These are interesting stats! I wanted to see how player skill distribution would affect things so I ran some simulations for the “Trent’s Barn” scenario as well.
I generated the player pool using a normal distribution to assign each player a ‘skill’ level that controls their chances of beating another player. With a basic normal distribution and swiss seeding, my results are pretty similar to @keefer’s for progressive strikes (ie, the event typically takes about 24-25 rounds to finish). However, things get interesting if I mix in an additional variable: “Number of ‘Pro’ players” (a “pro” player is basically an outlier that has about a 75% chance of winning any given match versus non-pro players). The results really highlight how two high skill players battling it out for one strike at a time can really draw out the end of the tournament:
Strikes: 24 ~ Players: 60 ~ ‘Pro’ players: 0 ~ Swiss groups: true
Average rounds: 24.9981 ~ Median rounds: 25
Probability that the event will take at least X rounds:
22 rounds: 95.0%
23 rounds: 75.0%
24 rounds: 50.0%
25 rounds: 31.0%
26 rounds: 19.0%
27 rounds: 11.0%
28 rounds: 7.0%
Strikes: 24 ~ Players: 60 ~ ‘Pro’ players: 2 ~ Swiss groups: true
Average rounds: 29.1298 ~ Median rounds: 29
Probability that the event will take at least X rounds:
23 rounds: 90.0%
24 rounds: 81.0%
25 rounds: 73.0%
26 rounds: 65.0%
27 rounds: 58.0%
28 rounds: 50.0%
29 rounds: 43.0%
30 rounds: 36.0%
31 rounds: 29.0%
32 rounds: 24.0%
33 rounds: 18.0%
34 rounds: 14.0%
35 rounds: 10.0%
36 rounds: 7.0%
Strikes: 24 ~ Players: 60 ~ ‘Pro’ players: 4 ~ Swiss groups: true
Average rounds: 26.2505 ~ Median rounds: 26
Probability that the event will take at least X rounds:
23 rounds: 89.0%
24 rounds: 70.0%
25 rounds: 51.0%
26 rounds: 37.0%
27 rounds: 26.0%
28 rounds: 18.0%
29 rounds: 12.0%
30 rounds: 8.0%