“Speaker points” is an appropriate but potentially misleading label for the individual scores debaters receive on their ballots. One widespread misconception (especially among LD debaters) is that the “speaker” in “speaker points” refers to the quality of a competitor’s speaking, i.e. his or her presentation. A quick look at the six metrics that determine speaker points – persuasiveness, organization, delivery, support, cross-examination, and refutation, or synonyms of those – reveals that presentation is only one of many factors at play. (In fact, on NCFCA ballots, “delivery” doesn’t even get its own category – it is grouped with “conduct.”)
So why are they called “speaker points”? Because they are the rankings given by the judge to individual speakers, as opposed to teams. (Of course, this makes a lot of sense in the context of TP, but in LD it is a bit strange. My theory is that this is just a terminological holdover. If anyone knows more about the history of this phraseology, I’d love to hear about it.)
This underscores the importance of analyzing your speaker points closely. If you came out of a tournament with low speaks, that doesn’t necessarily mean your presentation was off. It could mean any number of things – that your speeches were poorly organized, that you didn’t cite enough evidence, etc. That might seem overwhelming, but it is actually good news, because every one of your judges gives you a precise ranking on each metric. So by consolidating ballots, you can statistically analyze your performance relative to each one and narrow down your weak (and strong) points.
Let’s look at an example. Suppose that Jane Doe averaged 23 speaker points over the course of her last tournament and wants to know how to improve that average at her next tournament. She can organize the data from her ballots using a table like this one:
A few things immediately stand out once the data is organized this way. Jane’s problem is decidedly not her presentation – her judges were very impressed with her on that front. Indeed, her judges were so impressed that it would actually be counterproductive for Jane to try and modify her delivery. After all, she can’t do any better than a 5 average. (If you are new to debate but have a lot of experience in speech, your table may look a lot like Jane’s.)
Jane’s low speaks were also not due to excoriating criticism from a single judge. Although her judge in Round 2 gave her a substantially lower score than the average, her judges in rounds 4 and 6 did not rank her much better. (This is important, since it is often tempting to blame one judge’s unreasonableness for bringing down our average.)
So what should Jane focus on before her next tournament? Rows 4-6 clearly indicate that she needs to work on her support, cross-examination, and refutation. Admittedly, that is a bit broad, but Jane can look to her judges’ RFDs and comments for insight into what particular changes she should make.
And the fun doesn’t stop there. Notice that Jane’s speaker points vary, both overall and by category, with which side of the resolution she is defending. Her average overall speaks are 21.7 on AFF and 24.3 on NEG, so judges seem to like her NEG arguments better than her AFF arguments. Jane can even calculate, based on her category scores, what accounts for the bulk of this disparity: on average, her cross-examination score on AFF was 2.3, while on NEG it was 3.7. That is more than a 50% difference! Notice, too, that the common weak link in the two rounds Jane lost was her low CX score. These are both signs that Jane needs to focus on preparing better CX questions for her AFF case. The same goes for her support scores: while Jane clearly needs to improve her handling of evidence across the board, her average support score on AFF (2.7) was substantially lower than her average support score on NEG (3.3). So if she only has limited time to search for better evidence, she should focus on finding evidence for her AFF case first.
The moral of the story is that speaker points are not as enigmatic as they (almost always) seem. They are actually a treasure trove of statistical data that you can use to calibrate your pre-tournament preparation. I would strongly encourage you to collect your ballots and assemble a table like the one above. A few minutes of number-crunching can go a long way.