The gang's all here! The position players check in today, workouts for the whole team tomorrow. Soon games will be underway. Baseball is back and eight months of drama, excitement and fun are just ahead.
But some essential, but boring, things first. I so enjoy using statistics to analyze the world around me that students pinned a nickname on me years ago... Dr. Data. And it stuck and I liked it, because I enjoy trying to understand reality by representing it in different ways, numbers, images, narratives, metaphors, models. So it follows I have been intrigued by baseball stats since my first reading of Bill James in the mid-1980s (I was introduced to him by my son, Matt, who was in his teens and used James' stats to clobber me in game after game of Strato-Matic).
Long before my fandom, part of following baseball was keeping track of the stats and at an early age, like all of us, I relied on the time-honored numbers of batting average, on base percentage, slugging percentage and ERA. After James and other pioneering "sabrematricians," the game has been flooded with what are, today, called "analytics." And Michael Lewis popularized their use with his profile of A's general manager Billy Beane in Moneyball. So today we have BABIP and PECOTA and WAR and all manner of trying to understand what happens after a pitch is thrown.
The latest trend is the use of devices designed to breakdown physical performance for closer analysis. Within a couple of years the the video camera and speed gun have been supplemented by instruments, handheld devices, to register and calculate all manner of data on every single pitch or swing.
One of them, RAPSODO, tracks twelve different features of each pitch: things like spin rate and release angle and a lot of other more obscure details. Similar information is collected for hitting. I've never pitched so I have no idea how useful or valid any of these data might be, but it's clear some players and coaches pay attention and used to make adjustments to improve performance. Judging by the sight of them at the M's bullpen sessions, we must be, too.
This practice, I would argue, represents the best application of statistics: to make comparisons over time or between situations (good versus bad) to pitch or hit better in the future.
If you doubt the sensibility or efficacy of that approach look at Kyle Seager. The use of analytics to shift the position of infielders player by player (and sometimes pitch by pitch) had a crippling effect on Seager. In fairness he had a broken toe for much of last season, but it was frustrating to watch Kyle hit last year.
Against the radical shifts analytics commanded, he'd either futilely try to pull the ball where there
were no longer openings or weakly attack the opposite field. In 2016, before he started seeing radical shifting, Seager hit a career high .278 with 30 home runs. In '17 and '18, respectively, his average fell to .241 and .221. More telling perhaps, to use one of my favorite stats, BABIP (his batting average in balls in play, balls he actually hit) fell from .295 to .251.
Another way to look at it is to use a stat called WAR (wins above replacement) a rough way of gauging a player's contribution to his team's winning (compared to an average player). Seager's WAR has fallen from 5.1 in 2016 to 1.6 last year.
These numbers tell is his performance has fallen: 37 fewer hits over a season, roughly one less hit every four games. 10 more players left on base, 21 fewer rbi's, almost 4 less wins. (Thanks to Fangraphs.com for all these great numbers.
What's really scary, is that for all his difficulty in fighting the shift, he pulled the ball more (hit into the shift rather than to center or left) than he had the previous two seasons.
I know some fan don't like breaking the game down like this, it robs baseball of some of its art and romance. But in the end, baseball is just balls and strikes and even the smallest things add up.
But some essential, but boring, things first. I so enjoy using statistics to analyze the world around me that students pinned a nickname on me years ago... Dr. Data. And it stuck and I liked it, because I enjoy trying to understand reality by representing it in different ways, numbers, images, narratives, metaphors, models. So it follows I have been intrigued by baseball stats since my first reading of Bill James in the mid-1980s (I was introduced to him by my son, Matt, who was in his teens and used James' stats to clobber me in game after game of Strato-Matic).
Long before my fandom, part of following baseball was keeping track of the stats and at an early age, like all of us, I relied on the time-honored numbers of batting average, on base percentage, slugging percentage and ERA. After James and other pioneering "sabrematricians," the game has been flooded with what are, today, called "analytics." And Michael Lewis popularized their use with his profile of A's general manager Billy Beane in Moneyball. So today we have BABIP and PECOTA and WAR and all manner of trying to understand what happens after a pitch is thrown.
The latest trend is the use of devices designed to breakdown physical performance for closer analysis. Within a couple of years the the video camera and speed gun have been supplemented by instruments, handheld devices, to register and calculate all manner of data on every single pitch or swing.
One of them, RAPSODO, tracks twelve different features of each pitch: things like spin rate and release angle and a lot of other more obscure details. Similar information is collected for hitting. I've never pitched so I have no idea how useful or valid any of these data might be, but it's clear some players and coaches pay attention and used to make adjustments to improve performance. Judging by the sight of them at the M's bullpen sessions, we must be, too.
This practice, I would argue, represents the best application of statistics: to make comparisons over time or between situations (good versus bad) to pitch or hit better in the future.
If you doubt the sensibility or efficacy of that approach look at Kyle Seager. The use of analytics to shift the position of infielders player by player (and sometimes pitch by pitch) had a crippling effect on Seager. In fairness he had a broken toe for much of last season, but it was frustrating to watch Kyle hit last year.
Against the radical shifts analytics commanded, he'd either futilely try to pull the ball where there
were no longer openings or weakly attack the opposite field. In 2016, before he started seeing radical shifting, Seager hit a career high .278 with 30 home runs. In '17 and '18, respectively, his average fell to .241 and .221. More telling perhaps, to use one of my favorite stats, BABIP (his batting average in balls in play, balls he actually hit) fell from .295 to .251.
Another way to look at it is to use a stat called WAR (wins above replacement) a rough way of gauging a player's contribution to his team's winning (compared to an average player). Seager's WAR has fallen from 5.1 in 2016 to 1.6 last year.
These numbers tell is his performance has fallen: 37 fewer hits over a season, roughly one less hit every four games. 10 more players left on base, 21 fewer rbi's, almost 4 less wins. (Thanks to Fangraphs.com for all these great numbers.
What's really scary, is that for all his difficulty in fighting the shift, he pulled the ball more (hit into the shift rather than to center or left) than he had the previous two seasons.
I know some fan don't like breaking the game down like this, it robs baseball of some of its art and romance. But in the end, baseball is just balls and strikes and even the smallest things add up.
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