Team standings rules¶
In Tabbycat, you can choose how teams are ranked in the team standings. For example, at Australs, teams are ranked first on the number of wins, and second on their total speaker score. The setting that specifies how teams are ranked is called the team standings precedence. The team standings precedence is used:
- When displaying the team tab,
- Whenever a power-paired draw is generated, and
- When computing which teams are in the break.
When you choose the team standings precedence, you choose from a list of metrics. Then, in the standings, teams will be sorted first by the first metric, then by the second metric, and so on. You must choose at least one metric, and you can choose up to eight. Teams tied on all metrics will have the same rank.
If you like, you can also choose team standings extra metrics, which are metrics that will be shown in the team standings, but not used to rank teams.
|Wins||How many debates the team has won.|
|Points||How many points the team has. For two-team formats, this is just a synonym for wins, and differs only in column labelling. For BP, this is 3 points for a first, 2 for a second, 1 for a third and 0 for a fourth.|
|Points (2/1/0)||How many points the team has, where teams earn 2 points for a win, 1 point for a loss and 0 points for a forfeit.|
|Total speaker score||The sum of all speaker scores attained in all debates.|
|Average total speaker score||The average total speaker score over all debates the team has had, not counting debates where they or their opponents forfeited.|
|Average individual speaker score||The total substantive speaker score, over all debates the team has had and the number of speakers. Provides an equivalent metric to average total speaker score in no-reply formats, but within the substantive speech scoring range.|
|Speaker score standard deviation||The standard deviation of total speaker scores over all debates the team has had, not counting debates where they or their opponents forfeited. This metric is ranked in ascending order (smaller standard deviations ranked higher).|
|Sum of margins||The sum of all margins. Wins are positive, losses are negative.|
|Average margin||The average margin over all debates the team has had, not counting debates where they or their opponents forfeited.|
The sum of the number of wins of every team this team has faced so far.
This is also known in some circuits as win points, opp wins or opp strength.
The number of adjudicators that gave this team a win across all of their debates. Also known as the number of ballots or judges a team has.
In cases where the panel is smaller or larger than 3, this number is normalised to be out of 3. For example, if a panel of five splits 3–2, then the winning team is recorded as gaining 1.8 votes, and the losing team is recorded as gaining 1.2. This also means that solo adjudicators are always worth three votes.
|Number of firsts||The number of debates in which the team came first. Only makes sense for British Parliamentary.|
|Number of seconds||The number of debates in which the team came second. Only makes sense for British Parliamentary.|
If there are exactly two teams tied on all metrics earlier in the precedence than this one, then check if the teams have faced each other. If they have, the team that won their encounter is ranked higher. If they have seen each other more than once, the team that has won more of their encounters is ranked higher.
If there are more than two teams tied, this metric is not applied.
This metric can be specified multiple times. Each time who-beat-whom occurs, it applies to all the metrics earlier in the precedence than the occurrence in question.
|Who-beat-whom (in divisions)||As for who-beat-whom, but only compares for teams in the same division. That is, the metric applies whenever there are exactly two teams from the same division exactly tied.|
Speaker standings rules¶
The speaker standings precedence is only used in speaker standings (i.e., it doesn’t affect the operation of the tournament). As for team standings, the speaker standings precedence specifies which metrics are used to rank speakers, with the second metric tie-breaking the first, the third tie-breaking the second, and so on. The speaker standings extra metrics are metrics that will be shown in the speaker standings, but won’t be used to rank speakers.
|Total||The sum of all speaker scores attained by the speaker. Note that if a speaker misses a round, they’ll typically be relegated to the bottom of the speaker standings by this metric.|
|Average||The average of all speaker scores attained by the speaker.|
The average speaker score after excluding their highest and lowest speaker scores. Also known as the high-low drop, truncated mean or Olympic average.
If the speaker has only one or two scores, this metric just returns the average of those scores, without excluding any.
|Standard deviation||The standard deviation of all speaker scores attained by the speaker. This metric is ranked in ascending order (smaller standard deviations ranked higher).|
|Average speaker score||The average total speaker score over all debates the team has had, not counting debates where they or their opponents forfeited.|
|Number of speeches given||The number of speaker scores associated with the speaker. (In tournaments where teams can rotate speakers, this may not be all rounds.) This metric is normally used as an “extra” (unranked) metric, because it’d be weird to rank by number of speeches given, but you can if you want to.|
The motion balance page applies a statistical test to estimate the degree to which a motion is imbalanced. This is calculated by first making an underlying assumption that a motion is generally fair. This will be our null hypothesis: that, for a given motion, affirmative teams won the same number of times as negative teams.
Our chi-squared test will then be centred around disproving this hypothesis. If we disprove the hypothesis, we say that, in the context of this tournament and this draw, the motion ended up being unbalanced. However (technically speaking) if we fail to reject the null hypothesis, we would conclude that there is insufficient evidence to suggest that the motion was unbalanced in the context of this tournament.
The test proceeds by calculating the chi-squared stat, then running a series of tests. The tests are where we go a little off-book with respect to statistical methodology. Normally we would test at a single “level of significance” (ie. with a certain degree of certainty), but that’s insufficient in telling us how bad a motion ended up being. So, instead, we conduct a range of tests with a range of levels of significance, and calculate the minimum level of significance that causes our null hypothesis to be rejected. Using the minimum level of significance that rejects our null hypothesis, we can then grade the fairness of the motion on a scale. Motions whose tests fall below a certain threshold will be considered fair, while others will be graded based on the minimum.
For formats with topic selection, the same test is applied using the number of affirmative and negative vetoes in place of wins. The assumption here is that, during the time allotted for motion selection, teams estimate how appealing a motion is from their position, and then veto the topic that they feel is least favourable. Thus, the null hypothesis is that a motion that is perceived of as fair would be vetoed by affirmative and negative teams to an equal degree.