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I’m going to be honest, I have no idea what we were doing sometimes.

Now, I’d definitely say that our club had a relatively good level of organization. However, the seemingly inevitable degree of chaos in some of our practices drove me crazy. Aside from deciding how to assign and how to write briefs, we also struggled just in deciding which briefs to write.

Sometimes we would talk about cases which we needed briefs for, then lose focus and have to reemphasize the need for a brief on that topic (or forget to really assign it altogether). At other times, someone would be very vocal about needing a brief on some random case (normally being one they had lost to), dominating important discussion time. All of these practices inevitably took up valuable club time and/or hurt our preparation. In the end, we usually got by, but still I found myself asking why we didn’t have a better, more efficient, and more effective, way of prioritizing negative preparation. Thus, I came up with a great tool to aid the process.

Introducing, the Brief Prioritization Algorithm Spreadsheet (BPAS)

This spreadsheet contains an algorithm that, based on factors you input (explained later), tells your club or e-ring how much more they need to work on a case. In other words, it prioritizes which cases to prepare for. Now, before you look over the spreadsheet, I just want to say that I know some might think it looks complicated, but I can assure you that it isn’t nearly as difficult to use as it might look—especially given that I will be explaining everything: what it does, some of how it works, and most importantly, how to use it. With this foreword established, here is the download for the example template. (Note: as stated on the example template, the given attributes are just demonstrations of the algorithm, and are mostly random. When you actually want to use the template, you should use the blank version, given later).

What it does, and how

Essentially, the spreadsheet is a general caselist (i.e. not one just for a specific tournament) that you would maintain over the course of the year. But rather than just putting in case names, you also put in a few attributes such as how strong and popular the case is (again, to be further explained later). Based on the values you put in, it automatically gives you various results, the most important of which is “Need to develop Neg.”

How it works is a bit more complicated, and so I won’t go into the mathematical details. Still, I will say that one of the equation’s basic premises is as follows:

(The case’s popularity) X (The case’s strength)

For the “Need to develop neg” output, it also factors into the equation how much preparation you have already done, by subtracting that from the strength. Although that description is fairly inaccurate since it ignores many of the intricacies, it gets the general idea across.

Ultimately, though, you don’t need to know how it works; using it isn’t that complicated. I still can understand that the sight of the chart makes some viewers shut down and retreat. However, if this is you, I would still strongly urge you to at least try to understand as I explain how to use it, because I found it to be very beneficial.

Why you should use this

I can condense the primary benefits of this tool into two key, overarching points:

  • It saves you time. As stated before, trying to prioritize briefs primarily on paper or—even worse—by memory in club discussions is a very inefficient way of approaching the topic. Doing that requires thinking through each individual case, usually approaching it from a lot of random, inconsistent directions. However, this spreadsheet gives you clear checkpoints and goals, like a rubric: Estimate the case’s strength, popularity… etc. Not only is this helpful for the next reason, but also it is much quicker. Additionally, this way you have an easily accessible version of the caselist, which won’t be lost if someone happens to lose the copy. This is also very time efficient when the debaters are trying to provide input on their own or view what other people have said, meaning that it is more collaborative: with this, people only need access to a club’s Google Drive (or Onedrive, if you are unusual) folder to access/edit the most up-to-date version of the caselist. (And I do suggest that once you download the file, upload it to Google Drive).
  • It is more thorough and structured. Although it requires human input, the actual algorithm itself is primarily based on decision theory and debate experience. Thus, the algorithm is far less prone to common human biases or oversights. Of course, it is also only an algorithm, so human analysis (e.g. “The teams running this case at the upcoming tournament are not very good”) may help in certain cases. Nonetheless, from my experience using and constantly tweaking it, I have found that this program is at least a fantastic base tool for club prioritization.

Ultimately, this algorithm is a fantastic tool, as it amplifies and improves your efforts. Thus, correct use will substantially benefit not just you, but also your club (or e-ring).

How to use it: the basics

On the templates, you will see various columns with headers. Although some of these columns should be self-explanatory, I have attached explanations for all of the columns through document comments. These also contain some advice/suggestions, so it is worth looking at them.

Once you sufficiently understand the basic columns, you’ll then want to get the blank version of the template, which you can download here.

Now, I’ll explain the basic process to get the “Need to dev. neg” output for a case.

  1. First, put in the case’s name in a blank row under the “Case (short) Name” column.
  2. Estimate what you think the case’s strength value should be, then put it in the appropriate cell under “Strength.” I will give tips and benchmark suggestions on this later on, but it’s essentially on a scale of 1-10, (except for when you don’t know the case, so you have no idea what to put—in which situation you would just put a “?”).
  3. Do the same as above but with “Popularity”: estimate an appropriate value.
  4. If you have good reason to believe that the case will be at an upcoming tournament, put “True” in the appropriate cell under “@Tourn. Soon?” Otherwise, leave it blank.
  5. Finally, estimate how strong you or your club’s negative preparation is for it, on a (low-leaning) scale from 1-10. I say low-leaning because as explained in the comment, you naturally start at 0, and affirmative preparation tends to be greater than negative prep.

And then you are done! Now the algorithm will provide the “Need to dev. Neg,” value.

Some general tips/notes

Here are just some general tips and notes I learned in my experience using this caselist algorithm:

  • Get your club/e-ring involved. One of the benefits of this is its ability to convey information to people through simple numbers, in an easily accessible format. Another benefit is that it is easy to collaborate on. Trust me, in the end it will be less beneficial and much more difficult if you try to do this for yourself, alone.
  • Benchmarking the value scale. Unfortunately, people will inevitably interpret certain numbers differently than I, and thus the values they input may make the need to dev. neg output much higher or lower than intended. This is the issue of standardizing benchmarks not just between clubs but even amongst an individual club’s members: it requires getting everyone in a club roughly on the same page for values. I can’t give perfect benchmarks, but trust that with experience, the ratings should become more intuitive.
  • Estimating values by sorting and comparing. This is different from the benchmarking issue in that it’s where you can’t decide whether some case attribute should be a 5 or 6. However, I have a handy tip: If you are struggling to decide, then (carefully) sort all the other cases by the attribute in question, then decide where it fits by asking “Is it more or less popular than [some comparison case]?” But still, don’t worry too much about “being off by 0.5” (which is the maximum precision I recommend using: “wholes and halves”)
  • Make sure the row has equations in it. This is a troubleshooting note: if you delete an entire row, then you will also delete the equations (algorithms) in that row. If this happens, just copy and paste from a cell which does have the correct equation/algorithm.

A final note: an “advanced” option:

As previously mentioned, there is the option to “easy-tweak” the algorithm so as to better fit your preferred benchmark for the prioritization output. That is to say, suppose that you think “strength” should be a more (or less) important factor in the need to dev. neg output. You can click on the appropriate cell under “User-adjustable variables,” then set it to a slightly higher or lower number (i.e. probably between 0 and 2). This tweaks the algorithm for you without having to actually edit that (relatively) large and unwieldy function.

In reality, this function isn’t that complicated, but I know it can superficially appear complicated, so I’ll simply let you decide: it isn’t necessary to adjust this for the algorithm’s basic functioning, but you can’t delete the variables; you can only move them out of sight if needed.


No doubt, we like to stick with what we know. Tradition certainly is comforting, and just about anything related to technology will look daunting or complicated to some people. However, even if this is you, I would urge you to try using this system. As I’ve stated, it is far superior to  pen-and-paper methods that even our club tended to use. While it can’t solve all of your organizational issues, and it does require some effort, I can tell you that proper usage will significantly improve your club’s efficiency and efficacy in prioritization of cases. This allows for more time and focus on other tasks, such as coaching, feedback, practice, assignment of work, etc. Thus, I really hope to see more clubs adopting the BPAS!

Harrison Durland is a blogging intern at Ethos. Now a college student at Ole Miss, he is studying international affairs, Russian, (hopefully public policy,) and intelligence and security studies, seeking to do analyst work and perhaps later move into public policy or organizational administration. He began debate in his sophomore year of high school, in Stoa. Despite an unenthusiastic first year, he later found that he had a passion for debate, especially policy debate. His third and final year of high school debate was 2016, during which year he qualified to NITOC. His primary interests outside of debate and academics include his faith, ethics, and game and decision theory.

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