Results from the eight handicap of the 2026 series, held on 03/10/2026. The keys and explanations of the columns are at the bottom of the page.
| Pos | Bib | Name | Category | Gender | AG Time | Time | ID | HTime | Improvement (Secs) | Imp% | Race# | HPos | Points | Details |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | 721 | Melissa O’Hare | V50 | Female | 19:02.1 | 24:52.2 | 26604O’HareMelissa | 1250 | 108 | 8.64% | 8 | 1 | 100 | More details |
| 32 | 782 | Tracey Pearson | V50 | Female | 24:40.1 | 34:39.9 | 24458PearsonTracey | 1588 | 108 | 6.80% | 8 | 2 | 99 | More details |
| 25 | 771 | Geraldine Turner | V60 | Female | 18:56.5 | 29:11.4 | 21594TurnerGeraldine | 1218 | 82 | 6.73% | 8 | 3 | 98 | More details |
| 10 | 788 | Siobhan Weir | V40 | Female | 18:59.5 | 23:11.1 | 28653WeirSiobhan | 1205 | 66 | 5.48% | 8 | 4 | 97 | More details |
| 17 | 798 | Shing Man | V60 | Male | 19:22.7 | 24:51.7 | 22586ManShing | 1205 | 43 | 3.57% | 8 | 5 | 96 | More details |
| 21 | 750 | Nathalia Kamonlakorn | V60 | Female | 19:41.3 | 28:22.6 | 61KamonlakornNathalia | 1216 | 35 | 2.88% | 8 | 6 | 95 | More details |
| 13 | 719 | Sue Thomas | V50 | Female | 18:13.7 | 24:41.0 | 25261ThomasSue | 1116 | 23 | 2.06% | 8 | 7 | 94 | More details |
| 23 | 775 | Clare Murphy | V50 | Female | 20:30.6 | 28:27.7 | 24862MurphyClare | 1253 | 23 | 1.84% | 8 | 8 | 93 | More details |
| 24 | 737 | Imke Siegerist | V50 | Female | 21:35.4 | 28:52.9 | 55SiegeristImke | 1311 | 16 | 1.22% | 8 | 9 | 92 | More details |
| 34 | 714 | Janet Clarke | V60 | Female | 23:29.2 | 34:44.9 | 22905ClarkeJanet | 1426 | 17 | 1.19% | 8 | 10 | 91 | More details |
| 1 | 787 | David Weir | V40 | Male | 16:36.6 | 18:20.6 | 29144WeirDavid | 997 | 1 | 0.10% | 8 | 11 | 90 | More details |
| 4 | 773 | Scott Aiken | V50 | Male | 16:43.4 | 19:51.0 | 25729AikenScott | 1004 | 1 | 0.10% | 8 | 12 | 89 | More details |
| 27 | 705 | Christopher Thompson | V60 | Male | 22:36.5 | 29:48.5 | 21413ThompsonChristopher | 1357 | 1 | 0.07% | 8 | 13 | 88 | More details |
| 12 | 764 | Sebastian Ampofo | Senior | Male | 24:25.6 | 24:25.6 | 35193AmpofoSebastian | 1458 | -7 | -0.48% | 8 | 14 | 87 | More details |
| 35 | 707 | Kathy Allman | V60 | Female | 23:59.5 | 35:02.0 | 23067AllmanKathy | 1417 | -22 | -1.55% | 8 | 15 | 86 | More details |
| 37 | 706 | Sylvia Goodman | V70 | Female | 21:52.3 | 38:28.8 | 18516GoodmanSylvia | 1291 | -21 | -1.63% | 8 | 16 | 85 | More details |
| 19 | 740 | James Nash | V40 | Male | 24:34.2 | 25:22.2 | 37NashJames | 1449 | -25 | -1.73% | 8 | 17 | 84 | More details |
| 31 | 746 | David Paul | V70 | Male | 22:09.6 | 32:56.2 | 76PaulDavid | 1302 | -27 | -2.07% | 8 | 18 | 83 | More details |
| 8 | 786 | Edwin Coutts | V50 | Male | 19:54.0 | 22:40.7 | 27670CouttsEdwin | 1159 | -35 | -3.02% | 8 | 19 | 82 | More details |
| 3 | 774 | Jim Arrowsmith | V40 | Male | 17:42.2 | 19:33.0 | 29046ArrowsmithJim | 1025 | -37 | -3.61% | 8 | 20 | 81 | More details |
| 29 | 716 | Ian Barber | V60 | Male | 22:59.2 | 30:35.5 | 21028BarberIan | 1299 | -80 | -6.16% | 8 | 21 | 80 | More details |
| 6 | 813 | Robert Sikorski | V50 | Male | 18:46.7 | 21:24.0 | 27488SikorskiRobert | 1060 | -66 | -6.23% | 8 | 22 | 79 | More details |
| 20 | 778 | Christian Eldrett | V50 | Male | 24:05.3 | 28:21.3 | 26115EldrettChristian | 1327 | -118 | -8.89% | 8 | 23 | 78 | More details |
| 30 | 720 | Brian Bowie | V70 | Male | 22:36.4 | 31:32.0 | 19485BowieBrian | 1163 | -193 | -16.60% | 8 | 24 | 77 | More details |
| 36 | 731 | Natasha De Souza | Senior | Female | 33:50.2 | 38:21.3 | 31603De SouzaNatasha | 1694 | -336 | -19.83% | 8 | 25 | 76 | More details |
| 2 | 818 | Andy Naylor | V40 | Male | 17:26.7 | 18:33.0 | 30760NaylorAndy | 0 | -100.00% | 8 | 33 | 0 | More details | |
| 5 | 805 | Kat Kruse | V40 | Female | 17:29.7 | 19:58.5 | 31194KruseKat | 1077 | 28 | 2.60% | 8 | 27 | 0 | More details |
| 7 | 816 | Sasha Nenasheva | Senior | Female | 19:47.9 | 21:31.3 | 35160NenashevaSasha | 0 | -100.00% | 8 | 34 | 0 | More details | |
| 9 | 817 | Jeremy Newton | V60 | Male | 18:31.8 | 23:08.9 | 23674NewtonJeremy | 0 | -100.00% | 8 | 35 | 0 | More details | |
| 11 | 820 | Aimee Jenner | V40 | Female | 19:00.4 | 23:12.2 | 28861JennerAimee | 0 | -100.00% | 8 | 36 | 0 | More details | |
| 14 | 803 | Anna Sikorska | V50 | Female | 18:55.4 | 24:43.4 | 26473SikorskaAnna | 1122 | -13 | -1.16% | 8 | 31 | 0 | More details |
| 15 | 819 | Tony Riley | V50 | Male | 21:12.6 | 24:45.8 | 26420RileyTony | 1267 | -5 | -0.39% | 8 | 29 | 0 | More details |
| 16 | 815 | Mark O'Donoghue | Senior | Male | 24:00.9 | 24:47.8 | 32527O'DonoghueMark | 0 | -100.00% | 8 | 37 | 0 | More details | |
| 22 | 806 | Danielle Nash | Senior | Female | 25:47.9 | 28:26.9 | 33325NashDanielle | 1620 | 73 | 4.51% | 8 | 26 | 0 | More details |
| 26 | 812 | Diana Reyes | Senior | Female | 26:53.2 | 29:31.8 | 33887ReyesDiana | 0 | -100.00% | 8 | 38 | 0 | More details | |
| 28 | 793 | Koon Tang | V50 | Male | 25:05.4 | 30:01.8 | 1969-08-22TangKoon | 1515 | 10 | 0.66% | 8 | 28 | 0 | More details |
| 33 | 821 | Nikki Nufer | V60 | Female | 24:22.7 | 34:41.6 | 24057NuferNikki | 0 | -100.00% | 8 | 39 | 0 | More details | |
| 38 | 822 | Angela Coyle | V40 | Female | 32:55.6 | 38:33.3 | 30343CoyleAngela | 0 | -100.00% | 8 | 40 | 0 | More details | |
| 39 | 785 | Maeve McEldowney | V40 | Female | 33:14.7 | 38:34.5 | 30453McEldowneyMaeve | 1980 | -14 | -0.71% | 8 | 30 | 0 | More details |
| 40 | 759 | Lauren Tidmarsh | Senior | Female | 35:34.3 | 38:35.3 | 35839TidmarshLauren | 2055 | -79 | -3.84% | 8 | 32 | 0 | More details |
| 41 | 814 | Tali Swart | V60 | Female | 25:27.6 | 38:41.5 | 22085SwartTali | 0 | -100.00% | 8 | 41 | 0 | More details | |
| 42 | 811 | Nina Baker | V50 | Female | 29:38.0 | 38:43.0 | 26549BakerNina | 0 | -100.00% | 8 | 42 | 0 | More details |
Pos:
Bib:
Name:
Category:
Gender:
AG Time:
Time:
HTime:
Improvement (Secs):
Imp%:
HPos:
Points:
HPOS = Handicap position. This is 0 if the athlete does not currently have a handicap time set. They need to run 2, or more races, to set a handicap time.
POS = Actual finishing position based on the actual time.
BIB = Their number
Category = Age group
AG Time = Age-graded time. Uses the tables published by the World Masters Athletics.
Time = Actual finish time.
HTime = Handicap Time in seconds. The athlete’s best time for the handicap races. They need to have 2 races to set a handicap time.
Improvement = The improvement in seconds. Which is the Handicap Time minus the Age-Graded Time.
Imp % = Improvement as a percentage. Improvement in seconds divided by Handicap time.
Points = Points awarded for the race. The athletes need a handicap time to be awarded points.