Though Gary said it, that pinged my “Stuff Keith Hernandez Says” meter, and I trotted off to Baseball-Reference.com to look it up. Since 1973, six other pitchers who debuted in relief have two hit batsman. Were any of them as bad as Sborz?

We don’t have to go back too far to find someone who was. In 2002, Justin Miller of the Blue Jays made his debut against the Devil Rays and hit Chris Gomez, then Jason Tyner. Miller deserves special recognition – after that beautiful start, he held on to pitch 2 2/3 and got the win!

Honorable mention goes to Mitch Stetter of the Brewers. In a 2007 game against the Pirates, Stetter debuted in the last inning of a 12-2 blowout. He was on the winning side, though it ended up 12-3. Stetter hit Jack Wilson. He threw a wild pitch in the process of walking Nyjer Morgan, then iced the cake by plunking Nate McLouth. That was followed up with a groundout that scored Wilson and a merciful game-ending double play.

Today’s project: find Pythagorean expectations for each team, then find the difference between the actual and expected win percentages (“pythagorean difference”). Apply the pythagorean difference to the total number of games played to determine a team’s Wins Above Expectation by multiplying the total number of games by the pythagorean difference.

Practical application: none.

Discussion and numbers behind the cut.

I’m using OpenOffice.org Calc and numbers that I copypasted from ESPN.com, cited above. The formatting is a little wonky because this was just a play session for me, but here are my data in handy dandy PDF format (original spreadsheet format available upon request).

Wins Above Expectation is a measure of the economy of a particular baseball team – the ability to win more games than the number of runs scored would predict. I would therefore hypothesize it to be useful as a measure of defensive ability, though of limited use from a predictive standpoint because anything it would predict could be more elegantly but less surely predicted using other measures.

Speaking of elegance, I can’t help but notice that the pythagorean expected rank and the rank by run differential are the same in all cases. The elegance of run differential as a method of ranking teams’ performance hadn’t occurred to me before, but I’ll probably continue to use it. The use of pythagorean expectation is to predict a number of wins given a particular number of games. (The correlation between expected rank and rank by run differential is obvious after thinking it through, since pythagorean expectation is basically just a special case of differential.)

The Rays’ exceedingly economical performance isn’t surprising, since they’ve been squeaking out wins regularly all season. As a team, they rank second in MLB in the teams’ number of saves (a stand-in variable for close wins), behind only the LA Angels (who beat them in terms of WAE as well). There’s also no surprise that one of this season’s biggest punching bags, Toronto, is last in the league in negative WAE (that is, wins below expectation).  This is what happens when you lose tight games and get your wins by enormous margins. Lesson to be learned: consistent performance creates measuring statistics that closely match performance.

Edit to Add: I added an additional page of stats using the 1.81 exponent cited in the Wikipedia article. The numbers changed; the ranks didn’t.

Numbers are behind the cut.

Runs scored after the All-Star Game: 170 in 34 games, averaging 5.0 runs per game
Runs allowed: 128 in 34 games (120 earned)
Team ERA = 3.59
Pythagorean expected win percentage = 63.819
Actual record: 20-14, 58.824%
Diff = 4.995
(League average ERA = 4.80
League average runs = 170
League average runs allowed = 170)

Runs scored before the All-Star Game: 399, 95, 4.2
Runs allowed: 376 in 95 (344 earned), 3.63
Team ERA = 3.79
Pythagorean = 52.965%
Actual record = 47-48, 49.474%
Diff = 3.491
(League average ERA = 4.10
League average runs = 438
League average runs allowed = 421)

Most surprising to me is the way the American League’s average ERA has increased after the All-Star Break, whereas the Blue Jays’ has decreased. Where they performed slightly better than average before the break, they’ve upped their game considerably and widened the gap since the All-Star Game. In addition, they’ve stopped leaving Roy Halladay criminally unsupported, like they did at the beginning of the season. Their scoring per game has increased by almost a whole run. The problem, of course, is that the Jays aren’t distributing their runs evenly throughout the games they’re playing, which we see when we compare the difference between actual and expected winning percentage before and after the All-Star break.

The Jays are going to have to up their consistency, rather than merely their scoring, if they expect to be contenders.

(Data pulled from or computed using numbers pulled from ESPN.com.)