If the Red Sox miss out on Brian McCann and Jarrod Saltalamacchia, the presumed solution is to go after Dioner Navarro. Despite playing only half a season, Navarro had the 2nd best season of his career (by wins above replacement) and set career highs for batting average, OBP, and slugging (.300/.365/.492). For comparison, those numbers actually top expected $100 million dollar man Brian McCann's .256/.336/.461 line across the board. Granted, no one is expecting Navarro to top McCann in 2014. Despite that, the Red Sox would do well to avoid Navarro.
Friday, November 22, 2013
Friday, November 15, 2013
Starting Pitching Primer, or, Is John Lackey's Contract Just like the Sox?
The Red Sox find themselves in an interesting position with starting pitching, heading in to 2014. Most importantly, they don't need to be involved in the starting pitching free agent market. There is a paucity of starters on the market, especially top tier arms, and every pitcher available has some real question marks around them.
Masahiro Tanaka, from the Japanese Pacific League, is coming off an incredible 24-0 season and is expected to be posted. However, importing Japanese pitchers has been a mixed bag - sometimes you get Darvish, sometimes you get Daisuke, and the cost could be well over $100 million. Ubaldo Jimenez and Ervin Santana are the next best arms available, and both were good pitchers in 2013, but both were also among the worst pitchers in baseball in 2012. Santana is looking for 5 years, $100 million, and Jimenez is likely looking in the same neighborhood. Finally, Ricky Nolasco is the final pitcher in Keith Law's top 50 free agents. Nolasco is looking for 5 years, $80 million on a track record that doesn't look substantively different from Ryan Dempster's last fall. A solid, if unspectacular track record of throwing 180-200 innings. There is some value in that, but I'm of the mind it falls a lot closer to Dempster's two year, $26.5 million deal, than Nolasco's aspirations.
Masahiro Tanaka, from the Japanese Pacific League, is coming off an incredible 24-0 season and is expected to be posted. However, importing Japanese pitchers has been a mixed bag - sometimes you get Darvish, sometimes you get Daisuke, and the cost could be well over $100 million. Ubaldo Jimenez and Ervin Santana are the next best arms available, and both were good pitchers in 2013, but both were also among the worst pitchers in baseball in 2012. Santana is looking for 5 years, $100 million, and Jimenez is likely looking in the same neighborhood. Finally, Ricky Nolasco is the final pitcher in Keith Law's top 50 free agents. Nolasco is looking for 5 years, $80 million on a track record that doesn't look substantively different from Ryan Dempster's last fall. A solid, if unspectacular track record of throwing 180-200 innings. There is some value in that, but I'm of the mind it falls a lot closer to Dempster's two year, $26.5 million deal, than Nolasco's aspirations.
The defensive cost of losing Drew
Defensively speaking, how much would the hit be to switch from Drew and Bogaerts at shortstop and third, respectively, to Bogaerts and Middlebrooks? This is a tricky question to answer, because you're trying to project two very young, unpredictable players in Middlebrooks and Bogaerts. On top of that, Drew had a gruesome ankle injury at the end of 2010, which makes his 2011 numbers an outlier.
Bogaerts at short and third
In a comment on an earlier post, I was asked about what to make of the fact that Bogaerts was a plus defender at short and a well below average one at third. The simple answer here is not much. Bogaerts only logged 110 innings in the field and defensive stats don’t even stabilize in a single season, so his numbers overall aren’t meaningful, let along splitting them up by position. Scouts say that Bogaerts would be an about average shortstop, so the simplest thing would just be to give him a 0 for defense. At shortstop, that’s no small feat. At third, it becomes less clear. The rough estimate would be that he would be save 5 runs, per calculations by Tom Tango.
Friday, November 8, 2013
Looking Ahead: Losses
After winning the championship, the Red Sox have a lot of players coming off the books. These include core contributors, including the entire swath of up the middle hitters, and guys you may have forgotten will be getting a ring for 2013.
Oh yeah, you were on the team (2013 salary)
Joel Hanrahan ($7.04)
Just as with Bailey, Hanrahan came over in a trade with the Pirates, in one of the larger moves of the offseason (or so we thought at the time). Unfortunately, Hanrahan was bad, then injured. He pitched a mere seven innings ($1 million/inning!), before undergoing Tommy John Surgery. Given that he's a Proven CloserTM, someone will probably take a flier on him on a one year deal with a team option, but we're probably looking at a return around the All Star Break next season. He won't be a great loss, doubly so given the emergence of Uehara as a closer and Uehara's affordable $4.25 million option, which vested in September, but the $7 million is always nice to have.
Joel Hanrahan ($7.04)
Just as with Bailey, Hanrahan came over in a trade with the Pirates, in one of the larger moves of the offseason (or so we thought at the time). Unfortunately, Hanrahan was bad, then injured. He pitched a mere seven innings ($1 million/inning!), before undergoing Tommy John Surgery. Given that he's a Proven CloserTM, someone will probably take a flier on him on a one year deal with a team option, but we're probably looking at a return around the All Star Break next season. He won't be a great loss, doubly so given the emergence of Uehara as a closer and Uehara's affordable $4.25 million option, which vested in September, but the $7 million is always nice to have.
Wednesday, November 6, 2013
Rebooting the blog to bask in the brilliance of a championship
It's been an awfully long time, but I wanted to try and start writing again, in part to enjoy the current success of the Red Sox, and in part to get in the practice of writing a lot, to help with my dissertation.
I'll be looking at free agent losses, the roster as it stands for 2014, upcoming prospects, and free agents in the next week or two.
I'll be looking at free agent losses, the roster as it stands for 2014, upcoming prospects, and free agents in the next week or two.
Saturday, May 11, 2013
An Extremely Unusual Start
On Friday night, Alex Cobb had one of the most unusual lines you'll ever see from a starting pitcher.
4 2/3 IP, 13 K, 2 BB, 5 H, 2 ER
So, Cobb got a total of 14 outs. Twelve were via the strikeout (one of the strikeouts reached first on a wild pitch). Two of the hits were home runs. That means that Cobb only had five balls in play. Of those five balls in play, two were infield singles and two were ground outs.
Looking at the underlying numbers, Cobb was just as dominant. Of Cobb's 117 pitches, 77 were strikes, for an about average total. However, aside from the two home runs, the Padres were clear completely lost at the plate. They swung at 57 pitches and swung and missed at 22 of them (38%). For comparison, 10% is a good swinging strike rate, and Yu Darvish has the highest swinging strike rate on the season (16.7%).
To top it all off, Cobb managed to strike out four batters in the third inning. Despite that, he allowed a run, as Will Venable reached on a wild pitch strike out, stole second, stole third, and then scored on a balk. Cobb then struck out Yonder Alonso to end the inning. It was only the 30th time than an AL pitcher has struck out four players in an inning and the 60th overall. Striking out four batters in an inning is one of the rarest occurrences in baseball, just behind a perfect game and the unassisted triple play.
All told, this might have been the most dominant start when a starter didn't even manage to qualify for a win.
4 2/3 IP, 13 K, 2 BB, 5 H, 2 ER
So, Cobb got a total of 14 outs. Twelve were via the strikeout (one of the strikeouts reached first on a wild pitch). Two of the hits were home runs. That means that Cobb only had five balls in play. Of those five balls in play, two were infield singles and two were ground outs.
Looking at the underlying numbers, Cobb was just as dominant. Of Cobb's 117 pitches, 77 were strikes, for an about average total. However, aside from the two home runs, the Padres were clear completely lost at the plate. They swung at 57 pitches and swung and missed at 22 of them (38%). For comparison, 10% is a good swinging strike rate, and Yu Darvish has the highest swinging strike rate on the season (16.7%).
To top it all off, Cobb managed to strike out four batters in the third inning. Despite that, he allowed a run, as Will Venable reached on a wild pitch strike out, stole second, stole third, and then scored on a balk. Cobb then struck out Yonder Alonso to end the inning. It was only the 30th time than an AL pitcher has struck out four players in an inning and the 60th overall. Striking out four batters in an inning is one of the rarest occurrences in baseball, just behind a perfect game and the unassisted triple play.
All told, this might have been the most dominant start when a starter didn't even manage to qualify for a win.
Wednesday, May 1, 2013
On swinging strikes, swings in the zone (or lack thereof), and strike outs
Following up on the comment on my Clay Buchholz piece, I wanted to examine how the swing percentage on pitches in the strike zone impacted strikeout rate. Thanks to Fangraphs excellent and customizable leader boards, I was able to get data for pitchers from 2006 to 2012 who threw for 100 or more innings. I did not want to go much farther back, as strikeout rates have changed considerably over the last few years, but also, swinging strike percent and swing rate on pitches in the zone are not available prior to 2005.
What are the Data I'm Using?
So, given 100 IP in a single season from 2006 to 2012, I had 986 total records from 306 different pitchers to go with. For simplicity's sake, I'm going to treat each season as independent, even though we might expect different seasons from the same pitcher to be related.
Creating the Basic Model
I used these 986 data points to create a linear model predicting strike out rate in K/9 IP using the swinging strike percentage. I originally considered adding the season to the model, as strikeout rates have steadily risen over the time period I'm looking at. While season was a significant predictor of K/9 IP when it was included in the model (more recent = more strikeouts), it didn't add much to the overall fit and only had a very minor impact on the model overall. In ecology, we often describe this as statistically significant, but not biologically significant. It often happens when you have a large sample size. Also, in general, I strongly prefer the simplest model possible, especially when adding variables marginally improves the fit. Guess I'm just a big fan of Occam's Razor. Without further ado, the relationship between swinging strike rate and strikeout rate is:
K/9 = 71.8*(Swinging Strike %) + 0.7892
The R2 for this model was 0.6693, meaning that swinging strike rate explains 67% of the variability in K/9. That is a huge proportion, but it does leave room for improvement.
Examining the Residuals
A residual is the difference between the expected value from the model and the actual model. A positive residual means that the pitcher had more strike outs than our swinging strike model thought they should, while a negative residual means the opposite. In general, a lower residual means a better fit for the model, but you can also look at the residuals to see if there is anything consistent about them. In our case, we’ll look at the percent of swings on pitches in the zone. To do this, I ran another linear regression, trying to predict the residuals using the zone swing percent. This showed a significant trend, but with a much lower predictive value than the swinging strike rate (R2=0.136). When your residuals have a trend, it means that your model isn’t capturing something, but for our purposes, it is a good thing. It means that when pitchers have a lower rate of swings against pitches in the zone, they have a higher strikeout rate.
The Low Swing Pitchers
Over the entire data set, the average swing rate at pitches in the zone was 65%. Unlike some other stats, there isn’t as much variability in swing rates at pitches in the zone. The lowest average belongs to Doug Fister (55.5%, over the course of three seasons), while the highest belongs to Scott Baker (72%, over two seasons). Another way to look at the data is to examine whether or not the pitchers with the low swing rates on pitches in the zone have a high residual. In short, the answer is yes. Four pitchers averaged a zone swinging rate of 60% or lower, and had multiple seasons on record. Here are their numbers – remember, a positive residual means that a pitcher was striking out more batters than the model predicted.
So, we see a consistent pattern at the extreme ends. But, does swing rate on pitches in the strike zone explain most of the variability in K/9?
The Biggest Departures from the Model
To examine this, I looked at the pitchers who had: 1. The highest residuals, and 2. At least three years of data. If the biggest departures from our expected results (largest residuals), all have a lower than average swing rate on pitches within the zone, that would provide even stronger support that swing rate within the strike zone controls how . Of the 15 pitchers with the highest average residuals, only two have swing rates below 60% (Mussina and Wilson), while an additional six have zone swing rates slightly below average (60-64%), and five have zone swing rates that are about average (64%-66%). None of the pitchers with consistently high residuals have above average zone swing rates. Interestingly, many of the high residual pitchers who had average to slightly below average zone swing rates also walked a lot of batters. If you increase the number of walks you hand out, you'll also increase the number of batters you face, which in turn should lead to a higher K/9. For example, think about two different pitchers. Pitcher A strikes out 25% of the batters he faces and walks 10% (Yovanni Gallardo) and pitcher B strikes out 25% of the batters he faces and walks 6%. Pitcher B is clearly the superior pitcher, but pitcher A will likely have a higher K/9.
So What Does This All Mean?
I think there are several conclusions we can draw here. First, a lower swing rate at pitches in the strike zone does consistently lead to a higher K/9. Second, if a pitcher has a high swing rate at pitches in the zone, they probably won't outperform their predicted K/9 based on swinging strike rate. This makes sense; lots of swings will lead to more balls in play, which in turn will lead to fewer strikeouts. Third, there are other factors that aren't captured by just looking at swinging strike rate and zone swing rate. Most of the pitchers with the highest residuals in the model did not have extremely low zone swing rates. This may be attributable to "stuff" in general, so perhaps delving into PitchFX data would lead to a clearer picture there.
What are the Data I'm Using?
So, given 100 IP in a single season from 2006 to 2012, I had 986 total records from 306 different pitchers to go with. For simplicity's sake, I'm going to treat each season as independent, even though we might expect different seasons from the same pitcher to be related.
Creating the Basic Model
I used these 986 data points to create a linear model predicting strike out rate in K/9 IP using the swinging strike percentage. I originally considered adding the season to the model, as strikeout rates have steadily risen over the time period I'm looking at. While season was a significant predictor of K/9 IP when it was included in the model (more recent = more strikeouts), it didn't add much to the overall fit and only had a very minor impact on the model overall. In ecology, we often describe this as statistically significant, but not biologically significant. It often happens when you have a large sample size. Also, in general, I strongly prefer the simplest model possible, especially when adding variables marginally improves the fit. Guess I'm just a big fan of Occam's Razor. Without further ado, the relationship between swinging strike rate and strikeout rate is:
K/9 = 71.8*(Swinging Strike %) + 0.7892
The R2 for this model was 0.6693, meaning that swinging strike rate explains 67% of the variability in K/9. That is a huge proportion, but it does leave room for improvement.
Examining the Residuals
A residual is the difference between the expected value from the model and the actual model. A positive residual means that the pitcher had more strike outs than our swinging strike model thought they should, while a negative residual means the opposite. In general, a lower residual means a better fit for the model, but you can also look at the residuals to see if there is anything consistent about them. In our case, we’ll look at the percent of swings on pitches in the zone. To do this, I ran another linear regression, trying to predict the residuals using the zone swing percent. This showed a significant trend, but with a much lower predictive value than the swinging strike rate (R2=0.136). When your residuals have a trend, it means that your model isn’t capturing something, but for our purposes, it is a good thing. It means that when pitchers have a lower rate of swings against pitches in the zone, they have a higher strikeout rate.
The Low Swing Pitchers
Over the entire data set, the average swing rate at pitches in the zone was 65%. Unlike some other stats, there isn’t as much variability in swing rates at pitches in the zone. The lowest average belongs to Doug Fister (55.5%, over the course of three seasons), while the highest belongs to Scott Baker (72%, over two seasons). Another way to look at the data is to examine whether or not the pitchers with the low swing rates on pitches in the zone have a high residual. In short, the answer is yes. Four pitchers averaged a zone swinging rate of 60% or lower, and had multiple seasons on record. Here are their numbers – remember, a positive residual means that a pitcher was striking out more batters than the model predicted.
| Name |
Seasons
|
Residual (K/9)
|
Zone Swing %
|
| Doug Fister |
3
|
0.873
|
55.5%
|
| C.J. Wilson |
3
|
1.678
|
57.5%
|
| Jake Arrieta |
3
|
0.992
|
59.2%
|
| Mike Mussina |
3
|
1.476
|
59.4%
|
So, we see a consistent pattern at the extreme ends. But, does swing rate on pitches in the strike zone explain most of the variability in K/9?
The Biggest Departures from the Model
To examine this, I looked at the pitchers who had: 1. The highest residuals, and 2. At least three years of data. If the biggest departures from our expected results (largest residuals), all have a lower than average swing rate on pitches within the zone, that would provide even stronger support that swing rate within the strike zone controls how . Of the 15 pitchers with the highest average residuals, only two have swing rates below 60% (Mussina and Wilson), while an additional six have zone swing rates slightly below average (60-64%), and five have zone swing rates that are about average (64%-66%). None of the pitchers with consistently high residuals have above average zone swing rates. Interestingly, many of the high residual pitchers who had average to slightly below average zone swing rates also walked a lot of batters. If you increase the number of walks you hand out, you'll also increase the number of batters you face, which in turn should lead to a higher K/9. For example, think about two different pitchers. Pitcher A strikes out 25% of the batters he faces and walks 10% (Yovanni Gallardo) and pitcher B strikes out 25% of the batters he faces and walks 6%. Pitcher B is clearly the superior pitcher, but pitcher A will likely have a higher K/9.
| Name |
Seasons
|
Residual (K/9)
|
Zone Swing %
|
| Yovani Gallardo |
4
|
2.065
|
62.0%
|
| C.J. Wilson |
3
|
1.678
|
57.5%
|
| Erik Bedard |
3
|
1.623
|
64.2%
|
| Mike Mussina |
3
|
1.476
|
59.4%
|
| David Price |
4
|
1.343
|
64.8%
|
| Ubaldo Jimenez |
4
|
1.272
|
63.7%
|
| Tim Lincecum |
5
|
1.258
|
63.4%
|
| Zack Greinke |
5
|
1.179
|
64.7%
|
| Jonathan Sanchez |
4
|
1.176
|
62.3%
|
| Clayton Kershaw |
5
|
1.169
|
63.4%
|
| Oliver Perez |
3
|
1.154
|
64.7%
|
| Tommy Hanson |
3
|
1.148
|
63.0%
|
| Josh Beckett |
6
|
1.134
|
64.9%
|
| Daniel Cabrera |
3
|
1.119
|
64.8%
|
| Jon Lester |
4
|
1.112
|
65.7%
|
So What Does This All Mean?
I think there are several conclusions we can draw here. First, a lower swing rate at pitches in the strike zone does consistently lead to a higher K/9. Second, if a pitcher has a high swing rate at pitches in the zone, they probably won't outperform their predicted K/9 based on swinging strike rate. This makes sense; lots of swings will lead to more balls in play, which in turn will lead to fewer strikeouts. Third, there are other factors that aren't captured by just looking at swinging strike rate and zone swing rate. Most of the pitchers with the highest residuals in the model did not have extremely low zone swing rates. This may be attributable to "stuff" in general, so perhaps delving into PitchFX data would lead to a clearer picture there.
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