The team pitching recaps from the 2010 season are all linked to under the 2010 Season Recap tab. This is the fourth installment, focusing on the Indiana Hoosiers
The Indiana Hoosiers finished the Big Ten conference season just 12-12 while finishing a game over .500 at 28-27 when you include non-conference play. The Hoosiers had no problems plating runs, scoring 432 all season which was good for second best in the Big Ten. Unfortunately, they had problems preventing other teams from doing the same, allowing a league-high 444 runs.
Below are the raw stats for each of their pitchers in 2010:
The team pitching recaps from the 2010 season are all linked to under the 2010 Season Recap tab. This is the third installment, focusing on the Iowa Hawkeyes.
The Iowa Hawkeyes lost in the Big Ten tournament title game after a 13-11 conference record and 30-28 overall record. Including non-conference games, the Hawkeyes scored the 7th fewest runs and allowed the second most in the league — which would indicate that they played “above their heads” for the lack of a better term.
That said, it’s hard to argue with results. So how did their individual pitchers perform? Below is a table of the raw stats for Iowa’s pitchers:
After looking at the Michigan State pitching staff on Monday, I will now look at the remaining teams in the conference alphabetically. We’ll kick it off with the Fighting Illini.
Illinois was middle-of-the-pack in the Big Ten with 347 runs allowed on the season. However, that’s where the good news ends. As a team, they struck out the second fewest hitters as judged by their strikeout percentage (K/total batters faced) at just 12.5% while having the fourth highest walk and hit-by-pitch percentage as well. The likely cause for Illinois finishing fifth in the Big Ten in runs allowed is the league’s lowest BABIP against — just .329 which puts them about 5% below the league average of .345 — and in a sample of 2000 batters faced, that becomes a substantial amount of hits.
Below is the Illini’s individual pitching raw data:
|de la Torriente, B.||0.100||0.108||-0.009||0.333||7.38||750||231|
My plan to look at individual pitching is to look for each team. Each post should be shorter than long-winded posts on all of the teams at once. With this being a Michigan State baseball blog, after all, I’ve decided to focus on the Spartans first in the series.
The Spartans on the team level allowed a league-low 309 runs versus an estimated 311 which also led the Big Ten. AJ Achter, now a Minnesota Twin, really paced the Spartans’ staff. He led the team in innings pitched (98.2), strikeouts (74), and total batters faced (432). He also had a team-high 36 walks and 12 hit batters.
Kurt Wunderlich and Tony Bucciferro rounded out the normal 3-man rotation as they combined to start 76% of the Spartans games. Andrew Waszak’s 9 starts made him only other MSU pitcher to have more than 3 starts.
Below is each pitcher’s strikeout rate (K%; K/total batters faced), walk and hit-by-pitch rate (BB%; walks + HBP’s/total batters faced), net strikeout rate (K-BB%), batting average on balls in play (BABIP), total batters faced (TBF), Defense Independent ERA estimator (dERA) and estimated pitches thrown (Pitches):
On Wednesday I looked at how each team fared on the pitching side of things, relative to the league average. Now it’s time to focus on the offensive side of things.
But what do we look for? Pitching is pretty easy: you don’t want to let the batter put the ball in play and you don’t want to give up base runners via the walk or hit-by-pitch. But hitters are different as if you want to measure some amount of power, you need to take the context into the equation. And I don’t mean just how does Team A stack up against the conference average; that’s just half the battle. You must account for the park as well.
For that, I’m using 2006-2009 Total Park Factors (includes road parks as well) from Boyd’s World. I waffled on whether I should use a Strength of Schedule component or not and I really wanted to. However, the way I adjust for park factors and SOS, I need it in the form where 100 is average. Boyd publishes that data, but not until well after the season, so the only version I have of that (to my knowledge) is his pre-season “intended” strength of schedule ratings. Obviously those become outdated once the season has been played. So until further notice, I am not using a SOS adjustment in the offensive metrics that need adjust (basically, my power metric).
Like with pitchers, here’s the raw data for each club, sorted alphabetically:
After looking at some runs estimations on Tuesday that helped us derive expected win percentages, I figured it was a nice time to dive into pitching on the team level.
First, we’ll look at the raw numbers:
K% = K/Total Batters Faced
BB% = BB+HBP/Total Batters Faced
K-BB% = K% – BBHBP%
K/BB = Strikeout per every walk+hit batter
Like Mr. Pavlidis did above, I’ve added hit by pitches with walks, as they’re both essentially the same thing. It’s because of the inclusion of HBP’s that make Indiana and Penn State negative in K-BB%.
However, we can’t know how good Purdue’s K-BB% is until we know how they rate against the average Big Ten team.
To kick off my first analytical piece of the Big Ten as a whole, I’m going to present the Big Ten standings in a bit different fashion. One way to adjust the standings is to find a Pythagorean Record. Basically, you square a teams runs scored and runs allowed. You then take the squared runs scored and divide by the sum of the squared runs scored and runs allowed. I don’t want to say that this has become a “faux pas” among Sabermetrics recently, but it’s not the ideal method.
What I’m doing is using a Linear Weights-based BaseRuns method to estimate a runs scored and runs allowed total. In honesty, I’m stealing this from Patriot, a well-known Sabermetric writer.
Below, you will find the 2010 Big Ten standings based on a win percentage derived from my runs created/allowed methods: