BP Unfiltered: Everything You Could Have Learned This Week, 4/10/15

Hello, friends. I hope you're enjoying the first full week of games! Isn't it great to have all our suspicions and predictions from the offseason completely confirmed or disproved? (Wait, the season is how long?)

Monday
Managers (or whoever makes the lineup) might be getting smarter in their choices of no. 2 hitters! The Evolution of the 2-Hole, by August Fagerstrom, Fangraphs

Offense is down across baseball and so, naturally, we find lower OBPs across the board. That is, except for in the 2-hole. I didn’t include pitchers, so the 9-hole only has half the sample size and could be subject to some noisy fluctuation. But there’s no mistaking that the No. 2 spot in the order, at least for Game 1 of 162, is finally being better utilized by managers across baseball.

Tuesday
Who shares the blame/credit for a batted ball outcome? Sometimes it's the pitcher, sometimes it's the hitter, and sometimes it's just chaos: Chopping Up the Credit, by Russell Carleton, Baseball Prospectus

Here we’re able to use some rudimentary data to ask the question of whether batters and pitchers bear much responsibility for steering the ball toward the fielders or away from them. We see from the data that what blame is out there is split about equally between the batter and pitcher, but we learn that there is a lot of randomness. To say that the batter and pitcher have absolutely no control over the fate of a batted ball (once it leaves the bat) is silly, but we see that they don’t have all that much in the grand picture. The further the ball gets from the bat (and the more things it has to bounce off) the more randomness there is in the outcome.

Can a major-league pitcher succeed while constantly "pitching backward?" By following Masahiro Tanaka this season, we might find an answer: Meet the New Masahiro Tanaka, UCL Tear Survivor (So Far), by Ben Lindbergh, Grantland

Although on the surface the box score seems like a confirmation of Yankees fans’ worst fears, it’s possible to put a positive spin on Tanaka’s inaugural outing in 2015. Aside from his voluntary renouncement of the four-seamer, Tanaka’s stuff seemed almost the same as it was before we were worried about his elbow. He stills throws hard enough to get swings and misses with his off-speed stuff. And while Tanaka doesn’t have a great sinker, his forsaken four-seamer was worse. It’s probably too much to hope for addition by subtraction, but if Tanaka’s partially torn ligament doesn’t hurt his command,1 the new Tanaka should be a useful starter (if not an ace) for as long as his ligament lasts, even if he nibbles too much to go deep into games. Tanaka has already defied one of our fears — and his own — by avoiding surgery and surviving the spring UCL reaping so far. Now he has a chance to chart the non-fastballs frontier.

Pitchers might be working faster, judging by data from Opening Day: Pitcher Pace in a Very Tiny Sample, by Dave Cameron, Fangraphs

Starting pitchers were down about half a second from last year’s average time between pitches; relievers were down nearly a second and a half. When you add those changes up over the 4,000 pitches thrown yesterday, you find a reduction of about 48 minutes, or a little over three minutes per game.

That’s not a life-changing figure, and the time between pitches yesterday was still higher than it was from 2007-2011, but I wouldn’t think that walking back years of slowing pace is going to happen overnight. Even just beginning to reverse the trend would be a good first step, and the league could make small incremental steps back towards 20 seconds between pitches. And that’s essentially what we saw yesterday; good small steps.

Wednesday
Pitchers could potentially decrease the stress applied to their UCL by training certain muscle groups (Remember, ligaments ≠ muscles): Muscles matter in baseball injuries, by Erin Spain, Northwestern University — Full study, by James H. Buffi, Katie Werner, Tom Kepple and Wendy M. Murray, Northwestern University

To better understand the stress pitching puts on the UCL, the Northwestern researchers used a specific computer model developed from bone, muscle and ligament geometry measurements taken in cadavers, as well as muscle volume and strength measurements taken in living subjects. This is the same model developed and used by the researchers to study arm and hand control for advanced prosthetic devices.

They developed a computer simulation of a real high school pitcher’s throwing motion to investigate how individual muscles can affect UCL loading and how changes in muscle output can either relieve or exacerbate the load on the UCL.

“Our simulations illustrate that if the muscles were doing nothing, then the bones that make up the elbow joint could have been pulled apart during that single pitch. In contrast, we also were able to implement reasonable assumptions about muscle performance that showed how the very same pitch could result in no load on the UCL at all,” Murray said.

Thursday
We now have our first look at the distribution of Statcast's batted ball data: New and Probably Way-Too-Early Batted Ball Data, by John Choiniere, Beyond the Box Score

Whether it's an effect of the physical location of the data recorder or the stringer's own bias isn't important, though — the point is that relying on subjective classification rather than objective measurement reduces the utility of what the research finds. Further, the newly-available data, if/when it becomes available for all batted balls, could allow researchers and advanced-stats-type people to drop the existing batted-ball profile standard (LD%, GB/FB ratio, etc) in favor either of a continuous function that describes trajectories or a more refined set of classes that arises naturally from the data.

Scott Boras is good at his job: Ranking the MLB Player-Agency Hustle, by Stephen Shaw, Banished to the Pen

The main purpose of the post was to give you an idea of how the “cost-of-a-win analysis” can be related to all parties involved including player agencies. Put as much stock into the analysis as you want. I think it gives us a decent approximation of how well agencies are performing for their clients. Obviously, there is a lot more that goes into players choosing an agent like fees and perks, but if we isolate salary, this might shed some light on their negotiating skills.

Friday
PECOTA projections + team spring training performance = stronger predictive power! Spring Training Matters, by Rob Arthur, FiveThirtyEight

There’s a reason spring training has more to tell us about teams than individual players. It’s about signal versus noise. For an individual player, any set of 50 plate appearances (in spring training or otherwise) is extremely volatile and doesn’t say much about them individually. But bring together all the plate appearances of the nine players who make up a batting order and the volatility begins to cancel itself out. All of a sudden we have some sense of how good the nine are in aggregate.