Spartans Baseball

Previewing Day 2 of the Big Ten Conference Tournament

Mike — Thu, 26 May 2011 00:04:00 +0000

Well, day one is in the books on the Big Ten Tournament and Ohio State and Purdue picked up victories. Ohio State had an 8th inning rally which accounted for all five of their runs to beat Minnesota 5-3. Purdue was down 2-0 before firing off seven unanswered runs which, behind the right arm of Matt Morgan was plenty to cruise passed Penn State. You can find plenty of recaps around the web, so I won’t spend too much time on that.

Let’s look ahead to tomorrow where Ohio State will take on the top seed Illinois Fighting illini. Meanwhile, The Boilermakers win got them a match up with the second-seeded Michigan State Spartans. As a result of their losses, fifth-seeded Minnesota will play sixth-seeded Penn State, as well.

What I will do is use a method called the Log5 to determine the likelihood each team wins their games tomorrow.Before we can use the Log5, we must establish a talent level for each team. In my methods, there’s three ways we can go about it: using raw conference winning percentage, using a winning percentage based on actual runs scored and allowed, using a winnings percentage based on estimated runs scored and allowed.

First, let’s look at raw winning percentage. The winning percentage in conference play for each team is below:

Illinois: .625
MSU:.625
Purdue: .583
Minnesota: .542
OSU: .542
PSU: .529

From here, it’s just plug-and-chug with the winning percentages into the Log5 formula. The probabilities, based on W% alone, that each team will win the game tomorrow are:

Minnesota v. Penn State: Minnesota should win 54.2% of the time.
Michigan State v. Purdue: MSU should win 54.4% of the time.
Illinois v. Ohio State: Illinois should win 53.5% of the time.

Note: I’ve given Ohio State a 5% boost for the tournament being held in Columbus in all of the probabilities that I’ll list. They do have a home field advantage in playing, well, at home. In the Major Leagues home teams get between a 4-5% boost over a road team of equal strength. I’m just guesstimating here; I can see the argument for the home field advantage to be even greater in the college game.

Now, in a 24-game season like the conference schedule is in the Big Ten, almost anything can happen (as cliche as that sounds). The data suggests that Illinois is not as good as No. 1 seed and that Purdue is better than a No. 3 seed. What I’ll use now are runs scored/allowed in conference play to get a Pythagorean Win %. Using that, we get the following PW%’s for each team:

Illinois: .526
MSU: .580
Purdue: .584
Minnesota: .491
OSU: .540
PSU: .529

Now if we input those into the Log5 we get the following probabilities:

Penn State: 52.9%
Purdue: 50.4%
Ohio State: 56.4%

Illinois outscored their opponents by just ten runs, the same amount Penn State outscored their conference foes by. That put both teams fourth and fifth in the conference behind Purdue (+29), MSU (+29), and Ohio State (+14).

Now, we can get even more granular. Using my estimated runs scored and allowed — which is based on a runs created method and looks at all of the underlying statistics like hits, walks, strikeouts, home runs et al. — to get a winning percentage. Since I don’t have all of that data for only conference play, I’m going to use full-season data in this winning percentage. These are not adjusted for schedule, however, which is something that should be done with half of the teams games (or more) being against non-conference teams. The EW% for each team are:

Illinois: .519
MSU: .653
Purdue: .659
Minnesota: .414
OSU: .518
PSU: .606

Here, Illinois takes even more of a hit. The Illini were dead even in run differential (actual runs, not estimated) over the entire season (a.k.a. they were outscored by 10 runs in non-conference games) and their underlying statistics reflect this. Meanwhile, Penn State had a very solid year all around and Purdue is quite possibly the best team in the Conference. I wouldn’t be surprised to see them win the tournament. Speaking of, the EW%’s put into the Log5 yields these results:

Penn State: 68.5%
Purdue: 50.6%
Ohio State: 54.9%

Here, Penn State looks like a fine bet to advance over Minnesota with the Purdue-Michigan State game being a proverbial coin flip. Ohio State comes out ahead against Illinois, but remember they have a 5% boost due to location. Lower, raise that as you see fit and you’re essentially staring at another coin flip game.

Now, there are obviously caveats. The first being that I didn’t account for any injuries at all. The second, my estimated runs scored aren’t completely ideal — I’m in the process of recalculating them to work better in the lowered run environment of 2011 versus 2010. Truth be told, I’ve never been fully confident in them an always viewed them as estimations. Thirdly, none of the estimated runs scored/allowed figures are adjusted for park or competition. This is something that should be done.

All of that said, tomorrow features at the very least two games that should be entertaining to watch and a third that isn’t so grossly lopsided that it isn’t worth watching. For the record, I’ll take Michigan State, Ohio State and Penn State to win.

Presenting 2011 Stat Leaderboards

Mike — Wed, 06 Apr 2011 07:15:58 +0000

I just put together some statistical leaderboards for the 2011 campaign so far. You can find these easily under the 2011 Leaderboard tab on the top right and they will be updated at my discretion (read: when I have time). In an ideal world, I’ll have these updated every Monday or Tuesday, but I make no promises on that.

As of right now, I’ve compiled the team Defensive Efficiency Ratio and data for individual hitters, starting pitchers and relief pitchers. If you’re looking for regular earned run average or a hitters batting average, or on-base percentage, then these leaderboards are not for you.

What I’ve presented, I’ll quickly run through.

Hitters

What I’ve included is the percentage of plate appearances the hitter either strikes out, walks/gets hit by a pitch. Along with that, I’ve included a hitter’s isolated power which I have adjusted using these park factors from Boyd’s World. The data, unfortunately, is not adjusted for competition. As of right now, I’d have to calculate the strength of schedule adjustment on my own and, honestly, I’m not willing to really mess with that for the time being. I also have a Runs Above Average total for each hitter listed that is calculated using the Base Runs method.

Pitchers

I’ve broken pitchers into two leaderboards and three separate roles. For pitchers who start a game in 70% or more of their appearances, they are labeled as starting pitchers. For those who get a start in 40-69% of their appearances, I’ve dubbed them swingmen. Finally, those who start games in 39% of their appearances or fewer, they are relievers.

The stats I have for all pitchers are K% and BB%, like I do for hitters. These are the most important numbers to look at. Sure, I’ve got advanced data with an ERA replicate born out of the Base Runs method and then adjusted for park — from which I get the runs above average totals — but the meat of the data for pitchers is in the strikeout and walk/HBP totals.

Team Levels

I currently have Defensive Efficiency Ratio which is calculated just like it is at Baseball Prospectus. This is the best measure for defense at the major league level. I feel that this holds true — perhaps even more so — at the college level, as well.

For team hitting, I’ve included the teams K%, BB%, BABIP and park adjusted isolated power. Pitching, I have included K%, BB% and BABIP, but also give the teams’ traditional ERA as well as a base runs derived runs allowed metric.

I have a standings page as well. Here, you’ll find actual runs scored and allowed, estimated runs scored and allowed — derived, again, from the Base Runs method — and the team’s actual winning percentage, predicted win percentage and estimated win percentage. The difference between the three are explained on that page itself.

2011 Standings

Mike — Wed, 06 Apr 2011 07:02:20 +0000

Updated for games through Monday, April 5th.

TEAM	RS	RA	eRS	eRA	W%	PW%	EW%
MSU	141	91	170	104	0.708	0.708	0.741
PSU	150	98	169	111	0.680	0.703	0.703
PUR	204	154	233	159	0.630	0.638	0.685
IND	173	128	188	156	0.667	0.647	0.592
OSU	123	141	130	129	0.476	0.432	0.505
ILL	120	126	120	122	0.429	0.476	0.492
NW	114	139	124	156	0.333	0.403	0.386
MINN	79	106	95	120	0.526	0.359	0.384
IOWA	120	137	131	166	0.400	0.436	0.383
MICH	98	172	121	172	0.280	0.231	0.327

AVG	132	129	148	140

RS – Actual Runs Scored
RA – Actual Runs Allowed
eRS – Estimated Runs Scored calculated using the base runs method
eRA- Estimated Runs Allowed calculated using the base runs method
W% – Actual Winning Percentage
PW% – Predicted Winning Percentage; calculated using actual runs scored and allowed
EW% – Estimated Winning Percentage; calculated using estimated runs scored and allowed

2011 Team Pitching

Mike — Wed, 06 Apr 2011 06:49:58 +0000

Updated for all games through April 5th.

TEAM	K%	BB%	BABIP	ERA	pERA
ILL	0.146	0.142	0.316	4.96	7.70
IND	0.156	0.137	0.306	3.64	6.35
IOWA	0.151	0.133	0.348	4.36	9.02
MICH	0.144	0.144	0.354	5.42	9.10
MINN	0.164	0.108	0.340	4.63	8.36
MSU	0.156	0.096	0.301	2.83	5.70
NW	0.158	0.114	0.368	4.94	7.78
OSU	0.140	0.149	0.323	5.33	7.13
PSU	0.170	0.133	0.292	3.04	5.66
PUR	0.184	0.082	0.360	4.73	6.93

AVG	0.157	0.124	0.331

K% – Percentage of plate appearances a player strikes out in
BB% – Percentage of plate appearances a player walks OR draws a hit by pitch
BABIP – Batting Average on Balls In Play; a measure of ‘luck’
ERA – Earned Runs Allowed Per Nine Innings
pERA – An ERA replicate via the Base Runs method that is adjusted for park

2011 Team Hitting

Mike — Wed, 06 Apr 2011 06:41:33 +0000

Updated for games through Monday, April 5th.

TEAM	K%	BB%	BABIP	ISO*
ILL	0.145	0.110	0.295	0.099
IND	0.137	0.108	0.322	0.105
IOWA	0.174	0.126	0.311	0.072
MICH	0.188	0.113	0.295	0.073
MINN	0.196	0.098	0.322	0.108
MSU	0.126	0.100	0.347	0.122
NW	0.173	0.099	0.318	0.104
OSU	0.155	0.118	0.340	0.096
PSU	0.194	0.114	0.346	0.150
PUR	0.123	0.137	0.350	0.152

AVG	0.159	0.113	0.325	0.103

K% – Percentage of plate appearances a player strikes out in
BB% – Percentage of plate appearances a player walks OR draws a hit by pitch
BABIP – Batting Average on Balls In Play; a measure of ‘luck’
Iso* – Park-adjusted Isolated Power, Slugging Percentage minus Batting Average

2011 Relief Pitcher Leaderboards

Mike — Wed, 06 Apr 2011 06:17:12 +0000

Updated for games through Monday, April 5th. Minimum 75 batters faced

Rk	Name	Team	RL	K%	BB%	BABIP	pERA	paRAA
1	Martin, Chad	IND	SW	0.137	0.087	0.248	4.28	7.7
2	Walkling, Dave	PSU	RP	0.341	0.098	0.283	3.31	6.5
3	Ignas, Ryan	PSU	RP	0.117	0.096	0.236	3.63	6.3
4	Dearden, Matt	IND	RP	0.157	0.135	0.258	3.84	5.1
5	Wieber, Tony	MSU	RP	0.204	0.108	0.238	4.70	3.4
6	Korte, Brian	IND	SW	0.110	0.061	0.303	4.62	3.3
7	McKinney, Brett	OSU	RP	0.147	0.093	0.278	4.61	2.8
8	Jahns, Michael	NW	RP	0.146	0.146	0.299	5.37	1.8
9	Strack, Will	ILL	RP	0.198	0.139	0.292	5.48	1.6
10	Halstead, Ryan	IND	RP	0.156	0.091	0.286	5.46	1.3
11	Lala, Patrick	IOWA	RP	0.167	0.222	0.208	5.58	1.1
12	Wittgren, Nick	PUR	RP	0.222	0.046	0.333	5.75	1.0
13	Fangman, Tim	IOWA	RP	0.101	0.101	0.309	5.89	0.5
14	Carr, Matt	IND	RP	0.197	0.148	0.247	6.13	-0.1
15	Clark, Kyle	MICH	SW	0.129	0.099	0.302	6.22	-0.4
16	Quigley, Jack	NW	RP	0.175	0.125	0.346	6.34	-0.5
17	Haase, Joe	PUR	SW	0.158	0.082	0.336	6.43	-1.3
18	Ramer, Robert	PUR	RP	0.209	0.116	0.315	7.04	-2.0
19	Breedlove, Lance	PUR	RP	0.095	0.052	0.364	7.32	-3.3
20	Leininger, Drew	IND	SW	0.112	0.099	0.316	7.41	-4.8
21	Lakatos, Alex	MICH	RP	0.171	0.210	0.326	8.81	-4.8
22	Ballentine, Ben	MICH	RP	0.161	0.145	0.354	7.77	-4.9
23	Vangheluwe, Kevin	MICH	RP	0.227	0.148	0.385	8.67	-5.4
24	DeNato, Joey	IND	SW	0.196	0.155	0.377	8.26	-5.5
25	Mascarello, Blake	PUR	RP	0.186	0.062	0.410	8.16	-5.6
26	Waszak, Andrew	MSU	SW	0.078	0.126	0.346	9.48	-7.9
27	Pack, Chris	ILL	RP	0.108	0.118	0.333	10.16	-9.5
28	Sandquist, Ricky	IOWA	SW	0.063	0.168	0.406	15.27	-18.6

	AVG			0.157	0.124	0.331	7.44

RL – Pitcher’s roll. If a pitcher started 40-69% of his games, he is a swingman (SW) and 0-39% is a relief pitcher (RP)
K% – Percentage of batters faced a pitcher struck out.
BB% – Percentage of batters faced a pitcher walked and hit by pitch
BABIP – Batting Average on Balls In Play; a measure of ‘luck’
pERA – An ERA replicate via the Base Runs method that is adjusted for park
RAA – Using pERA to estimate a pitchers ‘true’ amount of runs contributed to his team.

2011 Starting Pitcher Leaderboards

Mike — Wed, 06 Apr 2011 06:12:17 +0000

Updated for games through Monday, April 5th. Minimum 75 batters faced

Rk	Name	Team	K%	BB%	BABIP	pERA	paRAA
1	Schreiber, Brad	PUR	0.259	0.068	0.278	3.76	9.3
2	Buccifero, Tony	MSU	0.136	0.060	0.290	4.48	8.6
3	Hill, Steven	PSU	0.182	0.051	0.275	4.53	8.4
4	Rucinski, Drew	OSU	0.170	0.080	0.321	4.41	5.1
5	Anderson, John	ILL	0.155	0.108	0.295	5.32	2.9
6	Wunderlich, Kurt	MSU	0.187	0.096	0.315	5.61	2.4
7	Walter, John	PSU	0.192	0.147	0.299	5.62	1.9
8	Morgan, Matt	PUR	0.205	0.143	0.271	5.58	1.4
9	Garner, David	MSU	0.168	0.102	0.333	5.95	0.5
10	Johnson, Kevin	ILL	0.165	0.106	0.305	6.11	-0.1
11	Wolosiansky, Dean	OSU	0.104	0.185	0.275	6.26	-0.5
12	Farrell, Luke	NW	0.209	0.092	0.375	6.28	-0.7
13	Monar, Blake	IND	0.193	0.140	0.385	6.84	-3.1
14	Brown, Nick	IOWA	0.140	0.118	0.294	7.10	-4.6
15	Tyson, Dan	NW	0.121	0.092	0.364	8.17	-6.7
16	Johnson, Heath	PSU	0.110	0.233	0.312	8.32	-7.2
17	Lubinsky, Austin	MINN	0.168	0.118	0.358	8.76	-7.4
18	Mills, Tyler	MICH	0.180	0.129	0.371	8.15	-7.5
19	Oakes, TJ	MINN	0.135	0.088	0.358	8.29	-8.3
20	Isaksson, Phil	MINN	0.099	0.083	0.308	9.00	-9.4
21	Greve, Greg	OSU	0.176	0.112	0.412	10.07	-11.1
22	Brooke, Francis	NW	0.168	0.029	0.481	10.11	-12.9
23	Hippen, Jarred	IOWA	0.140	0.084	0.348	9.14	-13.9
24	Dermody, Matt	IOWA	0.202	0.150	0.371	9.62	-14.2
25	Havey, Jack	NW	0.132	0.139	0.404	11.29	-17.3
26	Kimes, Corey	ILL	0.150	0.170	0.387	11.86	-18.7
27	Brosnahan, Bobby	MICH	0.101	0.107	0.386	10.82	-19.9

	AVG		0.157	0.124	0.331	7.44

K% – Percentage of batters faced a pitcher struck out.
BB% – Percentage of batters faced a pitcher walked and hit by pitch
BABIP – Batting Average on Balls In Play; a measure of ‘luck’
pERA – An ERA replicate via the Base Runs method that is adjusted for park
RAA – Using pERA to estimate a pitchers ‘true’ amount of runs contributed to his team.

2011 Individual Hitting

Mike — Wed, 06 Apr 2011 05:31:55 +0000

Updated for games through Monday, April 5th. Minimum 50 plate appearances.

Rk	Name	Team	PA	K%	BB%	BABIP	ISO*	PA RAA
1	Holm, Jeff	MSU	107	0.065	0.140	0.410	0.198	12.2
2	Deegan, Sean	PSU	119	0.176	0.059	0.407	0.306	10.8
3	Eckerle, Brandon	MSU	117	0.051	0.103	0.454	0.066	9.3
4	Johnson, Micha	IU	131	0.214	0.122	0.453	0.125	9.2
5	Boss, Torsten	MSU	109	0.147	0.101	0.429	0.247	9.1
6	Steranka, Jordan	PSU	119	0.143	0.076	0.368	0.296	7.6
7	Dickerson, Alex	IU	126	0.071	0.119	0.367	0.191	7.6
8	Perkins, Cameron	PUR	124	0.097	0.097	0.372	0.201	6.5
9	Cypret, Ryan	OSU	95	0.042	0.095	0.392	0.175	6.2
10	Talbott, Stephen	PUR	114	0.132	0.167	0.400	0.117	6.1
11	Spillner, Tyler	PUR	126	0.111	0.143	0.364	0.215	6.0
12	Snieder, Paul	NW	103	0.184	0.126	0.409	0.205	4.9
13	DeMuth, Dustin	IU	139	0.144	0.108	0.446	0.087	4.6
14	Solomon, Greg	OSU	67	0.239	0.045	0.522	0.220	3.7
15	DeBernardis, Joey	PSU	116	0.172	0.103	0.405	0.179	3.4
16	Plawecki, Kevin	PUR	122	0.033	0.131	0.382	0.061	2.9
17	Bridges, Ryan	PUR	93	0.129	0.129	0.359	0.214	2.8
18	Nick O’Shea	MINN	59	0.085	0.068	0.367	0.212	2.5
19	Livingston, Jack	NW	57	0.211	0.140	0.389	0.240	2.3
20	Serrato, Barrett	PUR	119	0.168	0.118	0.417	0.094	2.1
21	Stevens, Trevor	NW	114	0.149	0.175	0.392	0.070	1.9
22	Lashmet, Chris	NW	113	0.097	0.097	0.367	0.126	1.8
23	Schultz, Trip	MINN	63	0.111	0.175	0.318	0.151	1.6
24	Gominsky, Justin	MINN	82	0.159	0.085	0.435	0.061	1.5
25	O’Neill, Michael	MICH	110	0.173	0.091	0.375	0.113	1.3

Rk – Rank
PA – Plate Appearances
BABIP – Batting Average on Balls In Play; a measure of ‘luck’
K% – Percentage of plate appearances a player strikes out in
BB% – Percentage of plate appearances a player walks OR draws a hit by pitch
Iso* – Park-adjusted Isolated Power, Slugging Percentage minus Batting Average;
RAA – Runs Above Average using a Base Runs method and adjusted for park. Estimate of the number of ‘true’ runs a player contributes to his team.

2011 Team Defense

Mike — Wed, 06 Apr 2011 05:16:34 +0000

Updated for all games through April 5th.

Rk	TEAM	DER	Err	BIP	Out	xOut	Delta
1	IND	0.662	31	775	513	489	24
2	MSU	0.660	32	670	442	422	20
3	PSU	0.657	38	647	425	408	17
4	ILL	0.653	26	597	390	376	14
5	MINN	0.636	19	538	342	339	3
6	OSU	0.624	37	593	370	374	-4
7	IOWA	0.621	33	742	461	468	-7
8	MICH	0.611	37	736	450	464	-14
9	PUR	0.598	37	774	463	488	-25
10	NW	0.589	36	676	398	426	-28

	AVG	0.630

DER – Defensive Efficiency Ratio. Formula taken from Baseball Prospectus; Measures number of balls in play turned into outs by the team
ERR – Errors
BIP – Number of Balls In Play
Out – Number of Outs Converted
xOut – Expected number of outs converted if said team had a league average DER and had the exact same number of BIP as the team in question
Delta – Difference between expected outs and actual outs
List is sorted by team DER

B1G Ten Hitting Stars of the Weekend

Mike — Mon, 21 Feb 2011 23:19:15 +0000

After recapping briefly the pitching stars of the weekend, I just gathered the data for the hitters this weekend. I’ll keep it much like pitching post and give, statistically, the three stars for Big Ten-Big East challenge.

Third Star: Illinois shortstop Josh Parr had a very good weekend. The junior went 5-of-13 with a double, triple, a walk and a hit-by-pitch. All of that led to a .385/.467/.615 triple-slash line. This worked out to being worth around 2.4 runs above average.

Second Star: Senior outfielder for the Michigan State Spartans, Brandon Eckerle had a very good weekend for the Spartans — who as a team had a solid weekend. Eckerle when 8-for-12 with all eight hits being singles. He did tack on a 2-for-3 mark in base stealing. In case you’re keeping track — and love even-earlier-than-early-season stat lines, Eckerle’s sporting a .667/.692/.667 batting line through the first weekend of play. That’s worth 2.7 runs above average.

First Star: Boilermake junior Barrett Serrato gets the ‘first star’ nod this week for his 6-for-9 weekend. The infielder clubbed a double and a triple, drew a walk, and had a sac bunt which led to nine total bases in nine at-bats (13 plate appearances). He put up 2.9 runs above average with his effort.

Honorable Mentions:

Ohio State’s Josh Engle and freshman Josh Desze combined to post 4.2 runs above average. They went a combined 12-for-22 with two doubles and four walks to go against just three strikeouts.
Michigan State had a couple other standout performances. Jeff Holm (who was listed as one of Jeff Sackmann’s defensive All Americans) and Torsten Boss who went 9-for-22 with three doubles, two triples, two walks and two strikeouts.

A Forgettable Weekend:

Iowa Hawkeyes. All of them. Two runs in three games on the weekend — and their ace was lit up as well — is pretty pitiful. I don’t have team numbers calculated yet, but summing their individual hitters (not the right way to do it, but it’ll suffice) comes out to -15.5 runs above (below) average. Tyson Blaser had 11 hit-less plate appearances with his only contribution being a lone base on balls. He came in third worst in the Big Ten in runs above average at a -3.2. Zach McCool came in seventh worst with a couple singles in14 at-bats with a couple walks. Phil Keppler was just a bit better as the ninth worst hitter in the conference by going 0-for-8. Why is that better than McCool’s two-hits when Keppler didn’t even get on base once? Because he still made less outs than McCool even though he only reached as far as the batters box offensively.
Minnesota’s Justin Gominsky was 0-for-12 with three K’s. This comes in as the worst performance of the weekend for an individual hitter at -3.8 runs.
Northwestern’s Kyle Ruchim did pick up two hits, but it took 15 at-bats and 17 plate appearances. And he had two sac bunts. Add that up and he hit .133 for the weekend but only got on base at a .118 clip.