2011 Relief Pitcher Leaderboards

Posted: April 6, 2011 | Author: | Filed under: Big Ten Baseball, Runs Above Average | Leave a comment »

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

Posted: April 6, 2011 | Author: | Filed under: Big Ten Baseball, Runs Above Average | Leave a comment »

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

Posted: April 6, 2011 | Author: | Filed under: Big Ten Baseball, Runs Above Average | Leave a comment »

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

Posted: April 6, 2011 | Author: | Filed under: Big Ten Baseball, Defensive Efficiency, Research | Leave a comment »

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

Returning Adjusted Pitching Runs Above Average

Posted: December 14, 2010 | Author: | Filed under: Research, Runs Above Average | 16 Comments »

One of the things I wanted to look at with the Adjusted Runs Above Average pitching totals (aRAA) was what teams lost the most talent.

What I did was take the sums from my last post and then subtracted out all of the pitchers that were seniors, red shirt seniors, or juniors that signed with a Major League team in 2010. This left me with a returning aRAA total for the pitching staffs.

Some caveats. Obviously, these being my adjusted totals, they adjust for batting average on balls in play (BABIP) and thus, assume that pitchers have little control over their BABIP’s in college baseball. This is a truism for pro baseball, but whether this actually occurs in college ball, I am not sure. It’s just as likely that it does not extend to college baseball as it does.

Also, these aren’t projections. While I’ve done my best to adjust for ‘luck’ by assigning a league-average BABIP to all of the pitchers, this does not account for growth. In college basketball, the typical player improves the most from his freshman to his sophomore season. This is something that I think probably extends to baseball, too, but I’m not positive. Until I can study how pitchers “age” so-to-speak these are not ‘projections.’

That means these are just what the title implies: the total aRAA accumulated by each team’s pitchers in 2010 once you subtract graduates and early-draftees. Read the rest of this entry »

2010 Team Level Pitching Runs Above Average

Posted: December 13, 2010 | Author: | Filed under: Big Ten Baseball, Runs Above Average | 1 Comment »

I’ve completed my run through the Big Ten’s pitching staffs on an individual level with my Runs Above Average statistics and wanted to have a post that included the team totals.

Below, I have my Unadjusted Runs Above Average (uRAA).

Team uRAA
PUR 72
MINN 55
MSU 31
OSU 10
NW 4
MICH -2
IOWA -29
ILL -31
PSU -60
IND -84

Read the rest of this entry »

Exploring Team Defense

Posted: November 10, 2010 | Author: | Filed under: Defensive Efficiency, Research | 1 Comment »

One of the big banes of Sabermetrics is attempting to quantify team defense. There are nifty tools out there like Ultimate Zone Rating, Defensive Runs Saved, Total Zone (all three of which can be found at Fangraphs) that are the most “advanced” defensive metrics available for Major League Baseball.

However, Colin Wyers of Baseball Prospectus fame has questioned defense with a lot of logical fore-thought. The long and short of it is that the processes of the advanced metrics like UZR or John Dewan’s DRS systems aren’t necessarily the problem; it’s the data being put into it. There’s numerous biases that can enter the mix and that compounds things. Crap in, crap out, so-to-speak. If your data isn’t good, it’s almost going to ensure that there’s nothing of value that will come out of the formula.

One thing we do have, though, are outs and balls in play. I asked Colin, and he confirmed, that using Defensive Efficiency Ratio is the best metric for team-level fielding.

Now, since I don’t have any database skills at all, I can’t do a plays made/not made (which is essentially Sean Smith’s Total Zone metric that you find on Baseball Reference)  on an individual level like Jeff Sackmann does, but I can use B-Pro’s Def Eff formula and apply it to the Big Ten teams from 2007-2010.

Read the rest of this entry »

Adjusted Pitching Run Values for the 2010 Season

Posted: September 22, 2010 | Author: | Filed under: Research, Runs Above Average | 6 Comments »

I got some free time sooner than I thought, and really wanted to hammer out some Runs Above Average numbers for the 2010 season for individual pitcher. You can read a bit about it from my post on Monday. What I’ve done is used the same methodology, however, instead of the raw singles, doubles, triples, homers, sacrifice flies/hits numbers, I substituted league average rates. This gives a Defensive Independent Pitching Statistic-like (DiPS) element to the run valuations.

Basically, it is my attempt to neutralize things like batting average on balls in play and/or defensive impacts.

Below, you will find the top 25 pitchers sorted by my Adjusted BaseRuns Runs Above Average statistic. Read the rest of this entry »

Pitching Run Values for the 2010 Season

Posted: September 21, 2010 | Author: | Filed under: Research, Runs Above Average | 9 Comments »

Taking a break from my team-by-team review of what I consider to be the essential pitching stats — strikeout percentage, walk percentage, strikeout minus walk percentage, total batters faced — I’m going to bring out a run estimator. While looking at multiple statistics are the better way to go, it’s nice to have a handy singular number to give you an idea of a pitcher’s value to their team.

What I’ve done is pretty simple. I’ve used a Base Runs method to get an ERA estimate. Then, I found the league average for each year I have statistics (2008-2010, though I have team stats for the 2007 season, but have slacked on getting individual numbers for that year). From there, it’s a simple matter of subtracting the pitchers Base Runs ERA from the league average rate and multiple by innings pitched and divide by nine. Voila. Pretty simple. Then again, I did steal this from Patriot over at Walk Like a Sabermetrician.  Below is the top 25 in runs saved in 2010. Read the rest of this entry »

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