Baseball runs per game 2012

So obviously, you needed more runs to secure a win in than in And you did, using the formula above: But if you want to be really precise, you need to go further, and look at the scoring environment.

If you said it this past season, you were more than a run too low. This is a free article. If you enjoyed it, consider subscribing to Baseball Prospectus. Subscriptions support ongoing public baseball research and analysis in an increasingly proprietary environment. Subscribe to Baseball Prospectus. Thank you for reading This is a free article. Subscribe now. You need to be logged in to comment. Login or Subscribe. What the heck happened in to make the runs per win the highest it's ever been?

Future article? Reply to jfranco Rob Mains. I don't know whether I could squeeze a full article out of it, but basically, pythag broke that year. Put it this way: In , there were 11 teams that exceeded or fell short of their pythag projection by 3 or more wins.

Same in The difference, of course, is that there were 30 teams last year, just 16 in Reply to Rob. Chris weikel. A team in the American League will average. This large number of scoreless innings can be described by discrete probability distributions that account for teams scoring none, one, or multiple runs in one inning.

Runs in baseball are considered rare events and count data, so they will follow a discrete probability distribution if they are random. The overall goal of this post is to describe the random process that arises with scoring runs in baseball. The Poisson distribution describes count data like car crashes or earthquakes over a given period of time and defined space.

It predicted fewer scoreless innings and many more 1-run innings than what really occurred. The PD makes an assumption that the mean and variance are equal. The graph above shows an example of the application of count data distributions. The actual data is in gray and the Poisson distribution in yellow. The NBD is also a discrete probability distribution, but it finds the probability of a certain number of failures occurring before a certain number of successes.

From a conceptual stand point, the two distributions are closely related. The second section of the post will discuss the specific equations and their application to baseball. I separated the two leagues to get a better fit for the data. Using data from , the American League had an expected value of 0. Transcontinental air travel and night games became commonplace.

Run totals for this period, 4. It was certainly better than it is now. Sandy Koufax entered spring training in boasting a lifetime record with a 4. He had nearly as many strikeouts for innings pitched at that point of his career, though the young lefty was also averaging more than five walks every nine innings.

Then, on January 26, , as recounted by Bill James in the historical abstract, the Baseball Rules Committee expanded the strike zone, stating that it went from the shoulders to the bottom of the knee. Over the ensuing four seasons before his arm gave out, Koufax went with a 1. Following the offensive nadir that was , when teams averaged their lowest runs per game in 60 years, Major League Baseball lowered the mound from 15 inches to 10 inches.

In fact, certain pitchers got better while others continued to dominate. First, as Jim Bouton wrote during spring training in in Ball Four :. He majored in phys. Has to do with the hypotenuse of a right triangle decreasing as either side of the triangle decreases. Assuming the lower mound favored hitters, I asked Dierker how he compensated. Dierker said:. But my arm position was around three-quarters, and I think the higher, steeper mound is really the mound that gives a tall, straight overhand pitcher like Jim Palmer a better advantage.

He was complaining about the mound from the first inning on. But he was kind of low three-quarters with his arm position, and I think he preferred a little bit more gentle slope. Jim Palmer, for his part, averaged 19 wins and a 2. The next time someone trots out the myth, please, show them the chart above. Maybe tell them also that for the years Morris pitched in the majors, through , teams scored an average of 4. Scoring rose slightly, granted, but was below the average for baseball history of 4.

It was more or less average, with pitchers enjoying a slight edge and small ball an oft-used strategy of the day. The sooner this is better understood, the more that players like Dwight Evans, Dave Parker, and Dale Murphy may get recognition from Cooperstown.

The common suggested culprits for the spike in offense, I think, are steroids, expansion, weaker pitching and a livelier ball. And if that happens, fans can bet on more rule changes from Major League Baseball to liven the game. Great work! Straightforward in busting the myth that the DH greatly increased scoring — although there may be value in breaking down games with the DH versus those without the DH in the same era. Two other factors, harder to track, may be a lack of parity among teams and the impact of new ballparks — especially the introduction of night baseball and artificial turf.