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Boston College Baseball: Measuring The Pitching Staff's Success

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Just how good were the arms in getting the Eagles to the ACC Tournament?

Courtesy BC Athletics

More than maybe any other sport, baseball uses mathematics to predict and prognosticate how a team might perform. It can be used to measure how close they are to being successful, and it measures if the end is justifying the means.

As they've grown and developed, advanced, SABRmetric statistics can provide a glimpse into that performance. They've made some numbers that formerly were used to measure success obsolete, and they've replaced them with new statistics designed to dive into performance, taking into consideration certain success variables.

Some of the numbers are a little too deep. As the number of available stats grows, more categories can be used to bend performance to shape how successful a player or team is, regardless of how it looks or feels. So you have to determine which stats are right for you. It might seem a little bit of a buffet where you pick and choose what you think will work best for you, but I've always felt there are several key stats that are more essential than the rest.

Before the season, I detailed just how good the Eagles would need to be to get where they wanted to be. In the long and short of it, I predicted BC would need 569 hits and 801 total bases in order to score 318 runs. Those numbers would put them approximately at 32 wins in a 55-game schedule and qualify them, minimally, for the ACC Tournament.

As it turns out, the Eagles only scored 249 runs this year on 446 hits and 597 total bases. And instead of winning 58% of their games, which 32 out of 55 would've been, Boston College won 31 out of 50 - or 62% of their games. Even the Pythagorean Win-Loss, which measures a team's record based on how they performed (thereby determining how they "should have done") put the Eagles at 30 wins in 50 games, meaning the variance is negligible when we're dealing with one game.

That means the offense did exactly what it needed to do in order to win 31 games, and the pitching did exactly what it needed to do - whatever that means. The offense was good when it needed to be, and a number of players hit over .300 down the stretch in a swath of games. But over the course of the entire sample, their early season struggles and losses against teams like Clemson and Pittsburgh weight down the success they might've had at the end of the year. In the end, that number looks a lot worse than it probably was.

In the same respect, the pitching staff was electric down the stretch, especially the starting rotation. It's been the most talked about part of the team, the ability to mow down opposing hitters with three very different types of pitchers. But the question would always linger - if BC won that many games and were supposed to win that many games, with that much of a variance in low offensive output, was the pitching really that good?

Luckily, there's a stat for that.

SABRmetrics has a stat called a "Game Score" which allows us to measure statistical output to a pitcher's game. Game score assigns a number value to a pitcher at the start of the game, then adds and subtracts points based on production. A pitcher gains points for every out recorded and gains bonus points for innings after the fourth (thereby earning more points based on how much more quality his start is). He also gains points based on strikeouts.

They then lose points based on earned runs allowed, unearned runs allowed, walks and hits.

To sum it up easily, every pitcher starts a game with 50 points (equaling the 50-50 chance he has of winning a game before it starts). The maximum possible score a pitcher can have is 114, assuming he strikes out everybody he faces in a perfect game, and the highest score ever recorded was a 105 (Kerry Wood's 20-strikeout performance in which he allowed one hit and no walks). To most, a score of 65 or higher is considered a solid gem of a game.

Looking at the Boston College end-of-season rotation's scores, we can assume a couple of different things:

-The rotation of Jacob Stevens, Justin Dunn, and Mike King gave the team a 50-50 chance to win roughly 25 times in 31 starts - or 81% of the time (the Niagara game is omitted because it was a staff start featuring all three of the pitcher).

-The rotation gave them a 65% chance or better to win on roughly 13 of those occasions. So of that 81% where they were given a better chance of winning because of the rotation, 72% featured a probability of winning at or near 65% or better.

-Justin Dunn gave BC an automatic 65% or better every time he was on the mound, save for his second start while his pitch count was being stretched out at Notre Dame.

-This doesn't take into account Jesse Adams, who also won two games as a starter, including one over Ohio State.

-In games where the starters gave BC less than a 50% chance of winning, they still managed no decisions half of the time, meaning the hitting really wasn't all that bad. Losses for those games have deeper meaning that we're not currently looking at.

-This also doesn't take into account the midweek rotation, where things are not always as easily explained (staff starts, shortened outings, etc.).

It also means that Boston College's pitching staff might be the best of the rotations currently in the ACC Tournament.

For reference, here are the game scores of the weekend rotation. Justin Dunn cycled into the rotation starting with the Virginia series.

Jacob Stevens

Date Opponent Game Score Decision Team Result
2/19 Northern Illinois 67 Win Win
2/27 Chicago State 59 Win Win
3/5 North Dakota State 74 No Decision Loss
3/13 NC State 56 No Decision Win
3/20 Clemson 60 No Decision Loss
3/26 Pittsburgh 61 No Decision Loss
4/8 Virginia 69 Loss Loss
4/15 Notre Dame 49 Loss Loss
4/22 Louisville 64 Win Win
4/29 Virginia Tech 34 Loss Loss
5/6 Wake Forest 42 No Decision Win
5/21 Georgia Tech 48 No Decision Win

Mike King

Date Opponent Game Score Decision Team Result
2/19 No.Illinois 81 Win Win
2/27 Indiana State 59 Win Win
3/5 Northwestern 58 No Decision Win
3/11 NC State 70 Win Win
3/18 Clemson 30 Loss Loss
3/24 Pittsburgh 64 Win Win
4/1 Florida State 56 Loss Loss
4/10 Virginia 62 No Decision Win
4/17 Notre Dame 29 No Decision Loss
4/24 Louisville 34 Loss Loss
5/1 Virginia Tech 77 Win Win
5/7 Wake Forest 52 Win Win
5/19 Georgia Tech 30 Loss Loss

Justin Dunn

Date Opponent Game Score Decision Team Result
4/9 Virginia 66 No Decision Win
4/16 Notre Dame 52 Loss Loss
4/23 Louisville 67 Win Win
4/30 Virginia Tech 65 Win Win
5/7 Wake Forest 64 No Decision Win
5/21 Georgia Tech 76 Win Win