Archive for November, 2013

Power and Patience (Part III of a Study)

So, last week we hopefully learned a few things. Let’s continue looking at league-wide trends.

In terms of getting on base, not getting on base, hitting for power, and not hitting for power, there are actually four mostly-distinct periods in baseball history for each combination. Define these terms against the historical average and you get:

  • 1901-18 – Players aren’t getting on base or hitting for power

  • 1919-52 – Players are getting on base but not hitting for power

  • 1953-92 – Players aren’t getting on base but are hitting for power

  • 1993-pres-Players are getting on base and hitting for power

There are some exceptions, but this paradigm mostly holds true. Here’s another depiction of the “eras” involved:


OBP (.333)

ISO (.130)













The periods from 1901-52 and since 1993 really are quite distinct, but the 1953-92 period is the hardest to truly peg and kind of has to be squeezed in there. In fact, those figures are quite close to the historical average. Well, actually, the OBP before 1993 is just as much below the average as the OBP after 1993 is above it. When the same era, categorized by offense, includes both 1968 and 1987, there is going to be some finagling.

So, really, there hasn’t been a clear period in MLB history with above-average power and below-average on-base percentages, while the “Ruth-Williams Era” (1919-52) had below-average power (again, vs. the historical average) but above-average on-base percentages.

Still, breaking things down into four eras is too simplistic. What follows is a walk-through, not of every season in MLB history, but key seasons, using some of the “metrics” from the first two parts of this series.

1918: .207 XB/TOB, -.038 sISO-OBP, 95 OBP+, 57 ISO+

In 1918, MLB hitters earned .207 extra bases on average. By 1921, they were earning .300 extra bases after year-to-year gains of 19%, 8%, and 12%. How much of this was on account of the Sultan of Swat? In 1918, Babe Ruth was already earning .523 extra bases, but had only 382 plate appearances. In 1921, however, he had 693 plate appearances and averaged .717 extra bases. Without him, the 1918 and 1921 ratios change to .205 and .295, respectively. So he’s only responsible for .003 of the increase. (My guess from a couple weeks ago was way off. He’s still just one player.) Perhaps the effect on the power boom of his individual efforts is overstated. However, his success was clear by 1921, so his influence on how other hitters hit seems properly stated. While Ruth’s 11 HR in 1918 tied Tillie Walker for the MLB lead, five other players had 20+ home runs in 1921.

OBP was low in 1918, and most seasons up to that point, but the dead ball era really was mostly a power vacuum. OBP already had two seasons (1911-12) around the current average, even though it would not get back there until 1920.

1921: .300 XB/TOB, -.027 sISO-OBP, 104 OBP+, 90 ISO+

So we touched on the 1918-21 period moments ago. Power skyrocketed, but still to about 10% below its current norm. Meanwhile, OBP was well on its way to a long above-average stretch: OBP+ was 100 or higher every single year from 1920 through 1941.

1930: .364 XB/TOB, -.007 sISO-OBP, 107 OBP+, 112 ISO+

1930 was the most power-heavy MLB season until 1956 and is even today the second-highest OBP season in MLB history at .35557, just behind the .35561 mark set in 1936. Non-pitchers hit .303/.356/.449 in 1930. Ten players hit 35 or more home runs, including 40+ for Wilson, Ruth, Gehrig and Klein.

Like we’ll see in 1987, however, 1930 was really the peak of a larger trend: XB/TOB grew 6+% for the third straight year before dropping 14% in 1931 and another 12% in 1933 (with a 9% spike in 1932).

1943: .261 XB/TOB, -.028 sISO-OBP, 98 OBP+, 74 ISO+

World War II in general was a bad time for hitters, at least from a power standpoint, with 1943 the worst season among them, but 1945 almost as bad. From 1940-45, the XB/TOB ratio fell 23%. It remained low until 1947. (But even at its lowest point in this time frame in 1942, it was still a better year for power than 1918.) OBP, however, was actually at about its current historical average during the war (within one standard deviation of the mean throughout), so there wasn’t a total offensive collapse. However, it was the first time since the deadball era that OBP+ was below 100. Either way, perhaps the coming look at individual players will tell us what happened.

1953: .365 XB/TOB, .001 sISO-OBP, 103 OBP+, 108 OPS+

Thanks to an 11% increase in XB/TOB, it was finally “easier,” relatively, to hit a double or homer than it was to make it to base in the first place. Also playing a role, however, was the OBP; in 1950 it was only harder to hit for power because players were reaching base at a pretty good clip; the OBP+ and ISO+ that year (1950) were 106 and 110.

1968: .320 XB/TOB, .003 sISO-OBP, 93 OBP+, 84 ISO+

1968 is often considered perhaps the all-time nadir for Major League hitters outside of the dead ball era, and non-pitchers only earned an average of .320 extra bases per time on base that year. It wasn’t just power that suffered, however, although it did, but it was also the worst league-wide OBP in 51 years. In fact, OBP was so low, it was actually ever so slightly easier to hit for power in 1968 than it was to reach base.

The thing about 1968 is that, while 1969 featured a lower mound, no 1.12 ERA’s, and a solid recovery for both OBP and ISO, it didn’t automatically revert baseball hitters to their pre-mid-60s form. Power fluctuated wildly in the roughly 25-year period between 1968-93.

1977: .378 XB/TOB, .010 sISO-OBP, 100 OBP+, 108 ISO+

1977, rather than 1930 or 1987, may be really the flukiest offensive season in MLB history. ISO+ shot up from 83 to 108, after having not been above 96 since 1970. MLB hitters earned 26% more extra bases per times on base than in 1976, easily the biggest one-year increase in MLB history. XB/TOB then promptly decreased 10% in 1978; it’s the only time that figure has gone up 10% in one year and declined 10% the next. It was the only season where sISO was .010 above OBP from 1967-84. 35 players homered 25 times or more, the most in MLB history until 1987. 1977 was a banner year for getting on base as well, although, as usual, not as much as ISO. It was the highest OBP season from 1970-78 and one of four seasons from 1963-92 with an average OBP vs. the historical average.

1987: .416 XB/TOB, .023 sISO-OBP, 101 OBP+, 120 ISO+

1987 has a big reputation as a fluky power season, and players earned .416 extra bases per time on base that year, but that was “only” a 9% spike from the prior season. Additionally, XB/TOB had actually increased every year from 1982-87, except for a 2% drop in 1984. The 1987 season was mostly the peak of a larger trend, which came crashing down in 1988, when the ratio dropped more than 15% to .353 extra bases. The .400 mark would not be broken again until 1994’s .412, but after that point, this ratio would never fall below the 0.400 it was in 1995.

This season was, however, the only one in the Eighties with an OBP+ over 100. From 1963-92, in fact, OBP was at or above the historical norm in just four seasons (1970, 1977, 1979, 1987). As with power, however, OBP collapsed in 1988 more so than it had gained in 1987, falling to 1981 levels (97 OBP+).

1994: .412 XB/TOB, .017 sISO-OBP, 103 OBP+, 122 ISO+

XB/TOB leapt over 10% from 1992-93, and another 9.5% in 1994, ushering in a power era that hasn’t quite yet flamed out. 1994 was the year power really took off relative to OBP: in 1992, sISO and OBP were even; in 1993, the gap was still about half of what it would be in favor of sISO in 1994. 1994 also featured the highest ISO to that point, higher than even in the culmination of the mid-80’s power trend in 1987. While there would be some years between 1993 and 2009 with modest decreases in power, even in 2013, ISO+ was 112–its lowest mark since 1993. More on the current power and OBP environment momentarily.

1901-2013: Changes in XB/TOB

Extra bases per time on base was our first choice of metric. How has this particular one changed in certain years?

Overall, nine times has this ratio spiked at least 10% in one season: 1902-03 (+12%), 1918-19 (+19%), 1920-21 (+12%), 1945-46 (+11%), 1949-50 (+10%), 1952-53 (+11%), 1976-77 (+26%), 1981-82 (+12%), and 1992-93 (+10%).

Meanwhile, it decreased by 10 or more percent on six occasions: 1901-02 (-11%), 1930-31 (-14%), 1932-33 (-12%), 1941-42 (-11%), 1977-78 (-10%), 1987-88 (-15%).


We’ll try to make this a little more interesting: where is baseball going from here? Can we look at these trends throughout history and determine what the next few years might look like?

XB/TOB dropped 4.8% in 2013. It was the sharpest one-year drop since a 5.6% fall in 1992, but that season only preceded a power boom. Both were modest declines historically, and this one is unlikely to portend much. However, this year’s 112 ISO+ was a new low for the post-strike era.

Yet the bigger issue in 2013 was a stagnant OBP, which has been below the current average since 2009 after being above it every year since 1992. OBP never deviates very much from its norm, but 26/30 seasons from 1963-92 featured a below average OBP.

Will OBP continue to stay low? It has fallen every year since 2006, from .342 to .323, which represents the longest continuous decline in MLB history. It may be unlikely that it decreases further, but the below-average-since-2009 fact is worrisome if you enjoy offense. Stagnation for such a length of time has nearly always been part of a larger trend, mostly in the dead ball era and that 30 year period from 1963-92.

One thing we can probably say is that the “Steroid Era” is over. From 1993-2009, OBP+ was never below 101 and ISO+ never below 109. Take 1993 out of the sample, and ISO+ is never below 118, and from 1996-2009, 14 years, ISO was 20% or more above the historical norm every time.

But since 2009, that 20% threshold has never been reached, although 2012’s ISO+ of 119 comes close. Nonetheless, power from 2010-present has yet to reach mid-90s, early 2000s levels. Power could still increase in the future, but likely for reasons other than PED’s (although the Melky Cabreras and Ryan Brauns of the world always leave a doubt).

If I had to guess, power and home runs are here to stay, even if 2000’s .171 stands as the highest non-pitcher ISO for years to come. (That really is a crazy figure if you think about it: non-pitchers that year hit for power at roughly the career rates of Cal Ripken or Ken Caminiti. In 2013, they were down to more “reasonable” levels similar to Johnny Damon or Barry Larkin.)

The on-base drought is more of a concern for offenses, however, but because OBP is so consistent, that OBP drought could be persistent, but minor.

This concludes the league-wide observations of power and patience. Part IV next week will look at things like “X players with an OBP of Y and ISO of Z in year 19-something.” Part V will then look at individual players. Maybe we can even wrap up with the ones who started this whole series: Joe Mauer, Rickey Henderson, and Wade Boggs. I guess we’ll have to find out.

Current Edwin Encarnacion vs. Vintage Albert Pujols

Toronto Blue Jays 1B/DH Edwin Encarnacion had another great year with the bat in 2013. He posted a .272/.370/.534 line with a 148 wRC+ that was 6th in the AL. This was on the heels of a 2012 season where Encarnacion managed a .280/.384/.557 line with a 151 wRC+.

In his late-career resurgence, Encarnacion has become the rarest of players, a power hitter that rarely strikes out. Only Chris Davis and Miguel Cabrera had more home runs than Encarnacion’s 36. The previous year, Encarnacion slammed 42 home runs.

Meanwhile, Encarnacion struck out in only 10% of his plate appearances. Only seven qualified hitters struck out at a lower rate than Encarnacion. None of them had more than 17 home runs.

In fact, you’ll have to go back to the glory days of Albert Pujols (2001-11) to find someone who matched Encarnacion’s home run total with a similarly low strikeout rate.

Here’s a look at their numbers side by side.

Vintage Pujols 40 13.1 9.5
Encarnacion ’12-13 39 13.1 12.3

Pretty impressive, huh? Well, let’s dig even further. From 2001-11, the MLB average walk and strikeout rates were 8.5% and 17.3%, respectively. In 2012-13, they were 7.9%, and 19.9%, respectively. So, here are Pujols’ and Encarnacion’s numbers expressed as a percentage of the MLB average.

Vintage Pujols 222% 154% 55%
Encarnacion ’12-13 238% 165% 62%

So if we adjust for the MLB average, Edwin Encarnacion’s home run and walk rates from 2012-13 were better than those of vintage Albert Pujols. His strikeout rate was a shade worse. If I restricted the comparison to 2013, Encarnacion would be better in all three categories.

Does this mean that Encarnacion from 2012-13 has been the offensive equivalent of vintage Pujols? Well, not quite. Let’s revisit wRC+. Pujols’ average from 2001-11 was a robust 167. Encarnacion’s wRC+ from 2012-13 is 148. Where does this big difference come from?

Pujols in-play batting average in his prime years was .311. On the other hand, Encarnacion has just a .256 in-play average from 2012-13. That’s a very big difference. Only Darwin Barney had a worse in-play batting average than Encarnacion in that time frame.

Does Pujols hit more line drives? What’s the reason for this big split? Here are their batted-ball ratios.

Vintage Pujols 19.0 40.9 40.0 13.0
Encarnacion ’12-13 19.6 34.1 46.3 10.7

Pretty similar. Pujols hits more ground balls, Encarnacion does a better job of avoiding the infield fly. In fact, based on these ratios, you would expect Encarnacion to have a higher in-play average than Pujols.

Recently teams have been using a unique shift against Encarnacion, where they put three infielders on the left side of second base. Here’s a picture below.

This shift has been successful in taking away hits from Encarnacion. Since 2012, he’s hit just .222 on ground balls, compared to .262 for vintage Pujols. In 2013, just 25 of the 170 groundballs Encarnacion hit found a hole. Here’s a link to his spray chart.

On balls he pulls, Encarnacion has a .376 batting average. That might sound very good, but compare it to Pujols, who hit .477 on balls he pulled in his vintage years.

Edwin Encarnacion is an elite hitter. In terms of walks, strikeouts, and home runs, he’s every bit the hitter that Albert Pujols was during his prime years. Sure, his pull-heavy approach might allow the shift to take away some hits, but the shift can’t do anything about the balls he puts over the fence.

The Bill James Hall of Fame–Pitchers

The Hall of Fame (HOF) voting will be announced in a month or so, and with a very competitive ballot full of worthy new players, deserving holdovers and numerous players with suspicions hovering over their candidacy, it will be one of the most compelling ballots in years. There will be no shortage of analysis in the coming month, and I’ll add to it, but hopefully in a manner that helps clarify instead of confuse.

In his wonderful book “Whatever Happened to the Hall of Fame?” Bill James laid out criteria for two measures he invented to evaluate HOF resumes. He devotes Chapter 14 to describing one of them, the HOF Standards and an additional measure, the HOF Monitor on p359-61. At the risk of being 100% incorrect, the two systems complement each other very well–the Monitor essentially measure the successful seasons (number of hits, home runs, runs scored, etc.) while the Standards measures these numbers over a career (did a pitcher win 200 games? 250? 300? Did a hitter hit 350 home runs? 400? and so on). In a perfect world, a player does well on both scales–he has a long career filled with career milestones AND has years in which he is clearly the best in the game. Putting these two factors together goes very far in helping evaluate HOF worthiness.

The tests work on two different scales–James states that anything over 100 on the Monitor and 50 on the Standards places the player in the company of those already enshrined. Therefore, that creates a fun thing to measure–just how well do HOF inductees match up with James’ measures? This graph shows pitchers of recent vintage only (from around 1960 or so) and plots them on a scatter graph on both of these measures:

Yellow dots are HOF members. Take a moment and peruse the players in the upper right quadrant, those that meet both tests for Standards and Monitor. These are truly worthy of enshrinement and the names are understood as among the best pitchers in baseball history. Roger Clemens and Randy Johnson are far right because they were power pitchers who racked up huge numbers of strikeouts per season and over a career, whereas Greg Maddux was simply a dominant pitcher who got batters out however he could. It doesn’t matter either way–any serious discussion of the best pitchers of the past 25 years includes these three pitchers, no matter how different their styles were.

The others in the upper right quadrant are Pedro Martinez, Tom Glavine and Mike Mussina. Glavine and Mussina are on the 2014 ballot and will generate no shortage of discussion, some of which might even concern their career achievements. I won’t discuss the quirks and shortcomings of HOF balloting in this post but will do so over the next week or so at my blog Beyond The Scorecard. Mussina in particular will generate tremendous discussion since he “only” won 270 games, whereas somehow Glavine’s 35 more wins is a wide chasm. Leaving aside the uselessness of the win as a stat in modern baseball (I have more thoughts on that here, for starters), it sets up a magical threshold that is exceedingly difficult to attain, and yet rewards no shortage of pitchers who missed that mark.

Nobody suggests that James’ measures should be hard and fast rules, and he himself argues on p182 that it would be a “terrible idea,” but that doesn’t mean that some element of rigor can’t be applied to the review of these pitchers to see if they’re truly amongst the best in their generation. Jamie Moyer had more career wins than Pedro Martinez–is there anyone who seriously suggests that Moyer was a better pitcher than Martinez? We don’t use metrics to create artificial (and often capricious) cutoffs as much as give nuance and context to the numbers we see. Particularly as the role of the starting pitcher has changed over the years, these types of values are even more important. So what do we do with the pitchers in the lower right quadrant? There’s plenty of precedent for enshrinement but it appears that at least in recent years, egregious errors made in the past are becoming far fewer. Even the “worst” HOF inductee on this chart, Jim Bunning was inducted by a Veterans Committee in 1996 and is far from the worst selection the HOF has made.

My real point is that James’ measures hold up remarkably well when tested against actual inductees. Like just about everything else he’s done in baseball metrics (and for the Boston Red Sox), it’s a measure that adds true value and allows us to make informed decisions as we evaluate HOF candidates. It’s been almost 20 years since he conceived these measures and perhaps time will require tinkering with the numerical values (for example, is 300 wins still a reasonable upper limit for pitching wins? If not, what should it be dropped down to?) to reflect changes in the game. But the overall structure remains very robust and does an excellent  job of matching up our remembrances with actual events. As Bill savors his third World Series title while being associated with the Red Sox, he should also be remembered as the man who attempted (and very much accomplished) something very important–helping us accurately evaluate player careers and place them in the proper context.

There are several unlabeled dots due to space:

In the lower right quadrant there are four dots between Andy Pettitte and Justin Verlander–they are (from top to bottom) CC Sabathia (just to the left of Pettitte), David Cone (left of Morris), Ron Guidry (right below Cone) and Vida Blue (above Verlander)

In the lower left quadrant there are six dots right around Jim Bunning–they are Luis Tiant (right below), Kevin Brown (just to the left of Tiant), Dwight Gooden (left of Brown), Mickey Lolich (below Bunning), Mike Cuellar (just below Lolich), Orel Hershiser (to the left of Cuellar) and Johan Santana (left of Hershiser). Other notable pitchers in that quadrant are (going down the Monitor number) David Wells, Dave Stewart, Cliff Lee, Bret Saberhagen, Frank Viola, Bob Welch, Fernando Valenzuela, Kenny Rogers and Jamie Moyer.

Be sure to visit my blog for more thoughts on the Hall of Fame and other baseball stuff

Follow me on Twitter @ScottLindholm

Ranking Free Agent Pitchers by TIPS

TIPS is a new ERA estimator that I have created. The post on the estimator can be found here.

In short, TIPS is an estimator that attempts to measure pitcher skill completely independent from all other factors other than batter-pitcher relationships (removing defense, catchers, umpires, batted ball luck, etc.). The formula is:

TIPS 6.5*O-Looking(PitchF/x) – 9.75*SwStr% – 4.8*Foul% + C (around 2.60)

where: O-Looking(PitchF/x) = 1 – O-Swing% (PitchF/x), SwStr% = percent of pitches swung at and missed, Foul% = percent of contacts fouled off

The estimator was found to be the most predictive of any estimator in samples less than 70 IP.

I have taken the free agent custom leaderboards provided by Dave Cameron and ranked the pitchers by TIPS.

TIPS may not have as much power with starting pitchers, since the samples will be larger than 70 IP, but since these pitchers will be changing defense, park, and catcher, I believe it can be useful (when used with FIP and xFIP). Click this text for the starting pitcher leaderboard.

If you cannot view the google spreadsheet, here are the top free agent starting pitchers by TIPS. Yes, I know Lincecum has since signed, but he is still included.

1 Scott Kazmir 158 4.04 3.51 3.36 3.55
2 Shaun Marcum 78.1 5.29 3.64 4.22 3.57
3 Tim Lincecum 197.2 4.37 3.74 3.56 3.58
4 Dan Haren 169.2 4.67 4.09 3.67 3.64
5 A.J. Burnett 191 3.30 2.80 2.92 3.65
6 Tim Stauffer 69.2 3.75 3.55 3.20 3.70
7 Phil Hughes 145.2 5.19 4.50 4.39 3.71
8 Josh Johnson 81.1 6.20 4.62 3.58 3.72
9 Ricky Nolasco 199.1 3.70 3.34 3.58 3.75
10 Matt Garza 155.1 3.82 3.88 3.73 3.75
11 Tim Hudson 131.1 3.97 3.46 3.56 3.76
12 Hiroki Kuroda 201.1 3.31 3.56 3.60 3.78
13 Andy Pettitte 185.1 3.74 3.70 3.88 3.83
14 Ervin Santana 211 3.24 3.93 3.69 3.89
15 Aaron Harang 143.1 5.40 4.79 4.38 3.93
16 Roberto Hernandez 151 4.89 4.63 3.60 3.95
17 Roy Oswalt 32.1 8.63 3.08 3.39 3.96
18 Bruce Chen 121 3.27 4.12 4.93 4.02
19 Jeff Francis 70.1 6.27 4.54 3.82 4.02
20 Chris Capuano 105.2 4.26 3.55 3.67 4.04
21 Ubaldo Jimenez 182.2 3.30 3.43 3.62 4.04
22 Erik Bedard 151 4.59 4.38 4.61 4.09
23 Chad Gaudin 97 3.06 3.34 4.00 4.15
24 Jason Hammel 139.1 4.97 4.93 4.56 4.15
25 Paul Maholm 153 4.41 4.24 3.89 4.18
26 Jason Vargas 150 4.02 4.09 4.29 4.22
27 Edinson Volquez 170.1 5.71 4.24 4.07 4.23
28 Freddy Garcia 80.1 4.37 5.49 4.00 4.28
29 Roy Halladay 62 6.82 6.14 5.10 4.31
30 Barry Zito 133.1 5.74 4.92 4.81 4.34
31 Bartolo Colon 190.1 2.65 3.23 3.95 4.36
32 Wandy Rodriguez 62.2 3.59 4.42 4.00 4.36
33 Scott Feldman 181.2 3.86 4.03 3.96 4.37
34 Mike Pelfrey 152.2 5.19 3.99 4.54 4.53
35 Jon Garland 68 5.82 4.93 4.54 4.57
36 Joe Saunders 183 5.26 4.72 4.23 4.63
37 Ryan Vogelsong 103.2 5.73 4.91 4.50 4.70
38 Bronson Arroyo 202 3.79 4.49 3.97 4.72
39 Jake Westbrook 116.2 4.63 4.62 4.95 4.78
40 Jason Marquis 117.2 4.05 5.65 4.81 4.83

Kazmir, Marcum, Haren, Hughes, and Johnson all look like really good value signings (when comparing their ERA and FIP/xFIP/TIPS). Scott Kazmir is someone who I believe could be a legit number 2 guy moving forward if he can keep his velocity. I know Jason Marquis had a 4.05 ERA, but he is someone you should be wishing your team does not sign.

But now on to where TIPS really shines, relievers!

Here is the RHP leaderboard and LHP leaderboard. I am also providing the full combined leaderboard:

1 Edward Mujica 64.2 2.78 3.71 3.53 2.58
2 Manny Parra 46 3.33 3.07 2.79 2.95
3 Joaquin Benoit 67 2.01 2.87 3.16 3.00
4 Boone Logan 39 3.23 3.82 2.71 3.01
5 Jesse Crain 36.2 0.74 1.52 2.94 3.06
6 Joe Nathan 64.2 1.39 2.26 3.27 3.06
7 Javier Lopez 39.1 1.83 2.41 2.92 3.08
8 Oliver Perez 53 3.74 3.26 3.36 3.31
9 Matt Belisle 73 4.32 3.03 2.99 3.39
10 Fernando Rodney 66.2 3.38 2.84 3.11 3.41
11 David Aardsma 39.2 4.31 5.27 4.63 3.43
12 Chad Durbin 16 9.00 5.99 4.44 3.47
13 Jose Valverde 19.1 5.59 6.36 4.09 3.49
14 Jon Rauch 16.2 7.56 3.47 4.16 3.51
15 Carlos Marmol 49 4.41 5.19 4.53 3.54
16 Rafael Betancourt 28.2 4.08 3.22 4.22 3.54
17 Grant Balfour 62.2 2.59 3.49 3.42 3.55
18 Tim Stauffer 69.2 3.75 3.55 3.20 3.70
19 Matt Thornton 43.1 3.74 4.04 4.13 3.7
20 Chad Qualls 62 2.61 3.32 3.25 3.72
21 Michael Gonzalez 50 4.68 4.87 3.88 3.75
22 Luis Ayala 33 3.27 3.68 3.77 3.82
23 Kameron Loe 26.2 7.09 8.41 4.69 3.82
24 Jason Frasor 49 2.57 3.37 3.62 3.86
25 Scott Downs 43.1 2.49 3.09 3.3 3.86
26 LaTroy Hawkins 70.2 2.93 3.06 3.12 3.91
27 Rich Hill 38.2 6.28 3.82 4.12 3.96
28 Matt Guerrier 42.2 4.01 3.82 4.44 3.97
29 Jamey Wright 70 3.09 3.13 3.48 3.97
30 Eric O’Flaherty 18 2.5 4.1 3.8 3.97
31 Matt Lindstrom 60.2 3.12 3.15 3.87 4.00
32 Brandon Lyon 34.1 4.98 3.98 4.48 4.02
33 Mark Lowe 11.2 9.26 5.79 6.55 4.13
34 J.P. Howell 62 2.18 2.89 3.48 4.14
35 Joba Chamberlain 42 4.93 5.64 4.60 4.15
36 Chad Gaudin 97 3.06 3.34 4.00 4.15
37 Joe Smith 63 2.29 3.60 3.70 4.26
38 Matt Albers 63 3.14 3.49 3.82 4.35
39 Shawn Camp 23 7.04 7.05 4.93 4.42
40 Kyle Farnsworth 38.1 4.70 4.14 3.73 4.46
41 Kevin Gregg 62 3.48 4.10 4.38 4.54
42 Scott Atchison 45.1 4.37 3.75 4.02 4.57
43 Darren Oliver 49 3.86 4.05 3.74 4.72
44 Hideki Okajima 4 2.25 7.3 5.76 5.08
45 Brett Myers 21.1 8.02 8.72 4.80 5.28
46 Peter Moylan 15.1 6.46 6.18 5.86 5.37
47 Tim Byrdak 4.2 7.71 8.62 5.68 5.54

There are a few notable FA relief pitchers. Mujica, Benoit, Nathan, Rodney, Balfour, Hawkins, and Gregg all closed this year. Crain is a pitcher who could potentially close as well. Looking at the closers, Mujica is alone in the top tier by TIPS. Then Benoit, Crain, and Nathan are second tier. Rodney and Balfour are in the next tier, while Hawkins and then Gregg are in the final tiers. Gregg in particular looks like a RP that no team should touch. Parra and Logan make for some good LOOGY signs if teams are looking for left-handed relievers. There a quite a few names in this list that would do a fine job in filling out a bullpen. It goes to show that trading for bullpen pieces might be akin to trading your brother or sister your blueberry for their strawberry when there is a pack of strawberries on the counter. A bit of a random analogy, but it makes sense. The SP crop is much thinner than the RP crop. There are no big name or potential number 1 pitchers in the FA crop, which means teams that are looking to add to the front of their rotation might have to do so through trade.

On a bit of a side note, I wanted to talk a little more about TIPS. Why does TIPS really like Mujica? It loves his amazing 44.2% O-Swing% and his 12.5% SwStr% isn’t too shabby either. O-Swing% (I use the PitchF/x value), SwStr%, and Foul% are peripherals that you should be accustomed to looking at and understanding. Foul% is not readily available, but is not too hard to calculate. What value is good? What is bad? I will explain here:

To finish this off, I’d like to say Koji Uehara is a monster. 39.2% O-Swing% (Above Excellent), 18.5 SwStr% (Above Excellent), and 60.8% Foul% (Almost Excellent).

Why the Toronto Blue Jays Need to Extend Josh Johnson

In the Marlins deal last November, Josh Johnson was the main headlining piece along with Jose Reyes and Mark Buehrle. Then the Blue Jays added R.A. Dickey in December and the starting rotation looked to be very strong. Dickey, Morrow, Johnson, Buehrle, and Happ were all supposed to have strong seasons and hope for a 2013 World Series title was in abundance. Then came April. The rotation struggled, terribly. Josh Johnson seemed to be the worst infringer of them all. He was the worst disappointment of the season. But was he actually that bad?

Using all of the standard metrics for pitchers, Josh Johnson was brutal. He was 2-8 with a 6.20 ERA and 1.66 WHIP. He also only pitched 81 and a third innings. How could you possibly say he had a good season? Those stats look worse than 2012 Ricky Romero. If you take a look at his K/9 of 9.18 you see he had the best K/9 of his career. You also see that he had the worst BB/9 of his more recent years at 3.32. These two stats are a little deceiving in this case however. Because of his much longer innings, his K/9 and BB/9 would both be up as he faces more batters per inning. We then have to look at the rate per batter. He had a K% of 21.6%, which is just shy of his career average (not best, as K/9 suggests) of 21.9%. This makes his strikeout rate look less appealing but it is still very good. The adverse effect is applied to his walk rate, as his BB% was 7.8%. This mark is better than his last two years and better than his career average of 8.1%.

Now on to why I believe Josh Johnson will be a good starter next year and onward. In case you haven’t heard of them before, there are ERA-accompanying stats called FIP, xFIP, and SIERA. These stats try to eliminate events that are beyond the pitcher’s control (fielding independent pitching). FIP is calculated from K’s, BB’s, and HR’s to IP. xFIP is the same, except that it corrects the pitcher’s HR total to what it would be with a league average HR/FB rate. SIERA uses a more complex formula based on K%, BB%, and batted ball profiles (ground balls, fly balls, and pop ups) to approximate ERA. These three stats do a much better job of predicting future ERA than they do of current ERA. ERA fluctuates greatly from year to year and sample to sample for pitchers, while the guts of these metrics are more constant. ERA is not stable as it depends on luck in BABIP, HR/FB, and LOB as well as team defense. FIP is usually closest to the ERA of the sample, as it doesn’t account for HR/FB luck. SIERA is the best at predicting future ERA, followed closely by xFIP, FIP, and lastly, ERA.

So while Josh Johnson’s ERA is 6.20, his BABIP is an inflated .356 (compared to a career average of .305 and league average of .294) and this should regress back towards the mean. FIP has BABIP luck taken out of the equation and has Johnson with a FIP of 4.62. This is much lower than the 6.20 ERA, but 4.62 is still not very good for a pitcher of his price-tag. However FIP does not assume a league average HR/FB rate, this is where xFIP comes into play. Johnson’s HR/FB% this year is an abysmal 18.5% (compared to a 8.2% career average and 10.6% league average). It can be assumed that this will regress towards the mean as well next year. So accounting for this absurd HR/FB%, Josh Johnson had an xFIP of 3.60. That looks a little better doesn’t it? Especially since xFIP does a better job of predicting future ERA.

The one problem with using FIP and xFIP in this case however, is that they are based of rates with IP as the denominator. As I discussed earlier, due to the long nature of Josh Johnson’s innings, this would increase the K, BB, and HR per inning as more batters come to the plate. This is where SIERA comes into play as the best statistic to use in this case. SIERA, as mentioned prior, deals with rates where PA (or BF) is the denominator. It is also shown that batted ball profiles are somewhat controllable by the pitcher and have an impact on results. In most cases, xFIP and SIERA are very similar, but replacing the IP denominator with BF and including some batted ball profile gives SIERA the slight edge in predictability. Josh Johnson’s SIERA this year was 3.73, which is probably the best guess as to what we can expect his ERA to be going forward.

3.73 or 3.60 look excellent and amazing considering the results we saw. What a ray of hope! But what if he really was just more hittable this year? What if he wasn’t unlucky and batters can just hit him? This is what I will look into now.

Johnson’s injury history and the effect it has had on his velocity is well documented. He is not the same pitcher he was in ’09 and ’10.  He is a different pitcher now, but he has been this way for two years, not one. Josh Johnson is the same pitcher that he was in 2012 when he posted a 3.81 ERA for the Marlins (he might even be better). How is this possible you say? His ERA has jumped 2.39 runs! I will dive into all of his peripherals to prove that he hasn’t changed that much.

First let’s take a look at his velocity (I will be using PITCHf/x numbers for all values).

His average FB velocity in 2012 was 92.8mph, while this year it is 92.9mph. Slider velocity was 86.9mph and now is 86.1mph. Curve was 78.5mph and now is 79.1mph while his changeup was 87.6mph and now is 88.6mph. All of these velocities are very constant! There is nothing here inferring that he is more hittable than last year, let’s move on.

Let’s look at plate discipline to see if there is anything that suggests hittability. His O-Swing% (outside zone swing%) was 30.9% and now is 32.3%. This should decrease hittability if anything, since contact should be worse on pitches outside of the zone. His Z-Swing (zone swing%) is a constant 60.4% compared to last year. His O-Contact% is slightly up (59.5% to 61.9%) but this shouldn’t matter, as these pitches should be less hittable. His Z-Contact% is slightly down (90.9% to 89.6%), which should be good as it means more whiffs in the zone. His zone% in also slightly down (44.9% to 43.7%), but who cares if he doesn’t walk more batters. Lastly, his SwStr% (swinging strike%) is essentially constant (9.2% to 9.3%). Again there is nothing here to suggest that batters should be able to hit him better.

I have heard some people say that he just gets rattled when things go bad. I’d like to partially debunk this theory, as his pace (time between pitches) is essentially the same as last year (20.9s in 2012 and 21.0s in 2013). Pitchers who are rattled generally take more time between pitches. There’s not really any other stats that can prove otherwise, as all his peripherals are fairly constant.

The one main difference that is notable in his peripherals between 2012 and 2013, is his 2-seam fastball use. He has used his two-seamer 13.3% of the time compared to only 4.8% last season. This difference has come at an expense of all three of his secondary pitches, which are all slightly down in usage. Is his two-seamer a bad pitch? It’s certainly not his best. I would take pitch values from this year with a grain of salt, as they are all low due to his bad luck, but his two-seamer has been below average for three years in a row: -1.94 RAA/100 pitches (runs above average) in 2011, -2.43 RAA/100 in 2012 and -1.99 RAA/100 in 2013.  Other than his changeup since his velocity decline (which went from average to well below average), the two-seamer has been consistently his worst pitch. The fact that he is using it more is not a good thing, but this is easily corrected if it is pointed out to him. It has nothing to do with a lack of ability. His above average curve and slider have taken a hit in usage and this needs to be corrected.

Pitch selection hasn’t been too much of an issue for him in terms of strikeouts and walks however. Both his K% and BB% are trending the right direction from last year. His K% is up 0.9%, while his BB% is down 0.4%. These both suggest he has improved since last year, and his xFIP and SIERA mirror that. xFIP has gone from 3.73 to 3.60 while SIERA has improved from 3.86 to 3.73. He has been getting better at pitching with his reduced velocity, not worse (as it appears on the surface).

One counter argument to this could be that he’s just throwing more meatballs down the middle that are getting hit, but also mean he walks less and strike out more. This was partially debunked by his lower zone% and lower z-contact% from before, but I want a little more proof that this is not the case. FanGraphs, with the help of PITCHf/x, is an amazing website that, in addition to all these fancy stats, also provides heat maps for pitchers to see exactly where they are throwing the ball.

Here are Johnson’s 2012 heat maps:

And here are his 2013 heat maps:

Not much difference is there? He enjoys throwing down and away the most, and this hasn’t changed at all. In case you’re wondering, there is less yellow in 2013 because he’s thrown about half as many pitches.

Another theory I have heard would be that his pitches are straighter now. I will look into this. This actually might have a case. His movement on each pitch has decreased since last year (around .6 inches for each pitch). However, we need to look into the numbers a little deeper. PITCHf/x movement in the z-direction (up or down) excludes gravity and gives a movement number in which the ball would move without gravity. What does this mean if we have positive movement values (which Johnson does with every pitch except his curve)? It means that, without gravity, each pitch would move up. In reality, gravity is much larger than this movement force and the balls drop. So a larger positive movement number means that the ball will drop less than a smaller movement value, and therefore have less movement. Johnson’s fastball and this two-seam fastball (to a larger extent), both have less rise this year, this means they actually have more drop. His slider is about the same while his changeup and curve are showing slightly less drop. I might say this is a problem, but his curve was his best pitch this year while his changeup has been bad for 2 years anyways and should just be a show pitch. I would be more concerned if he was showing less movement in the horizontal direction, but this isn’t the case. With the exception of his changeup (which is moving less), each pitch’s horizontal movement is almost identical to 2012. All things considered, nothing here suggests that he is any more hittable, especially considering his batted ball profile.

One last thing to look at is to see if batters are getting better contact aside from high home run rates is batted ball profile. Again these almost look identical to 2012. His line drive rate is slightly up (23.6% to 24.2%). It isn’t much, but still a small concern. His ground ball rate is related and took a small hit (46.2% to 45.1%). His fly ball rate is slightly up too (30.2% to 30.7%), but that’s not a problem either. His infield fly ball rate is also up (7.2% to 8.6%) which is actually good since they are almost always an out. His infield-hit rate is up (5.1% to 5.9%) showing some more of his bad luck. Again, SIERA takes batted balls into consideration and it wasn’t too concerned with his rates with the 3.73. There are some xBABIP formulae out there that predict what BABIP should be based on batted balls. These formulae are better at suggesting if a pitcher (or batter) has changed their true talent BABIP (instead of getting lucky) then actually predicting BABIP.  Using Steve Staud’s xBABIP that uses LD%, FB%, and IFFB%, Josh Johnson’s 2012 and 2013 xBABIPs are nearly identical (.3163 to .3159). Matt Swartz’s xBABIP uses GB% and K% and yields .2894 in 2012 and .2880 in 2013. This is almost exactly the same again. This suggests that Josh Johnson’s true talent BABIP has not changed and that he has been getting very unlucky. There is no large or conclusive outliers in Josh Johnson’s stats suggesting that he his any different of a pitcher than in 2012.

Another thing that I would like to add is that Josh Johnson has been very consistent at preventing home runs and having a HR/FB rate that is less than league average. This is shown by his 8.2% career average and that he has posted HR/FB rates lower than league average in every year of his career except 2013. This causes his FIP to be consistently lower than his xFIP and SIERA (has been every year save 2013). So while xFIP and SIERA are the best estimators of ERA, Josh Johnson usually outperforms them in FIP. He had an excellent 3.40 FIP last year and was just a bit unlucky with LOB%, which cause his ERA to higher at 3.81. Using all of this information and the proof that Josh Johnson hasn’t changed, it would be safe to say that his ERA should be around 3.55 next year (if he were still in the NL) if everything keeps trending the same way.

There are two more things to consider though: league change and age. The AL ERA this year is 0.26 runs higher than the NL ERA. This can be accounted for in the 3.55, which brings him back to around 3.70-3.90. Age is another thing to consider, Josh Johnson is going from 29 to 30 years old. As a pitcher, this actually gives him an approximate 0.05 decrease in ERA. This generalization is shown in this graph from Baseball Prospectus. Taking this into consideration I believe we will see Josh Johnson post an ERA between 3.65 and 3.85 next year.



So let’s say we have Johnson posting a 3.75 ERA next year. A full season of Johnson should be around 3.0 WAR, cut his innings in half (injury risk) and that’s still 1.5 WAR. With wins being worth approximately $9M next year, Josh Johnson could realistically be worth anywhere from $13.5M to $27M, depending on injuries. A qualifying offer will be around that $13.5M. So even with a qualifying offer, the downside is that you will pay what you get, while the upside is much better. You can’t really lose. However I don’t think the Jays need to pay him $13.5M. Remember, he posted a 6.20 ERA this year. GMs around the league, as well as agents, will want to stay away from a bad, injury-prone pitcher. I believe the Jays could extend Johnson at around $11M/year over three years. At this price you could most certainly expect positive value from him. There are not really any cases like this to compare the situation with, so predicting possible contracts is a shot in the dark, but no matter the contract, I am positive it will be worth it. The Blue Jays definitely need to extend Josh Johnson as soon as possible. It is one of the best buy low opportunities they’ll ever encounter.

SkaP: A New Metric to Measure Hitting Prowess

Before I explain to you what this new metric – SkaP – does, I am first going to warn you that I can’t provide you with a formula or individual statistics for it.  It’s a theory right now, and something for which I need access to data I don’t have in order to find a formula.

This statistic was inspired in part by Colin Dew-Becker’s article the other day here on FanGraphs Community Research.  In his article, he argued that the the way a hit or out is made matters – not just the result of the hit or out.  A single to the outfield, for example, is more likely to send a runner from first to third or from second to home than an infield single.  Likewise, a flyout is more likely to advance runners than a strikeout is.

This statistic was also inspired in part by UZR.  UZR attempts to quantify runs saved defensively by a player partially by measuring if they make a play that the average fielder would not.  In the FanGraphs UZR Primer, Mitchel Lichtman explains that

“With offensive linear weights, if a batted ball is a hit or an out, the credit that the batter receives is not dependent on where or how hard the ball was hit, or any other parameters.”

This means that a line drive into the gap in right-center that is a sure double but is caught by Andrelton Simmons ranging all the way from shortstop (OK, maybe that was an exaggeration) will only count for an out, even though in almost any other situation it would be a double.  The nature of linear-weight based hitting statistics (and most other hitting statistics as well) is that they are defense-dependent.  Hitters have been shown to have much more control over their batted balls than pitchers do, which is why so far only pitchers have commonly used defense-independent statistics, but it would probably be useful for hitting too, no?

Now, if we want a defense-independent and linear weights-based hitting statistic, it would not be possible to formulate something similar to the hitting equivalent of the current model  of tERA (or tRA) because that generalizes all batted balls into categories such as grounders, line drives, or fly balls, because hitters can control where and how hard and at what angle their batted balls are hit at least to some extent.  Instead, what I would use is something more similar to a hitting equivalent of this version of tERA I found on a baseball blog.  What that article proposes is something much more detailed than what we have now (by the way, tERA has been supplanted by SIERA, but is still an interesting theory).  Their idea is that instead of finding expected run and out values for grounders, line drives, and fly balls, find the expected run value for a ball, to use their words, “with x velocity and y trajectory [that] lands at location z.”  This is similar to UZR in that exact (or as close to exact as possible) batted-ball data is processed and the expected run/out values are calculated.

So now for the statistic:  SkaP, or Skill at (the) Plate, is a number that uses all that batted-ball data to find the expected run and out values of each at-bat.  It would weight the following things:  home runs (although maybe a regressed version could use lgHR/FB%*FB instead), walks, strikeouts, HBP, and each ball put in play by the player.  This makes it so that it is not defense-dependent, and so that Andrelton Simmons catching that sure double does not penalize the hitter.  I haven’t calculated this statistic, though, so I don’t know if this would be best as a rate, counting, or plus-minus statistic (maybe all three?).

There’s one catch to this, however:  Skill at the Plate is really only a measure of skill at the plate.  It doesn’t account for some batters’ ability to stretch hits or beat out infield singles.  Billy Hamilton is going to be more likely to reach on an infield single than Prince Fielder.  However, this stat would treat them both the same, and not reward Hamilton’s speed for allowing him to reach base on what might have most likely been an out.  It would be very hard to separate defense independence and batter-speed independence for hitting statistics, though, and I’m not sure it’s possible to do without an extreme amount of effort.  Maybe a crude solution would be to quantify a player’s speed using Spd, UBR or BsR and add it somehow to this statistic.

I can’t calculate this myself, as I don’t have access to Baseball Info Solutions’s (or some other database that tracks batted balls) data.  FanGraphs does, however, and I would love to see this looked into further.