Foreign Direct Investment International Arbitration Moot


Foreign Direct Investment International Arbitration Moot

Matches Results 2022 *

Preliminary Rounds

Match winner underlined. * =match result pending

Time Room Claimant Respondent
Sat 2:301Bedjaoui (6, 420.2500)Colliard (6, 471.2500)
Sat 2:302Gonzales (6, 377.2500)Tarassov (6, 530.2500)
Sat 8:001Winiarski (6, 300.2500)Tarassov (6, 456.2500)
Sat 8:002Barwick (6, 300.2500)Gonzales (6, 421.2500)
Sat 12:003Tarassov (6, 457.2500)Barwick (6, 300.2500)
Sat 12:001Bedjaoui (6, 443.2500)Winiarski (6, 300.2500)
Sat 12:002Colliard (6, 452.2500)Oxman (6, 427.2500)
Sun 2:301Barwick (6, 300.2500)Oxman (6, 482.2500)
Sun 2:302Gonzales (6, 395.2500)Bedjaoui (6, 436.2500)
Sun 8:002Oxman (6, 526.2500)Gonzales (6, 405.2500)
Sun 8:001Colliard (6, 390.2500)Winiarski (6, 300.2500)
Sun 12:002Oxman (6, 398.2500)Bedjaoui (6, 389.2500)
Sun 12:003Tarassov (6, 485.2500)Colliard (6, 452.2500)
Sun 12:001Winiarski (6, 300.2500)Barwick (6, 300.2500)

Victors by Group

GroupTeamWinsSBVotesz-points
1Tarassov45121805.60
1Colliard3591642.60
1Oxman3481710.60
1Bedjaoui2171565.60
1Gonzales1131475.60

Within each group, teams are ranked according to:
Wins are matches won on the basis of the total raw scores of the arbitrators (caveat: raw scores can be distorted by individual high- or low scoring arbitrators).

SB is the total number of wins of those teams against which a team has won. This criterion takes account of the strength of the opposition that a team has faced.Neustadtl Sonneborn-Berger

Votes is the total number of arbitrators who in each match have given a team’s two advocates a higher aggregate point total. This criterion flattens out the phenomenon of high- or low scorers and takes account that matches may be won 3:0, 2:1 or even rarely 1:2.

z-Points are the total normalised points a team has received in its four preliminary round matches. It is rare that over four matches two teams would have exactly the same number of total points, so this can provide a tie-break where other methods fail. Normalising using the “z-score” formula (using standard deviations) seeks to mitigate inequities caused by high- or low scorers (caveat: the quality of statistical methods depends on many variables).

Teams that won no matches are not shown above.