Update (April 16th): Since originally posting I have added an age-weighted component to the numbers, so that newer results have a larger influence than older ones. As you can see from the changes in the numbers, this adds another interesting dimension to the numbers in this post.
I love getting feedback on my analysis and predictions – very often, they trigger some new, interesting way of looking at the data. For example, Linsey Corbin made the following remark to me:
I wish there was a way that your predictions could show consistency. One thing I pride myself on is being fairly consistent across the board.
Thanks for the suggestion, Linsey (and great to see you back to racing)! I have been looking at different ways of attacking this question, here is what I was able to come up with. I will continue to monitor these numbers for upcoming races, maybe and I’ll include them in future predictions.
Deviation
In statistics, there are a number of way to measure how “consistent” a set of data is. The most common way to express variability in data sets is the “Standard Deviation“. StdDev basically measures the distance of data points from the average value – the more “outliers” there are and the further off they are, the higher the standard deviation.
This was my first try of analyzing consistency. The data analysis part is pretty simple, as the function is built into all kinds of programs. However, the results were not very helpful: In essence it helped identify athletes that had one or more sub-standard results, e.g because of walking large parts of the marathon in a race. For example, Lucy Gossage showed up as an inconsistent athlete with a large deviation, but that was almost exclusively a result of her marathon walk resulting in an 11:32 finish in Kona 2014. It also didn’t value “good” results: The difference of a good result to an average – maybe 30 minutes or so – is much smaller than that of a bad result – walking easily adds an hour to the overall time.
Identifying Non-standard Results and Quantifying Consistency
Even when looking at the deviation of results of each athlete did not lead to a good measure, it formed the basis for another way of looking at the data. In the familiar “bell shape” curve of the normal distribution, 68% of results fall within one standard deviation around the average. When looking at the difference between an athlete’s “expected time” and their actual finishing time, roughly 68% of the results are within 20 minutes of the expected time. Based on this I classify results within 20 minutes of the expected finishing time as “normal”, and any result quicker as “better” results and anything slower and DNFs as “sub-par” results.
I can then aggregate all the results of an athlete into a figure like this:
Linsey Corbin: 83% +17% -0% (18)
Older results have less of a meaning than newer, so adding in an aging component gives the following numbers:
Linsey Corbin: 79% +21% -0% (18)
Each part has the following meaning:
- Linsey Corbin: Name of the athlete
- 79%: Fraction of normal race results
- +21%: Fraction of “better than expected” race results
- -0%: Fraction of “sub-par” race results (including DNFs)
(Note: Technically, Linsey has at least one DNF in her Ironman races – she didn’t finish IM Texas in 2011. This is a limitation in my data – I have only been including DNF’s since 2014.)
- (18): Total number of Ironman-distance results (including DNFs)
Average numbers are about 68% of normal results and roughly 15-20% each of better and sub-par results, but these numbers vary wildly between athletes.
Examples
Here are some more numbers from well known athletes – put into different groups. (As I have updated my algorithm a bit since posting for the first time, I am also including the originally posted numbers in [square brackets].)
Stable Athletes
- Andy Potts: 100% +0% -0% (13) [originally posted: 100% +0% -0% (13)]
- Yvonne Van Vlerken: 84% +0% -16% (23) [originally posted: 91% +0% -9% (23)]
- Lucy Gossage: 92% +0% -8% (12) [originally posted: 91% +0% -9% (11)]
- Sebastian Kienle: 85% +12% -3% (11) [originally posted: 82% +9% -9% (11)]
These are athletes where predictions are a very good indicator of how they’ll perform on race day – they usually perform on a very similar level from race to race.
Normal Stability
- Jodie Swallow: 55% +0% -45% (10) [originally posted: 78% +0% -22% (9) – she has since DNF’d in South Africa]
- Caroline Steffen: 92% +8% -0% (20) [originally posted: 75% +25% -0% (20)]
- Meredith Kessler: 65% +14% -20% (23) [originally posted: 70% +17% -13% (23)]
- Andreas Raelert: 48% +0% -52% (19) [originally posted: 63% +0% -37% (19)]
- Luke McKenzie: 51% +30% -19% (26) [originally posted: 62% +23% -15% (26)]
For these athletes predictions give a good indication, but it is also interesting whether there is a higher potential for an “up-side”, better-than-expected result (larger percentage of faster results, e.g. Carolin Steffen) or for a “down-side” result (larger percentage of sub-par results, e.g. Jodie Swallow or Andreas Raelert). For other athletes, the day could go either way (e.g. Meredith Kessler or Luke McKenzie).
Lower Stability
- Sarah Piampiano: 41% +47% -12% (14) [originally posted: 50% +43% -7% (14)]
- Luke Bell: 23% +5% -72% (26) [originally posted: 38% +12% -50% (26)]
- Dede Griesbauer: 41% +18% -40% (26) [originally posted: 32% +32% -36% (25)]
- Tim O’Donnell: 14% +63% -23% (11) [originally posted: 27% +45% -27% (11)]
- Pete Jacobs: 5% +16% -79% (26) [originally posted: 15% +42% -42% (26)]
Then there are athletes that have a lower fraction of “normal” results. Here it’s also interesting to look at the upside (e.g. Sarah Piampiano, Tim O’Donnell) or downside potential (e.g. Luke Bell). Some athletes’ results are very hard to predict from previous numbers – for example Dede Griesbauer and Pete Jacobs have had a good fraction of great results but also slower, disappointing results.
Update (Apr 27th): Brad Kahlefeldt