So if you read this blog often (and if not, why not, gentle reader?) you’ll know that two of my big life interests are data and theatre. When I try to explain about the data to the theatre people they either get vastly excited like I’m some kind of wizard, or the conversation dries up very quickly and then we all mutually, silently stare into our beer for an indeterminate amount of time. (Don’t even ask what happens when I try to explain about the theatre to the data people.) I thought I might try to find some middle ground by outlining a couple of examples that fit in both spheres.
For instance, you may have noticed recently that UCL did some regression analysis to determine that Eurovision participating countries aren’t actually in a conspiracy to deny certain countries the top slot year after year—various countries do indeed vote in groups which influence the outcome of the top slots each year, but according to researchers (as far as I can tell from what they’ve done) the lower rankings are pretty much an arbitrary jumble each year, so it’s not that the groups which are voting together are trying to force any one country or group of countries out, they’re just aligning with their favourites. (What, you don’t think Eurovision is theatre? If nothing else, Eurovision certainly speaks to the European theatre of politics.)
Another example of what could be done with theatrical data occurred to me after my recent review of Titus Andronicus at the Globe Theatre: let’s say that you had a copy of this production’s script with line numbers and the run time (otherwise known as the prompt book, commonly used by stage managers/sundry theatrical-organizing-type persons to make sure all the staging and sound cues and things are happening at the right time. They generally look like this. ) And let’s say the Globe were keeping a detailed log of each first-aid incident (passing out, throwing up, etc.) which included the time of each incident that occurred at every performance. You could then aggregate the data to show the most common scenes and lines for ‘incidents of distress’ during Titus across all the performances.
You might, as a Shakespearean scholar, be sitting there thinking, “But I know which bits of the play are goriest! Here, let me quote you from memory the bits that people are passing out during.” But if the night I went by was any indicator, people weren’t having ‘incidents’ immediately during the most violent and disturbing parts of the play—in fact, most of the fainting on my night seemed to take place during long speeches. One possibility that this suggests to me is that the adrenaline experienced by the audience during the really distressing portions of the play is keeping them upright and attentive, then when they go to relax a bit during the calmer bits they suddenly find themselves overwhelmed. I’d be interested to hear other hypotheses as well, of course, being neither a medical student nor a scholar of Early Modern theatre!
Clearly this could give a slightly macabre cache of production facts for the Globe: record number of fainters per performance, overall severest ‘incident of distress,’ number of performances in a row without people having incidents, most consistently distressing scene/act/monologue, etc. But how else could this data be useful? Well, if you find that there are clearly defined points in the show where people tend to need assistance, it might help the Globe manage their volunteer steward response during each performance, or their general first aid capacity, or even their risk management for future performances.
If you were able to expand your data set to include ticket information, you’d even be able to pick out relationships between the distressed people and where they were sat in the theatre. Is there a particular box which is especially risky, or low-risk? I imagine it’s mainly the groundlings passing out, but as they move around a great deal during the performances it would be pretty hard to run any serious analysis about which parts of the pit are most fainting-prone. Unless you also have, say, CCTV of the audience for each performance, meaning you could mark data points on a floor plan of the yard–again, tying this back to specific scenes and lines for each performance and aggregating across all performances.
With all of that and a friendly graphic designer, you could work all of the above into a nifty (if gruesome) infographic or even an animation encapsulating this production of Titus’s impact on the audience. (The falling-over kind of impact, anyway.)
I’m hoping what you’ll take away from this post is that there are all kinds of ways to engage with theatre and that you can do so on multiple levels simultaneously. My experience of the visceral, graphic nature of this production of Titus is no less arresting just because I also want to chart it. Learn a bit about data, theatre folks. You might find it rewards you with surprising riches. Or at least, you can pretend to be a wizard.