The historic start of the club residing on Chicago’s north side has obscured some pretty amazing things going on at US Cellular Field, as the White Sox have raced out to the best record in the American League. Hopes weren’t all that high entering the season, with the club’s only spring-training noise emanating from the aftershocks of Drake LaRoche-Gate.

A month-plus in, however, the poor-fielding and weak-hitting Chisox of 2015 are a distant memory. A fine starting staff, led by perennial Cy Young candidate Chris Sale and his wingmen Jose Quintana, Carlos Rodon and Mat Latos, are thrilled to find that most of the batted balls they allow are finding leather this time around.

About those batted balls: much is being made of the fact that Chris Sale is posting the best, small-sample traditional numbers of his career while pitching to much more contact than in the recent past. Today, let’s dig inside the numbers a little bit to see whether Sale is, in fact, new and improved.

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Major League Baseball has taken steps toward becoming a truly global game in recent years. Cuban players have joined their Venezuelan, Dominican and other Latin American counterparts in making a significant impact on today’s game, and talent from the Far East, particularly from the Japanese and Korean Leagues, has made its presence felt as well.

This year’s most heralded Japanese rookie is Kenta Maeda, who signed a long-term deal with the Dodgers this past offseason. After concerns were raised following a medical examination, he signed a deal that was heavily discounted from the originally negotiated terms, paying him $25 million over an eight-year period. This put the Dodgers in a fantastic position: a low-risk, potentially high-reward scenario. One month in, the Dodgers simply have to be thrilled as Maeda’s posted a 3-1, 1.41 mark with a 28/6 strikeout-to-walk ratio in 32 innings.

Sure, the season remains young, and the sample sizes are small, but it’s not too early to form some early hypotheses regarding whether Maeda is for real. Today, let’s use granular batted-ball data, examining his plate-appearance frequency and production by BIP type data, to see how Maeda is getting it done, and whether we can expect his success to continue moving forward. Read the rest of this entry »

It’s almost time to rip the first page from the regular-season calendar, and many players and moments have already left indelible marks that will live on in our memories. From Trevor Story to Kenta Maeda, from the Cubs and Nationals on the good end to the Twins and Astros on the bad, it’s been an exciting ride thus far.

There are a number of dominant pitching performances already in the books, with Jake Arrieta’s second no-hitter in as many years an obvious highlight. Just a week before his vanquishing of the Reds, the Phils’ Vincent Velasquez and the Cards’ Jaime Garcia unfurled identical game scores of 97 in complete game victories over the Padres and Brewers, respectively. Since it’s still early in the season, and sample sizes remain quite small, let’s use batted-ball data in a more laid-back, fun manner, and attempt to split some hairs among these three gems, and crown one as April’s most impressive pitching performance.

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Over the last few weeks in this space, we’ve conducted ball-in-play based analyses of position players’ and starting and relief pitchers’ 2015 performance. Last time, we considered AL relievers. Today we’ll present the last installment of this series, focusing on NL relief pitchers. It’s admittedly a little dicey to evaluate relief pitchers in this manner. The sample sizes are much smaller, and filled with more noise. Still, it’s a worthwhile exercise that can show us the different ways in which closers, set-up men, et al, get it done.

First, some background on the process. I identified the 214 relief pitchers from both leagues who yielded the most batted balls in 2015, making sure that all team save leaders were included in the sample. From that group, I selected 28 pitchers from each league for further scrutiny. Pitchers are listed with their 2015 league mates; those who were traded during the season will appear in the league in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Relief Pitcher BIP Profiles

	AVG MPH	FB/LD MPH	GB MPH	POP%	FLY %	LD%	GB%	ADJ C	K%	BB%	ERA-	FIP-	TRU-
Jansen	88.07	91.64	86.56	7.4%	46.3%	11.1%	35.2%	85	40.0%	4.0%	64	56	43
Kimbrel	89.42	92.05	86.82	3.9%	30.5%	19.5%	46.1%	62	36.4%	9.2%	69	70	45
A.Chapman	83.53	86.39	79.65	8.1%	33.0%	21.8%	37.1%	83	41.7%	11.9%	41	49	51
Storen	87.16	90.47	84.11	5.1%	32.6%	23.9%	38.4%	75	29.4%	7.0%	87	73	58
AJ.Ramos	86.80	90.72	82.82	4.4%	35.9%	16.4%	43.4%	76	31.4%	9.4%	59	80	60
W.Smith	88.72	91.15	88.36	1.4%	37.5%	15.3%	45.8%	91	34.5%	9.1%	67	61	63
Romo	84.14	90.03	80.39	4.1%	27.6%	23.4%	44.8%	95	30.9%	4.4%	83	53	64
Strop	89.71	90.49	89.35	4.6%	24.3%	19.7%	51.3%	74	30.0%	10.7%	74	81	64
Kelley	88.13	92.83	85.75	4.9%	33.0%	19.4%	42.7%	88	30.7%	7.3%	66	67	66
Melancon	87.99	92.05	85.13	3.3%	19.3%	19.8%	57.5%	73	21.2%	4.8%	59	75	66
Benoit	83.22	90.00	77.46	4.3%	32.1%	17.3%	46.3%	69	24.8%	9.1%	63	98	66
Dyson	88.41	91.73	87.26	1.9%	12.5%	16.8%	68.8%	73	23.0%	6.8%	66	76	67
Familia	86.23	90.18	85.06	2.5%	19.1%	20.1%	58.3%	90	27.9%	6.2%	50	71	69
R.Delgado	84.95	89.42	82.25	5.7%	35.0%	18.0%	41.2%	67	23.7%	10.7%	80	97	69
H.Rondon	87.60	89.05	85.96	1.6%	25.6%	20.4%	52.4%	84	24.6%	5.3%	43	69	70
Maurer	84.09	88.76	78.90	4.7%	25.5%	22.1%	47.7%	68	18.9%	7.3%	81	86	70
Fr.Rodriguez	85.64	89.08	82.26	2.1%	27.9%	23.6%	46.4%	97	28.7%	5.1%	55	72	71
Grilli	87.98	91.56	82.00	5.9%	41.2%	25.9%	27.1%	104	32.1%	7.1%	76	57	73
Rosenthal	87.76	91.57	87.65	4.5%	30.5%	19.2%	45.8%	93	28.9%	8.7%	55	63	74
Ziegler	88.89	89.20	88.51	0.5%	13.1%	13.6%	72.8%	65	13.7%	6.5%	45	89	75
Papelbon	88.57	90.98	89.24	2.8%	32.2%	15.3%	49.7%	95	21.5%	4.6%	54	95	81
Giles	88.55	90.67	87.58	2.2%	31.1%	21.9%	44.8%	107	29.2%	8.4%	46	54	82
Casilla	86.68	92.23	81.26	2.6%	27.1%	23.9%	46.5%	102	25.4%	9.4%	77	100	89
Nicasio	85.93	89.70	82.34	2.5%	29.3%	24.8%	43.3%	94	25.0%	12.3%	103	74	90
Jeffress	86.72	89.96	84.98	0.0%	18.0%	23.8%	58.2%	109	23.5%	7.7%	65	80	95
Cishek	86.47	90.89	83.03	0.6%	31.5%	21.8%	46.1%	98	19.8%	11.1%	92	102	103
Axford	91.65	93.10	91.09	1.3%	25.8%	16.8%	56.1%	120	24.8%	12.8%	92	85	112

First, a little background. The larger group of 214 relievers had a cumulative strikeout rate of 22.2% and walk rate of 8.2%. Both rates are higher than the comparable marks for starters (19.8% and 7.0%, respectively). The larger group of relievers also conceded less authoritative contact than starters, allowing lesser overall (88.02 mph for relievers, 88.46 mph for starters), FLY/LD (91.24 vs. 91.78) and grounder (85.76 vs. 86.30) authority. With regard to BIP frequency, relievers outpaced starters in the key grounder-rate category by 45.6% to 45.2%, and matched them in pop-up rate (3.2%).

The subset of relievers listed above generally represents the cream of the relief crop. Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitcher’s Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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Over the last few weeks in this space, we’ve conducted a ball-in-play based analysis of position players’ and starting pitchers’ 2015 performance, the most recent post featuring an examination of starting pitchers in the AL West. Next, we’ll take a similar look at relief pitchers. It’s admittedly a little dicey to evaluate relief pitchers in this manner. The sample sizes are much smaller, and filled with more noise. Still, it’s a worthwhile exercise that can show us the different manners in which closers, set-up men, et al, get it done.

First, some background on the process. I identified the 214 relief pitchers from both leagues who yielded the most batted balls in 2015, making sure that all team save leaders were included in the sample. From that group, I selected 28 pitchers from each league for further scrutiny. Pitchers are listed with their 2015 league mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

First, a little background. The larger group of 214 relievers had a cumulative strikeout rate of 22.2% and walk rate of 8.2%. Both rates are higher than the comparable marks for starters (19.8% and 7.0%, respectively). The larger group of relievers also conceded less authoritative contact than starters, allowing lesser overall (88.02 mph for relievers, 88.46 mph for starters), FLY/LD (91.24 vs. 91.78) and grounder (85.76 vs. 86.30) authority. With regard to BIP frequency, relievers outpaced starters in the key grounder rate category by 45.6% to 45.2%, and matched them in pop-up rate (3.2%).

The subset of relievers listed above generally represents the cream of the relief crop. Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitcher’s Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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With just over a week of the regular season in the books, it’s high time we concluded our division-by-division, ball-in-play-based analysis of 2015 starting-pitcher performance. Last time, we considered the AL Central. Today, it’s the AL West.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Starting Pitcher BIP Profiles – AL West

	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	ADJ C	K %	BB %	ERA –	FIP –	TRU –
Keuchel	85.78	90.55	83.52	2.4%	17.2%	18.7%	61.7%	83	23.7%	5.6%	62	73	69
McHugh	86.16	89.25	85.12	3.9%	30.7%	20.0%	45.4%	85	19.9%	6.2%	97	89	78
F.Hernandez	88.81	92.10	87.70	2.0%	24.9%	16.9%	56.2%	92	23.1%	7.0%	88	95	79
Gray	88.85	91.89	87.55	2.5%	28.2%	16.6%	52.7%	86	20.3%	7.1%	68	86	80
Iwakuma	88.71	91.87	87.20	2.1%	29.0%	18.5%	50.4%	100	21.5%	4.1%	88	93	82
McCullers	89.16	92.62	85.87	3.0%	28.8%	21.8%	46.5%	99	24.8%	8.3%	80	81	83
Richards	87.48	92.35	85.20	2.9%	25.1%	17.1%	54.9%	88	20.4%	8.8%	91	96	85
Weaver	86.82	91.59	82.47	6.0%	40.5%	19.0%	34.4%	86	13.5%	4.9%	116	120	89
Shoemaker	87.37	91.81	83.55	3.9%	38.5%	18.5%	39.2%	101	20.4%	6.2%	111	114	90
Happ	89.72	91.82	89.71	4.1%	30.0%	24.3%	41.6%	104	21.1%	6.3%	90	85	91
Hahn	86.56	90.70	84.23	1.3%	21.6%	24.5%	52.6%	92	15.8%	6.2%	84	88	92
Kazmir	87.67	92.37	84.43	2.6%	34.7%	19.8%	42.9%	101	20.3%	7.7%	77	99	93
Santiago	89.38	92.96	85.73	5.9%	47.7%	16.5%	29.9%	99	20.9%	9.2%	90	119	94
Elias	88.40	91.68	86.41	3.3%	33.1%	19.4%	44.2%	98	19.8%	9.0%	103	113	94
T.Walker	90.60	92.96	88.48	3.9%	35.1%	22.4%	38.6%	115	22.2%	5.7%	114	101	95
CJ.Wilson	90.07	92.52	88.70	3.4%	31.6%	21.9%	43.1%	103	19.9%	8.3%	97	100	96
J.Chavez	89.21	93.11	85.85	5.4%	28.6%	22.9%	43.1%	110	20.2%	7.1%	104	96	99
Gallardo	88.53	89.92	87.69	2.4%	26.3%	22.0%	49.3%	96	15.3%	8.6%	85	100	102
C.Lewis	89.47	92.22	86.47	3.5%	40.8%	22.0%	33.7%	111	16.5%	4.9%	116	104	104
Feldman	88.34	90.80	87.63	1.7%	25.8%	23.6%	48.9%	101	13.5%	6.0%	97	108	105
Heaney	89.95	93.39	86.67	4.0%	35.5%	22.2%	38.3%	117	17.8%	6.4%	87	93	109
Graveman	90.07	93.10	87.59	1.3%	27.3%	21.4%	50.0%	115	15.3%	7.6%	101	115	116
N.Martinez	89.04	91.44	87.38	3.6%	30.1%	24.0%	42.3%	109	13.8%	8.2%	99	124	116
AVERAGE	88.53	91.87	86.31	3.3%	30.9%	20.6%	45.2%	100	19.1%	6.9%	93	100	93

Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitchers’ Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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The 2016 season is in its early stages, though sample sizes obviously remain way too small to take very seriously. So let’s just sit back and enjoy the 2016 games for now, and continue our ball-in-play-based analysis of 2015 starting pitcher performance. Two more to go. Last time, we looked at the AL East. Today, the AL Central is on tap.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Starting Pitcher BIP Profiles – AL Central

	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	ADJ C	K %	BB %	ERA –	FIP –	TRU –
Sale	85.93	89.75	81.38	3.5%	31.8%	22.1%	42.6%	97	32.1%	4.9%	85	68	61
Carrasco	88.70	92.53	86.33	2.0%	27.8%	19.0%	51.2%	98	29.6%	5.9%	91	71	69
Verlander	87.42	89.65	87.67	6.3%	39.2%	19.9%	34.6%	77	21.1%	6.0%	84	87	69
Kluber	88.05	91.96	84.76	2.8%	33.1%	21.7%	42.4%	105	27.7%	5.1%	87	74	75
Price	87.95	90.96	85.42	4.0%	32.5%	23.1%	40.4%	98	25.3%	5.3%	61	69	76
Salazar	89.76	92.12	87.89	2.2%	35.2%	18.7%	43.9%	102	25.8%	7.0%	86	90	81
C.Young	87.62	90.88	85.31	8.6%	49.3%	16.6%	25.5%	85	16.6%	8.6%	76	113	82
T.May	88.41	90.14	87.90	4.0%	35.6%	21.4%	39.0%	100	22.4%	5.3%	100	81	83
Milone	87.23	91.32	84.67	5.5%	29.9%	23.0%	41.6%	84	16.8%	6.6%	98	107	84
Quintana	88.10	91.28	86.50	1.9%	27.8%	23.2%	47.1%	100	20.5%	5.1%	84	79	86
An.Sanchez	87.65	90.75	85.37	5.0%	34.1%	21.0%	40.0%	98	20.9%	7.4%	124	118	89
K.Gibson	88.70	93.34	86.39	2.4%	24.4%	19.8%	53.4%	92	17.7%	7.9%	96	99	91
Samardzija	87.98	90.32	87.00	4.0%	35.8%	21.2%	39.0%	102	17.9%	5.4%	124	105	94
Danks	87.01	90.87	83.60	4.4%	36.3%	21.1%	38.2%	92	16.2%	7.3%	117	112	94
Ventura	89.81	93.03	86.97	2.1%	25.1%	20.6%	52.2%	109	22.5%	8.4%	102	89	96
Bauer	88.53	91.45	87.42	5.6%	35.1%	20.1%	39.2%	106	22.9%	10.6%	113	108	97
Volquez	87.96	90.60	86.38	1.6%	31.3%	21.1%	46.0%	100	18.2%	8.5%	89	95	99
Rodon	89.00	91.92	87.77	2.4%	27.4%	23.4%	46.8%	114	22.9%	11.7%	94	97	105
E.Santana	90.42	93.12	88.94	5.5%	32.1%	21.5%	40.9%	111	17.9%	7.9%	100	104	108
Pelfrey	88.37	91.68	87.35	1.8%	24.6%	22.6%	51.0%	102	12.0%	6.3%	106	100	109
Duffy	89.32	91.75	89.42	6.5%	30.0%	24.7%	38.8%	111	17.4%	9.0%	102	110	112
Hughes	90.27	91.99	88.99	4.5%	35.9%	24.2%	35.3%	133	14.4%	2.5%	110	117	124
Simon	90.55	94.43	87.21	4.1%	30.6%	21.7%	43.6%	123	14.3%	8.3%	126	119	129
Guthrie	89.09	90.88	87.84	3.3%	36.5%	25.8%	34.4%	126	12.7%	6.6%	148	140	132
AVERAGE	88.49	91.53	86.60	3.9%	32.6%	21.6%	42.0%	103	20.2%	7.0%	100	98	94

Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitchers’ Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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Teams are setting their Opening Day rosters, another page is about to flipped on the wall calendar, heralding the dawn of the regular season. Never mind those pesky Opening Day temperature forecasts of sub-40 degrees in my neck of the woods. Today, we’ll open the second half of our ball-in-play-based analysis of 2015 starting pitcher performance. Most recently, we examined the NL West. We begin our look at the junior circuit with the AL East.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Starting Pitcher BIP Profiles – AL East

	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	CON	K %	BB %	ERA –	FIP –	TRU –
M.Estrada	88.57	91.95	84.82	6.1%	46.2%	15.5%	32.2%	74	18.1%	7.6%	78	110	75
Pineda	88.14	92.07	86.03	2.7%	27.2%	21.9%	48.2%	103	23.4%	3.1%	109	83	79
Archer	90.10	92.00	89.29	2.6%	31.3%	20.0%	46.1%	112	29.0%	7.6%	81	72	81
Warren	88.06	90.97	85.42	3.2%	28.8%	22.8%	45.2%	87	19.5%	7.3%	82	90	82
Gausman	87.43	91.26	85.45	4.5%	33.6%	17.1%	44.7%	97	21.9%	6.2%	106	102	83
E.Ramirez	89.30	92.38	87.25	4.1%	27.7%	20.4%	47.8%	94	18.9%	6.0%	94	94	87
Tanaka	89.86	93.21	87.21	3.2%	30.6%	19.2%	47.0%	111	22.8%	4.4%	88	99	89
W-Y.Chen	87.61	91.74	86.37	5.5%	33.9%	20.1%	40.5%	100	19.3%	5.2%	83	104	89
Karns	89.20	92.03	87.87	3.4%	33.1%	21.6%	41.9%	104	23.4%	9.0%	92	102	92
Odorizzi	88.82	92.35	85.90	4.3%	36.4%	22.0%	37.3%	106	21.4%	6.6%	84	90	92
Sabathia	87.86	90.45	86.08	3.1%	29.3%	21.7%	45.9%	99	18.9%	6.9%	118	117	93
E.Rodriguez	87.58	91.39	85.74	4.8%	28.6%	23.5%	43.0%	98	18.8%	7.1%	96	98	93
Miley	87.52	92.13	84.23	2.1%	28.3%	20.8%	48.8%	96	17.7%	7.7%	111	95	95
U.Jimenez	88.07	91.63	86.54	3.5%	25.3%	22.1%	49.1%	104	21.2%	8.6%	102	100	95
Dickey	88.08	91.72	85.02	5.1%	32.2%	20.8%	41.9%	93	14.3%	6.9%	98	112	97
Porcello	88.99	92.36	86.53	1.7%	30.8%	21.8%	45.7%	118	20.2%	5.2%	123	103	101
Buehrle	87.23	92.50	83.94	2.8%	29.9%	21.4%	45.9%	97	11.0%	4.0%	95	106	102
Eovaldi	88.28	90.86	86.79	1.8%	24.2%	21.8%	52.2%	107	18.0%	7.3%	105	85	103
J.Kelly	90.01	91.89	89.68	1.7%	27.7%	25.1%	45.6%	108	18.8%	8.4%	120	104	103
M.Gonzalez	89.56	92.76	86.50	3.4%	32.4%	23.9%	40.3%	107	17.5%	8.2%	122	125	106
Tillman	90.33	91.92	90.16	4.8%	30.5%	21.2%	43.5%	102	16.2%	8.6%	124	111	106
Hutchison	88.39	92.36	85.36	4.4%	32.0%	24.0%	39.6%	123	19.4%	6.6%	139	110	111
AVERAGE	88.59	91.91	86.46	3.6%	30.9%	21.3%	44.2%	102	19.5%	6.8%	102	101	93

Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitchers’ Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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The NCAA Final Four is set, and we’re inside a week until baseball games actually start to mean something. Today, we’ll reach the halfway point of our ball-in-play-based analysis of 2015 starting pitcher performance. Yesterday, it was the NL Central. Now, the NL West.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Starting Pitcher BIP Profiles – NL West

Name	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	ADJ C	K %	BB %	ERA –	FIP –	TRU –
Kershaw	84.91	89.47	83.07	2.7%	25.5%	21.8%	50.0%	88	33.8%	4.7%	55	51	56
Greinke	87.78	91.04	86.02	3.1%	29.8%	19.1%	48.0%	76	23.7%	4.7%	43	71	64
Bumgarner	87.46	90.80	85.46	4.3%	31.3%	22.7%	41.7%	92	26.9%	4.5%	75	74	70
T.Ross	87.79	90.13	86.55	2.0%	17.9%	18.6%	61.5%	79	25.8%	10.2%	84	76	73
Ch.Anderson	88.52	91.59	87.00	3.6%	30.8%	23.6%	42.0%	94	17.3%	6.3%	110	106	94
Bettis	88.06	92.09	85.67	1.4%	27.1%	22.2%	49.3%	95	19.5%	8.4%	108	99	95
R.Ray	90.50	91.77	90.24	2.2%	32.4%	22.2%	43.3%	105	21.8%	9.0%	90	91	99
Heston	89.25	92.84	86.64	2.5%	23.5%	21.0%	53.0%	99	18.9%	8.6%	101	103	100
Shields	89.69	93.14	86.52	3.5%	30.8%	20.8%	44.9%	117	25.1%	9.4%	100	114	101
Cashner	88.79	92.08	87.09	2.7%	27.2%	22.7%	47.4%	106	20.5%	8.2%	111	99	101
Kennedy	89.73	92.44	87.13	3.0%	35.7%	22.8%	38.5%	121	24.4%	7.3%	110	116	101
B.Anderson	88.98	93.65	86.70	0.4%	18.1%	15.2%	66.3%	98	15.5%	6.1%	95	101	102
Bolsinger	88.41	91.70	86.79	1.3%	27.8%	17.8%	53.1%	105	21.0%	9.7%	93	100	102
De La Rosa, J.	86.07	90.84	83.67	1.2%	26.1%	20.7%	52.0%	104	21.1%	10.2%	107	107	103
Despaigne	87.41	90.40	85.69	1.7%	25.4%	22.4%	50.5%	96	12.6%	5.9%	149	122	105
De La Rosa, R.	89.13	90.55	88.30	2.4%	30.4%	18.1%	49.1%	107	18.5%	7.8%	120	123	106
Hellickson	90.14	93.64	87.19	1.5%	35.0%	21.1%	42.4%	112	19.0%	6.8%	118	114	107
Vogelsong	88.34	92.78	85.48	2.1%	34.0%	19.2%	44.7%	104	18.1%	9.7%	120	116	109
Rusin	88.60	92.64	85.62	2.7%	24.5%	20.8%	52.1%	109	14.5%	6.9%	137	121	116
Collmenter	86.12	91.84	79.40	5.2%	34.8%	25.6%	34.5%	121	12.6%	4.8%	97	119	128
Kendrick	89.70	93.37	86.46	2.7%	36.5%	22.0%	38.8%	127	12.7%	7.2%	162	157	140
AVERAGE	88.35	91.85	86.03	2.5%	28.8%	21.0%	47.8%	103	20.2%	7.4%	104	104	99

Most of the column headers are self explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitchers’ Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

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Over the last few weeks in this space, we took a position-by-position look at the ball-in-play (BIP) profiles of 2015 regulars and semi-regulars to gain some insight into their potential performance moving forward. As I wrote the following, snow fell outside my window in blatant disregard for the dawn of baseball season. Regardless, we continue our similar BIP-centric analysis of qualifying 2015 starting pitchers, division by division. We began with NL East starters. Today’s second installment focuses on the NL Central.

First, some ground rules. To come up with an overall player population roughly equal to one starting rotation per team, the minimum number of batted balls allowed with Statcast readings was set at 243. Pitchers are listed with their 2015 division mates; those who were traded during the season will appear in the division in which they compiled the most innings. Pitchers are listed in “tru” ERA order. For those who have not read my previous articles on the topic, “tru” ERA is the ERA pitchers “should” have compiled based on the actual BIP frequency and authority they allowed relative to the league. Here we go:

Starting Pitcher BIP Profiles – NL Central

Name	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	ADJ C	K %	BB %	ERA –	FIP –	TRU –
Arrieta	84.89	88.56	82.79	2.1%	20.7%	21.0%	56.2%	73	27.1%	5.5%	45	60	58
Lester	87.44	91.15	85.87	2.5%	26.8%	21.8%	48.9%	91	25.0%	5.7%	86	75	76
J.Garcia	87.88	92.01	85.92	1.1%	21.2%	16.5%	61.2%	81	19.0%	5.9%	62	77	79
Hendricks	88.24	91.05	87.22	2.4%	24.5%	21.8%	51.3%	91	22.6%	5.8%	101	86	80
G.Cole	89.08	91.69	86.86	1.8%	27.8%	22.4%	48.0%	99	24.3%	5.3%	67	68	81
C.Martinez	87.63	91.79	85.99	1.7%	23.7%	20.1%	54.5%	91	24.4%	8.3%	77	82	81
F.Liriano	86.36	90.48	84.08	2.5%	23.9%	22.4%	51.2%	99	26.5%	9.1%	87	82	84
Fiers	88.51	91.60	85.65	5.3%	36.8%	20.3%	37.6%	96	23.7%	8.4%	95	103	87
Lackey	88.59	90.95	88.04	3.9%	29.5%	20.6%	46.0%	96	19.5%	5.9%	71	92	91
Hammel	89.02	92.20	85.68	1.5%	35.7%	24.5%	38.3%	112	24.2%	5.6%	96	94	92
Haren	88.54	91.85	86.51	5.4%	43.8%	20.2%	30.6%	94	17.2%	5.0%	92	118	92
Wacha	87.48	91.62	85.94	3.6%	28.4%	22.2%	45.8%	95	20.1%	7.6%	87	99	92
Cueto	87.27	90.32	85.58	4.3%	31.3%	21.8%	42.5%	105	20.3%	5.3%	88	91	95
DeSclafani	89.10	92.07	87.39	3.4%	30.3%	21.2%	45.1%	101	19.2%	7.0%	104	94	97
Locke	87.21	90.70	85.61	1.5%	23.4%	24.1%	51.0%	96	17.5%	8.2%	115	101	100
Lynn	88.96	91.86	88.72	3.2%	31.0%	21.6%	44.2%	107	22.2%	9.1%	78	88	100
J.Nelson	86.62	90.74	84.48	3.1%	26.3%	20.0%	50.6%	102	19.7%	8.6%	105	105	101
Burnett	90.48	94.10	88.84	2.0%	22.1%	22.5%	53.4%	113	20.5%	7.0%	82	86	105
Jungmann	87.64	91.94	84.67	2.4%	30.7%	20.6%	46.3%	110	21.4%	9.4%	97	101	105
Leake	89.64	92.98	87.23	2.2%	24.4%	21.6%	51.8%	111	15.3%	6.3%	95	108	114
Morton	89.99	94.09	87.72	2.0%	19.5%	21.2%	57.3%	102	17.1%	7.3%	123	107	117
Garza	88.27	91.05	87.61	4.3%	28.6%	22.1%	45.0%	111	15.6%	8.6%	144	127	120
Lohse	88.20	92.44	84.26	3.0%	35.2%	23.3%	38.6%	122	16.2%	6.5%	150	131	123
W.Peralta	89.79	93.96	87.11	1.9%	26.5%	19.9%	51.6%	122	12.6%	7.7%	121	124	136
Lorenzen	88.78	91.11	86.55	1.9%	29.3%	28.2%	40.5%	128	16.1%	11.1%	138	138	139
AVERAGE	88.22	91.69	86.25	2.8%	28.1%	21.7%	47.5%	102	20.3%	7.2%	96	98	98

Most of the column headers are self-explanatory, including average BIP speed (overall and by BIP type), BIP type frequency, K and BB rates, and traditional ERA-, FIP-, and “tru” ERA-. Each pitchers’ Adjusted Contact Score (ADJ C) is also listed. Again, for those of you who have not read my articles on the topic, Unadjusted Contact Score is derived by removing Ks and BBs from opposing hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100. Adjusted Contact Score applies league-average production to each pitchers’ individual actual BIP type and velocity mix, and compares it to league average of 100.

Read the rest of this entry »