How much does pitching on a downhill plane affect a pitcher's ability to get ground balls?
Here we are in the middle of the Information Age, with access to more data than the human mind can possibly process, and yet the dissemination of baseball information has been muted by a language barrier. Baseball fans are becoming increasingly savvy about the nuances of the game, with sophisticated analytical tools at their disposal, but access to the dynamics of play on the field is often clouded by a filter of scout-speak. If we were playing poker, then the dealer would need to remind the scouts in seats eight and nine of the “English only at the table” rule in order to prevent them from trading secrets that fly under the radar of other players.
There are dozens of entries in the pitching section of the scout-speak dictionary, from “command” and “control” to “arm action.” One of these buzzwords is “downhill plane,” a term that refers to pitch trajectory that has a steep slope on its approach toward the hitter. It seems to follow that pitchers who possess a high release point would induce a higher rate of ground balls. The logic behind the idea is simple enough, as anyone who has thrown a tennis ball against a wall can attest, but the statistical evidence paints a different picture.
Mike continues his investigation of HITf/x data to glean more insights into whether pitchers can prevent hits on balls in play.
In the first part of this study, I used detailed batted ball speed information from HITf/x to examine the degree of skill that batters and pitchers had in quality of contact made or allowed. Here, I will look deeper into the question of why some batted balls fall for hits and others do not.
Are pitchers able to apply certain skills when a game calls for it?
One of the pitchers I enjoyed watching the most while I was growing up was Tom Glavine. Even though I was a Phillies fan and frequently saw him victimize my favorite team, I was impressed by the expertise he demonstrated on the mound, and how he perfected his craft. Glavine remains the premier example of a pitcher who out-pitched his peripheral statistics; he was greater than the sum of his parts. For the amount of strikeouts, walks, and ground balls that Glavine got in his career, he should never have been able to keep runs off the scoreboard as well as he did.
A look at the variance in ground-ball rates between pitchers in the majors and minors.
The idea behind SIERA, as opposed to say FIP, is that the key factors in a pitcher's profile are his walk rate, strikeout rate, and ground-ball rate. Those factors become the prime shapers of other statistics—hits, home runs, and ultimately runs—that go into a pitcher's value.
Ground-ball pitchers have several skills that traditional statistics do not account for.
There are two more important reasons why Skill-Interactive Earned Run Average's (SIERA) is so successful at predicting the following year's ERA. First, most other Defense-Independent Pitching Statistics, like FIP and xFIP, assume that pitchers have no control over their Batting Average on Ball in Play (BABIP), but we know that they do have some control. I have shown before that pitchers with high strikeout totals and low ground-ball rates tend to allow fewer hits per ball in play, and thus lower BABIPs. Of course, BABIP is subject to so much luck that it is nearly impossible to discern a pitcher's true ability to prevent hits on balls in play from his historical BABIP. That is why last year's FIP is much better at predicting this year's ERA than last year's ERA is. It strips ERA of BABIP (and sequencing) altogether and assumes league-average BABIP for all pitchers and random sequencing.
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.
Look at which direction some hitters with high batting averages on balls in play are likely headed in 2011.
Last week, I discussed several pitchers who were pitching well in front of or well behind their peripherals using SIERA. This week, I will discuss several hitters who have particularly high BABIPs, and how much of that performance is skill versus luck.
A look at some pitchers who have had good luck this season and some who haven't.
When Eric Seidman and I introduced SIERA in February, we were very careful to show that it predicts future ERA better than current ERA does. While Defense Independent Pitching Statistics are not a foolproof way to measure pitchers, using them as a guide to dig further into the numbers can be very helpful. Last October, I spent a couplearticles analyzing Cole Hamels’ performance, and I highlighted how little was different between his 2008 and 2009 season, and how I expected his performance to improve as his luck neutralized. Sure enough, Hamels has seen his ERA fall back toward 2008 levels in 2010. In June, I disappointed Rockies fans by explaining the luck that had led to Ubaldo Jimenez’s 1.16 ERA at that time. Sure enough, he has a 4.36 ERA since that article was posted. Eric and I wrote on the Diamondbacks’ starters, stressing the bad luck that Dan Haren had seen to that point in the season. He had a 5.35 ERA, but it has been 3.59 since that article was posed and Haren has also been traded to the Angels. My point is not to cherry pick successes, but to prove that this type of analysis works. I certainly cannot be right every time I say a pitcher’s ERA is likely to fall or rise, because luck plays a role in pitching to a very large degree and luck by its very nature can reoccur. However, this type of analysis will prove prophetic more often than not.
A look at the surprise home run hitters of 2010, relative to their pre-season PECOTA forecasts.
On Tuesday night in Kansas City, Blue Jays right fielder Jose Bautista launched his major league-leading 26th home run, continuing one of the most unexpected power surges in recent memory. Long known as a journeyman with decent patience and a modicum of power, few expected Bautista at this stage of his career to suddenly turn into a long-ball machine. It’s always fun to see players suddenly show a propensity for the long ball—perhaps we identify with players who manage the baseball equivalent of the young Marty McFly balling up his fist and decking Biff with an unexpected haymaker.