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Archive for July 2010

Fun with Numbers

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Some of these are stupid-good, stupid-bad, or just plain stupid.

Austin Jackson’s BABIP is 0.436, somewhat supported by a 28.3% line-drive rate, 46.9% ground ball rate, and just 3.2% of his flyballs staying on the infield.  He has a 37-point advantage over Josh Hamilton.

Jose Bautista‘s slugging percentage is more than twice his batting average (0.548 to 0.242), meaning that he averages more than 2 bases per hit.  34 singles, 21 doubles, 2 triples, and 27 home runs will do that.

On the other hand, Juan Pierre has 11 extra base hits (10 doubles and a triple) in 431 plate apperances, leading to a slugging percentage (0.282) that is not only 32 points higher than his batting average (0.250) but is also lower than his on-base percentage (0.321).

Justin Morneau, Miguel Cabrera, Joey Votto, and Josh Hamilton are simply in another league in terms of ability to hit a baseball.  Easily the most valuable offensive players so far in 2010.

Votto has yet to hit a pop up on the infield.

Almost 60% of Mark Reynolds‘ batted balls are fly balls.  Fortunately, 20% of those leave the ballpark.  On the opposite side of the spectrum, Derek Jeter has hit fly balls only 15.7% of the time in 2010.

Erick Aybar has more bunt hits than 13 teams.

Ryan Howard sees fewer fastballs than anyone in baseball; David Eckstein sees the most.  Insert size joke here.  After Eckstein in that department are Jason Kendall and Pierre.  Yeah, I’d thrown them strikes too.

47% of the time Vladimir Guerrero sees a pitch that isn’t a strike, he swings.  He’s followed by Jeff Franceour, who gets noticeably worse results with his method than Vlad does.

Reynolds, Howard, Adam Dunn, and Carlos Pena all miss 50% or more of the pitches they swing at that aren’t strikes.  To be fair, Reynolds only hits 70% of the pitches he swings at that are strikes; his overall contact rate of 61.7% is the worst is the major leagues by almost 8 percent.

Albert Pujols sees fewer first-pitch strikes than anyone in baseball (46 percent). #BadIdeas #GoAhead&WalkHim

Clue Haywood leads the league in most offensive categories, including nose hair.  When this guy sneezes, he looks like a party favor.  Just wanted to see if you were still paying attention.

Would you have guessed that the best pitcher in baseball so far has been Josh Johnson?

Your Cliff Lee status update: 101 strikeouts, 7 walks, 14.43 K/BB.

Nick Blackburn is 107th in home run rate and 109th in strikeout rate among qualified starters.  That’s a quick way to 6.66 ERA.

Trevor Cahill is sporting a 0.220 BABIP, leading to an ERA that is 1.11 runs below his FIP.  Topping that, Tim Hudson has a BABIP of 0.231 BABIP and a strand rate of 83.2%, resulting in a 2.47 ERA and a 4.25 FIP.

On the flip side, Brandon Morrow, Francisco Liriano (SCARY), Scott Baker, and Justin Masterson all sport ERAs that are more than a run higher than their FIPs, thanks to BABIPs ranging from 0.337 to 0.357.

Masterson’s problem almost certainly is that he induces too many ground balls, because the Indians’ infield defense sucks (also, those same guys can’t hit) at turning them into outs.  He gets 3 ground balls for every fly ball, which leads the league among starters.

Phil Hughes leads the major leagues in run support, getting over 8 runs per game in his starts; next closest is Blackburn at 7.01 runs per game.  At the other end are Ted Lilly and Roy Oswalt, who get 2.43 and 2.51 runs per game, respectively.

Upside, Carlos Marmol is at 16.92 strikeouts per nine innings; downside, also at 6.46 walks per nine innings.

BABIP is a lot more volatile for relievers because of the fewer innings pitched, but Chad Qualls is at 0.452 with a 51.2% strand rate.  Yikes.  His ERA is twice his FIP (8.49 to 4.14).

Lastly, Ubaldo Jiminez, by month:

IP ERA ER HR BB SO K/9 BB/9 K/BB HR/9 AVG BABIP LOB% FIP
Mar/Apr 34.1 0.79 3 0 14 31 8.13 3.67 2.21 0.00 0.185 0.251 91.7% 2.52
May 46 0.78 4 1 12 39 7.63 2.35 3.25 0.20 0.156 0.202 93.1% 2.67
Jun 32.2 4.41 16 4 14 32 8.82 3.86 2.29 1.10 0.264 0.325 75.5% 4.12
Jul 21.1 7.59 18 2 15 18 7.59 6.33 1.20 0.84 0.220 0.262 41.1% 4.75

That’s why they don’t give out Cy Youngs in May.

Written by Dan Hennessey

July 26, 2010 at 8:25 PM

Posted in Uncategorized

Carlos Santana

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Part of me wants to make 20 “Black Magic Woman” jokes.  Part of me wants to just post a picture of Casey Blake and laugh maniacally.  But the baseball fan in me is winning, so today I want to talk about Carlos Santana’s first 35 games in the major leagues.

Now, from what you’re about to read, it’s going to sound like I think he’s the greatest thing since sliced bread, but we’re too late for that; there’s already a young catcher with that claim to fame.  I’m not suggesting that he’s as good as he’s played so far.  I’m not suggesting that some of the “he’s on pace for…” statistics I’m about to deliver are anywhere near credible; I think we’re all smarter than that.  I just want to demonstrate how remarkably good the 23-year old catcher has been so far.

Santana was called up on June 11th and was immediately inserted into the number 3 spot in the Tribe’s lineup.  He went o for 3 with a walk (foreshadowing!) but followed it up the next night with a home run and a double.  The Indians were 23-36 before Santana joined the team and are 17-18 since.  He’s started 34 of the 35 games, 31 at catcher and 3 at DH, and pinch-hit in the 35th game.

In his 35 games, Santana has a slash line of 0.292/0.443/0.566.  Hint: that’s a good start.  He’s got 6 home runs and 13 doubles in 113 at-bats; couple that with 32 walks against only 20 strikeouts in 149 plate appearances and you’ve got a really valuable hitter.  His batting value by number of runs is 13.7, which puts him 45th in the major leagues; remember, he didn’t play the first two months. That same number is already second amongst catchers, behind only Geovany Soto.

His 21.5% walk rate is out-of-control good for a rookie, and it might not be a complete fluke.  His minor league walk rates were in the mid-teens, and his strikeout rates were around the 17.7% he’s posting now.  His HR/FB% is 15.0%, which seems slightly high, but isn’t unreasonable.  He’s seeing 4.28 pitcher per plate appearance, which would put him 15th in the major leagues.

His weighted on-base average is 0.435, which places him fourth in baseball amongst hitters with at least 100 plate appearances.  He’s behind Josh Hamilton, Justin Morneau, and Miguel Cabrera, and just ahead of Joey Votto, Kevin Youkilis, and Albert Pujols.

In 35 games, he’s been worth 2.2 wins; on a 162-game pace, that would be 10.2 wins above replacement.  That’s MVP territory.  Remember, I’m not saying it will continue, just that what he’s done shouldn’t be overshadowed by what’s happening by other rookies in Washington and San Francisco.  Amongst catchers, he’s fifth in WAR, behind Miguel Olivo, Brian McCann, Soto, and Joe Mauer.

Basically he’s doing everything a hitter is supposed to, and he’s 23 years old and in his first month plus in the big leagues.

The major question mark when he was called up was whether he was ready to be a major league catcher defensively.  It seems as though he’s met the challenge to this point.  The following table shows the statistics of Tribe pitchers with the four different catchers used so far.  Now, I’m not big on “the catcher directly affects the pitching staff” stuff, particularly because there’s no evidence to suggest that such a correlation exists.  But it certainly seems like Santana isn’t hurting anything at the very least.

Catcher G IP ERA HR BA OBP SLG CS % HR/9 K/9 BB/9 K/BB
Lou Marson 45 378.1 4.92 41 0.286 0.369 0.442 38.1% 0.98 5.71 4.19 1.36
Carlos Santana 31 266.1 4.22 21 0.266 0.332 0.390 34.8% 0.71 6.09 3.28 1.86
Mike Redmond 22 162.1 3.99 12 0.263 0.344 0.391 7.4% 0.67 5.00 4.16 1.20
Chris Gimenez 2 20.1 2.66 1 0.266 0.366 0.380 0.0% 0.45 6.72 5.37 1.25

I love Carlos Santana because he’s fun to watch hit (he takes some man-sized swings) and he’ll be an Indian (hopefully  still hurting baseballs) until he’s at least 30.  It’s fun when one of the best prospects in a sport bursts onto the scene for your team and crushes initial expectations.  Let’s hope Santana never does this.

Lastly, just because it’s fun:

Miss you Casey.

Written by Dan Hennessey

July 21, 2010 at 10:20 PM

Posted in Uncategorized

How OK is Striking Out?

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In the last post, I hypothesized that an increase in strikeouts makes it easier for pitchers to record perfect games and no-hitters.  While that might be true, it does not necessarily mean that hitters are faring worse than they used to; it simply means that there are more feast-or-famine days for major league teams. Using data from 1955 up through the current season, I looked at what hitters are doing while at the plate to determine if they are using their plate appearances as efficiently as they can.

In order to do this, I simply looked at how many runs are scored per game and how many strikeouts are recorded per game (shown in the first chart below).  The number of runs per game jumped sharply in the early 1990s, just as strikeouts were increasing.  Between 1992 and 1993, strikeouts jumped 6.6% per nine innings as runs scored increased by 7.0% per nine innings.  Obviously, hitters were doing more productive things in their non-strikeout at-bats.

I then looked at the weighted on-base average (wOBA) of the league from 1955 to 2010.  I’ve talked about wOBA before, but to clarify, wOBA averages the run value of each positive action achieved by a hitter; the resulting number is scaled to on-base percentage to make it easier to understand.  Therefore, an average hitter has a wOBA near 0.335, a great hitter is 0.400 or higher, and a poor hitter would be under 0.300, just like OBP.

The following chart tracks the league-average wOBA and the strikeout rate of the league for the last 55 years:

During the late 1960s (when the rules had been changed to favor pitchers), strikeouts went up and wOBA went down; when the rules were readjusted to even the playing field, hitters were back to their pre-dead ball era numbers and strikeouts decreased.  Beginning in the mid-1980s and continuing through the mid-1990s, strikeouts went up significantly as did the league’s wOBA.  This wOBA graph mirrors the runs scored graph, as it should, since wOBA attempts to place run values on each action assigned to a hitter.  Until this season (which, as I said in the previous post, is the most offensively-challenged season since 1992), the wOBA and average number of runs scored stayed fairly constant even as strikeouts continued to increase.

The formula for wOBA is as follows: wOBA = (0.72 * NIBB + 0.75 * HBP + 0.90 * 1B + 0.92 * RBOE + 1.24 * 2B + 1.56 * 3B + 1.95 * HR) / PA, where NIBB is non-intentional bases on balls, RBOE is the number of times reached base on error, and PA is plate appearances.  So the variables shown are the numbers needed to determine if what hitters are doing something that is counteracting the strikeouts.  Here’s a nasty looking chart if you want to see the numbers for yourself.

Year R/G 1B 2B 3B HR SO HBP NIBB wOBA
1955 4.48 6.26 1.32 0.28 0.90 4.38 0.21 3.37 0.327
1956 4.45 6.17 1.35 0.29 0.93 4.64 0.19 3.31 0.326
1957 4.31 6.32 1.37 0.27 0.89 4.84 0.21 3.01 0.319
1958 4.28 6.20 1.37 0.27 0.91 4.95 0.20 3.02 0.320
1959 4.38 6.19 1.40 0.24 0.91 5.09 0.20 3.02 0.319
1960 4.31 6.15 1.39 0.27 0.86 5.18 0.20 3.10 0.319
1961 4.53 6.16 1.39 0.26 0.95 5.23 0.20 3.20 0.324
1962 4.46 6.28 1.33 0.26 0.93 5.42 0.22 3.12 0.322
1963 3.95 6.00 1.27 0.24 0.84 5.80 0.22 2.67 0.304
1964 4.04 6.12 1.31 0.23 0.85 5.91 0.21 2.65 0.307
1965 3.99 5.94 1.29 0.24 0.83 5.94 0.22 2.74 0.305
1966 3.99 6.04 1.28 0.25 0.85 5.82 0.21 2.55 0.303
1967 3.77 5.96 1.26 0.24 0.71 5.99 0.23 2.58 0.299
1968 3.42 5.90 1.19 0.21 0.61 5.89 0.24 2.44 0.292
1969 4.07 6.11 1.24 0.22 0.80 5.77 0.23 3.08 0.313
1970 4.34 6.16 1.35 0.24 0.88 5.75 0.21 3.15 0.319
1971 3.89 6.18 1.27 0.21 0.74 5.41 0.21 2.87 0.310
1972 3.69 6.06 1.25 0.20 0.68 5.57 0.20 2.78 0.304
1973 4.21 6.41 1.34 0.20 0.80 5.24 0.19 3.02 0.319
1974 4.12 6.49 1.34 0.22 0.68 5.01 0.20 2.98 0.318
1975 4.21 6.41 1.41 0.23 0.70 4.98 0.20 3.11 0.321
1976 3.99 6.48 1.35 0.25 0.58 4.83 0.18 2.90 0.315
1977 4.47 6.36 1.53 0.28 0.87 5.16 0.19 2.96 0.324
1978 4.10 6.27 1.47 0.24 0.70 4.77 0.18 2.91 0.318
1979 4.46 6.43 1.53 0.25 0.82 4.77 0.18 2.91 0.324
1980 4.29 6.56 1.51 0.26 0.73 4.80 0.16 2.79 0.320
1981 4.00 6.35 1.43 0.24 0.64 4.75 0.17 2.86 0.314
1982 4.30 6.40 1.50 0.23 0.80 5.04 0.16 2.85 0.318
1983 4.31 6.33 1.53 0.24 0.78 5.15 0.17 2.87 0.319
1984 4.26 6.40 1.48 0.23 0.77 5.34 0.16 2.86 0.317
1985 4.33 6.12 1.53 0.23 0.86 5.34 0.17 2.97 0.318
1986 4.41 6.11 1.55 0.20 0.91 5.87 0.19 3.07 0.320
1987 4.72 6.12 1.61 0.21 1.06 5.96 0.20 3.11 0.326
1988 4.14 6.15 1.52 0.20 0.76 5.56 0.22 2.76 0.312
1989 4.13 6.18 1.50 0.21 0.73 5.61 0.19 2.87 0.313
1990 4.26 6.20 1.55 0.21 0.79 5.67 0.20 2.96 0.319
1991 4.31 6.14 1.54 0.21 0.80 5.80 0.22 3.03 0.318
1992 4.12 6.20 1.56 0.20 0.72 5.59 0.23 2.94 0.317
1993 4.60 6.31 1.64 0.21 0.89 5.80 0.26 3.00 0.327
1994 4.92 6.25 1.79 0.22 1.03 6.18 0.27 3.16 0.333
1995 4.85 6.24 1.72 0.20 1.01 6.30 0.30 3.26 0.333
1996 5.04 6.29 1.76 0.19 1.09 6.46 0.31 3.25 0.335
1997 4.77 6.17 1.77 0.19 1.02 6.61 0.32 3.20 0.332
1998 4.79 6.13 1.80 0.18 1.04 6.56 0.33 3.16 0.331
1999 5.08 6.20 1.80 0.19 1.14 6.41 0.33 3.45 0.341
2000 5.14 6.11 1.83 0.20 1.17 6.45 0.32 3.50 0.341
2001 4.78 5.91 1.81 0.19 1.12 6.67 0.39 2.97 0.327
2002 4.62 5.90 1.79 0.19 1.04 6.47 0.36 3.05 0.326
2003 4.73 5.99 1.82 0.19 1.07 6.34 0.38 3.00 0.328
2004 4.81 6.03 1.84 0.18 1.12 6.55 0.38 3.06 0.330
2005 4.59 6.02 1.82 0.18 1.03 6.30 0.37 2.88 0.326
2006 4.86 6.09 1.88 0.20 1.11 6.52 0.37 2.97 0.332
2007 4.8 6.15 1.89 0.19 1.02 6.62 0.36 3.04 0.331
2008 4.65 6.02 1.86 0.18 1.00 6.77 0.34 3.09 0.328
2009 4.61 5.92 1.80 0.20 1.04 6.91 0.33 3.18 0.329
2010 4.46 5.93 1.79 0.18 0.94 6.93 0.32 3.08 0.325

The first thing I notice is that singles are at an all-time (since 1955) low, supporting the theory that hitters are sacrificing contact in order to drive the ball.  Also at an all-time low are triples, though that number has been fairly stable since 1986 and might have more to do with today’s ballparks (I’m a big believer that triples are usually hard hit balls that hit a wall funny or are misplayed; obviously there are stadiums with huge outfields and big alleys for triples, but they are the exception).  Hitters are certainly walking more than they did in the 1960s and 1970s, but it’s seen a significant decrease since the turn of the century (3.50 walks per nine innings in 2000 to 2.97 walks per nine innings in 2001).  The number of hit batters is up, probably because today’s hitter stands on top of the plate with no fear, some thanks to the body armor they are allowed to wear to the plate.  The largest increases have been in home runs and doubles, the latter of which has seen a nearly 50% increase in the last 40 years.

Looking back at the run values for each event, a typical double produces 0.3 more runs than a single and a home run produces a full run more than a single.  Strikeouts have gone up, but so have walks (slightly) and hit batters (nearly double in some seasons).  The total number of hits has increased slightly, but the extra value in extra base hits more than makes up for the strikeouts.

This exercise showed us basically what we expected; hitters hit the ball less often, but make up for hit by reaching base more on balls not in play (walks, HBP, and home runs).  The increase in extra base hits provides more than enough value to make up for the decrease in singles and the overall decrease in balls in play.  So even if a hitter strikes out 40+ percent of the time he goes to the plate, he still can be an above average hitter.

955 4.48 6.26 1.32 0.28 0.90 4.38 0.21 3.37 0.327
1956 4.45 6.17 1.35 0.29 0.93 4.64 0.19 3.31 0.326
1957 4.31 6.32 1.37 0.27 0.89 4.84 0.21 3.01 0.319
1958 4.28 6.20 1.37 0.27 0.91 4.95 0.20 3.02 0.32
1959 4.38 6.19 1.40 0.24 0.91 5.09 0.20 3.02 0.319
1960 4.31 6.15 1.39 0.27 0.86 5.18 0.20 3.10 0.319
1961 4.53 6.16 1.39 0.26 0.95 5.23 0.20 3.20 0.324
1962 4.46 6.28 1.33 0.26 0.93 5.42 0.22 3.12 0.322
1963 3.95 6.00 1.27 0.24 0.84 5.8 0.22 2.67 0.304
1964 4.04 6.12 1.31 0.23 0.85 5.91 0.21 2.65 0.307
1965 3.99 5.94 1.29 0.24 0.83 5.94 0.22 2.74 0.305
1966 3.99 6.04 1.28 0.25 0.85 5.82 0.21 2.55 0.303
1967 3.77 5.96 1.26 0.24 0.71 5.99 0.23 2.58 0.299
1968 3.42 5.90 1.19 0.21 0.61 5.89 0.24 2.44 0.292
1969 4.07 6.11 1.24 0.22 0.80 5.77 0.23 3.08 0.313
1970 4.34 6.16 1.35 0.24 0.88 5.75 0.21 3.15 0.319
1971 3.89 6.18 1.27 0.21 0.74 5.41 0.21 2.87 0.31
1972 3.69 6.06 1.25 0.20 0.68 5.57 0.20 2.78 0.304
1973 4.21 6.41 1.34 0.20 0.80 5.24 0.19 3.02 0.319
1974 4.12 6.49 1.34 0.22 0.68 5.01 0.20 2.98 0.318
1975 4.21 6.41 1.41 0.23 0.70 4.98 0.20 3.11 0.321
1976 3.99 6.48 1.35 0.25 0.58 4.83 0.18 2.90 0.315
1977 4.47 6.36 1.53 0.28 0.87 5.16 0.19 2.96 0.324
1978 4.1 6.27 1.47 0.24 0.70 4.77 0.18 2.91 0.318
1979 4.46 6.43 1.53 0.25 0.82 4.77 0.18 2.91 0.324
1980 4.29 6.56 1.51 0.26 0.73 4.8 0.16 2.79 0.32
1981 4 6.35 1.43 0.24 0.64 4.75 0.17 2.86 0.314
1982 4.3 6.40 1.50 0.23 0.80 5.04 0.16 2.85 0.318
1983 4.31 6.33 1.53 0.24 0.78 5.15 0.17 2.87 0.319
1984 4.26 6.40 1.48 0.23 0.77 5.34 0.16 2.86 0.317
1985 4.33 6.12 1.53 0.23 0.86 5.34 0.17 2.97 0.318
1986 4.41 6.11 1.55 0.20 0.91 5.87 0.19 3.07 0.32
1987 4.72 6.12 1.61 0.21 1.06 5.96 0.20 3.11 0.326
1988 4.14 6.15 1.52 0.20 0.76 5.56 0.22 2.76 0.312
1989 4.13 6.18 1.50 0.21 0.73 5.61 0.19 2.87 0.313
1990 4.26 6.20 1.55 0.21 0.79 5.67 0.20 2.96 0.319
1991 4.31 6.14 1.54 0.21 0.80 5.8 0.22 3.03 0.318
1992 4.12 6.20 1.56 0.20 0.72 5.59 0.23 2.94 0.317
1993 4.6 6.31 1.64 0.21 0.89 5.8 0.26 3.00 0.327
1994 4.92 6.25 1.79 0.22 1.03 6.18 0.27 3.16 0.333
1995 4.85 6.24 1.72 0.20 1.01 6.3 0.30 3.26 0.333
1996 5.04 6.29 1.76 0.19 1.09 6.46 0.31 3.25 0.335
1997 4.77 6.17 1.77 0.19 1.02 6.61 0.32 3.20 0.332
1998 4.79 6.13 1.80 0.18 1.04 6.56 0.33 3.16 0.331
1999 5.08 6.20 1.80 0.19 1.14 6.41 0.33 3.45 0.341
2000 5.14 6.11 1.83 0.20 1.17 6.45 0.32 3.50 0.341
2001 4.78 5.91 1.81 0.19 1.12 6.67 0.39 2.97 0.327
2002 4.62 5.90 1.79 0.19 1.04 6.47 0.36 3.05 0.326
2003 4.73 5.99 1.82 0.19 1.07 6.34 0.38 3.00 0.328
2004 4.81 6.03 1.84 0.18 1.12 6.55 0.38 3.06 0.33
2005 4.59 6.02 1.82 0.18 1.03 6.3 0.37 2.88 0.326
2006 4.86 6.09 1.88 0.20 1.11 6.52 0.37 2.97 0.332
2007 4.8 6.15 1.89 0.19 1.02 6.62 0.36 3.04 0.331
2008 4.65 6.02 1.86 0.18 1.00 6.77 0.34 3.09 0.328
2009 4.61 5.92 1.80 0.20 1.04 6.91 0.33 3.18 0.329
2010 4.46 5.93 1.79 0.18 0.94 6.93 0.32 3.08 0.325

Written by Dan Hennessey

July 20, 2010 at 10:27 PM

Posted in Uncategorized

Perfect Games are Easier(?)

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Loyal reader Mac asked me the other day why there have been so many perfect games and no-hitters, both completed and those that came up just short.  Just in this season we’ve had Dallas Braden, Roy Halladay, and Armando Galarraga (yeah, I’m counting it) throw perfect games and Ubaldo Jiminez and Edwin Jackson throw no-hitters.  Last season there was also a perfect game (Mark Buerhle) and a no-hitter (Jonathon Sanchez).  Before I theorize why these are occurring more often, I wanted to think about how often should these things happen, and once I’ve established the parameters for a no-hitter or a perfect game, are they more likely now?

First, let’s start with no-hitters.  I’m going to simplify slightly by talking about 9 inning no-hitters only.  To do this a pitcher has to get 27 outs.  Now, this isn’t to say that you have to get 27 different hitters out.  Because we are only talking about no-hitters, the possibility exists for a runner who got there via error or walk to be thrown out on the bases (double play, caught stealing, etc.).  So we’re talking about slightly less than 27 at-bats that need to pass without a hit.  Since it’s probably not a full at-bat that we could erase by doing this, I’m going to continue given that the pitcher has to get 27 different guys out.

The league-average batting average last year was 0.263.  So the random chance of getting 27 consecutive outs is 0.0264% (meaning that each game should see 0.000264 no-hitters, or a no-hitter will occur every 3,788 games).  There are 2,430 games in a major league schedule, and each game has 2 chances for a no-hitter.  Multiplying the 0.0264% chance by 4,860  results in about 1.28 no-hitters per season.  Since 1998 (as long as there have been 30 teams), there have 23 been no-hitters (including 6 perfect games), or roughly 1.8 no-hitters per season.  Since 1875 there have been 267 no-hitters, or roughly 1.98 no-hitters per season.  Remember that for many of these seasons there were fewer than 30 teams, but the 0.263 BA is near the median of league batting averages through the history of the game.

This year the batting average is down to 0.259 so far, the lowest it’s been since 1992 (ERA is the lowest it has been since 1992 also).  In the history of baseball, the league batting average is 0.2634.  The lowest it’s been in the last 100 years was 0.237 in 1968, the famed “Year of the Pitcher.”  Anyway you slice this, there have been more no-hitters than we might expect throughout the history of the game.

Just to show what difference the change in batting average makes, here’s a table showing the number of no-hitters we would expect given the current number of games in a major league season:

BA No-Hitter? # Games Per NH
0.240 0.0605295% 1652
0.245 0.0506486% 1974
0.250 0.0423306% 2362
0.255 0.0353362% 2830
0.260 0.0294616% 3394
0.265 0.0245334% 4076
0.270 0.0204041% 4901
0.275 0.0169483% 5900
0.280 0.0140597% 7113
0.285 0.0116483% 8585
0.290 0.0096377% 10376
0.295 0.0079635% 12557
0.300 0.0065712% 15218

Looking at this a little more in-depth, strikeouts are at an all-time high also, as teams are striking out roughly 7 hitters per nine innings.  Obviously, striking out more hitters leads to fewer balls in play.  So the league is arriving at its average BA by a different route.  Examining this on a BABIP-basis, the percentages change; the following table shows what the odds are if you only have get 20 outs on balls in play.

BABIP No-Hitter? # Games Per NH
0.285 0.1219329% 820
0.290 0.1059661% 944
0.295 0.0919988% 1087
0.300 0.0797923% 1253
0.305 0.0691347% 1446
0.310 0.0598387% 1671
0.315 0.0517382% 1933

Now we’re in the realm of 2 no-hitters per season.  Every additional hitter that is struck out makes a big difference; the following table assumes a 0.300 BABIP and shows the difference based on the number of outs needed.

Outs Needed No-Hitter? # Games Per NH
22.0 0.0390982% 2558
21.5 0.0467313% 2140
21.0 0.0558546% 1790
20.5 0.0667590% 1498
20.0 0.0797923% 1253
19.5 0.0953700% 1049
19.0 0.1139890% 877

Moving to perfect games, this one is slightly easier to check.  The league-average on-base percentage last season was 0.333.  Not accounting for reaching via error, a pitcher needs to retire 27 hitters without any reaching base.  The odds for this are 0.001784% (or a perfect game every 56,053 games, roughly 11 seasons).  Like I said earlier, there have 6 perfect games since 1998, or roughly 1 perfect games every 2.1 seasons.

Since 1875 there have been only 20 perfect games (21 counting Galarraga’s), or roughly 1 every 6.8 seasons; however, 8 of the 20 (or 9 of the 21) perfect games have occurred in the last 20 years. There have also been 10 instances of a perfect game being broken up on the 27th hitter (including Galarraga); there have been 9 instances of the first man reaching base and then the pitcher retires the next 27 hitters, and there have been 8 instances of no-hit, no-walk, no-hit-batter games where the only baserunner reached via error.

The frequency is definitely increasing, and the addition of teams throughout the league helps in two ways: one is that there are more opportunities for it to happen, and the other is that hitting talent is more spread throughout the league than it’s ever been.  A good pitcher has a better chance of running into a lineup with weak spots.  The league average for BA and OBP are propped up by weak pitchers.

The increase in strikeouts, seen in the table below, has definitely helped.  The value placed on defense over the last several years has probably helped.  We could play the steroids game, but I’d rather not get into it.  There’s probably an argument for the tension of the late inning situations getting to the hitters more than the pitchers, but there’s no way to measure that.  There’s definitely a way to measure how the situation gets to umpires, relating to the possibility that the strike zone expands when a pitcher has something historic going.  As much as anything else, it could also be a lot of randomness, the same randomness inherent in every no-hitter and perfect game where every batted ball finds a glove and every throw finds it target.

Decade K/9 Year K/9
2000s 6.59 2010 6.94
1990s 6.14 2009 6.91
1980s 5.34 2008 6.77
1970s 5.15 2007 6.62
1960s 5.70 2006 6.52
1950s 4.40 2005 6.3
1940s 3.55 2004 6.55
1930s 3.32 2003 6.34
1920s 2.81 2002 6.47
1910s 3.67 2001 6.67
1900s 3.43 2000 6.45
1890s 2.55 1999 6.41
1880s 3.48 1998 6.56
1870s 1.42 1997 6.61

Written by Dan Hennessey

July 15, 2010 at 10:40 PM

Posted in Uncategorized

Magglio Ordonez

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Magglio Ordonez broke in with the White Sox during the 1997 season and took over in right field full-time in 1998.  In his 8 seasons with the White Sox, he was an above-average player; he made four All-Star teams and finished in the Top 12 in the AL MVP voting twice.  During his last season in Chicago, he only played 52 games because of injury, and at age 30, the White Sox decided to let him go.  He signed a 5-year, 75 million dollar deal with Detroit to last from 2005 to 2009, which also had 18 million dollar club options for 2010 and 2011 that would vest if he met certain playing time criteria.   If Ordonez hit 540 plate appearances or 135 starts in 2009 (or 1,080 PA or 270 starts in 2008-2009), his 2010 option would become guaranteed.  Same with 2010 regarding his 2011 option.

Well, 2009 didn’t go so well for Maggs and there was a lot of chirping about whether or not the Tigers should bench him to avoid having to pay him in 2010 and 2011.  He struggled with injuries in 2004 and 2005 (his first year in Detroit) and hit for less power in 2006, making him just a slightly above-average hitter for a rightfielder.  In 2007, he bounced back in a BIG way, finishing 2nd (deservedly so) to A-Rod in the MVP voting.  He remembered that he didn’t have to swing at everything, played a fairly good right field, and to be fair, got a little lucky with some extra hits finding their way.  His 0.363 batting average was inflated by a 0.381 BABIP, 60 points higher than any other season in his career  to that point (previous BABIPs ranged from 0.290 to 0.321).  He didn’t hit more line drives or hit a lot more fly balls for home runs; nothing in his profile suggests this was any part his skill.  Nonetheless, it was a memorable season for a good player.

In 2008, Ordonez lost 45 points from his batting average, 60 points from his on-base percentage, and 100 points from his slugging percentage.  The main culprits were BABIP and his inability to control the strike zone, but it brought him back to pre-2007 levels.  In 2009, the drop was even worse.  In 131 games, he hit 9 home runs; his home run rate dropped to a career low (8.0% home run per fly ball) and his ISO was a 0.118.  In the end, his option vested despite the fact that he was at-best an average major leaguer in 2008 and 2009; the Tigers would owe him 18 million dollars in 2010.

I have to admit, I didn’t know this at the time, but his wife was diagnosed with thyroid cancer in 2009.  Whether the Tigers played him because they thought he gave them the best chance to win (the way he was playing, he didn’t and they had other options), or because they were going to stick by one of their guys as he and his family were going through a tough time, we might never know.  But clearly it affected Ordonez and his play suffered; whose wouldn’t?

So far though, he’s proved to be worth it.  His 0.314/0.386/0.481 line resembles his career line.  His power has come back to some extent, but given that he’s now 36, his peak power days are probably gone.  He does have 10 home runs, the second highest walk rate of his career, and the second highest home run rate of his career.   He’s hitting more line drives and fewer fly balls.  He’s played in 73 of Detroit’s 85 games and has been worth roughly 2.6 wins, which places him 17th in the American League among position players.  Given that he’s on pace to be close to a 5-win player in 2010, his 18 million dollar option is almost a bargain (as much as pay 18 million dollars for something can be a bargain).

Also, who had any idea that Magglio Ordonez was a career 0.312 hitter or that he had 2,063 hits in his career? That 0.312 BA is tied for 88th all-time and is 11th among active players.  His career OPS is 0.884 (90th all-time) and his OPS+ is 127.  He hasn’t reached the true decline phase of his career, so these averages might be weighed down by his final seasons, but just from a cursory look at things, he might be one of the best 300 or so hitters of all-time.  That might not sound like a lot, but it’s nothing to sneeze at either.  He’s not going to make it to 3,000 hits and he only has 287 career home runs (not enough for a corner outfielder in this era to make a case for the Hall of Fame), but Magglio Ordonez has been a damn good hitter for a long time.

Written by Dan Hennessey

July 11, 2010 at 6:46 PM

Posted in Uncategorized

This is Just Fun

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Ryan Franklin somehow removed 0.99 wins tonight all by himself.  Congratulations, Ryan!

Written by Dan Hennessey

July 6, 2010 at 10:03 PM

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The Blue Jays

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I watched these guys play five times in the last week, once as the home team in an NL ballpark against the Phillies and four times on TV against the Tribe (HOLLER SWEEP), and every night their lineup was awful.  On June 5, they were 33-24 and just 3.5 games behind Tampa Bay in the AL East and 2 behind the Yankees for the wild card.  Since then, they have gone  8-19 and are done.  They have eight players with over 200 at-bats, and it reads like an All-Star team:

Vernon Wells: 2.4 WAR

Alex Gonzalez: 2.2 WAR

Adam Lind: -1.3 WAR (yeah, it’s negative)

Lyle Overbay: 0.2 WAR

Jose Bautista: 2.1 WAR

Aaron Hill: -0.1 WAR

Fred Lewis: 0.3 WAR

John Buck: 1.4 WAR

There’s only one issue – the only thing these guys are good at is hitting home runs.  They lead the league in home runs (by almost 10 percent over the Red Sox) but are 28th in on-base percentage.  They have the 4th highest strikeout rate and a middle of the pack walk rate; on top of that, their team BABIP is 0.264, which is dead last and almost comically low.  They hit the most total fly balls and the most infield fly balls (which almost never go for hits) and as a result, the BABIP makes sense.  Still, it’s the lowest team BABIP in 20 years.  As a team, they are hitting only 0.237, which is better only than the Astros.

First, the good players…at least those that have played well so far.  Vernon Wells responded from some harsh criticism with his best season since 2007 and an All-Star berth.  His has a career high ISO matched by a career high HR/fly ball percentage; it’s also matched by a career high infield fly ball rate.  He’s swinging at everything and seeing fewer strikes than ever.  Enjoy it Toronto fans, because this isn’t going to last.  Alex Gonzalez has never been a good offensive player.  Sure he’s hit some home runs and plays a good enough shortstop to be passable, but this is only the second time in 12 seasons that he’s been an above average hitter.  His story pretty much matches that of Wells; he’s swinging at everything and hitting more home runs, but that’s about it.  Also at age 33, the Blue Jays should be shopping him hard to any contender that calls.

Jose Bautista has kicked around for awhile and pretty much always sucked.  In half a season this year though he’s got 21 home runs and a 0.360 OBP, easily the best in his career.  Again, Bautista’s fly ball percentage is up, but at the expense of his groundballs, not his line drives.  He also hasn’t been as hack-crazy as his aforementioned teammates; there’s no reason to think he can’t keep this going through the end of the year and end up with 35 or so home runs and close to 4 WAR.  John Buck was cast off by the Royals (yikes) after last season, but has proven to be a valuable piece to the Blue Jays.  His walk rate is at an all-time low, his OBP is barely 0.300, and his 0.274 batting average is BABIP-inflated.  Another easy sell opportunity, particularly given that one of the Blue Jays best prospects is J.P Arencibia, who is currently crushing at Triple-A.

Overbay and Lewis don’t really interest me, so I’m going to skip them.  Aaron Hill…Ooooohhhh, Aaron Hill.  He’s hitting 0.189.  He’s been worth 10 runs less than replacement hitting (yep, that’s 1 win).  He’s walking more than ever and striking out nearly as often,  but his BABIP is 0.183(!).  8.5% of the balls he’s hit have been line drives; that’s the lowest in the majors by almost 4 percent.  Unfortunately for him, those line drives have become fly balls and are not leaving the ballpark (he has the fourth highest flyball percentage in the majors).  He too is swinging at everything (do the Blue Jays have a hitting coach?) and it’s not working.  He wasn’t as good as he was last year when he hit 0.286 with 36 home runs and 37 doubles, and he’s not nearly this bad.  But for half of a season he’s been truly awful.

Lastly, we come to Adam Lind.  Lind played his first full season in 2009, hitting 0.305 with 35 home runs.  He was the 15th best hitter in baseball (as estimated by FanGraphs), tied with Matt Holliday and Shin-Soo Choo.  This year he’s lost 100 points from his batting average because…(bet you can’t guess) – he’s hitting more fly balls and swinging at more crap!  He’s being pitched basically the same way he was in 2009 (at least in terms of the pitches he’s seeing), but is striking out 50 percent more.  As a DH and occasional left fielder, his job is to hit, and he’s not doing it.  That’s a quick way to be worse than a replacement player.

By the way, someone named Dwayne Murphy is the Blue Jay hitting coach, and his claim to fame seems to be that he took a lot of pitches to let Rickey Henderson steal bases.  Good work, Dwayne.

Written by Dan Hennessey

July 6, 2010 at 10:01 PM

Posted in Uncategorized