The difficulty is trying to figure out what these 15 games mean to the future. As an analyst who is somewhat statistically inclined, this is the part of the year where I am a bit uncertain what I can actually write about. I was discussing baseball with one of my friends at lunch on Friday in front of the Capitol and discussing how everything I have begun writing so far has not inspired me to finish any of the pieces. She, a peripheral Red Sox fan who enjoys the Fenway experience, replied how it is nice people write critical pieces, but so little has actually happened and so much is left to occur. In other words, the famed words: Small Sample Size. SSS has been an often used and often overused term for those of us who engage statistics with more vigor than most. This means often that in moments like this when our counting stats are minimal in power, we need to lean more on qualitative analysis.
But what does winning April mean? The Orioles have a shot at it being one game behind the Yankees with a little over a week left to play.
To try to answer this question, I looked at all fourteen American League teams last year. Specifically, I took their monthly winning percentages and ran some R2s comparing a single month's winning percentage against the average winning percentage of the other months. By doing this, we can see to how each month's performance was able to relate to the final total.
First off, we see there is an amazing correlation between a team's August record in 2011 and what that team's record was in every month excluding August. Second, this is just one year with only 14 teams. I think it is prudent not to automatically assume that August is the defining month. It is also unfortunate that if it is the defining month then it makes the July trade deadline a bit murky. Regardless, it appears that maybe April's record is not incredibly useful in predicting the final record for a team.
Month R2 April 0.05 May 0.11 June 0.05 July 0.07 August 0.59 September 0.05
A secondary question can then emerge: how many games does it take to see how well a team will perform in the future? To try to answer this, I used the same data and generated R2s comparing cumulative records against the remaining record.
The story here is likely that the more games you play, the more certain you are of the true value of a team (measured as wins). By that I mean, 81 games played gives you a decent idea of the true value of a team and 81 games left to play allows for that talent to represent what they are truly worth. Having 130 games in the bag will help you know the value of a team even more, but with only 32 games left, you can have some interesting things happen that can skew a record.
Date R2 May 1st 0.05 June 1st 0.16 July 1st 0.41 August 1st 0.18 September 1st 0.05
Finally, I decided to run a quick regression comparing individuals months (April, May, June, and July) to a club's final record.
Again, I caution against taking these numbers as anything definitive due to what I assume to be a small sample size. I find it interesting that the coefficient (weighted value of a month's record related to the final total record) would be the most valuable and that the coefficients trend downward. I would like to see this with a more robust data set. Second, I also find it striking that the record for July did not meet the level of significance I set (P = 0.05). I also wonder how a more robust data set would affect that. It should also be noted that standard error here is roughly 0.1 for each month's coefficient, so that is a pretty wide range that leaves none of the months statistically significant in difference to each other.
Month Coefficient P April 0.39 0.006 May 0.30 0.02 June 0.25 0.03 July 0.16 0.07
I am not exactly sure what to conclude here other than me want to see what happens with a larger data set. The first two exercises suggest that winning the April pennant has little bearing on what happens for the rest of the year. The third exercise provides numbers for a convenient narrative that the beginning of the season appears to establish a perspective for the rest of the year. That narrative would mean that if a team starts off poorly, then for some reason that performance will have an effect on the rest of the season more so than whatever is accomplished in May, June, or July. Tempering that perspective is the relatively large standard error.