Tigers Sabermetric Leaders

Here are some Tigers sabermetric leaders from Lee Sinnis' Sabermetric Baseball Encyclopedia:

Runs Created = Projection of runs produced by a player based on singles, doubles, homers, walks, stolen bases and other things that a player does offensively.

Cabrera 60
Guillen 52
Granderson 52
Ordonez 51
Polanco 49

Runs Created Per Game = A projection of how many runs a team would average if every player on the team was cloned to that player (at least 150 plate appearances).

Granderson 6.65
Thames 6.61
Cabrera 5.93
Ordonez 5.81
Guillen 5.66

Offensive Winning Percentage = Projected team winning percentage if each offensive player was cloned to that player and the team had average pitching/defense (at least 150 plate appearances).

Granderson .602
Thames .600
Cabrera .551
Ordonez .542
Guillen .530

Runs Created Above Average = A difference between a player's Runs Created total and a total for the average player who used the same amount of his team's outs (no plate appearance limits).

Granderson 11
Thames 7
Santiago 7
Joyce 7
Cabrera 6

Support Neutral Wins (and Losses) = Projected number of wins (losses) given league average offensive support

Verlander 9-7
Galarraga 7-4
Robertson 6-8
Rogers 6-7
Bonderman 4-3

Runs Saved Above Average = Number of runs saved by a pitcher over an average pitcher's runs allowed.

Galarraga 13
Verlander 6
Bonderman 2
Rogers -1
Robertson -15

Mid-season FIP update

Today, I'll continue my mid-season analysis of the Tigers looking at Fielding Independent Pitching (FIP or FIP ERA). FIP is a measure of how well a pitcher performs in events which he can control without the influence of fielders - home runs, strikeouts, walks and hit batsmen. The formula is

(HR*13+(BB+HBP)*3-K*2)/IP, plus a league-specific factor added to make it equivalent to a real ERA.

While real ERA is affected by fielding, FIP gives you an idea of how well a pitcher performed regardless of how much fielding support he received. FIP was invented by Tangotiger.

Table 1 below indicates how Tigers starters rank among 48 American League starters with 80 or more innings pitched so far this year. The data were extracted from the Hardball Times site. The columns of the table are described below:

ERA = actual ERA.

FIP = Fielding Independent Pitching ERA

DER = the Defensive Efficiency Ratio (proportion of balls in play converted into outs) of the team when the given pitcher is on the mound.

FIP-ERA = FIP minus ERA.

LOB% = percentage of runners put on base which have been left stranded.

The table shows that none of the Tigers are in the top half of the league in FIP. Justin Verlander (4.09) and Nate Robertson (4.11) lead the Tigers and are ranked 27th and 28th respectively. Since FIP tends to be a decent indicator (more so than ERA) of future success, this would normally be a bad sign for the second half. The good news is that most of the damage was done early and they are pitching a lot better now.

Armando Galarraga's FIP (4.39) is about a run higher than his ERA (3.40) which suggests that he is probably not pitching as well as his ERA indicates. The component which drags down his FIP is his 3.3 walks per game which is in the bottom ten in the league. One factor which is likely helping his ERA is his .774 DER (third in the league). This indicates that he may be benefitting from some combination of above average fielding support and luck. I would expect his actual second half ERA to be closer to 4.39 than 3.40.

In contrast to Galarraga, Robetson's FIP (4.11) is more than a run lower than his actual ERA (5.23). Despite his third worst in the AL ERA, Robertson has a good BB/ 9 IP ratio (2.5) and a respectable K/ 9 IP ratio (6.0). Unlike The Big Cat, Nate has a very low .661 DER (third worst in the AL) so he may be getting poor fielding support or perhaps has been unlucky.

In conclusion, the Tiger starters have done well so far this year even if they have improved substantially since mid-May. In the second half, we can probably expect Robertson's ERA to improve and Galarraga's ERA to regress based on their ERA/FIP differentials. I would also expect Verlander and Rogers to have better ERAs in the second half simply because they are pitching better now than they were in the first half of the season.


Table 1: FIP for Tigers starters (through June 30, 2008)

Rank	Pitcher	IP	ERA	FIP	DER	FIP-ERA	LOB%
27	Verlander	108	4.42	4.09	.729	-0.33	67.2
28	Robertson	96 1/3	5.23	4.11	.661	-1.12	69.4
36	Galarraga	82	3.40	4.39	.774	0.99	69.4
43	Rogers	100	4.59	4.90	.702	0.31	70.8
---	Bonderman	71 1/3	4.29	4.98	.709	0.69	74.0

Mid-season WPA update

With the season approaching the halfway point, I'm going to start looking at Tigers leaders in various areas. I'll start with the relief pitchers. It's difficult to measure the performance of relievers for a couple of reasons: (1) They pitch so few innings that their statistics can be influenced heavily by a couple of really bad outings. (2) Their actual value depends on game situations more than any other player. Using ERA to evaluate relievers is problematic because relievers often come in with runners on base and give up other pitcher's runs. So a pitcher could have a low ERA without actually being that effective. FIP ERA which is based on walks, strikeouts and home runs allowed rather than runs allowed is better but it still does not consider the game environments in which a reliever pitched.

In one instance, Fernando Rodney comes into the game with a one run lead and two runners on base in the 8th inning. In another game, Bobby Seay comes into the game with a 6 run lead and nobody on base. Now suppose each pitches a perfect inning. Using ERA or FIP, they would both get the same credit for that inning but Rodney's performance had more impact on the outcome of the game.

The Win Probability Added (WPA) statistic gives players (hitters, starters, relievers) credit based on the effect each play has on a team's probability of winning. These probabilities vary depending on the game score, the runners on base and the number of outs before and after each play. They are based on the results of thousands of games worth of data looking at every possible situation over and over.

More concretely, WPA works as follows. Suppose Bobby Seay comes into the game in the top of the 8th with a 2 run lead, 0 outs and a runner on first. There is a .787 (78.7%) expectancy that a team will win the game given that situation. Suppose Seay strikes out the first batter. There is now one out and the probability of winning has gone up to .848. Thus, the strikeout was worth .848-.787=.061

Now suppose the next batter after that doubles home a run. The Tigers now have a one run lead with a runner on second and one out. The probability of winning goes down to .693. So Seay loses points on that batter: .693-.848=-.155.

If you add up all the gains and losses for all the batters Seay faces you get his WPA. WPA doesn't necessarily solve the problem of small sample sizes but it is a reasonable stat for relievers because it gives more weight to plate appearances which have a strong impact on winning and losing games. Table 1 below lists The WPAs for Tigers relievers with 10 or more appearances in 2008. Table 2 lists the American League leaders with 25 or more appearances. These data were abstracted from the Fan Graphs web site.

Todd Jones is the leading Tigers reliever according to WPA (1.34) so far this year. This ranks Jones 13th in the American League. If we ranked Jones according to his by ERA (3.86) or WHIP (1.38), he would not have finished in the top 30. He ranks better on WPA because, like most closers, Jones comes into a lot of critical situations and gets positive results a lot more often than negative results. The Tigers lowest WPA (-1.41) belongs to Francisco Cruceta who, of course, is no longer with the team.

Table 1: WPAs for Tigers relievers in 2008 (through June 27)

Name	G	WPA
Jones	32	1.34
Miner	30	0.48
Rapada	14	0.32
Dolsi	17	0.13
Lopez	23	0.06
Seay	29	0.01
Bautista	16	-0.14
Cruceta	13	-1.41

Table 2: AL reliever WPA leaders in 2008 (through June 27)

Name	Team	G	WPA
Rodriguez	Angels	37	2.86
Nathan	Twins	34	2.77
Soria	Royals	33	2.66
Rivera	Yankees	33	2.39
Howell	Rays	29	2.10
Johnson	Orioles	31	1.99
Sherrill	Orioles	37	1.97
Downs	Blue Jays	34	1.90
Guardado	Rangers	31	1.73
Mahay	Royals	34	1.71

Now, which pitchers typically worked in the most pressing situations? To answer this question, we can use Leverage Index (LI) which measures how critical a given plate appearance is to determining the final result of a game. An LI of one is average. An LI of more than one indicates a high leverage plate appearance which has a potentially high impact on the outcome of the game. An LI of less than one is a low leverage plate appearance. pLI is Leverage Index per Plate Appearance.

Table 3 below lists The pLIs for Tigers relievers with 10 or more appearances so far in 2008. Table 4 lists the American League leaders with 30 or more appearances. Not surprisingly, closers dominated the AL leader board. If you noticed that Francisco Cruceta pitched in a lot of high leverage situations, you were right. Cruceta had the highest pLI (1.78) of any Tiger before he was removed from the Tigers roster. Of those still on the roster, Jones (1.35) and Freddy Dolsi (1.31) have been used in high leverage situations most often. Again, all of these data were pulled from Fan Graphs.


Table 3: Leverage Indexes for Tigers relievers in 2008 (through June 27)

Name	G	pLI
Cruceta	13	1.78
Jones	32	1.35
Dolsi	17	1.31
Miner	30	1.07
Seay	29	0.88
Bautista	16	0.79
Lopez	23	0.54
Rapada	14	0.47

Table 4: AL reliever Leverage Index Leaders in 2008 (through June 27)

Name	G	WPA
Jones	32	1.34
Miner	30	0.48
Rapada	14	0.32
Dolsi	17	0.13
Lopez	23	0.06
Seay	29	0.01
Bautista	16	-0.14
Cruceta	13	-1.41

Sharing the blame

David Berri, who authored the book Wages of Wins and writes the Wages of Wins Journal,
asked me the other day how well we could have expected the Tigers to perform this year given what the players did last year. He also wanted to know which players are underperforming and by how much. There is no definitive answer to these questions but one approach is Win Shares.

Bill James developed Win Shares as a way to estimate the number of wins a player contributes to a team based on his individual statistics. In the Win Shares approach, everything a player does (hitting, fielding, base running and pitching) is summarized with one number. Each win is worth three win Shares. For example, a player with 30 Win Shares is considered to have contributed 10 wins to his team.

The algorithm for determining Win Shares is much too complicated to be detailed here. If you want to learn more, you can read Win Shares written by Bill James and Jim Henzler and published by STATS, Inc. If you want a much briefer version, you can go to Baseball Graphs
The win shares algorithm has been critiqued and altered over the year and these changes are described at The Hardball Times.

I obtained the 2007 and 2008 Win shares statistics from The Hardball Times. THBT updates the win shares every couple of weeks and I thank Dave Studeman for running them the same day I told him I was hoping to use them in this article. Table 1 shows the win shares for batters in 2007 versus 2008. Basically, I calculated expected win shares for 2008 based on 2007 win shares and expected plate appearances in 2008. Using Miguel Cabrera as an example, here is how to read the table:

PA = 680 plate appearances in 2007
WS= 30 win shares in 2007

EPA= 700 expected plate appearances in 2008. This was done mostly to account for acquisitions made during the off-season. You would not, for example, expect Brandon Inge to have the same number of appearances in 2008 as a part-time player as he had in 2007 as a full-time player. Consequently, you would also not expect him to have as many win shares.

EWS=31 expected win shares in 2008 given 2007 win shares and EPA in 2008.

WS=6 win shares in 2008 as of June 5.

PWS= 17 projected win shares in 2008. This is calculated by simple extrapolation assuming Cabrera will perform the same way all season as he did through June 5.

WS diff= PWS-EWS

W diff= WS diff/3 because each win is worth 3 win shares.

Table 2 is similar to table 2 except it presents win shares for pitchers and uses innings pitched to determine expected win shares.


Table 1: Win Shares for Tigers batters - 2007 vs 2008

	2007				2008		2008 - 2007
Player	PA	WS	EPA	EWS	WS	PWS	WS diff	W diff
Granderson	676	26	650	25	3	8	-17	-5.67
Jones	495	15	500	15	0	0	-15	-5.00
Cabrera	680	30	700	31	6	17	-14	-4.67
Ordonez	679	36	650	34	8	22	-12	-4.00
Sheffield	593	16	500	13	1	3	-10	-3.33
Renteria	543	18	600	20	4	11	-9	-3.00
Polanco	641	23	600	22	6	17	-5	-1.67
Raburn	148	5	150	5	0	0	-5	-1.67
Guillen	630	19	600	18	6	17	-1	-0.33
Rodriguez	515	13	450	11	4	11	0	0.00
Santiago	74	2	150	4	3	8	+4	+1.33
Inge	577	13	250	6	4	11	+5	+1.67
Thames	284	7	200	5	4	11	+6	+2.00
Others	----	----	200	4	3	8	+4	+1.33
Totals	----	----	6200	213	52	144	-69	-23

Table 2: Win Shares for Tigers pitchers - 2007 vs 2008

	2007				2008		2008 - 2007
Player	IP	WS	EIP	EWS	WS	PWS	WS diff	W diff
Verlander	202	16	215	17	2	5	-12	-4.00
Willis	205	7	215	7	0	0	-7	-2.33
Zumaya	34	3	60	6	0	0	-6	-2.00
Robertson	178	8	185	8	1	3	-5	-1.67
Byrdak	45	4	50	4	0	0	-4	-1.33
Rodney	51	3	60	4	0	0	-4	-1.33
Seay	46	6	50	6	1	3	-3	-1.00
Miner	54	5	60	6	1	3	-3	-1.00
Jones	61	6	60	6	1	3	-3	-1.00
Grilli	80	4	60	3	1	3	0	0.00
Rogers	63	3	125	6	2	6	0	0.00
Bonderman	174	7	200	8	4	11	+3	+1.00
Galarraga	17	1	25	1	4	11	+10	+3.33
Lopez	9	0	25	0	4	11	+11	+3.67
Others	----	----	60	2	2	6	+4	+1.33
Totals	----	----	1450	84	23	65	-19	-6.33

Looking at the totals row in each table, we can see how many team win shares the Tigers would accumulate in 2007 if they performed the same way in 2008 as 2007. It comes out to 297 expected win shares in 2008 (213 for the batters and 84 for the pitchers). That is 99 expected wins assuming no injuries or player regression. So far this year, they have accumulated 75 win shares (52 and 23). Extrapolating that over 162 games, it comes to 209 win shares (144 for hitters and 65 for pitchers). That is equivalent to 70 wins.

So, if they keep up the same pace, the Tigers will lose 29 more games this year than expected based on their performance in 2007. Some of it is injuries to Curtis Granderson and Gary Sheffield and a few others but they haven't been hit that hard by injuries this year. So which players are underperforming the most?

Table 1 illustrates that six position players are underperforming by three or more wins over a full season.

Curtis Granderson (5.67 wins) - We can't blame Granderson for his hand injury but even if we lower his expected plate appearances to reflect time missed for the injury, it still comes to 4+ wins of underperformance.

Jacque Jones (5 wins) - Some people were not expecting much from him but he should have been expected to be a decent platoon player. Instead he had zero win shares at the time of his release.

Miguel Cabrera (-4.67) - He is tied for second on the team in win shares but he has simply underperformed. There is no excuse for him other than he is trying to adjust to a new league.

Magglio Ordonez (-4.00) - I'll excuse Ordonez for not repeating his career year of 2007. If I had used a three year average as the comparison rather than just last year, he would be performing just as expected. So, let's not blame Magglio.

Gary Sheffield (-3.33) - Sheffield has been hurt but he was hurt last year too so I wasn't expecting a full season of plate appearances from him. He has produced very little while playing though.

Edgar Renteria (-3.00) - Renteria had one of his best seasons last year but even if we take a three year average, he is still underperforming by more than 2 games.

On the pitching side, there are three players underperforming by 2 or more games:

Justin Verlander (-4.00) - He is apparently healthy and simply underperforming.

Dontrelle Willis (-2.33) - has been injured most of the season.

Joel Zumaya (-2.00) - has been injured all season.

My conclusion is that most of the team underperformance compared to 2007 is due to three things:

(1) the underperformance of the following players: Cabrera, Verlander, Granderson, J.Jones, Renteria and Sheffield.

(2) injuries to Zumaya, Granderson, Willis and Sheffield.

(3) Magglio Ordonez should not have been expected to repeat his amazing 2007 season.

So, for those who want to point the finger, there is plenty of blame to go around. For those who like to make excuses, there is some reason for that too. Regardless of how you look at it, it's been a very disappointing season for the Tigers.

Bondo's peripherals going south

Watching Jeremy Bonderman, from 2004 through the first half of 2007, you got the feeling that, while maddeningly inconsistent, he was very close to developing into a top pitcher. He had two good fastballs (four seamer and two seamer) and one of the best sliders in the game. His fielding independent statistics were excellent - high strikeouts, low walks and lots of ground balls with relatively few homers allowed. For someone reason, Jeremy's ERAs never matched his other stats but there was reason for optimism for the still young pitcher.

Then in the middle of last season starting somewhere around that ten run meltdown versus the Angels on July 29, he lost his control and his pitches didn't seem to be as sharp as in the past. It grew into more than just a slump. It was a miserable second half that ended when he revealed that he had been hiding an injury to his elbow for several weeks. The table below shows that his peripherals went down hill drastically in the second half starting with the Angels game. His strikeouts per game dropped from 8.1 to 6.0, his walk rate rose from 1.8 to 4.2 and his home run rate climbed from 1.1 to 1.5.

It was actually a relief to find out that he was injured and that it was apparently not a serious one as it gave us some confidence that he would be able able to rebound this year. Instead, the table reveals that his strikeout rate (5.0) and walk rate (5.8) are even worse this year than they were in the second half of last season. He has gone from a pitcher with one of the best K/BB ratios in the league to one who walks more than he strikes out. I can't say whether he still has a health problem and I don't know a lot about pitching mechanics but the trend is troubling.

Table: Bonderman's stats: 2004-2008

Year	IP	K/9 IP	BB/9 IP	HR/ 9 IP	ERA
2004	184	8.2	3.6	1.2	4.89
2005	189	6.9	2.7	1.0	4.57
2006	214	8.5	2.7	0.8	4.08
2007 (through July 24)	127	8.1	1.8	1.1	3.69
2007 (July 25 - end)	48	6.0	4.2	1.5	8.50
2008	45	5.0	5.8	1.4	4.80

Pitching to the score

You often hear pitchers talk about how they pitch to the score. That is, they do their best pitching when the game is close and let up somewhat when their team gives them a big lead. Jack Morris, for example, has said on a number of occasions that the reason for his relatively high career ERA (3.90) was that he pitched to the score. He says he was more interested in getting wins and pitching deep into games than he was about his ERA and that he would give up a lot of his runs in games where his team had built up a big lead. I don't doubt that pitchers pitch differently according to the situation but how much does it affect their actual performance?

A few years ago, Joe Sheehan at Baseball Prospectus examined Morris' claim in "The Jack Morris Project". He tracked every inning of Morris' career and looked at his performance in different game situations - ties, one run leads, six run leads, etc. Joe did not find that Morris pitched much better in close games than he did with big leads. He did however, admit that he was not really sure what a pitcher's performance would look like if he actually did pitch to the score.

My goal here is to expand upon Sheehan's project by looking at a large group of pitchers rather than just one. From that, I should be able to get a better idea of what pitching to the score looks like and which pitchers have such a tendency. An example of how the analysis works is this: If a starting pitcher begins an inning with a two run lead, his runs allowed during that inning go into the "up by two runs" category. Using the retrosheet databases, I looked at every inning pitched by every starting pitcher between 1990 and 2007 (excluding 1999 because the data were not available that year). The results are shown in Table 1 below.

Table 1: MLB ERA by Score - 1990-2007 (excluding 1999)

	Total	Up 7+	Up 6	Up 5	Up 4	Up 3	Up 2	Up 1
IP	460,868	12,270	7,770	12,361	19,151	29,278	43,883	65,792
ERA	4.45	4.57	4.60	4.73	4.49	4.51	4.31	4.33

	Tied	Down 1	Down 2	Down 3	Down 4	Down 5+
IP	165,728	49,910	29,090	15,201	6,822	3,612
ERA	4.39	4.37	4.63	4.89	4.77	5.69

You can see that the overall ERA for starting pitchers was 4.45 and you should also notice that the lowest ERAs do indeed come in innings where the margin is small: 4.39 in tie games, 4.33 when up by one run, 4.37 when down by one run, etc. The worst ERAs occur in innings where the pitchers are behind by a significant margin: 4.89 when down by three, 4.77 when down by four, etc. That should be expected though because a pitcher who is behind by a lot of runs must not have been pitching well prior to the inning and would tend to continue to struggle going forward. More interestingly, the ERAs in innings where pitchers had big leads were also relatively high: 4.73 with five run leads, 4.60 with six run leads, etc.

Table Two presents annual data comparing the performance of pitchers in close games (tied, down by one, up by one) and blow outs (leading by five or more runs). I computed a ratio as follows:

(ERA in close games/ERA in blow outs) X 100

A ratio of 100 would mean that pitchers performed the same in innings where the margin was small as in innings where they had big leads. A ratio under 100 would indicate that they did better with when the score was close, whereas a ratio of over 100 would indicate a better ERA in innings beginning with a large lead.


Table 2: MLB ERA - Up by five runs versus within one run

year	total ip	total ERA	IP up 5+ runs	ERA up 5+ runs	IP within 1 run	ERA within run	ratio
Total	460,868	4.45	32,401	4.64	281,430	4.37	94
1990	25,521	3.97	1,718	3.99	15,783	3.99	100
1991	25,587	4.02	1,775	4.20	16,013	3.97	94
1992	26,098	3.85	1,528	4.18	16,643	3.75	90
1993	27,730	4.26	1,840	4.56	17,075	4.20	92
1994	19,472	4.55	1,506	4.53	11,699	4.49	99
1995	23,907	4.53	1,783	4.87