Stat Watch: Week 5

Long post as I get this out before the EMU game at 3pm TUESDAY (bumped up a day to get the better weather) at the Fish.  No preview for EMU as one has already been done here.  You can check out the recap of the game at EMU here.

Stat Watch

In this edition of stat watch, we’ll catch back up with team hitting, and get caught up on offense. Pitching numbers are still rough, but we’ll at least take a look at the leader boards and look at the potential starting rotation for our upcoming 5-game weeks of the conference season. I’ll have a mix of Excel Graphs and ManyEyes (individual statistics). At this point, I can’t get the ManyEye’s visuals to embed, allowing you to play with the data and charts. So instead, its back to the basic Excel graphs.

Team Hitting

battinggraph5

Above is the game by game batting average (blue), on-base percentage (red), and slugging percentage (yellow) for the team as it has accumulated over the season.  As you can see, we appear to be reaching a fairly consistent level of production over the last 7 games or so.  Game 12 is the last game of the Siena series, so everything after that would include the Arizona series, @EMU, and the IPFW series.

Our current batting average is .321, on-base percentage .410, and slugging percentage of .495.  These are pretty solid numbers.  The average batting average for the NCAA (last assembled in April 08) was approximately .292.  Over the last few years, the NCAA average has been in the mid .280s.

As far as slugging, I have yet to find an NCAA-wide statistic, so I’ll compare it to the last few years of Michigan.  The last five years final numbers are .489, .478, .417, .429, and .413.  We’re still early in this year, but we look to be doing rather well for ourselves in the power department, at least compared to previous teams.

When looking at other Big10 teams, we can get a slightly better idea of where we compare this year.  Keep in mind that there is a definite difference in competition faced.

Team Record RPI BA OB% SLG%
Minnesota 13-6 20 .309 .432 .526
Ohio State 17-2 22 .350 .405 .550
Illinois 12-4 48 .322 .411 .438
Michigan 14-5 117 .321 .410 .495
Penn State 11-8 143 .308 .396 .401
Purdue 8-9 194 .286 .381 .411
Indiana 7-13 220 .338 .412 .500
Michigan State 6-14 227 .253 .339 .356
Iowa 6-10 228 .289 .382 .451
Northwestern 4-14 238 .252 .323 .344

I have the table sorted by RPI, so theoretically, the teams the have done well against better competition should be at the top.  Michigan places 4th in the Big10 in batting average, 4th in on-base percentage, and 4th in slugging percentage.  Go figure we’re currently 4th in RPI.  It makes sense as Ohio State has been destroying every pitching staff they’ve seen (mostly inferior teams).  Indiana is scoring a ton of runs, but they are giving them up at a startling rate (check out this football score of 28-17 in a loss to Northern Iowa).  Overall, I’d say we’re doing pretty well.

RBI vs Left on Base

While this isn’t the greatest measure of how timely a hitter is, it does offer a little insight.  In the graph below, we can see how many RBIs a player has and how many runners he has stranded to end an inning.  Click on the graph to see it enlarged.

Green, as it should be obvious is good, and purple isn’t horrible, but isn’t all too great.  Ideally, you want to score all of your base runners, but you will always leave some here and there.  For those of you not familiar with the “left on base” stat, it’s pretty self explanatory.  If a runner is left on base to end an inning, they are charged to the batter for not hitting them in.   I had started looking at individual batting averages with runners in scoring position, but going back through 15 games to check each at bat is time consuming.  I’m hoping to track it better through the conference season.

The players that are doing well in this graph are LaMarre and Cislo; Urban and Crank are also doing pretty well.  All four of these guys have a higher number of RBIs than they do LOB.  On the flip side, McLouth is doing surprisingly poor.  The hot start he had to the season has masked his current trouble with runners on.  Over his last 8 games, he has 3 RBI (all in one game) while stranding 7 in 26 total at bats.   As a team right now, we score 1.7 RBIs per runner stranded.  McLouth only knocks in .7 RBI per runner stranded, nearly half as many as the rest of the team.  For a clean up hitter, you’d like to see him doing much better.

Leader Board – Offense

Average On Base% Slugging%
Player AVG Player OB% Player SLG%
Kevin Cislo .366 Kevin Cislo .494 Ryan LaMarre .662
Ryan LaMarre .365 T-Kenny Fellows .447 Mike Dufek .658
Kenny Fellows .354 T-Ryan LaMarre .447 John Lorenz .537
Runs Batted In
Runs Walks + HBP
Player RBI Player R Player BB/HBP
Ryan LaMarre 28 Kevin Cislo 27 Kevin Cislo 18
Mike Dufek 18 Ryan LaMarre 17 Anthony Toth 15
Kenny Fellows 16 T-Dufek/Toth 16 Mike Dufek 13
Steals Doubles Home Runs
Player SB Player 2B Player HR
Kevin Cislo 7 T-Kevin Cislo 7 T-Mike Dufek 6
T-Ryan LaMarre 5 T-Mike Dufek 7 T-Ryan LaMarre 6
T-Nick Urban 5 Nick Urban 5 Jake McLouth 4

Minimum 35 ABs

Pitching

Individual stats are still meaningless at this point, but as a team, some trends are starting to finally form, so we’ll stick with just this for right now.

opponentper9inning5

In this graph we see the Per 9 innings stats for earned runs, strikeouts, walks, and hits.  The trend I find most interesting is how closely our ERA is following our walks per nine innings.  The only reason the two diverted recently was the Arizona game where we gave up 14 runs on 20 hits and 1 walk.  At least we were hitting the strike zone consistently that game?

The strikeouts per nine innings is rather extraordinary.  The recent NCAA trends for strikeout per nine innings have been between 6.6 and 7.1.  We’re at an even 9.  On the other end of the spectrum, the number of hits given up per nine innings is a little bit high.  To examine that we’ll look at opponent batting stats.

opponentbatting5

We’re actually doing a pretty good job here.  The team opponent batting average is just .294, so about average for the NCAA.  I went through 20-30 team statistic pages today, and our .331 opponent-on-base percentage seems to be on par with the rest of the NCAA, maybe slightly better than average.  Slugging Percentage (.495) appears to be close to average, just to the high side of that same sample.

What I gather from all this is that our pitching staff is throwing remotely average as a team.  This isn’t too much of a surprise watching the games.  The inconsistency in some of the back end of the bullpen is taking away from the really great appearances.  Once we gain some consistency, we should see all these numbers improve slightly.  The next 6 games should bring a few of the numbers down considerably.

Leader Board – Pitching

Starter* ERA Innings Starter* Ks
Player ERA Player INN Player Ks
Chris Fetter 2.30 Chris Fetter 31.1 Chris Fetter 31
Eric Katzman 2.84 Eric Katzman 25.1 Eric Katzman 27
Travis Smith 6.63 Travis Smith 19 Travis Smith 23
Relief** ERA Relief App Relief** Ks
Player ERA Player APP Player Ks
Matt Miller 2.13 Tyler Burgoon 8 Matt Miller 16
Mike Wilson 2.92 Matt Miller 7 Tyler Burgoon 14
Mike Dufek 3.38 T-Dufek/Gerbe 5 Mike Wilson 13
Oppon BA Walks+HBP/9IP Saves
Player BA Player
BB/HBP Player Sv
Matt Miller .170 Chris Fetter 2.01 T-Matt Miller 2
Mike Wilson .229 Mike Dufek 3.37 T-Tyler Burgoon 2
Mike Dufek .233 Brandon Sinnery 4.15 Mike Dufek 1

*At least half of their appearances are starts
**Less than half appearances are starts

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