Kenanga Man of Steel Triathlon 123

Filter age groups

Registered vs. Finished / DNF / DNS, by age group

Click a legend entry (Finished / DNF / DNS) to hide that status across every group. Use the checkboxes above to focus on specific age groups — the table below stays in sync.

Age group breakdown

Fastest splits

Swim (top 5)

Bike (top 5)

Run (top 5)

Fastest female splits

Inge Prasetyo (bib 859, Women 45-49) swept all three: fastest female swim, bike, and run.

Most competitive age groups, by average finish time

Ranked fastest to slowest average finish time among finishers. Groups with very few finishers (n < 5) are not statistically meaningful — one fast or slow athlete swings the whole average — so treat those with caution.

Headline finding: among groups with a statistically meaningful field, Men 35-39 is clearly the most competitive age group — 37 finishers averaging 6:26:36, over 40 minutes faster than the next-largest group, with a fast winner (4:47:21) pulling the whole field rather than one outlier skewing it.

The complete triathlete vs. the specialist

Using percentile rank across all three disciplines (231 finishers with complete Swim/Bike/Run splits).

Most dominant across all three legs (best average percentile)

Teh Kuok Yuen (bib 663) is the standout: top-10 in every single leg, and the outright race winner. Also worth highlighting — the correlation between leg ranks across the whole field is Swim↔Bike 0.49, Swim↔Run 0.50, Bike↔Run 0.71. Bike and run performance track each other much more closely than either does with swim, which makes sense: swimming is a more specialized skill, while bike and run fitness overlap more.

Biggest specialists (largest spread between best and worst discipline percentile)

Simon Peter (604) is a clean example of a pure swim specialist: 2nd-fastest swimmer in the entire field, but mid-to-back-of-pack on the bike and run. (Bib 716's swim/bike/run mismatch is unusually extreme — see Course notes, where it's flagged as a data-quality item rather than simply a "specialist" story.)

Biggest movers (comeback stories)

Position gained/lost, tracked by cumulative time at each checkpoint (swim finish → bike finish → race finish), among the 231 finishers with complete splits.

Biggest run comebacks (bike-finish position → final position)

Five of the six biggest run comebacks belong to Men 35-39 — which helps explain why that age group posted the fastest average finish time in Competitiveness: it wasn't just a fast front pack, the whole field surged late.

Biggest bike comebacks (swim-exit position → bike-finish position)

Transition efficiency

Fastest T1 (swim → bike)

Fastest T2 (bike → run)

Photo finishes

Three exact dead-heat ties to the second (different age groups, so no placing conflicts).

Country breakdown

9 nationalities were represented. Unsurprisingly for a domestic race, 90% of the field (239 of 265) was Malaysian.

Indonesia's 11 athletes averaged over an hour faster than the Malaysian field — driven by several of the race's fastest overall finishers (Marlingga Alfedo Prima, Andy Wibowo, Andy Lordianto, Inge Prasetyo) being Indonesian. Small-sample caveat applies to every country except MAS and INA.

By age group (only groups with international entries shown)

• Men 25-29 years (n=16): 15 MAS, 1 INA
• Men 35-39 years (n=41): 38 MAS, 1 BRN, 1 GBR, 1 INA
• Men 40-44 years (n=50): 47 MAS, 2 INA, 1 BRN
• Men 45-49 years (n=32): 27 MAS, 3 INA, 1 HKG, 1 SGP
• Men 50-54 years (n=27): 24 MAS, 2 GBR, 1 HKG
• Men 55-59 years (n=12): 9 MAS, 2 SGP, 1 INA
• Men 60-64 years (n=9): 7 MAS, 1 BEL, 1 GBR
• Men 65 years & above (n=3): 2 MAS, 1 NED
• Women 18-24 years (n=3): 1 MAS, 1 SGP, 1 USA
• Women 35-39 years (n=3): 2 MAS, 1 INA
• Women 40-44 years (n=8): 7 MAS, 1 INA
• Women 45-49 years (n=11): 10 MAS, 1 INA

Fully Malaysian groups: Men 18-24, Men 30-34, Women 25-29, Women 30-34, Women 50-54, Women 55-59, Women 60-64. Interesting angle: the international field skews toward the masters brackets (Men 45+ has multiple countries represented in 4 of its 5 groups) rather than the younger open categories.

Course distance & timing-data integrity notes

The advertised course was 2 km swim / 98 km bike / 23 km run. Several athletes' own GPS devices measured closer to 94 km on the bike and just under 23 km on the run. This section documents what the official timing data does and doesn't support as an explanation.

Methodology: the bike course was timed as 4 laps (Bike Lap 0-3 + Bike Finish) and the run as 2 laps split into halves (Run Lap 1.1 / 1.2 / 2.1 / 2.2 + Finish) — each a separate timing-mat checkpoint, in addition to the aggregate Swim/Bike/Run leg times used elsewhere in this report.

• Of 265 registered athletes, 28 have no valid bike-leg time and 33 have no valid run-leg time at all (this mostly overlaps with the 31 DNF/DNS athletes in Participation).
• Among finishers, a handful have a valid overall leg time but are missing one or more intermediate lap-mat reads. In most of these cases the leg time is entirely consistent with the athlete's pace on their other two legs — a normal-looking finish with a gap in the mat data — which is the signature of an ordinary chip/timing-mat read failure (common at lap and turnaround points in mass-participation multisport events), not a shortcut.

• Two officially-finished athletes (bibs 745 and 822) have zero valid run-leg mat data at all despite an official finish time on the board — the biggest simple data gap in the field, and also worth a data-quality check.
• These few cases, even taken at face value, are far too small a sample (at most 1-3 athletes out of 234 finishers) to explain a field-wide ~4 km bike shortfall. A more likely explanation for the overall distance gap is that GPS watches systematically under-measure distance on multi-lap courses with turnarounds (the recorded track "cuts the corner" on every U-turn relative to the road actually ridden/run), an effect that compounds over 4 bike loops and 2 run loops. It's also possible the course was simply marked/measured slightly short of the advertised distance. Comparing raw GPX files from several independent athletes against the official course map would be a more conclusive way to settle this than inferring it from timing data.