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April 28, 2014 MoonshotHow Quickly Do Team Results Stabilize?With the end of April looming, we can begin to shed some of our fears regarding small sample size. Statistics like strikeout and walk rates have passed critical thresholds on their march toward stabilization, and so we are beginning to get a first look at how well individual players will perform. The requisite earlyseason loss of ~20% of each team’s starting rotation to the failure of a certain crucial ligament has taken its toll, resulting in a clearer picture of who will make each team’s starts. All of which is to say, we can begin to turn our attention to matters larger than individual players. Since the ultimate goal of every team is to win a championship—and the best way to win a championship is simply to field a very good team—the question of utmost importance is simply: How good is my team? In light of this question, I examine here how quickly team quality stabilizes over the course of a season. At a fundamental level, good teams are defined by 1) scoring lots of runs, and 2) not allowing the other team to score many runs. Therefore, I take as my measurements of quality runs scored per game and runs allowed per game. While there is a simple relationship between the number of runs scored/allowed and wins (via the Pythagorean expectation), that relationship is quite noisy. First and foremost, the noise results from sequencing, or the luck a team has in apportioning its runs to individual games. A bad team may thus end the season with an excellent record and a playoff berth, despite an underlying lack of quality. Nevertheless, all else being equal, good teams (those that score many runs and don’t allow many runs) are more likely to make the playoffs and win championships than bad teams.
Estimating Quality
19 comments have been left for this article.

You say: "In general, a given team’s projected RS number is going to be somewhere between PECOTA’s projection and the RS number the team has accrued so far."
Why would it ever be anything else? How, for instance, do we account for the Orioles who are outperforming their PECOTA so far, but are projected to finish below that level?
Mathematically, a linear model has the variables in it (in this case, RS so far and PECOTA projected RS), and also an intercept. So if the intercept for a particular model is say .1, and the RS and PECOTA numbers are each pointing towards 4.2 RS, the model might spit out 4.1, because of that intercept.
Generally, I would take the above numbers with a grain of salt. They are provided for illustrative purposes, rather than as definitive predictions. The point of this article was to show how quickly RS/RA stabilized, and to demonstrate that preseason predictions still carry some weight (and will until ~ game 100). If people are interested in maximally accurate predictions, maybe I can do a followup with some more sophisticated models.
(With that said, it's also possible I just made a mistake in entering the numbers in the table. I'll go back and check to make sure.)