We saw in Part Three that the R^2 between OBP and ISO for the annual average of each from 1901-2013 is .373. To find out the correlation between OBP and ISO at the individual level, I set the leaders page to multiple seasons 1901-2013, split the seasons, and set the minimum PA to 400, then exported the 16,001 results to Open Office Calc.

(Yes, sixteen thousand and one. You can find anything with FanGraphs! Well, anything that has to do with baseball. Meanwhile, Open Office operating on Windows 7 decides it’s tired the moment you ask it to sum seven cells. At least it gets there in the end.)

The result was .201, so we’re looking at even less of a correlation in a much larger sample compared to the league-wide view. Are there periods where the correlation is higher?

Recall from Part Two that from 1994-2013 the R^2 for the league numbers was .583. Using individual player lines (400+ PA) from those seasons increases our sample size from 20 to 4107 (again splitting seasons). This gives us an R^2 of .232. That’s a little higher than .201, but not very much so.

All in all, it’s not the most surprising thing. On-base percentage and isolated power, mathematically, basically have nothing in common other than at-bats in the denominator. Given that, any correlation between them at all (and there is some) suggests that it either helps players hit for power to be an on-base threat, or vice versa. Not that one is necessary for the other, but there’s something to it. And throughout history, as we saw in Part One, a majority of players are either good at both aspects of hitting, or neither.

In fact, it’s the exceptions to that rule that triggered this whole series, those higher-OBP, lower-ISO players. Again from part one, there were 12 19th century, 21 pre-1961 20th century, and 3 post-1961 20th-21st century players with a career OBP over .400 and ISO below .200.

Much of this can probably be attributed to that consistency OBP has had historically relative to ISO that we observed a couple weeks ago. Continuing with the somewhat arbitrary 1961 expansion era cutoff, from 1961-present, 168 players with 3000 PA have an ISO over .200 and 18 have an OBP over .400; from 1901-60, it was 43 with the ISO over .200 and 31 with the OBP over .400. The .200+ ISO’s are split 80-20% and the .400+ OBP’s are split about 60-40%. The latter is the much smaller gap, as we’d expect. (Some players whose careers straddled 1961 are double-counted, but you get the basic idea.)

But let’s see if we can trace the dynamics that brought us to this point. What follows is basically part of part one in a part three format (part). In other words, we’re going to look at select seasons, and in those seasons, compare the number of players above and below the average OBP and ISO. Unfortunately, it’s hard to park-adjust those numbers, so a player just above the average ISO at Coors and a player just below it at Safeco are probably in each other’s proper place. But that’s a minor thing.

After the league-wide non-pitcher OBP and ISO are listed, you’re going to see what might look like the results of monkeys trying to write Hamlet. But “++” refers to the number of players with above-average OBP and ISO; “+-” means above-average OBP, below-average ISO; “-+” means below-average OBP and above-average ISO; and “- -” means, obviously, below-average OBP and ISO. The years were picked for various reasons, including an attempt at spreading them out chronologically. Notes are sparse as the percentages are the main thing to notice.

1901: .330 OBP, .091 ISO. Qualified for batting title: 121. 35% ++, 25% +-, 12% -+, 28% – –

1908: .295 OBP, .069 ISO. Qualified for batting title: 127. 41% ++, 23% +-, 8% -+, 28% – –

The sum of OBP and ISO was its lowest ever in 1908.

1921: .346 OBP, .117 ISO. Qualified for batting title: 119. 42% ++, 24% +-, 8% -+, 26% – –

Baseball rises from the dead ball era. Still relatively few players are hitting for power while not getting on base as much.

1930: .356 OBP, .146 ISO. Qualified for batting title: 122. 45% ++, 23% +-, 3% -+, 29% – –

The best pre-WWII season for OBP and ISO. Almost nobody was about average at hitting for power while not as good at reaching base. Two-thirds of qualifiers had an above-average OBP vs. fewer than half with an above-average ISO.

1943: .327 OBP, .096 ISO. Qualified for batting title: 106. 41% ++, 24% +-, 10% -+, 25% – –

World War II, during which OBPs stayed near the average but ISOs tanked. That would not necessarily appear in these numbers, because the players in this segment are categorized vs. each year’s average.

1953: .342 OBP, .140 ISO. Qualified for batting title: 88. 44% ++, 22% +-, 14% -+, 20% – –

The first year where sISO exceeded OBP was easily the lowest so far in terms of players below average in both OBP and ISO. (Note: So few players qualified on account of the Korean War.)

1969: .330 OBP, .127 ISO. Qualified for batting title: 121. 45% ++, 17% +-, 14% -+, 23% – –

1983: .330 OBP, .131 ISO. Qualified for batting title: 133. 43% ++, 16% +-, 17% -+, 25% – –

1969 and 1983 were picked because of their historically average league-wide numbers for both OBP and ISO. The percentages for each of the four categories are about equal in both seasons.

2000: .351 OBP, .171 ISO. Qualified for batting title: 165. 39% ++, 16% +-, 15% -+, 29% – –

The sum of OBP and ISO was its highest ever in 2000.

2011: .325 OBP, .147 ISO. Qualified for batting title: 145. 50% ++, 17% +-, 12% -+, 21% – –

2012: .324 OBP, .154 ISO. Qualified for batting title: 144. 44% ++, 24% +-, 14% -+, 18% – –

2013: .323 OBP, .146 ISO. Qualified for batting title: 140. 45% ++, 24% +-, 17% -+, 14% – –

Originally, this part ended with just 2013, but that showed an abnormally low “- -” percentage, so now 2011-13 are all listed. From 2011 to 2012, the split groups (above-average at 1 of the 2 statistics, “+-” or “-+”) increased sharply while the number of generally good and generally bad hitters decreased. From 2012 to 2013, there was almost no change in qualifiers based on OBP (the “++” and “+-” groups). Among those with below-average OBPs, the number with above-average power increased as the number with below-average power decreased. Most significantly, 2011-13 has produced an overall drop in players who are below average at both.

I don’t want to draw too many conclusions from this set of 12 out of 113 seasons. But a few more things come up besides the recent decline in players below average in both OBP and ISO.

Regarding “++” Players

Unsurprisingly, limiting the samples to qualifiers consistently shows a plurality of players to be good at both the OBP and ISO things.

Regarding “- -” Players 

Essentially, until 2012, this group was always at least 1/5 of qualifiers, and usually it was 1/4 or more. The last couple years have seen a decline here. Is it a trend to keep an eye on in the future (along with the league-wide OBP slump from Part 3)?

Regarding “++” and “- -” Players

Meanwhile, the majority of players will be above average at both getting on base or hitting for power, or below average at both. The sum of those percentages is just about 60% at minimum each year. Of the ten seasons above, the lowest sum is actually from 2013, mostly on account of the 14% of players who were below average at both.

This also means that it’s a minority of players who “specialize” in one or the other.

Regarding “+-” vs. “-+” Players

The “-+” players, those with below-average OBPs and above-average ISOs, show the best-defined trends of any of the four categorizations. In general, before 1953, when OBP was always “easier” to be good at than ISO (via OBP vs. sISO as seen in Parts 2 and 3), you saw fewer ISO-only players than you see today. Either they were less valuable because power was less a part of the game and of the leagues’ offenses, or they were less common since it was harder to exceed the league average.

The number of OBP-only players is more complicated, because they too were more common in the pre-1953 days. But they have jumped in the last two years from 1/6 of qualifiers from ’69-’11 to 1/4 of qualifiers in 2012 and 2013. Overall, the recent decline in “- -” players has come at the expense of “+-” players. This can also be interpreted as indicating that players are becoming better at reaching base while remaining stagnant at hitting for power (important distinction: that’s compared to the annual averages, not compared to the historical average; as we saw last week, OBP is in a historical decline at the league level).

Conclusion

The key takeaway for all of this is that there are always going to be more players who are above-average in both OBP and ISO or below average in both. Even if the correlations between OBP and ISO on the individual level aren’t overly high, clearly more players are good at both or at neither.

This isn’t just on account of players with enough PA to qualify for the league leaders being better hitters in general, because while the number of players above-average in both who qualify is always a plurality, it’s almost never a majority. It takes a number of players who are below-average at both to create a majority in any given year.

In terms of OBP-only players and ISO-only players, the former have almost always outnumbered the latter. This is sufficiently explained in that reaching base is often key to being a good hitter, while hitting for power is optional. (That’s why OPS has lost favor, because it actually favors slugging over OBP.) Even when batting average was the metric of choice throughout baseball, those who got the plate appearances have, in general, always been good at getting on base, but not necessarily at hitting for power.

Next week this series concludes by looking at the careers of some selected individual players. The most interesting ones will be the either-or players, with a significantly better OBP or ISO. We won’t look much at players like Babe Ruth or Bill Bergen, but instead players like Matt Williams or Wade Boggs. Stay tuned.

Baseball fans have been treated to incredible starting pitching performances in recent years, with several ace staffs leading their teams to regular-season and postseason success. Initially, I set out to examine the number of innings pitched by AL starting rotations because I expected that there would be a big disparity from team to team. And more specifically, I thought that the percentage of innings pitched by a team’s starting rotation would correlate positively to either its W-L record, or more likely, its Pythagorean W-L record.

I gathered five years of data (2009 – 2013 seasons) and calculated the Starting Pitcher Innings Pitched Percentage (SP IP%). This number is simply the number of innings a team’s starters pitched divided by the total innings the team pitched. If a starter was used in relief, those innings didn’t count. I only looked at AL teams, because I assumed that NL starting pitchers could be pulled from games prematurely for tactical, pinch-hitting purposes, while AL starters were likely to stay in games as long as they weren’t giving up runs, fatigued, or injured.

Two things struck me about the results:

1. There was little correlation between a team’s SP IP% and its W-L record or its SP IP % and Pythagorean W-L record

2. The data showed little variance and was normally distributed

I looked at 71 AL team seasons from 2009 – 2013 and found that on average, AL Teams used starting pitchers for 66.8% of innings, with a standard deviation of 2.83%. The data followed a rather normal distribution, with teams SP IP% breaking down as follows:

Over two-thirds of the teams (48 of 71) fell within the range of 63.6 to 69.2 SP IP%, which is much less variance than I expected to find.  And only three seasons fall outside the range of two standard deviations from the mean: two outliers on the negative end and one on the positive end. Those teams are:

Negative Outliers:

2013 Minnesota Twins: 60.06 SP IP%

2013 Chicago White Sox 60.25 SP IP%

Positive Outlier:

2011 Tampa Bay Rays 73.02 SP IP%

Taken at the extreme, these numbers show a huge gap in the number of innings the teams got out of their starters. Minnesota, for example, got only 871 innings out of starters in 2013, while the 2011 Tampa Bay Rays 1,058 innings in a season with fewer overall innings pitched. Another way of conceptualizing it would be to say that Minnesota starters pitched averaged just over 5 1/3 innings of each nine-inning game in 2013, while the 2011 Rays starters pitched nearly 6 2/3 innings. But when the sample is viewed as a whole the number of innings is quite close, as seen on this graph of SP IP% for the last five years:

The correlation between SP IP% and team success (measured via W-L or Pythagorean W-L) was minimal. (The Pearson coefficient values of the correlations were .1692 and .1625, respectively).  Team victories are dependent on too many variables to isolate a connection between team success (measured via team wins) and  SP IP%;  a runs scored/runs allowed formula for calculating W-L record was barely an improvement over the traditional W-L measurement. Teams like the Seattle Mariners exemplify the issue with correlating the variables: their starters have thrown above-average numbers of innings in most of the years in the study, but rarely finished with a winning record.

What I did find, to my surprise, was a relatively narrow range of SP IP% over the last five years, with teams distributed normally around an average of 66% of innings. In the future, it might be helpful to expand the sample, or look at a historic era to see how the SP IP% workload has changed over time. The relative consistency of SP IP% over five seasons and across teams could make this metric useful for future studies of pitching workloads, even if these particular correlations proved unsuccessful.

During the 2013 baseball season, the City of Chicago approved a $500 million plan to renovate Wrigley Field and build an adjacent office building and hotel.  Included in the renovation plan is the proposed construction of a large video board behind the left field bleachers and signs advertising Budweiser behind the right field bleachers.  The Cubs have delayed the start of this project, however, because the owners of the rooftop businesses across from the ballpark have threatened to file a lawsuit against the Cubs because the proposed signage will obstruct the views of the field from their respective rooftop businesses.

Rooftop Litigation History

Detroit Base-Ball Club v. Deppert, 61 Mich. 63, 27 N.W. 856 (Mich., 1886)

Disputes over neighbors viewing ballgames are nothing new.  In 1885, John Deppert, Jr. constructed a rooftop stand on his barn that overlooked Recreation Park, home to the National League’s Detroit Wolverines, future Hall of Famer Sam Thompson and a rotation featuring the likes of men named Stump Wiedman, Pretzels Getzien and Lady Baldwin.  The Wolverines claimed that they had to pay $3000 per month for rent and that the 50 cent admission fees, helped to offset this cost.  They were thereby “annoyed” by Deppert charging people, between 25 to 100 per game, to watch the games from his property and asked the court to forever ban Deppert from using his property in this manner.

Deppert countered that the ballgames had ruined the quiet enjoyment of his premises, that ballplayers often trespassed on his land in pursuit of the ball and that he often had to call the police to “quell fights and brawls of the roughs who assemble there to witness the games.”  He further claimed that his viewing stand had passed the city’s building inspection and that he had the legal right to charge admission and sell refreshments.

The trial court dismissed the Wolverines case and the ball club appealed.  The Supreme Court of Michigan agreed that the Wolverines had no right to control the use of the adjoining property; therefore, Deppert was within his rights to erect a stand on his barn roof and sell refreshments to fans that wanted to watch the game.  Furthermore, there was no evidence that Deppert’s rooftop customers would otherwise have paid the fees to enter Recreation Park.

Similarly, the rooftops of the buildings across the street from Shibe Park were frequently filled with fans wanting a view of the Philadelphia Athletics game action.  While never happy about the situation, Connie Mack was pushed too far in the early 1930s when the rooftop operators started actively poaching fans from the ticket office lines.  Mack responded by building the “Spite Fence,” a solid wall that effectively blocked the view of the field from the buildings across 20^th Street.

Lawsuits were filed but the “Spite Fence” remained in place throughout the remainder of the use of Shibe Park, later renamed Connie Mack Stadium.

The Current Dispute

Chicago National League Ball Club, Inc. v. Skybox on Waveland, LLC, 1:02-cv-09105 (N.D.IL.)

In this case, the Cubs sued the rooftop owners on December 16, 2002 seeking compensatory damages, disgorgement to the Cubs of the defendants’ profits and a permanent injunction prohibiting the rooftop owners from selling admissions to view live baseball games at Wrigley Field, among other remedies and under several causes of action.  According to the complaint, the Cubs alleged that the defendant rooftop operators “…have unlawfully misappropriated the Cubs’ property, infringed its copyrights and misleadingly associated themselves with the Cubs and Wrigley Field.  By doing so, Defendants have been able to operate multi-million dollar businesses in and atop buildings immediately outside Wrigley Field and unjustly enrich themselves to the tune of millions of dollars each year, while paying the Cubs absolutely nothing.”

In their statement of undisputed facts, the defendants countered that the rooftops had been used to view games since the park opened on April 23, 1914 as home of the Chicago Federal League team and that the Cubs conceded that their present management knew the rooftop businesses were selling admissions since at least the late 1980s.

In May 1998, the City of Chicago enacted an ordinance authorizing the rooftops to operate as “special clubs,” which allowed them to sell admissions to view Cubs games under city license.  The City wanted their piece of the action and interestingly, the Cubs made no formal objection to the ordinance.  Based on the licensure and lack of any opposition from the Cubs, the rooftop owners made substantial improvements to enhance the experience and to meet new City specifications.

By January 27, 2004, the Cubs had reached a written settlement with owners of 10 of the defendant rooftop businesses which assured that the Cubs “would not erect windscreens or other barriers to obstruct the views of the [settling rooftops]” for a period of 20 years.  The remaining rooftop owners later settled and the case was dismissed on April 8, 2004, just days ahead of the Cubs home opener set for April 12^th.

After the 2004 agreement legitimized their businesses, the rooftop owners made further improvements to the properties.  Long gone are the days that a rooftop experience meant an ice-filled trough of beer and hot dogs made on a single Weber.  The rooftop operations are now sophisticated businesses with luxurious accommodations, enhanced food and beverage service and even electronic ticketing.

As a result of the settlement agreement of Cubs’ 2002 lawsuit, the team now has legitimate concerns that a subsequent lawsuit by the rooftop owners to enforce the terms of the contract could ultimately result in the award of monetary damages to the rooftop owners; cause further delays in the commencement of the construction project due to a temporary restraining order; or, be the basis of an injunction preventing the Cubs from erecting the revenue-producing advertising platforms for the remainder of the rooftop revenue sharing agreement.

It is obvious that the rooftop owners need the Cubs more than the Cubs need them; however, the Cubs wanted their piece of the rooftop owners’ profits (estimated to be a payment to the Cubs in the range of $2 million annually) and now the Cubs have to deal with the potential that their massive renovation project will be held up by the threat of litigation over the blocking of the rooftop views.

Note: I have no idea if I’m the first to do this, but quite frankly I don’t care.

Every year, the Gold Gloves are awarded, and people get pissed off about whom they are awarded to. Every year, the Silver Sluggers are also awarded, and…well, no one really gives a fuck about the Silver Sluggers. Why? Hell, I don’t know. They don’t have the “storied tradition” of the Gold Gloves, the “time-honored legends” or the…uh…”legendary honors”? Look, people like to use weird cliches about how things used to be, and then Mike Bates writes quasi-racist articles¹ about it.

Personally, I enjoy the Silver Sluggers–sarcastically using them as superlatives for a player (“He’s won four, he must be good!”), looking forward to the nominations and announcements of the winners, but most importantly, arguing over them. It’s no secret that most awards are controversial–not just in baseball, but in all walks of life. People have differing opinions, and the technology available today makes it easier than ever for those opinions to be spouted furiously for the whole world to hear. In baseball, though, we are different. We have FACTS! We have EVIDENCE! We have STATISTICS!

What was the point of that disjointed rant? As I mentioned earlier, there has been many a bad pick for the Gold Gloves. However, the same is also true for the Silver Sluggers, and aside from Jeff Sullivan, no one seems to give a damn about it. Well, given that I am no one (see what I did there?), I decided that a damn should be given about it. I tracked down all of the Silver Slugger winners, back to 1980 (when they were first awarded), and saw if their wRC+ was the best at their respective position². What did I find?

Well…There were quite a few snubs. There are now 34 seasons of Silver Sluggers, which means there are 613³ Silver Slugger winners. Of those, 226 (36.9%) were undeserving by my methodology. Most of these were forgivable oversights, but some were simply awful choices; I have presented to you today several of the latter, for your viewing pleasure.

Below, you’ll see the 10 worst Silver Sluggers of all time, as measured by difference between the winner’s wRC+ and the deserved winner’s wRC+.

10. AL Outfield–1991

Winner: Joe Carter (123 wRC+)

Deserving Winner: Danny Tartabull (168 wRC+)

In his last season with the Royals before he headed to the Bronx (and to Seinfeld), Tartabull had the best season of his career, putting up 4.5 WAR for the Royals despite accruing only 557 plate appearances. His fielding was just as poor as it had ever been (-21 Def), meaning that all of his excellence had to be derived from his offense, and it was. In those 557 plate appearances, he batted .316/.397/.593, for a .430 wOBA and a 168 wRC+, highest among all outfielders. But was he good enough to win the award that is given to the best offensive players? Evidently not, as that honor went to Joe Carter and his .273/.330/.503 triple-slash, .361 wOBA, and 123 wRC+. Tartabull was clearly superior to Carter, so why did he get robbed?

It wasn’t for a lack of consistent position–although he would become a full-time DH later in his career, Tartabull started in right field for 124 of the 132 games that he played. Looking at traditional stats, Carter is only marginally better than Tartabull (33 HRs and 108 RBIs for the former, 31 HRs and 100 RBIs for the latter), and he still had a 43-point lead in batting average. Neither of them had won any Silver Sluggers prior to this, although Carter would win one the following year⁴. In this case, I suppose the voters picked Carter because he played the most, even if his aggregate offense was worth less than half that of Tartabull (23.4 wRAA  to 47.9 wRAA). As you’ll soon see, this oversight was acceptable compared to some of the other egregious ones.

9. AL Designated Hitter–1998

Winner: Jose Canseco (110 wRC+)

Deserving Winner: Edgar Martinez (156 wRC+)

The 35-year-old Martinez was still going strong at this point, putting up at least 5 WAR for the fourth of six consecutive years. His 5-win season in 1998 was primarily based on his ability with the bat, as he played 150 of his 154 games at DH. The Mariners were certainly happy with his production, as he hit .322/.429/.565, for a .427 wOBA and a 157 wRC+. However, a certain time-traveling outfielder was instead rewarded with the Silver Slugger, and it’s not hard to see why.

While Martinez hit for a good amount of power, Canseco outslugged him by a mile, or at least in the one area the voters care about. Martinez only had 29 round-trippers, compared to 46 for Canseco. Yes, Canseco also only batted .237 with a .318 OBP, .354 wOBA, and 110 wRC+, but that’s not important–he hit 46 dingers!

Reputation probably didn’t play a huge role with this one, as each player had won three times before (1992, 1995, and 1997 for Martinez; 1988, 1990, 1991 for Canseco⁵). The (theoretical) ability to drive in runners also wasn’t important, as the two players had nearly identical RBI lines (102 for Martinez, 107 for Canseco); moreover, both were equally durable (672 PAs for Martinez, 658 PAs for Canseco). In the end, the ability to hit the ball out of the park was what stole the award from Martinez, even though both rate stats and cumulative stats (12.7 wRAA for Canseco, 53.5 for Martinez) agreed that other factors were important as well.

8. AL Third Base–1995

Winner: Gary Gaetti (111 wRC+)

Deserving Winner: Jim Thome (158 wRC+)

In his first season qualifying for the batting title, Thome didn’t disappoint, as he gave the Indians six wins above replacement level; he was solid with the glove (1.1 Def at third), but his work with the bat set him apart: He smashed 25 home runs in 557 plate appearances, while hitting .314/.438/.558 with a .433 wOBA and 158 wRC+. Nevertheless, he would be disappointed at season’s end–no, not because the Indians lost the World Series, but because he got robbed of an award to measure the best offensive players at any given position!

Anyway, while Thome’s blossoming power was nothing to shrug at, Gaetti’s power was even more impressive, as he hit 35 homers in only 21 more plate appearances. However, his game suffered everywhere else, as he batted only .261, got on base at a .329 clip, and had a wOBA and wRC+ of .360 and 111, respectively. Both of them played the majority of their games at third base, so both were judged against each other; Thome, though, was unarguably better, which was reflected in wRC+ and wRAA (46.2 for Thome, 13.1 for Gaetti). However, the voters have a tendency to not listen to rational arguments, so Gaetti’s superior home run and RBI totals (96 compared to 73 for Thome) gave him the sought-after crown.

7. AL Outfield–1994

Winner: Kirby Puckett (124 wRC+)

Deserving Winner: Paul O’Neill (171 wRC+)

Because 1994 was shortened by the strike, counting stats from this season have to be taken with a grain of salt. One counting stat in particular was the deciding factor in this race, and I’ll soon reveal what it was. O’Neill was in pinstripes for the second of nine straight seasons, and he lived up to the lofty standard that the garb carries. In 443 plate appearances, O’Neill had 4.3 WAR, despite a Def of -10.7; this was due, then, to the fact that he demolished his way to a .359/.460/.603 line, with a .450 wOBA and 171 wRC+. But did the voters care? No, because a wife-beater was supposedly better.

Puckett was certainly good in 1994, hitting .317/.362/.540 with a .381 wOBA and 124 wRC+ in 482 plate appearances. O’Neill, though, had more than double the wRAA (43.6 to 19.3), and the sizable wRC+ lead; in addition, ONeill actually outhomered him, 21 to 20, and had the aforementioned advantage in batting average. Going down the Triple Crown checklist, that leaves one category: RBIs. O’Neill brought 83 runners home–an acceptable total, to say the least. Puckett, however, blew him out of the water, with 112 RBIs–in 108 games! That’s pretty impressive, if you care about such things, and God knows the voters care. Hence, the Silver Slugger was not given to its rightful owner, all because of one useless stat.

6. AL Designated Hitter–1996

Winner: Paul Molitor (114 wRC+)

Deserving Winner: Edgar Martinez (163 wRC+)

Should Martinez make the Hall of Fame? Probably. Will he make the Hall of Fame? Given his recent history, I’m inclined to say no. Would winning two deserved Silver Sluggers have helped his case? Well…Again, nobody really cares about this thing, so probably not. But the point of all of these rhetorical questions is: Martinez was a boss in 1996 (as he was in 1998). The second of six straight five-win seasons, Martinez was a full-time DH, meaning that he had to crank out the offense constantly if he wanted to remain a high performer. He most certainly did crank, to the tune of a .327/.464/.595 triple-slash, with a .450 wOBA and 163 wRC+ in 634 trips to the plate. You wouldn’t know that from looking at the awards, though, as the guy that deserves to be in Cooperstown was shut out by the only guy at his position that is in Cooperstown. What caused this?

While Martinez didn’t hit a whole lot of long balls–his .269 ISO was derived primarily from his 52 doubles, not his 26 homers–Molitor was even worse, hitting only 9 round trippers in 728 plate appearances. What the voters proved in 1996 was that they didn’t depend solely on primitive statistics like “home runs” to determine a player’s worth. They used advanced statistics for the modern age, like batting average and runs batted in! In those regards, Molitor had clear advantages over Martinez, with a .341 average and 113 RBIs. Now, Molitor’s dearth of walks and power meant that his OBP and SLG were a mere .390 and .468, respectively, which in turn meant that his wOBA was .372 and his wRC+ was 114, which in turn meant that he was completely inferior to Martinez in rate and counting stats (23.0 wRAA, compared to 62.2 for Martinez), but he had 113 RBIs! And a .341 average! That’s gotta count for something!

This was not, however, the only big-boned brouhaha that brewed in 1996…

5. NL First Base–1996

Winner: Andres Galarraga (123 wRC+)

Deserving Winner: Jeff Bagwell (173 wRC+)

In Bagwell’s second of four 7-WAR seasons, he put up some serious numbers for the Astros, hitting .315/.451/.570 with a .433 wOBA and a 173 wRC+ in 719 plate appearances as a first baseman; with a -7.8 Def, he needed to mash to earn his keep. Galarraga was also a relatively poor defender (-7.5 Def), so the same went for him. He also hit quite well, or so it would appear; his triple-slash was .304/.357/.601, which gave him a .402 wOBA in 691 plate appearances at first–not that far off from Bagwell. Why, then, was the wRC+ gap so large?

‘Twas about the elevation, dearie. Galarraga played for the Rockies, meaning he played half of his games at Coors Field, meaning he was expected to hit like a monster. While the aforementioned batting line was rather good by major-league standards, it was merely adequate by the mountain standard, and his 123 wRC+ and 39.3 wRAA reflected that. By contrast, Bagwell played in the Astrodome half of the time, which was not particularly good to hitters as a whole⁶; thus, his 173 wRC+ and his 60.1 wRAA.

Obviously, the voters were unaware of the effects a player’s home park can have on his all-around production, or else they would have discounted Galarraga’s 47 home runs and 150 RBIs. With this next case (well, these next few cases, really), though, there’s no excuse.

4. NL Pitcher–1985

Winner: Rick Rhoden (18 wRC+)

Deserving Winner: Mike Krukow (71 wRC+)

My theory is that the voters are all secretly supporters of the DH, and they all want to see it implicated across both leagues. How else can you explain 15 of the 34 pitchers (44.1%) that have won being undeserving, or that the four worst picks (of any position) were all pitchers? Anyway, Krukow was quite good (for a pitcher) with the bat in 1985, slugging his way to a .218/.259/.345 line, with a .271 wOBA and a 71 wRC+; looking at more traditional stats, he hit one home run and had three RBIs in 66 trips to the plate. He was also pretty good with the arm, accruing 3.1 WAR in 194.2 innings for the Giants in his second of six years by the bay.

Rhoden was also solid on the mound in his seventh of eight years with the Pirates, putting up 2.6 WAR in 213.1 innings pitched. He won the Silver Slugger the year before (and actually deserved to), so maybe the voters were just lazy and assumed he hit well the next year. Make no mistake, though–he did not hit well at all in 1985. His triple-slash was an anemic .189/.211/.230, meaning his wOBA was .200 and his wRC+ was 18; he also went homerless, and had only 6 RBIs. His offense (or lack thereof) cost the Pirates 7.2 runs, three times that of Rhoden (-2.4). For reasons that escape me, that performance was apparently Silver Slugger-worthy, and now the wrong man has gone home with the award for yet another year. But don’t you worry–it gets much, much worse…

3. NL Pitcher–1998

Winner: Tom Glavine (37 wRC+)

Deserving Winner: Mike Hampton (91 wRC+)

Hampton is best remembered for two things: Signing the largest contract in baseball history (for the time) with the Rockies and proceeding to stink up the joint before getting traded to the Braves; and being a pretty damn good hitter. Like, a better career wRC+ than Ozzie Guillen good. Yeah, that’s a bad comparison to make, whatever. The point is, Hampton could hit, and 1998 was no exception–in his penultimate year with the Astros, he had a .262/.348/.328 batting line, which translated to a .312 average and a more than satisfactory 91 wRC+. Glavine, on the other hand, was a less than satisfactory hitter, both for his career and in this year⁷. He batted a mere .239/.250/.282, which only gave him a .237 wOBA and a 37 wRC+. Cumulative stats reflect this as well, as Hampton’s offense was worth 5.6 runs more than Glavine’s (-1.2 to -6.8 wRAA). Triple-crown stats don’t reveal anything–neither player homered, although Glavine had seven RBIs to Hampton’s two.

The reason for Glavine’s victory here was likely twofold. One, Glavine pitched better than Hampton, with the former’s 2.47 ERA in 229.1 innings dwarfing the latter’s 3.36 ERA in 211.2 innings. Second, Glavine had a better reputation, which is where it gets complicated. See, Hampton was a good hitter, and Glavine wasn’t (as footnote 7 should make perfectly clear); however, according to reputation, both of these men were good hitters (for their position), as they took home a combined nine Silver Sluggers. The difference between the two? 1998 was the end of Glavine’s run of Silver Sluggers, whereas the next year (i.e. 1999) was the first of five straight for Hampton⁸. In this case, Glavine’s notoriety, which was built up prior to 1998, won him the award, while Hampton’s fame won him a few later (see footnote 8).

Without a doubt, the 1998 pitcher’s Silver Slugger was one of the worst in the history of the award. Sadly, there are two years that were even worse.

2. NL Pitcher–1983

Winner: Fernando Valenzuela (20 wRC+)

Deserving Winner: Tim Lollar (78 wRC+)

Lollar’s career was pretty unremarkable–he put up 2.5 WAR in 906.0 innings for four teams. In 1983, he pitched for the Padres, and he was in line with his career numbers–0.4 WAR and a 4.61 ERA in 175.2 innings. At the plate, though, he was a revelation–well, comparatively speaking. He hit .241/.292/.345 in 65 plate appearances, which gave him a .285 wOBA and a 78 wRC+, best in the National League among qualified pitchers. Valenzuela’s career was notably more remarkable, as his career WAR was 38.5 over 2930.0 innings for six teams. In the year in question, he pitched well for the Dodgers, accruing 3.9 WAR over 257.0 innings (with a 3.75 ERA). Hitting did not work out quite as well, to say the least: In 105 plate appearances, his triple-slash was .187/.194/.253, which translated to a .199 wOBA and a 20 wRC+.

Lollar was much better than Valenzuela, by both advanced and basic stats–they both hit one homer, but Lollar had 11 RBIs to Valenzuela’s 7. Lollar’s offense only cost the Padres 1.7 runs below average, whereas Valenzuela’s took nearly 10 runs away from the Dodgers. This is one of the more puzzling awards (though not as puzzling as the next one); my best guess is that Valenzuela rode on the coattails of his incredible rookie year in 1981⁹. Unfortunately, this was not the darkest hour for the prestigious honor that is the Silver Slugger award; no, that time would come six years later, in a travesty greater than any that came before it,

1. NL Pitcher–1989

Winner: Don Robinson (43 wRC+)

Deserving Winner: Bob Knepper (111 wRC+)

Given that Bob Knepper’s career wRC+ is 3–yes, three–I’m inclined to believe that his 1989 season was a fluke. The second-to-last season of his career, 1989 didn’t go well for him as a pitcher–he put up a 5.13 ERA while costing the Astros and Giants -0.8 wins over 165.0 innings. As a hitter, though, he was never better–somehow, he managed to get on base in 32.7% of his trips to the plate, with a decent .372 slugging percentage for good measure. His competence in these two areas was enough to compensate for his sub-Mendozan batting average (.186) and bring his wOBA and wRC+ to .324 and 111, respectively.

The antithesis to this would be Robinson, who was quite good on the mound (at least by traditional stats), with a 3.43 ERA in 197.0 innings for the Giants, but was completely ineffective at the plate (even for a pitcher). The owner of a respectable career wRC+ of 60, Robinson sunk down to 43 in 1989, as he only batted .185/.195/.309 (.226 wOBA). In what world was that worth more than Knepper? A world where the voters for most major awards rely on archaic means of measuring player performance–i.e. homers and RBIs. Knepper only knocked one out once in 55 plate appearances, while Robinson did it thrice in 82 PAs; Robinson also out-ribbied Knepper, seven to three. When the dust had settled, Knepper was worth 0.5 wRAA, while Robinson was worth -5.4; despite this, the Silver Slugger went to Robinson.

***

Having finally finished with this torturous exercise, I now see why people don’t place any value in the Silver Sluggers. They’re pointless awards, given out solely on reputation and not actual performance. Anyone who takes them seriously is just aksing for…Wait, what’s that? The Orioles won HOW MANY Silver Sluggers?

In summation: The Silver Slugger is the best award in baseball, and it’s a shame that the level of respect for it is as low at it is.

———————————————————————————————–

¹Just to be clear: I thoroughly enjoyed the article, and don’t consider Bates to be racist in any way.

²A little bit about the methodology: I decided on wRC+ (as opposed to, say Off) for two reasons. First, I wanted to see who the best hitters were, not the best offensive players, meaning baserunning was not to be included. Is that small-minded? Probably. Is the award in question called the Silver Slugger/Baserunner, and is the award itself a combination of a silver bat and a silver pair of cleats? Certainly not. Second (and more rationally), I wanted to measure the best hitters overall, not in terms of aggregate value; using Off or wRAA would benefit players that played longer. To pull some numbers out of my ass as an example, a guy with a 140 wRC+ is a better hitter than a guy with a 130 wRC+, but if the latter received 700 plate appearances while the former only received 550, Off or wRAA (or any counting stat) wouldn’t reflect that. But, just to be safe, I also put each player’s wRAA somewhere in the writeup.

³In 2004, there were two AL catchers that won–neither of whom was deserving.

⁴That would also be undeserved; Carter’s 120 wRC+ in 1992 paled in comparison to Shane Mack and his 142 wRC+.

⁵Oddly enough, all of those were deserving. Canseco’s wRC+s of 169, 157, and 152 in 1988, 1990, and 1991, respectively, were among the three best among qualified outfielders in those years, and Martinez’s wRC+s of 165, 182, and 164, respectively, were the best among qualifying DHs in those years.

⁶Side note: Why did this never happen? Come on, people–I expected more from you.

⁷Glavine won four Silver Sluggers over the course of his career (in 1991, 1995, 1996, and 1998). Care to speculate as to how many of those were justified? That’s right, none! In those years, Glavine’s wRC+s were 50, 41, 81, and 37, respectively, when Tommy Greene (94 wRC+ in 1991), Kevin Foster (65 wRC+ in 1995), Jason Isringhausen (84 wRC+ in 1996), and Hampton were far better. Also, in case you were wondering, Glavine’s career wRC+ is 22.

⁸Of those five, three (1999, 2001, and 2002, with wRC+s of 111, 106, and 112, respectively) were the right choice, and two (2000 and 2003, with wRC+s of 56 and 52, respectively, when Omar Daal and Russ Ortiz had wRC+s of 83 and 81, respectively) were not.

⁹He won the Silver Slugger in that year as well, and that was also undeserved, as his 55 wRC+ was outshined by Gaylord Perry’s 71 wRC+.

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:

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%).

2014-???

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.

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.

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.

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.

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 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

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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.

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:

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).

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.

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.