The infield fly ball is the second worst outcome for a hitter besides a strikeout. With almost 100% of all popups turning into outs, a hitter, who is prone to skying the ball into the infield, will generate more outs and therefor a lower batting average. Several factors make a pitch more likely to be hit as an infield fly ball, but the key factor is the batter’s mechanics.
So much of what makes pitchers effective at the major league level is their ability to keep hitters off-balance. Sure, a 95 mph fastball with movement and a Lord Charles curveball help, but even these physical tools are only as effective as a pitcher’s ability to create uncertainly in the hitters mind from pitch to pitch.
One — admittedly crude — way of looking at this is whether a pitcher throws the type of pitch that’s expected in a given count. Does a pitcher throw fastballs in “fastball counts”, or do they throw off-speed pitches? Pitchers that throw counter to expectations are often said to “pitch backwards”. The Rays’ James Shields is someone that has been referenced as such a pitcher over the past few years.
But exactly how backwards does Shields pitch? And who are some other pitchers that fit into this category?
David Wright experienced a resurgence of sorts in 2012. After four straight outstanding offense seasons, Wright’s offensive production dipped significantly in 2009 — from a 141 wRC+ to 125. In 2011, Wright’s wRC+ declined all the way to 116.
But this year, the old David Wright reappeared and the 29-year-old third basemen posted a 140 wRC+. The Mets, encouraged by Wright’s year at the plate, have not only picked up his 2013 option (which was predictable), but have also continued discussions for a long-term contract extension.
How likely we are to see Wright put up similar numbers in the future is debatable.
Regardless, one thing was clear: Wright was making better decisions at the plate in 2012. And while his plate discipline numbers were positive (e.g. -2.1% O-Swing), the overall change didn’t seem to capture how well Wright’s plate approach improved.
In an effort to tease this out beyond the basic plate discipline metrics, R. J. Anderson used Mike Fast’s “correct” decision-making approach to look at how Wright’s decision-making improved in the past three season. Anderson calculated the percentage of “correct” pitches Wright swung at in 2012, compared to the two previous seasons. He found Wright had improved his decision-making by 7%.
I decided to take an even narrower view than Anderson and focused only on the location of balls Wright swung at that were just off of the plate, or that were off the black.
In the Oct. 15 issue of ESPN the Magazine, Tim Kurkjian wrote this when talking about young pitchers with injury histories:
GM Billy Beane doesn’t require power, he wants outs without walks. Plus strike throwers generally have good mechanics that help prevent injury. Beane also isn’t afraid to go with young pitchers, what at least in theory are less likely than older ones to get injured.
I’ve written quite a bit this year about pitcher aging — specifically, trends in velocity loss for pitchers. There are two general findings that I want to revisit today and apply to pitchers from 2012; the predictive power of velocity loss in July and end of season velocity, and the impact of losing velocity in one season on next season’s velocity.
First, a pitcher’s velocity will tend to vary throughout the year. Trying to get a read on whether a pitcher is having trouble velocity-wise during a season is difficult if you simply compare to last year’s overall velocity. So I compared a pitcher’s velocity in each month to their velocity the previous year in that same month and found that pitchers who lose at least 1 mph of velocity in July are 13.7 times more likely to finish the entire year down at least 1 mph.
Second, 91% of pitchers that do finish a season down at least 1 mph compared to the previous season will lose additional velocity the following season (average decline of 1.6 mph), with only 7% regaining some (but, likely, not all) of that velocity back.
With the close of the 2012 season, I checked back on how well July-over-July velocity trends predicted full season declines as well as which pitchers ended the season losing over 1 mph off of their fastball.
I have finally had enough time to muddle through the 2012 MLB transaction data and have compiled a complete disabled list (DL) data set for the year(second link). Let’s get right to the data.
One of the oldest truisms in baseball is that, to be successful, pitchers need to pitch inside. Establishing the inside of the plate allows pitchers to more effectively use the outer-half of the plate — and get batters to swing and miss or make weak contact more often on pitches thrown to the outer part of the zone. But it isn’t easy to pitch inside. Pitchers who lack the ability to get away with throwing inside tend to stay away from that part of the plate for fear that hitters will drive those pitches for extra-base hits. This can lead to hitters cheating on outside pitches and can force pitchers to throw fatter pitches as a result of throwing behind in the count.
So who were this year’s best pitchers when it came to throwing inside? I dove into our PITCHf/x data and found out.
On Monday, I took a look at how the new ball in Japan’s NPB league may be affecting the predictability of Japanese talent. The first signs are good: It looks like the new, standardized baseball in Japan — a ball which mimics the MLB design, starting in 2011 — may be resulting in more direct skill translations into the MLB. This means MLB franchises will be able to identify priority athletes better and extend more appropriate contracts and posting fees.
In the comments of that article, KJOK of Seamheads.com suggested — quite rightly — that the conversion conversation should go two ways:
…I think it is important to examine the players that went TO Japan since the new ball was introduced, and see how they also performed against expectations.
Let’s do just that.
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All the way back in May, I came up with a pretty simple way to calculate “expected strikes” based on data available at FanGraphs. I don’t know if I was the first person to do this, and it’s so simple I’d be surprised if I were, but I remember me so I’m linking to me. Once you have expected strikes, you can compare that total to actual strikes, and maybe then you can learn something about the pitcher(s) or the catcher(s) or about something else. I”ll explain further!
FanGraphs provides for you total pitches, total strikes, and plate-discipline data based on PITCHf/x data. By using zone rate, you can come up with pitches in the zone, which leads to knowing pitches out of the zone, which leads to knowing swings at pitches out of the zone. Based on those numbers, you can end up with an expected strikes total. You’re way ahead of me — I probably don’t need to explain this in great detail.
In September, teams are allowed to expand their rosters and the Rockies did that in 2011 by calling up Wilin Rosario. Rosario showed a bit of pop, but had some problems making contact. Going into 2012, questions about his ability to not strike out existed. By using a small sample size of a hitter’s swing and contact values, a better estimate of his walk and strikeout rates can be estimated.
The Rockies began the 2012 season with Ramon Hernandez as their #1 catcher and Wilin Rosario was slated as the backup even though Rosario was a highly touted ranked prospect (#49 in 2011, #87 in 2012). The main reason the Rockies didn’t have any faith in Rosario was his plate discipline. In the minors, his BB% ranged between 4.5% and 8.7% and his K% between 19.2%-29.9%. In 57 MLB plate appearances, his BB% was 3.5% and his K% was 35.1%. These values forced people to have reservations about him being able to stick in the majors.
In the 2011 FG+ fantasy preview, Paul Swydan wrote the following on Rosario:
Swinging at every pitch thrown to you is only a good strategy for a hitter if you have enough bat control to hit or foul off nearly every pitch thrown to you (see Guerrero, Vladimir). Wilin Rosario is not this type of hitter, and his acceptable plate discipline in the low minors has steadily worsened as he has moved up the Rockies’ organizational ladder. ….. Rosario still needs to fine tune his game — particularly his plate discipline — and is unlikely to contribute to your team no matter where he starts the season.
Instead of using BB% and K%, a player’s estimated K% and BB% can be determined by using swing and miss values. To get an idea of this value, I created a formula using (See Appendix) O-Swing%, K-Swing%, O-Contact% and K-Contact% plate discipline values.
By plugging Rosario’s 2011 plate discipline numbers into the spreadsheet, his 2011 plate discipline numbers would be 22% K% and 6% BB%. While the BB% is fairly close to his actual value (4%), the K% is off by 13 percentage points.
With questions surrounding his plate discipline in 2012, he saw is K% end up at 23%. This was within 1% point of what his 2011 estimated K%. With reasonable plate discipline, he was able to put up a decent season (1.8 WAR in 426 PA). Using a second method to calculate a Rosario’s K% and BB% helps to get a better picture of his true talent level.
Rookies, like Rosario, are called up and get a small number of plate appearances. By using a player’s plate discipline numbers, the player’s walk and strikeout rates can be estimated. The estimate can help determine if the player’s talent level is significantly different than their stats suggest.
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Appendix
I wanted a formula to help estimate a player’s K% and BB% using the plate discipline values available at FanGraphs. The formula create wouldn’t be a prediction (as it contains no regression) or stat that stabilizes fast.
I took every player that had over 200 PAs in a season from 2002, when plate discipline numbers are first available at FanGraphs, to 2012. I ran a linear regression against over 3500 seasons and came up with the following two formulas:
BB% ((NIBB-IBB)/PA)
BB% = -0.228 x O-Swing% -0.139 x Z-Swing% – 0.030 x O-Contact% -0.257 x Z-Contact% + 0.437
R-Squared = 0.45K% (K/PA)
K % = 0.248 x O-Swing% -0.345 x Z-Swing% – 0.153 x O-Contact% -0.837 x Z-Contact% + 1.169
R-Squared = 0.79
I have gone ahead and saved people some time and uploaded a spreadsheet to the Google Docs that will automatically do the calculations.
To use the sheet.
1. Download the spreadsheet by using the “Download As” feature under File.
Go to the players page at FanGraphs, minimize minor league data, go to the Standard stat area and copy the all the data going back to 2002.
2. Go to the downloaded spreadsheet and paste the data with the upper left corner being the left yellow box.
3. Go back to the player’s FanGraphs page and copy the (non-Pitch F/X) Plate Discipline values.
4. Go back to the downloaded spreadsheet and paste the data with the upper left corner being the right yellow box.
5. Once the data has been added to the spreadsheet, the player’s real and estimated K% and BB% will be calculated.
