suddenly can’t, and now their golden parachute—a year-and-a-half of a bona fide superstar—is probably sequestered for the time being by the law against selling low. For Machado, his perplexing down season (a la Bryce Harper’s 2016) could be one calendar fresher in the minds of potential suitors.
"To me it just means getting rid of all these stats. Everything they're throwing at us nowadays."
Every once in a while, a quote about sabermetrics pops up that makes me pause for a moment, and last week we got one from none other than Orioles center fielder Adam Jones. Responding to a question about how to make baseball “cool” again, Jones went off on a rather interesting tangent:
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Brian McCann lives his life 23.4 feet per second at a time.
Yesterday, Statcast released the comprehensive leaderboards for its newest metric: Sprint Speed, measured in terms of “feet per second in a player's fastest one-second window.”
These were derived by measuring when runners were likely to be heading at top speed, taking multiple bases (other than from second on an extra-base hit). The full list is available here for your perusal: there are a few surprises, like the proximity of John Jaso to Jonathan Villar, or the sad state of Troy Tulowitzki. But for the most part, it’s an intuitive ordering of names, what you’d want when measuring baseball players.
Cardinals teammates Matt Carpenter and Tommy Pham provide two sides to the same story.
Things are grim for the Cardinals right now. They’ve been disappointing, and worse, they’ve been frustrating. They cough up leads, they make mistakes on the bases and in the field, and the offense is really stuck in neutral. Coming into the season, they had a clear offensive core: Yadier Molina, Matt Carpenter, Aledmys Diaz, Randal Grichuk, Dexter Fowler, and Stephen Piscotty. Two months in, Molina looks too old; Diaz looks too young; Grichuk is in the Florida State League for some reason; and Fowler, Carpenter, and Piscotty simply aren’t producing at a level that might allow them to make up for that.
Pitching metrics disagree when it comes to the D'backs lefty, but does his Statcast data reveal his true skill level?
Robbie Ray has been much talked about by both fantasy owners and baseball fans. Sam Miller profiled Ray fantastically in this ESPN piece from November. Depending on which pitching stats you place the most emphasis on, Ray’s 2016 season varied anywhere from significantly below average (run prevention) to above average (Fielding Independent Pitching, Deserved Run Average). Ray’s 4.90 ERA ranked fourth-worst among the 64 starting pitchers who threw at least 170 innings last year, and his park-adjusted ERA- of 112 ranked 54th out of 64. His 3.88 DRA ranked 31st. His 3.76 FIP ranked 21st.
How exit velocity, spin rate, and other new metrics can help guide your player choices this season.
As a fantasy owner, I like to look at what the progressive front offices inside the game are doing in their player evaluation. What are the brightest minds in baseball doing to pick players? I often find concepts and ideas that I carry over to my player evaluation in my leagues to help me construct my fantasy rosters.
The Pirates' struggles this year might have more to do with the entire rest of the league than Pittsburgh's pitchers themselves.
Francisco Liriano has been a Pittsburgh Pirates success story. Signed as a free agent for $1 million after compiling a 5.34 ERA, 4.29 FIP, and 4.02 DRA in 156 2/3 innings split between the Twins and White Sox in 2012, he became a hero in Travis Sawchik’s book about the 2013 Pirates and their embrace of analytics, Big Data Baseball. In Liriano’s case, the approach was to junk his four-seam fastball, focus on his sinking two-seam fastball, and generate a lot of groundballs for shifted Pirates infielders to gobble up. The success of this strategy was evident through last year:
What you need to know before your sweeping take about a player's exit velocity.
Note: Baseball Prospectus has removed the leaderboards mentioned in this article. Thank you for your interest in our work and for your patience as we attempt to resolve this issue.
Last year, the folks at MLB Advanced Media started publishing what is commonly described as “exit velocity”: the pace at which the baseball is traveling off the bat of the hitter, as measured by the new Statcast system.
As a statistic, exit velocity is attractive for several reasons. For one thing, it is new and fresh, and that’s always exciting. It also makes analysts feel like they are traveling inside the hitting process, and getting a more fundamental look at a hitter or pitcher’s ability to control the results of balls in play.
However, we’ve seen many people take the raw average of a player’s exit velocities and assume it to be a meaningful indication, in and of itself, of pitcher or batter productivity. This is not entirely wrong: Raw exit velocity can correlate reasonably well with a batter’s performance.
But this use of raw averages also creates some problems. First, if you use exit velocity as a proxy of player ability, then you must also accept that one player’s exit velocity is a function of his opponents, be they a batter or pitcher. Put more bluntly, a player’s average exit velocity is biased by the schedule of the player’s team.
Second, and much more importantly, we have concluded Statcast exit velocity readings, as currently published, are themselves biased by the ballpark in which the event occurs. This goes beyond mere differences in temperature and park scoring tendencies. In fact, it appears that the same player generating the same hit will have its velocity rated differently from stadium to stadium, even if you control for other confounding factors.