The Rangers finally get to toss their ace against the Yankees' grizzled veteran.
Cliff Lee: 3.18 ERA, 3.03 SIERA Lee’s post-season mystique seems to grow by the start, and he now will carry that into a pivotal Game Three for the Rangers. Lee had an incredible pair of performances to carry the Rangers past the Rays in the ALDS, and the Texas needs him to come up big again in the ALCS. In two starts this postseason, Lee has allowed no walks but struck out 21 batters in 16 innings pitched; he has only allowed two runs, and gotten two critical wins for the Rangers. Lee had a pair of wins in last year’s World Series for the Phillies over the Yankees as well, though he did struggle with a big lead toward the end of his second start in Game Five. Even still, Lee is one of the very best pitchers in the league and presents a major challenge to the Yankees. As I wrote before his Game Five start in the ALDS, this is exactly why Jon Daniels traded for Lee with only a couple months remaining on his contract—the value of a big-time ace in the postseason is high. If Lee can pitch the Rangers even further into the postseason, this will come out looking like a win for Daniels.
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Breaking down the pitchers who will take the hill for the Phillies and Giants in Game Two of the NLCS.
Jonathan Sanchez: 3.07 ERA, 3.70 SIERA
Sanchez’s modus operandi has been that he strikes many batters out, but walks many as well. When faced with the Braves in Game Three of the NLDS, Sanchez only followed the first part of that plan—he walked just one hitter while striking out 11 of the 25 batters he faced. Sanchez does not mess around with left-handed batters, so he should be able to attack and have some success against some of the Phillies’ bigger bats. However, the myth often relayed about the Phillies’ lineup is that they do worse against lefties. The numbers simply do not bear this out, as they have hit slightly better as a team against southpaws for several consecutive years. The reason is that outside of sluggers like Howard, Utley, and Ibanez, the rest of the team is right-handed or switch-hitters who do better from the right side. Sanchez will need to use his nasty slider to get past the Phillies’ lefties, but must also keep from allowing the righties and switch-hitters to reach base if he is going to give the Giants a win in Game Two.
The ALCS continues, while the Phillies and Giants begin the NLCS with a pair of Cy Young winners toeing the rubber.
Yankees at Rangers Phil Hughes: 4.19 ERA, 4.00 SIERA Hughes pitched six scoreless innings in the clinching Game Three of the Yankees’ ALDS sweep of the Twins. He struck out six and walked only one, sprinkling four hits along the way. He certainly showed that he had whatever mentality people were afraid he lacked in the ALDS, and now he is slotted to start both of his games on the road in the ALCS.
The Yankees will send their ace to the mound, but the Rangers are without Cliff Lee in Game One.
Yankees at Rangers CC Sabathia: 3.18 ERA, 3.75 SIERA Sabathia struggled to throw strikes in his start in Game One of the ALDS, but the Yankees’ bats bailed him out and won the game anyway. As noted in last week’s summary re-printed below, Sabathia’s walk rate had increased in recent years, threatening his ability to be as economical with his pitches. As evidence he threw 111 pitches in six innings last week. Even still, Sabathia is back in to start Game One in Texas.
Another look at Rays' David Price and Rangers' Cliff Lee, who are set to square off in the fifth and deciding game of the ALDS.
Rays vs. Rangers David Price: 2.72 ERA, 3.82 SIERA
Price struck out eight and did not walk anyone in Game One, but he surrendered a couple home runs and got outpitched by Lee. The walks do bode well for Price, who has struggled with free passes at times in 2010. He makes the biggest start of his life tonight in the first LDS Game Five since 2005. Below is the discussion of Price from prior to Game One:
Breaking down the strengths and weaknesses of the men who will toe the rubber in Game Four of Giants-Braves NLDS
Braves vs. Giants Madison Bumgarner: 3.00 ERA, 3.88 SIERA Bumgarner, the highly touted prospect, appeared to be the Giants’ fifth starter heading into spring training, but he struggled with his fastball velocity and did not make the team. However, the Giants gave him his shot in late June and he did not disappoint over 18 starts. His velocity is back, helping him strike out a respectable 18.2 percent of hitters in 2010. He was also very good at limiting walks in his rookie season, issuing them to only 5.5 percent of batters. Bumgarner also generated a 46 percent ground ball rate, which is about average as well. The key to Bumgarner’s success in 2010 was his control, but he also had some luck go his way. He had a similar rate on home runs per fly ball that other Giants’ starters had, and his overall BABIP was actually a high .314 despite the Giants’ overall team BABIP of .286. However, Bumgarner allowed just a .253 BABIP with runners in scoring position and a .269 BABIP with runners on base overall. Thus, despite a modest WHIP of 1.31, his baserunners were frequently stranded. The ability to induce a weaker BABIP with runners on base is not something that pitchers tend to consistently repeat. While some pitchers are better at dodging home runs with men on and better at pitching to contact with the bases empty, the results do not tend to carry over to BABIP. Bumgarner is certainly beatable, despite his low ERA. He is not going to issue walks often, so the Braves will need to make solid contact when they get a pitch to hit.
A breakdown of the starting pitchers in Saturday's post-season games.
Rangers vs. Rays Matt Garza: 3.91 ERA, 4.29 SIERA Garza maintained a similar ERA in 2010 to last year’s 3.95, but his SIERA rose from 3.83 to 4.29 due a drop in his strikeout rate. Garza struck out 22 percent of hitters in 2009 only to whiff 17.5 percent in 2010. Garza maintained his ERA because his BABIP was .247 with men on and .208 with runners in scoring position. His walk rate fell from 9.2 to 7.4 as well, which meant that he allowed a similar number of baserunners even though he allowed more hits due to the decreased strikeout rate. However, Garza is likely to allow more of those runners to score as his BABIP in high-leverage situations increases. Garza may look like a solid second starter with his ERA, but this season he pitched like an average pitcher.
Breaking down the starters in Friday's post-season action.
Phillies vs. Reds Roy Oswalt: 2.76 ERA, 3.33 SIERA Oswalt joined the Phillies in late July and posted a 1.74 ERA in 12 starts and an inning of relief. The Phillies won 10 of his 12 starts, but his SIERA of 3.33 was a dead ringer for his 3.31 SIERA in his 20 starts with the Astros. Oswalt’s .227 BABIP with the Phillies was due to very few ground balls finding holes and outfield flies staying catchable more often than can be expected in the future. However, both his 2010 SIERA in Philadelphia and in Houston were far better than his recent years’ SIERAs of 3.89, 3.63, and 3.86 from 2007-09. The reason is that Oswalt struck out more hitters than he had since his 2001 rookie year, raising his strikeout rate from 18.2 percent in 2009 to 23.1 percent in 2010. Oswalt began using his changeup more in 2010 with the Astros early on, and began using it even more after working with Phillies pitching coach Rich Dubee. Thus, Oswalt has the sixth-best SIERA among all starters in the playoffs and 13th overall, after finishing only 33rd in the majors in 2009.
Breaking down the starters in today's post-season games.
Giants vs. Braves Tim Lincecum: 3.43 ERA, 3.16 SIERA Lincecum’s ERA increased by 0.95 runs in 2010, but his SIERA only went up by 0.43 runs. His strikeout rate did decline from his lofty 2008 and 2009 levels of 28.6 and 28.8 percent to 25.8 in 2010. While striking out as many hitters as any starting pitcher did during his Cy Young years, Lincecum was able to get away with mediocre walk and ground-ball rates. However, as his velocity declined, Lincecum became slightly more hittable and batters were able to get more runs off of him. Lincecum did put up a career-best 50 percent ground-ball rate in 2010, suggesting that he is learning how to pitch smarter. However, he also had some bad luck as well—his BABIP was .315, primarily due to a 20.9 percent line-drive rate. This sounds bad, but line-drive rate is the least persistent pitcher statistic. In his career, Lincecum has allowed a .301 BABIP, so there is little reason to expect this to change. He is still one of the top 10 pitchers in the league, and this will not be an easy matchup for the Braves. Lincecum will still strike out about a quarter of hitters he faces, which means the Braves need to take advantage when they do make contact and work pitch counts consistently. Two starts against Lincecum are not going to be easy, though, and the righty gives the Giants a real ace to try to start each post-seasn series with a win.
Previewing some of the Game One pitching matchups for the upcoming division series.
We’re revving up for the postseason that starts today, where pitching matchups are sure to be highlighted. I’ll be discussing the difference between ERAs and SIERAs of pitchers in October games, and some of the reasons for those discrepancies. My goal is to be more detailed than simply looking at BABIP and declaring a pitcher lucky or unlucky, instead really getting at what the pitcher does to get hitters out and whether his SIERA is more indicative of his skill level than his ERA. If nothing else, we can gain a solid understanding of where pitchers excel and where they struggle, and how it will all affect the games.
Is having pitch data available helpful in determining a pitcher's walk rates?
Last week, I looked at Predicting Strikeouts with Swing and Whiff Rates, breaking down pitch-by-pitch data to see if things like swinging-strike rates could provide more enlightenment when combined with the previous year’s strikeout rate to predict future strikeout rate. The answer was mostly negative. This was primarily due to two reasons. One was that much of the data on pitch locations is poor, and ensuing discussions highlighted just how poor it is. The other reason, however, is that strikeout rate is the quickest statistic to stabilize over small samples, so one year of strikeout data does a very good job of predicting subsequent strikeout data already. However, this week I will look at walk rate, and attempt to determine whether this data is more useful in predicting future walk rates. There is certainly evidence of value added in this case, far more so than with predicting strikeouts.