Running Characteristics of Female Triathletes Qualifying for the 2022 Ironman World Championship

As a continuation to my previous entry, in this post, I analyzed trends among the females I tested in 2022 who were training for Ironman distances & focused on those who targeted qualifying for the Ironman World Championship in the same year.

Important to note that the info provided next is not a definitive statement regarding Ironman female age group racing. The goal is to identify common traits for female amateurs performing well at the top of their respective age groups. 

Like my last entry, I included the data of female triathletes I tested racing Ironman or pursuing a Kona qualification. In addition, I looked at the data between October 2021 & August 2022 to include popular end-of-the-year North American races such as Ironman Florida, Cozumel & Arizona until races offering the final slots for the 2022 world championship.

My run data sample is larger versus cycling, as some of the females I tested only did a run test versus both sports. I tested around 45 female triathletes, 32 were pursuing a championship spot & 75% achieved their goal. Below is a breakdown of the age group placing for those earning a sport for the championship

Next is the relationship of blood lactate accumulation (BLa) at their first inflection point, known as the first Lactate Threshold (LT1) for all athletes tested. 80% of all subjects had a BLa of 1.9 mmol/L or lower

For those that qualified in the age groups between 30 and 55 years old, 58% had a lactate accumulation of 1.7 mmol/L or lower at LT1, 25% with a lactate accumulation between 1.8-1.9 mmol/L at LT1, and 8% had lactate accumulation of 2 mmol/L at LT1. None of the females I tested with a lactate accumulation of 2.1 mmol/L or higher at LT1 earned a slot.

Below I also included the pace (minute/mile) at their respective LT1 and LT1 and the delta between each. Unlike the data collected for cycling, in the athletes I tested, the delta average was ~14% LT1 vs. LT2 pace, while for cycling, the delta was more than ~20%>. 

Depending on the athlete's fitness, a top age grouper can generally race near LT1 pace (within ~2% pace-wise or ~5 bpm heart rate-wise). However, I found no significant relationship between lactate accumulation at LT1 and their ability to run near LT1 for Ironman. Below you can see the avg LT1 and Ironman pace (min/mile) and the delta by age group placing. 

Those finishing 1st-2nd ran ~6.5% slower than LT1 pace, those placing 3rd-4th ran ~2% slower than LT1 pace, and those placing 5th ran ~9% slower. It's possible those at the front of their age group may run slower than LT1 pace as they may pace the swim/bike more aggressively, start the run a bit too fast, don't fuel adequately, or lack the durability to run at LT1 pace and thus, forming a strategy with this in mind may work best. 

Still, diving deeper into this and comparing the bike vs. run performances, training volume & distribution, etc., in the next post will be interesting.

Finally, the following table shows the lactate accumulation at LT1, LT1 pace, and the LT1 pace vs. Ironman pace delta for the best performances qualifying for Kona. And yet again, unlike the previous cycling delta, it isn't easy to make definitive observations regarding BLa at LT1, pace, etc. 

Something to notice for the 25% of the athletes seeking a Kona slot but didn't achieve their goal, lactate accumulation was in the 1.9> mmol/L range, LT1 vs. LT2 pace delta was in the 15%> and their LT1 pace vs. Ironman pace delta was above 13%. Still, drawing any definitive conclusion from all the data collected is difficult.

In summary, for the 2022 season, some of the characteristics female triathletes exhibited to qualify for the Ironman Wilrd championship 30-55 age groups cycling-wise were: 

1. A lactate accumulation of ~<2.0 mmol/L at LT1.

2. An LT1 vs. LT2 pace delta of ~14% or lower

3. An LT1 vs. Ironman pace delta of 9% or lower

Next post, I'll discuss possible trends between their bike vs. their run metabolic profiles plus the training distribution & load of those I have access to their training data.