Opening Day lies just beyond the horizon, though the weather forecasts in many parts of the country don’t seem to want to pay attention. 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. Next, we’re going to take a similar approach with regard to starting pitchers, division by division. We’ll begin today with the NL 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 will be 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 East

Name	AVG MPH	FB/LD MPH	GB MPH	POP %	FLY %	LD %	GB %	ADJ C	K %	BB %	ERA –	FIP –	TRU –
DeGrom	87.53	90.60	85.66	3.1%	31.6%	20.9%	44.4%	85	27.3%	5.1%	65	69	66
Scherzer	87.26	90.88	83.85	5.9%	39.5%	18.6%	36.0%	98	30.7%	3.8%	72	71	66
Harvey	87.74	90.35	85.91	3.5%	32.6%	17.9%	46.0%	83	24.9%	4.9%	69	78	68
Syndergaard	86.06	89.08	84.88	3.5%	30.1%	19.9%	46.5%	93	27.5%	5.1%	83	83	70
Strasburg	88.95	92.11	87.48	4.9%	29.4%	23.4%	42.2%	102	29.6%	5.0%	89	72	72
S.Miller	87.18	90.54	85.39	3.0%	31.1%	18.2%	47.7%	76	19.9%	8.5%	77	88	81
Hamels	88.16	91.33	86.30	3.8%	27.6%	20.9%	47.7%	105	24.4%	7.1%	94	89	89
Colon	89.07	92.05	86.77	2.8%	34.1%	20.8%	42.3%	101	16.7%	2.9%	107	98	95
Zimmermann	88.52	91.89	85.82	4.5%	31.8%	21.7%	42.0%	105	19.7%	4.7%	94	96	95
Niese	88.71	92.05	86.65	1.2%	23.5%	20.8%	54.5%	90	14.7%	7.1%	106	113	99
G.Gonzalez	88.58	92.28	85.99	1.2%	25.5%	19.5%	53.8%	105	22.3%	9.1%	97	78	99
Teheran	89.27	92.26	86.76	3.5%	32.8%	24.0%	39.7%	105	20.3%	8.7%	104	113	103
Roark	86.20	91.26	81.99	2.2%	28.4%	21.7%	47.8%	101	15.0%	5.6%	112	121	104
Latos	88.60	93.65	84.06	2.3%	29.6%	24.2%	43.9%	114	20.2%	6.5%	127	95	105
Koehler	89.98	93.58	87.98	2.5%	33.1%	18.4%	46.0%	99	17.1%	9.6%	105	116	107
A.Wood	87.92	91.00	85.76	2.4%	25.1%	23.0%	49.5%	107	17.4%	7.4%	98	95	109
Fister	88.22	91.04	85.89	1.2%	32.9%	21.3%	44.6%	106	14.0%	5.4%	107	117	111
Phelps	89.76	91.64	87.33	3.1%	32.1%	23.0%	41.8%	114	16.0%	6.9%	115	103	117
Harang	90.67	93.16	88.61	5.3%	38.4%	20.2%	36.1%	110	14.4%	6.8%	125	124	118
Wisler	90.66	93.40	86.89	5.9%	37.3%	23.2%	33.6%	118	15.1%	8.4%	121	126	128
J.Williams	89.13	92.57	85.99	2.3%	27.8%	22.8%	47.1%	121	13.4%	6.2%	149	134	129
W.Perez	90.32	93.69	87.96	1.1%	27.7%	20.3%	50.9%	126	14.2%	9.9%	123	125	141
AVERAGE	88.57	91.84	86.09	3.1%	31.0%	21.1%	44.7%	103	19.8%	6.6%	102	100	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.

Read the rest of this entry »

With the Ides Of March upon us, let’s complete our position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. We’ll wrap it up with a look at catchers.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Fewer catchers qualify because of the physical demands of the position; players at other positions generally miss time only due to injury and platooning. Even the best non-Salvador Perez catchers need at least a day off per week to remain fresh.

Also, bear in mind that catchers earn playing time primarily for reasons not covered here today, i.e., defensive skills such as receiving/framing, handling the running game and the pitching staff. What the player brings to the table in those categories determines just how much a club is willing to sacrifice at bat. Today, we’ll look at the building blocks of their offensive games, to see what direction they might be headed in the near term. Let’s begin with a truncated AL field of qualifiers:

Read the rest of this entry »

As we become accustomed to a steady diet of spring-training baseball, let’s continue to take a position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. We’ve already looked at all the various infield positions — plus left field and center. Today, let’s complete our tour of the outfield by focusing on right fielders.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Let’s start with the AL RFs.

Ball-In -Play Overview – AL RF

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
N.Cruz	92.80	97.25	88.71	3.5%	30.5%	20.4%	45.7%	195	25.0%	9.0%	160	38.6%	36.5%	24.8%
J.Bautista	93.83	95.79	92.41	7.0%	41.8%	13.9%	37.3%	121	15.9%	16.5%	149	53.3%	28.1%	18.7%
JD.Martinez	91.86	95.95	85.66	2.1%	41.4%	22.3%	34.2%	181	27.1%	8.1%	140	41.1%	34.7%	24.2%
Springer	89.64	93.75	85.61	1.1%	29.0%	24.5%	45.4%	141	24.2%	11.1%	126	34.2%	38.7%	27.1%
Choo	91.63	95.86	88.52	0.7%	27.8%	20.7%	50.9%	136	22.5%	11.6%	125	44.1%	34.2%	21.7%
Beltran	91.63	93.76	90.03	3.3%	39.6%	21.6%	35.6%	111	16.0%	8.5%	121	44.4%	35.6%	20.1%
Reddick	89.00	91.74	86.82	3.7%	36.9%	21.0%	38.4%	94	11.2%	8.4%	113	35.6%	35.3%	29.1%
C.Young	85.13	89.06	84.18	8.9%	38.1%	17.4%	35.6%	110	20.5%	8.4%	111	58.8%	24.4%	16.8%
Trumbo	92.86	94.22	93.22	5.0%	35.3%	18.0%	41.6%	130	24.2%	6.6%	108	37.1%	32.9%	30.0%
Calhoun	86.87	91.23	81.86	2.5%	32.9%	22.8%	41.8%	115	23.9%	6.6%	104	42.6%	34.3%	23.1%
Souza Jr.	89.16	95.06	84.68	3.6%	31.1%	20.0%	45.3%	140	33.8%	10.8%	99	45.9%	34.2%	19.9%
Moss	89.39	94.48	83.25	3.1%	44.0%	20.4%	32.5%	118	28.1%	9.3%	90	49.4%	30.6%	20.1%
A.Garcia	88.91	92.68	85.77	1.7%	25.0%	24.5%	48.8%	100	23.5%	6.0%	89	36.1%	40.4%	23.6%
T.Hunter	89.09	93.00	85.61	3.4%	30.9%	17.3%	48.4%	90	18.5%	6.2%	89	40.9%	35.9%	23.3%
Chisenhall	87.25	90.14	86.07	8.3%	31.5%	19.5%	40.6%	84	19.1%	6.4%	78	40.2%	33.8%	26.0%
Rios	88.81	89.72	88.57	2.8%	35.2%	22.2%	39.8%	76	16.3%	3.6%	73	42.6%	35.8%	21.6%
AVERAGE	89.87	93.36	86.94	3.8%	34.4%	20.4%	41.4%	121	21.9%	8.6%	111	42.8%	34.1%	23.1%

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 BIP by field sector (pull, central, opposite). Each player’s OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

Read the rest of this entry »

As the first wave of spring-training games begin, let’s continue to take a position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. We’ve already looked at all the various infield positions — and, just yesterday, left field. Only three more to go; today, it’s the center fielders’ turn.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Let’s start with the AL center fielders. Hmmm, I wonder who’s at the top of the list?

BIP Overview – AL CF

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
Trout	93.17	96.67	89.25	1.0%	37.4%	24.4%	37.2%	196	23.2%	13.5%	176	38.2%	33.2%	28.7%
Cain	90.86	92.70	89.12	0.9%	30.4%	23.2%	45.5%	127	16.2%	6.1%	126	33.7%	38.1%	28.2%
Eaton	87.64	89.53	88.72	2.8%	24.5%	22.0%	50.7%	117	19.0%	8.4%	122	29.2%	36.8%	34.0%
Betts	91.16	91.89	91.72	4.5%	37.9%	19.5%	38.2%	108	12.5%	7.0%	118	40.3%	36.5%	23.1%
A.Jones	89.03	93.97	85.92	4.3%	32.0%	17.8%	45.8%	114	17.6%	4.1%	109	44.3%	34.0%	21.7%
R.Davis	87.44	90.83	85.65	6.1%	27.4%	22.4%	44.1%	109	20.5%	5.9%	104	34.9%	34.2%	30.9%
Burns	82.99	85.28	83.06	5.8%	22.3%	21.6%	50.3%	97	14.6%	4.7%	100	29.6%	36.9%	33.5%
Kiermaier	87.65	89.93	88.15	3.9%	25.4%	22.9%	47.8%	98	17.8%	4.5%	98	41.3%	36.0%	22.7%
Pillar	85.35	88.02	86.23	6.5%	30.2%	21.9%	41.4%	88	13.5%	4.5%	96	42.9%	30.2%	26.9%
Hicks	89.32	92.37	86.01	3.6%	31.8%	22.9%	41.8%	91	16.9%	8.7%	95	35.8%	34.4%	29.9%
DeShields	85.81	88.08	84.30	2.0%	31.7%	19.0%	47.4%	94	20.5%	10.8%	95	36.7%	36.7%	26.6%
A.Jackson	89.38	91.33	87.97	0.5%	24.1%	24.3%	51.1%	112	23.9%	5.5%	95	36.0%	37.9%	26.0%
Gose	87.33	89.58	87.93	1.9%	23.3%	20.8%	54.0%	112	27.1%	8.4%	91	31.0%	35.7%	33.3%
Ellsbury	86.86	88.36	87.47	3.2%	27.4%	24.1%	45.3%	80	17.2%	7.0%	84	37.8%	35.1%	27.0%
Marisnick	85.21	90.06	82.11	3.9%	34.5%	19.7%	41.9%	104	28.2%	4.8%	81	42.6%	30.3%	27.1%
Bourn	85.17	87.93	83.85	1.6%	26.4%	24.8%	47.3%	71	22.2%	9.5%	64	33.4%	39.5%	27.1%
AVERAGE	87.77	90.41	86.72	3.3%	29.2%	22.0%	45.6%	107	19.4%	7.1%	103	36.7%	35.3%	27.9%

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 BIP by field sector (pull, central, opposite). Each player’s OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

Read the rest of this entry »

As the calendar mercifully flips to March, it won’t be long until meaningful major league baseball games will be played in a ballpark near you. Meanwhile, let’s continue our series of position-by-position looks at the ball-in-play (BIP) profiles of 2015 regulars and semi-regulars. We’ve already looked at all the various infield positions, so today we’ll begin our outfield review in left field.

First, let’s review some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Let’s begin with the AL left fielders.

BIP Overview – AL LF

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
Cespedes	93.18	96.53	89.46	3.4%	34.5%	20.4%	41.7%	156	20.9%	4.9%	137	43.7%	34.8%	21.5%
Brantley	89.11	91.93	87.35	1.7%	30.0%	22.5%	45.8%	107	8.6%	10.1%	130	42.7%	32.7%	24.6%
Gordon	89.00	91.50	86.61	3.0%	34.6%	24.8%	37.6%	123	21.8%	11.6%	120	45.7%	36.3%	18.0%
S.Smith	89.41	91.86	88.29	0.7%	37.3%	19.7%	42.3%	112	21.9%	10.4%	117	38.7%	34.8%	26.5%
Guyer	86.72	90.73	84.81	6.0%	28.6%	21.2%	44.2%	95	15.8%	6.5%	115	44.7%	32.7%	22.6%
Rasmus	90.24	93.42	84.02	5.1%	46.5%	20.0%	28.4%	159	31.8%	9.7%	113	52.8%	27.0%	20.2%
Gardner	88.22	91.69	86.48	2.1%	31.8%	20.8%	45.3%	101	20.6%	10.4%	105	34.9%	34.5%	30.7%
De Aza	86.63	89.12	83.31	0.9%	36.8%	23.4%	39.0%	118	23.0%	8.5%	104	37.1%	39.2%	23.7%
Dv.Murphy	88.79	90.24	87.98	4.1%	28.4%	16.7%	50.8%	92	12.5%	5.1%	101	38.7%	39.6%	21.7%
E.Rosario	87.90	91.11	83.52	4.8%	35.8%	20.3%	39.1%	129	24.9%	3.2%	99	39.0%	35.8%	25.2%
Tucker	90.55	91.38	90.90	4.8%	31.0%	17.7%	46.6%	106	21.1%	6.2%	99	43.1%	33.6%	23.3%
Me.Cabrera	90.19	91.11	91.38	2.7%	27.2%	23.9%	46.3%	84	12.9%	5.9%	97	36.9%	35.4%	27.7%
H.Ramirez	91.16	95.39	89.24	3.6%	26.0%	20.4%	50.0%	91	16.5%	4.9%	90	37.1%	39.5%	23.4%
DeJesus	87.87	91.16	85.21	3.0%	29.8%	23.5%	43.7%	68	16.4%	6.6%	76	42.0%	33.2%	24.8%
Aviles	87.10	88.20	87.75	4.3%	30.2%	16.3%	49.2%	56	12.0%	6.3%	61	38.0%	36.8%	25.2%
AVERAGE	89.07	91.69	87.09	3.3%	32.6%	20.8%	43.3%	106	18.7%	7.4%	104	41.0%	35.1%	23.9%

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 BIP by field sector (pull, central, opposite). Each player’s OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

Read the rest of this entry »

Camps are open, players either are or aren’t in the best shape of their lives, and everyone’s starting to tire of watching bullpens and PFP. Let’s continue to take a position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. We’ve already looked at first basemen and designated hitters and second baseman and shortstops; today, let’s complete the infield with a look at third basemen.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Without further ado, let’s kick it off with the AL third sackers.

BIP Overview – AL 3B

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
Donaldson	92.43	96.71	88.50	3.6%	34.3%	17.3%	44.8%	156	18.7%	10.3%	155	42.9%	33.5%	23.7%
Valencia	92.90	95.57	90.61	2.6%	27.7%	17.2%	52.4%	149	21.2%	7.7%	134	47.8%	34.0%	18.3%
Machado	92.44	94.03	92.43	5.4%	33.1%	17.8%	43.7%	123	15.6%	9.8%	131	38.1%	36.7%	25.2%
Moustakas	89.85	91.47	90.64	6.3%	35.1%	18.8%	39.9%	103	12.4%	7.0%	120	39.2%	33.4%	27.4%
Seager	89.25	91.82	86.86	4.0%	36.8%	24.0%	35.2%	99	14.3%	7.9%	118	44.4%	31.8%	23.8%
Longoria	90.26	93.98	86.31	4.4%	36.0%	20.6%	39.0%	111	19.7%	7.6%	111	41.9%	32.1%	26.0%
Beltre	89.88	92.95	87.34	2.9%	32.8%	22.7%	41.6%	96	10.5%	6.6%	110	38.8%	38.6%	22.6%
Freese	89.56	94.29	86.28	1.9%	26.2%	17.5%	54.4%	113	22.8%	6.6%	109	38.1%	30.6%	31.3%
Valbuena	89.89	93.98	85.34	4.2%	41.3%	20.3%	34.2%	101	21.5%	10.1%	103	44.7%	29.3%	26.0%
Plouffe	90.81	93.39	88.57	4.9%	35.8%	18.2%	41.1%	101	19.6%	7.9%	99	42.7%	34.6%	22.7%
Castellanos	88.36	90.59	85.37	0.5%	39.9%	23.3%	36.2%	118	25.5%	6.6%	98	36.2%	34.5%	29.3%
Lawrie	90.06	94.39	87.11	1.9%	30.8%	18.5%	48.8%	111	23.9%	4.7%	92	38.1%	36.7%	25.2%
Headley	87.20	90.48	84.31	3.6%	26.8%	26.6%	43.1%	96	21.0%	7.9%	92	44.3%	35.6%	20.0%
Sandoval	89.20	91.95	88.85	4.3%	28.0%	18.8%	48.9%	74	14.5%	5.0%	76	29.8%	39.5%	30.8%
AVERAGE	90.15	93.26	87.75	3.6%	33.2%	20.1%	43.1%	111	18.7%	7.6%	111	40.5%	34.4%	25.2%

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 BIP by field sector (pull, central, opposite). Each player’s OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

Read the rest of this entry »

As we count down the days until spring-training games begin, let’s continue to take a position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. We’ve already looked at first and second baseman; today, let’s go all the way to the “good” end of the defensive spectrum and examine the shortstops.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Without further ado, let’s kick it off with AL shortstops.

BIP Overview – AL Shortstops

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
Correa	90.58	94.66	87.39	2.6%	26.5%	22.4%	48.6%	130	18.1%	9.3%	132	35.5%	34.2%	30.4%
Lindor	89.08	92.05	88.13	3.1%	25.6%	20.6%	50.8%	118	15.8%	6.2%	122	34.6%	36.1%	29.3%
Bogaerts	88.13	90.84	87.53	2.7%	23.1%	21.5%	52.7%	114	15.4%	4.9%	108	33.8%	34.2%	32.1%
Miller	90.71	94.00	89.64	2.6%	28.8%	20.2%	48.4%	99	20.3%	9.5%	106	32.9%	35.5%	31.7%
Cabrera	87.76	91.61	85.25	5.3%	38.3%	20.7%	35.8%	106	19.4%	6.5%	105	47.9%	27.7%	24.4%
E.Escobar	86.06	89.55	82.32	3.1%	35.6%	19.3%	42.0%	107	19.3%	6.3%	102	41.5%	34.2%	24.2%
Iglesias	83.33	87.17	82.95	3.5%	19.7%	21.0%	55.9%	82	9.7%	5.5%	100	34.6%	36.4%	29.0%
Semien	87.56	91.54	82.12	3.3%	35.5%	23.1%	38.1%	106	22.0%	7.0%	95	42.0%	33.3%	24.7%
Gregorius	86.05	88.67	84.75	3.2%	30.9%	21.2%	44.7%	81	14.7%	5.7%	90	38.5%	35.0%	26.5%
Reyes	85.06	85.92	85.83	7.7%	28.2%	20.0%	44.1%	80	11.9%	5.0%	84	42.5%	35.7%	21.8%
Aybar	85.25	87.87	84.72	1.4%	25.0%	21.0%	52.6%	69	11.4%	3.9%	81	40.1%	37.5%	22.4%
Andrus	87.28	89.71	87.36	3.1%	28.7%	21.1%	47.1%	69	11.8%	7.0%	80	43.7%	31.0%	25.2%
A.Ramirez	84.66	87.57	83.34	3.3%	25.8%	21.4%	49.5%	66	10.9%	5.0%	79	38.1%	42.3%	19.5%
J.Ramirez	86.15	87.73	86.63	4.8%	31.4%	16.2%	47.6%	55	11.0%	9.0%	70	44.2%	30.4%	25.4%
A.Escobar	84.11	86.51	83.31	3.0%	26.9%	22.3%	47.8%	61	11.3%	3.9%	68	31.5%	38.3%	30.2%
Hardy	87.94	90.69	86.51	3.4%	29.7%	17.5%	49.4%	64	20.1%	4.6%	53	40.1%	40.4%	19.5%
AVERAGE	86.86	89.76	85.49	3.5%	28.7%	20.6%	47.2%	88	15.2%	6.2%	92	38.8%	35.1%	26.0%

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 BIP by field sector (pull, central, opposite). Each player’s OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

Read the rest of this entry »

As we count down the days until spring-training games begin, let’s continue to take a position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. Last time, we reviewed first basemen and designated hitters; today, let’s take a look at second basemen.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Without further ado, let’s kick it off with AL second sackers.

BIP Overview – AL Second Basemen

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
Forsythe	89.22	92.10	87.23	3.2%	37.4%	19.8%	39.6%	115	18.0%	8.9%	123	39.1%	36.3%	24.6%
Altuve	86.29	90.26	83.92	3.0%	32.2%	18.1%	46.7%	104	9.7%	4.8%	122	45.3%	35.5%	19.1%
Kipnis	89.88	92.58	87.37	1.9%	26.2%	26.8%	45.0%	119	16.7%	8.9%	121	35.3%	36.1%	28.6%
Zobrist	89.05	92.22	86.89	3.6%	28.8%	18.6%	49.0%	93	10.5%	11.6%	120	45.7%	31.7%	22.6%
Cano	90.88	94.74	88.06	1.1%	24.2%	24.2%	50.5%	110	15.9%	6.4%	118	36.3%	41.1%	22.7%
Kinsler	86.38	88.29	84.69	5.2%	35.5%	25.4%	33.9%	98	11.9%	6.4%	113	41.9%	34.1%	24.0%
Pedroia	88.47	91.77	86.27	4.5%	27.3%	17.7%	50.5%	100	12.0%	8.9%	113	40.1%	38.6%	21.3%
Schoop	90.79	94.36	88.89	5.3%	32.4%	19.3%	43.0%	142	24.6%	2.8%	110	43.2%	31.0%	25.8%
Odor	88.44	92.93	86.35	7.6%	32.1%	14.6%	45.8%	105	16.8%	4.9%	107	46.9%	31.6%	21.5%
Dozier	87.51	92.32	80.34	8.7%	35.4%	22.6%	33.3%	104	21.0%	8.7%	101	60.2%	24.2%	15.6%
Holt	86.65	88.85	85.46	1.1%	22.4%	23.8%	52.7%	100	19.1%	9.0%	96	33.4%	40.1%	26.5%
Giavotella	85.62	87.87	83.78	3.0%	27.5%	23.7%	45.8%	75	11.8%	6.4%	96	36.7%	38.6%	24.7%
Goins	87.36	89.16	86.62	3.1%	24.8%	18.0%	54.1%	81	19.4%	9.1%	86	34.1%	36.4%	29.5%
Drew	86.53	88.56	84.51	6.1%	40.5%	15.7%	37.7%	66	16.6%	8.6%	78	47.0%	33.2%	19.8%
Sanchez	86.07	89.15	84.31	2.0%	21.1%	22.8%	54.1%	67	19.3%	4.5%	66	30.8%	36.5%	32.7%
Sogard	84.52	86.71	84.17	3.1%	30.5%	22.0%	44.3%	60	12.5%	5.7%	66	35.6%	39.9%	24.5%
Infante	84.41	86.88	83.75	3.8%	33.9%	21.0%	41.2%	57	15.2%	2.0%	49	43.5%	34.5%	22.0%
AVG	87.53	90.51	85.45	3.9%	30.1%	20.8%	45.1%	94	15.9%	6.9%	99	40.9%	35.3%	23.9%

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 BIP by field sector (pull, central, opposite). Each player’s OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

Read the rest of this entry »

Football is behind us, and large trucks are on their way to Florida and Arizona, bearing loads of baseball-related cargo. To tide us over until spring-training games kick in next month, let’s take a position-by-position look back at the ball-in-play (BIP) profiles of 2015 semi-regulars and regulars to see if we can find any clues as to their projected performance moving forward. Today, we’ll take a look at first basemen and designated hitters.

First, some ground rules. To come up with an overall player population roughly equal to one player per team per position, the minimum number of batted balls with Statcast readings was set at 164. Players were listed at the position at which they played the most games. There is more than one player per team at some positions and less at others, like catcher and DH. Players are listed in descending OPS+ order. Without further ado, let’s kick it off with AL first basemen.

BIP Overview – AL First Basemen

Name	Avg MPH	FB/LD MPH	GB MPH	POP%	FLY%	LD%	GB%	CON	K%	BB%	OPS+	Pull%	Cent%	Opp%
Cabrera	93.8	96.9	91.7	1.1%	31.6%	25.2%	42.1%	163	16.0%	15.1%	170	35.8%	30.7%	33.5%
Davis	92.2	97.1	86.4	1.7%	41.8%	24.7%	31.8%	213	31.0%	12.5%	146	56.0%	26.5%	17.6%
Teixeira	89.9	93.9	86.1	3.8%	38.5%	18.9%	38.8%	134	18.4%	12.8%	146	55.5%	28.9%	15.7%
Colabello	91.1	94.8	88.0	2.5%	24.4%	25.2%	47.9%	193	26.7%	6.1%	142	34.5%	39.1%	26.5%
Abreu	92.0	94.1	90.5	3.4%	28.7%	20.7%	47.3%	146	21.0%	5.8%	135	37.6%	35.9%	26.6%
Hosmer	90.6	94.4	88.6	2.5%	21.9%	23.4%	52.2%	119	16.2%	9.1%	122	36.8%	34.6%	28.7%
Pujols	92.0	93.5	90.8	4.1%	38.1%	15.9%	41.8%	90	10.9%	7.6%	118	45.8%	34.9%	19.3%
Moreland	92.1	96.6	87.9	3.8%	30.8%	19.8%	45.6%	134	21.7%	6.2%	116	44.8%	32.7%	22.5%
Cron	88.8	93.5	84.9	6.7%	30.4%	18.4%	44.5%	110	20.3%	4.2%	106	33.8%	38.8%	27.4%
Gonzalez	89.2	93.7	85.5	4.9%	28.1%	22.7%	44.3%	114	20.0%	4.3%	106	49.1%	34.3%	16.6%
Santana	90.8	93.5	90.1	7.0%	30.1%	18.3%	44.5%	88	18.3%	16.2%	103	53.4%	28.6%	18.0%
Canha	90.4	93.4	88.6	5.7%	34.5%	17.8%	42.0%	104	19.8%	6.8%	102	42.8%	34.5%	22.7%
Carter	92.6	97.3	84.4	4.5%	47.3%	18.4%	29.8%	131	32.8%	12.4%	100	39.6%	36.3%	24.1%
Mauer	89.5	93.8	87.1	0.8%	19.4%	24.1%	55.7%	90	16.8%	10.1%	96	30.5%	37.5%	32.1%
Napoli	89.8	94.6	84.6	4.8%	37.3%	15.5%	42.4%	108	25.2%	12.2%	96	39.3%	35.9%	24.8%
Morrison	91.1	92.6	90.8	4.0%	35.0%	16.3%	44.7%	75	15.9%	9.2%	92	41.7%	34.3%	24.0%
Loney	85.9	87.0	86.2	2.1%	30.9%	24.2%	42.7%	73	8.8%	5.9%	90	38.2%	33.6%	28.2%
AVG	90.7	94.2	87.8	3.7%	32.3%	20.6%	43.4%	123	20.0%	9.2%	117	42.1%	33.9%	24.0%

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 BIP by field sector (pull, central, opposite). Each players’ OPS and Unadjusted Contact Score (CON) is also listed. For those of you who have not read my articles on the topic, Contact Score is derived by removing Ks and BBs from hitters’ batting lines, assigning run values to all other events, and comparing them to a league average of 100.

Read the rest of this entry »

Success is often best measured relative to expectations. I am a lifelong Philadelphia Eagles fan. Rich Kotite is one of the few coaches in Eagle history to finish with a career record over .500; Chip Kelly is another. I watched Kotite coach; he very well might have been the single worst head coach, in any sport, whom I have ever had the pleasure to watch. While most coaches are hired to take over foundering or rebuilding clubs, Kotite had taken over Buddy Ryan’s exceedingly young and talented club, coming off of three consecutive playoff appearances. He torched it in record time, then had a dire run with the Jets.

In an offbeat kind of way, Stephen Strasburg is a baseball equivalent of Rich Kotite. Though he has compiled a 54-37 record and 3.09 ERA — and produced a scintillating 901/192 strikeout-to-walk ratio (K/BB) in 776.2 career innings — most would agree that he has failed to accomplish as much as expectations would have suggested. Kotite went nowhere with a young, three-time playoff Eagles’ team that he inherited from the fired Buddy Ryan; Strasburg, meanwhile, has only received Cy Young Award votes in one season, finishing ninth in the 2014 balloting, to cherry-pick one piece of data.

Well, I’m here to tell you that this is quite likely the year that Strasburg’s perfect storm could engulf the National League. And the timing would be quite fortuitous, given the amount of cash a fully actualized Strasburg could command on the free market, as he enters free agency following the 2016 season.

Read the rest of this entry »