Post-Final Swimming Analysis: When the Stopwatch Does Not Tell the Whole Story

At the women's 100m freestyle final at this year's Australian national championships, a number appeared on the results board that almost nobody in the stands noticed. The runner-up had a reaction time 0.07 seconds faster than the winner, a gap considered significant at this level. She also had a higher average speed over the first two lengths. But at the wall, she finished 0.12 seconds behind. A coach standing next to me muttered: "She swam faster and still lost." That statement is physically wrong. But it touches a problem the results board never prints: early-race speed and late-race speed are two different stories, and only one of them decides the medal.

This is not the first time I have watched a swimmer win on every average metric but lose at the finish. In five years covering swimming for the Australian market, I have learned that every lane is a sequence of small decisions, and the stopwatch only records the final sum of them. Behind it lie dozens of hidden variables: underwater depth, stroke rate, breathing count, turn angle at the wall, and the coach's decision about how to distribute effort across each length.

Context of the analysis

The women's 100m freestyle at the Australian national level has a peculiarity that outsiders rarely notice: the density of competition is so high that the gap between the top four places is often under 0.3 seconds. That means a small error in the start phase or a missed turn can push you from gold to fourth. With such a large sample size, reading the final time alone is an extremely irresponsible way to interpret the result.

My data set for this season has three sources: the official split board from the organisers, stroke-rate data measured by sensors fitted in the goggles of the top two swimmers, and high-speed video for analysing the underwater phase. My approach is to place two curves side by side: the instantaneous speed curve by metre and the stroke-rate curve. When these two curves diverge, that is where the data is saying something the coach cannot see on paper.

Before getting to the specific numbers, I want to state a principle I hold myself to: every analysis must begin with a research question, not a conclusion. For this final, my question was simple: why did a swimmer with a better reaction time and higher speed over the first two lengths finish behind? If I can answer it, we have an analytical model that can be applied to other events.

Core analysis: the evidence chain

Let us start with the raw data. Swimmer A, the winner, had a reaction time of 0.68 seconds. Swimmer B, the runner-up, had 0.61 seconds. At the first 50m, Swimmer B hit the wall at 25.41 seconds, 0.09 seconds faster than A. At the second 50m, Swimmer B finished with a 27.18-second split, while A swam 26.90 seconds. That is the entire story in two numbers.

But stopping there is too simple. What is worth noting is this: Swimmer B did not lose on speed. She lost on stroke count. Sensor data shows that over the second 50m, B's stroke rate dropped from 52 strokes per minute to 47, while her distance per stroke rose only from 2.05m to 2.12m. She chose a slower rhythm but did not make up for it with greater propulsion. This imbalance is the direct cause of the 0.28-second gap over the second length.

This is the point most analysts miss: a swimmer does not lose because she swims slowly, but because she chooses the wrong ratio between stroke rate and stroke length. Swimmer A did the opposite. Over the second 50m, her stroke rate rose from 49 to 51 strokes per minute, and her distance per stroke stayed at 2.08m. She did not swim with more force; she simply held a steady rhythm and let her fitness turn into closing speed.

This leads to a counterintuitive conclusion: in the 100m freestyle, a good start and a fast first length can be a trap if the swimmer has no plan for distributing effort over the final 25m. My data across 12 women's 100m freestyle finals over three years shows that in 9 of 12 cases, the leader at the 50m mark was not the champion. That ratio is much higher than the spectator's intuition.

Another variable the results board does not record: the depth of the underwater phase after the start. Swimmer B surfaced at 12.3m, earlier than the optimal range recommended for the 100m freestyle (around 14-15m). She saved time off the block but traded it for lost glide. Swimmer A surfaced at 14.7m. In a race where each metre is worth about 0.85 seconds at peak speed, surfacing 2.4m early means B shifted into the more energy-expensive kicking phase almost 2 seconds sooner than A. The fitness A saved from a better underwater phase is exactly what she used in the final 25m.

I cross-checked this data with a simple regression model, using the second 50m time as the dependent variable and three independent variables: reaction time, underwater depth, and stroke rate over the first 50m. The result showed that underwater depth was the variable most strongly correlated with the second split (a correlation coefficient of about 0.62 in the sample of 12 races), stronger than even reaction time. This is information any coach should consider when planning training for 100m freestyle swimmers.

The counterintuitive angle

But before you nod along with the conclusion above, I must issue a warning about the limits of the very data I have just presented. A correlation coefficient of 0.62 does not mean underwater depth determines 62% of performance. Correlation is not causation. It is possible that both are the result of a third variable I have not measured: the ability to stay calm under pressure. Swimmers who are good at the underwater phase are often also psychologically stable, and it may be that stability itself is what helps them distribute effort correctly.

Moreover, a sample size of 12 races is far too small to draw a firm conclusion. A swimming season has hundreds of races, and deriving a rule from 12 of them shows only a trend, not a law. I once watched a model with 99% probability collapse right on the betting table. Kazan was the day I learned that numbers do not protect you from what you cannot measure.

There is one more factor the machine does not record: the coach's decision. Swimmer B may have been instructed to swim fast over the first 50m to apply psychological pressure on her rival. If that was the tactic, then losing by 0.12 seconds is a failure of tactics, not of fitness. But we cannot know that from the outside. This is the zone of ambiguous data that I always force myself to acknowledge before making a judgment.

The limits of the data

I must admit there is a zone in this analysis that numbers cannot reach. The swimmer's feeling as she hits the wall at the first 50m, the roar of the crowd, the pressure of an international selection spot waiting in her file – these are all data, but we do not yet have the tools to measure them in milliseconds. I once wrote in a EURO piece that emotion is also data, we just lack the tools. That holds true in swimming as well.

A swimmer is not a machine with fixed technical specifications. She sleeps less because she is anxious, she swims in a pool at a different temperature from her training pool, and she has a family waiting for the result at home. None of that appears on the split board, but all of it appears in every stroke of the final 25m.

Signals for the next cycle

What I will track in the coming meets is not the final time but three metrics: the depth of the surfacing phase, the ratio of stroke rate between first and last length, and the final 25m split. If a swimmer improves her final 25m split without raising stroke rate, that is a sign of an optimised underwater phase. If the opposite, it is a sign she is swimming on power rather than technique.

Numbers have no gender, but the people who read them do. Every time I sit down to analyse a lane, I remind myself that behind that 0.12-second number is a woman who spent four years training for that moment. Data has no gender, but the way we use data to judge an athlete is full of bias. The question I want to leave for the next meet is not who will win, but: how are we measuring a swimmer's performance, and what are we leaving out every time we look at the results board?