Experimental verification of the logit score

One method for ranking teams that I introduced to the debate community is the logit score. The logit score is derived from a logistic regression. The logit score combines a team's record, speaker points, and its opponents' strength into a single number. Because the logit score factors in record and points, it is performance-based, but that record is adjusted by opponent strength, making the logit score more fair than record alone. A win against a good team is "worth" more than a win against a weak team. If you take the worst opponent a team beat and the best opponent it lost to, and average those together along with the team's average speaker points, then you're approximating the team's logit score. Due to how the logit score is calculated, it is the likeliest team strength that explains its results: its record and its points.

I had previously looked for empirical support for the logit score in a college debate season. I took the real results for the entire season and used them to calculate each team's logit score. I then used those to retrodict the winner in every single match-up that had actually happened, with the higher ranked by logit score team retrodicted to win the round. The logit score did this better than every other ranking method I also tested, slightly edging out median speaker points, and doing better by a goodly margin than the win-loss record. Despite this success, there was the nagging concern that the logit score was being derived from an entire season's worth of information. This empirical support could not show if the logit score would work for a single tournament.

Therefore I set out to do an experiment. I created a simulation tournament in a program, and ran and re-ran it hundreds of times. I tested various tournament conditions, from random prelims to a typical method of power-matching to pre-matching (like a round robin). I looked to see whether in these kind of conditions--using only the information available in a tournament--the logit score fared as well in comparison to record-based rankings and to speaker point-based rankings.

The results are that, in any condition, the logit score is a vast improvement on the win-loss record, but not quite as good as speaker points. It may surprise people to realize that speaker points, even though they vary considerably from judge to judge, are the best information to rank teams. A team's median speaker points isn't affected too much by one judge. Speaker points are rich data when you only have six or eight rounds to rank a team.

However, I believe many in the community would not prefer to use speaker points alone. If nothing else, ignoring wins and losses gives a perverse incentive to teams to speak pretty and ignore winning key arguments. The logit score is a solid, thoughtful compromise. The logit score is based on both wins and points, so there's no perverse incentive to ignore key arguments--nor is there an incentive to ignore effective, mellifluous communication. Although the logit score is slightly less accurate for a single tournament than speaker points alone, the logit score is far more accurate than win-loss record is. The logit score is, in other words, a vast improvement on the status quo method--a compromise in name only.

The Logit Score: a new way to rate debate teams

I recently published an article on a new debate team-rating method I invented, called the logit score. I hope the logit score will take its place among win-loss record, average speaker points, median speaker points, opponent wins, ranks, and so on as an effective way to rate (and thus rank) debate teams at a tournament.

What is the logit score?

The basic idea is simple: the logit score combines win-loss record, speaker points, and opponent strength into one score using a probability model. In other words, the logit score is the answer to the question, "Given these speaker points and these wins and losses to those particular opponents, what is the likeliest strength of this team?"

Let's take a step back and acknowledge a truth not universally acknowledged in debate: results should be thought of as probabilities, not certainties. A good team won't always beat a bad team--just usually. Off days, unusual arguments, mistakes, and odd judging decisions all contribute to a slight risk of the bad team winning. The truly better team won't always prevail. That means actual rounds need to be thought of as suggesting but not definitively proving which team is better. Team A beats team B. Team A is probably better, but then again, they could have had off day, been surprised by a weird argument, or had a terrible judge. If team A got much, much higher speaker points, it was very likely the better team. If team A only edged out team B by a little bit, then the uncertainty grows.

That's where the logit score comes in. Estimating team A's actual, true strength depends on putting together all of those probabilities and uncertainties into one model. I won't get into the specifics (the details are in the article), but the basic idea is using a logistic regression to put the probabilities for wins and losses to specific opponents as well as specific speaker points received together. The logit score for a team means: "If team A were estimated to be stronger, these results would be a bit more likely, but those other results would be far less likely. If team A were estimated to be weaker, these results would be far less likely, even though those other results would be a bit more likely. This logit score is the proper balance that makes all the results most likely overall." Because it factors in all the results in one probability model, the logit score isn't sensitive to outliers: unusually high or low speaker points, losses to outstanding teams, and wins over terrible teams don't affect the logit score much at all.

Does the logit score have any empirical results to back it up?

Yes. This is the bulk of my article.

I took a past college debate season, used those results to give every team a logit score, and then looked to see how well logit scores "retrodicted" the actual results in a season. That is to say, how often did the higher logit scoring team win rounds against the lower logit scoring team? As a baseline of comparison, I also did the same kind of analysis by ranking the teams by win-loss record.

The logit score rankings got slightly more rounds correct than the win-loss record rankings.

The slightly higher accuracy is not, on its own, a reason to rush to adopt logit scores. It merely proves that the logit scores aren't doing anything crazy. For the most part, the logit scores reshuffles teams ever so slightly with their nearest peers. The moves are slight ups or downs, not drastic shifts.

The real reason to consider using logit scores is that (a) they are less sensitive to outliers, which can matter a lot for a six or eight round tournament; and (b) they factor in more information. Win-loss records only use speaker points as a tiebreaker; it's secondary. Measures of opponent strength usually come third. In other words, a team with a really tough random draw and goes 4-2 as a result of dropping the first two rounds might miss out on breaking if no 4-2s break--win-loss record comes first and opponent strength won't factor in in that scenario. The logit score on the other hand--because wins, points, and opponents are all factored in at once--could reflect that this team is in fact very strong because it only lost two rounds to very good opponents. (See how important it is to be less sensitive to outliers?) More information also rewards well-rounded teams: those that win rounds on squeakingly close decisions and don't receive great speaker points are penalized more under a logit score system than a win-loss-then speaker points-system.