Very accurate music reviews are perhaps not so useful

Back in august i downloaded all album reviews from, a hip music website mainly dealing with genres such as rock, electronica, experimental music, jazz etc. In addition to a written review, each reviewed album is given a score by the reviewer from 0.0 to 10.0, to one decimal accuracy. In other words, a reviewed album is graded on a 101 point scale. But does it make sense to have such an accurate grading scale? Is it really any substantial difference between two records with a 0.1 difference in score? Listening to music is a qualitative experience, and no matter how professional the reviewer is, a record review is always a subjective analysis influenced by the reviewers taste, mood and preconceptions. To quantify musical quality on a single scale is therefore a hard, if not impossible, feat. Still, new music releases is routinely reviewed and graded in the media, but i don’t know of anyone having a grading system to the accuracy that Pitchfork does. Usually there is a 0 to 5 or 0 to 10 scale, perhaps to the accuracy of a half. There are sites like Metacritc and Rotten Tomatoes (for film reviews) that has a similar accuracy to their reviews, but they are both based on reviews collected from many sources. In the case of Pitchfork, there is usually just one reviewer (with a few reviews credited to two or more people). As far as i know pitchfork has no guidelines on how to interpret the score or what criteria they use to set the score and it may just be up to the reviewer to figure out what to put in the score.

Anyway, I extracted the information from the reviews i downloaded and put it into a .csv file. This gave me data on 13330 reviews which i then loaded into R for some plotting with ggplot2. Lets look at some graphs to see how the scores are distributed and try to find something interesting. First we have a regular histogram:

When I first saw it I was not expect the distribution to be so right skewed. I expected the top to be around maybe 5 or 6. I calculated the mean and median which are 6.96 and 7.2, respectively. Lets look at a bar plot, where each bar corresponds to a specific score.

Now this is interesting. We can clearly see four spikes around the top, some scores are clearly more popular than others. ggplot2 clutters the ticks on the x-axis so it is difficult to see exactly which scores it is (this seems to be a regular problem with ggplot2, event the examples in the official documentation suffers from this) Anyway, I found out that the most popular scores are 7.5 (620 records), 7.0 (614 records), 7.8 (611 records) and 8.0 (594 records). Together, 18.3% of the reviewed records has been given one of these four scores. From this it seems to be some sort of bias towards round or ‘half round’ numbers. I guess we humans have some sort of subconscious preference for these kinds of numbers. If we now look closer at the right end of the plot, we see the same phenomena:

The 10.0 ‘perfect’ score is way more used than the scores just below it. So it appears to be harder to make a ‘near perfect’ album than a perfect one, which is kind of strange. If I were to draw some conclusion after looking at these charts, it would be that a 101 point scale is too accurate to be useful for distinguish between albums that differ little in their numeric scores. I also wonder if this phenomenon can be found in other situations where people are asked to grade something on a scale with similar accuracy.

Looking at monthly distribution of births in Norway

A news story earlier this week reported an increased number of births during the summer months in Norway. According to the story the peak in births used to be in the spring months, nine months after summer vacation, but is now during the summer. The midwifes thinks this change is because of the rules for granting a place in preschool day care. Children born before september 1st are legally entitlet to a place in day care.

Anyway i decided to try to visualize this. I found some data at the Statistics Norway website, loaded it into R, cleaned it, restructured it etc. and made this animation with ggplot2 showing the monthly distribution of births from year 2000 to 2011. I decided to include data for the years before 2005 since that is when the current left wing coalition took office and they had a program for universal access to day care. It is hard to spot a definite trend, but the graph for 2011 shows a clear top in the summer months. It will be interesting to see if this becomes clearer the next couple of years. Also, if this becomes a continuing trend, it would be interesting to look at surveys in family planning and see if there has been more of it the last couple of years.

The birthIndex on the y-axis is not the precise number of births for a given month, but is corrected for the number of days in the month. This makes the different months comparable.