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April 2016

Ironman Australia 2016 (May 1st) – Predictions

IMAUSLogo Update (Apr 27th): Brad Kahlefeldt announced on Instagram that he suffered a labral tear during his last long training run after avoiding a dog attack. He hopes to be back to running after a short break and then race IM Cairns.

Previous Winners

Year Male Winner Time Female Winner Time
2005 Chris McCormack (AUS) 08:25:44 Lisa Bentley (CAN) 09:13:20
2006 Chris McCormack (AUS) 08:20:42 Lisa Bentley (CAN) 09:19:44
2007 Patrick Vernay (NCL) 08:21:49 Rebekah Keat (AUS) 09:12:59
2008 Patrick Vernay (NCL) 08:31:32 Chrissie Wellington (GBR) 09:03:54
2009 Patrick Vernay (NCL) 08:24:53 Chrissie Wellington (GBR) 08:57:10
2010 Patrick Vernay (NCL) 08:23:54 Carrie Lester (AUS) 09:23:46
2011 Pete Jacobs (AUS) 08:29:28 Caroline Steffen (SUI) 09:29:54
2012 Paul Ambrose (AUS) 08:17:38 Michelle Gailey (AUS) 09:34:57
2013 Luke Bell (AUS) 08:30:23 Rebecca Hoschke (AUS) 09:34:55
2014 Elliot Holtham (CAN) 08:35:18 Melissa Hauschildt (AUS) 09:28:43
2015 Paul Ambrose (AUS) 08:35:53 Michelle Bremer (NZL) 09:38:24

Last Year’s TOP 3

Male Race Results

Rank Name Nation Swim Bike Run Time
1 Paul Ambrose AUS 00:47:34 04:39:24 03:05:22 08:35:53
2 Luke Bell AUS 00:44:58 04:49:01 03:01:01 08:38:34
3 Brian Fuller AUS 00:48:28 04:49:53 03:08:30 08:49:39

Female Race Results

Rank Name Nation Swim Bike Run Time
1 Michelle Bremer NZL 00:53:13 05:18:15 03:23:17 09:38:24
2 Jessica Fleming AUS 00:56:26 05:11:50 03:30:35 09:42:18
3 Michelle Gailey AUS 00:52:54 05:24:59 03:28:56 09:50:51

Course Records

Leg Gender Record Athlete Date
Total overall 08:17:38 Paul Ambrose 2012-04-29
Swim overall 00:44:24 Luke Bell 2014-05-04
Bike overall 04:31:25 Paul Ambrose 2012-04-29
Run overall 02:47:20 Tim Van Berkel 2012-04-29
Total female 08:57:10 Chrissie Wellington 2009-04-05
Swim female 00:48:42 Melissa Ashton 2006-05-11
Bike female 05:00:57 Carrie Lester 2010-03-28
Run female 03:01:06 Lisa Bentley 2006-05-11

Course Rating

The Course Rating for IM Australia is 04:32.

Race Adjustments for IM Australia

Year Adjustment Swim Adj. Bike Adj. Run Adj. # of Finishers Rating Swim Rating Bike Rating Run Rating
2006 01:23 02:10 -07:18 00:32 30 01:23 02:10 -07:18 00:32
2007 03:43 01:45 -05:11 00:32 36 02:33 01:58 -06:15 00:32
2008 01:05 00:00 -02:59 00:48 24 02:03 01:18 -05:09 00:37
2009 02:39 -00:16 -04:18 02:26 28 02:12 00:55 -04:57 01:04
2010 08:03 04:55 02:30 00:04 18 03:22 01:43 -03:27 00:52
2011 02:12 02:16 -02:28 01:46 14 03:11 01:48 -03:17 01:01
2012 05:11 02:22 01:24 03:52 9 03:28 01:53 -02:37 01:26
2013 12:01 01:37 -03:18 07:56 9 04:32 01:51 -02:42 02:14
2014 11:02 03:35 -02:25 08:23 15 of 17 05:15 02:03 -02:40 02:55
2015 -01:55 02:36 -00:10 -00:37 18 of 24 04:32 02:06 -02:25 02:34

KPR points and Prize Money

IM Australia is a P-2000 race. It has a total prize purse of 50.000 US$.

Male Race Participants

This is the first time I’m including a “Consistency” in my pre-race post. It shows the fraction of races where an athlete has been about as expected (the first number), significantly faster (the number with the “+”) or significantly slower (including DNFs, the number with the “-“) and also the total number of IM starts. For more information have a look at my post on “Consistency of Athletes“.

Rank Bib Name Nation Expected Time Rating Exp. Swim Exp. Bike Exp. Run Consistency Overall
1 3 Brad Kahlefeldt AUS 08:22:52 08:42:29 00:44:05 04:46:31 02:47:16 43% +0% -57% (2) (59)
2 4 David Dellow AUS 08:24:37 08:26:12 00:46:02 04:41:35 02:52:00 58% +3% -39% (12) 14
3 7 Dougal Allan NZL 08:28:47 08:36:07 00:56:30 04:30:56 02:56:20 70% +13% -17% (6) 39
4 1 Paul Ambrose AUS 08:34:11 08:54:58 00:47:17 04:40:06 03:01:48 47% +4% -48% (19) 103
5 2 Luke Bell AUS 08:34:31 08:59:15 00:45:08 04:44:53 02:59:29 23% +5% -72% (26) 119
6 14 Mitch Dean AUS 08:45:02 09:00:07 00:46:37 04:50:15 03:03:09 100% +0% -0% (1) (124)
7 6 Tim Reed AUS 08:47:15 08:59:59 00:45:44 04:44:43 03:11:48 23% +28% -50% (3) (122)
8 8 Casey Munro AUS 08:47:29 09:02:28 00:45:05 04:43:37 03:13:47 47% +0% -53% (4) (133)
9 5 Clayton Fettell AUS 08:48:54 08:46:08 00:45:12 04:46:42 03:11:59 20% +0% -80% (6) 72
10 17 Jarmo Hast FIN 08:49:42 08:53:41 00:48:41 04:55:29 03:00:32 48% +6% -46% (13) 97
11 15 Michael Fox AUS 08:51:33 09:06:50 00:46:53 04:57:32 03:02:08 100% +0% -0% (1) (155)
12 13 Simon Cochrane NZL 08:53:25 09:05:31 00:49:07 04:54:56 03:04:22 78% +16% -5% (15) 153
13 11 Simon Billeau FRA 08:55:05 08:55:59 00:52:15 04:46:05 03:11:45 52% +0% -48% (16) 109
14 16 Adam Gordon AUS 09:03:16 09:19:33 00:52:35 05:06:16 02:59:26 13% +61% -26% (3) 199
15 18 Darren Jenkins AUS 09:11:58 09:19:21 01:02:04 05:05:08 02:59:46 80% +0% -20% (7) 198
16 19 James Lubinski USA 09:16:06 09:37:00 00:59:50 05:03:26 03:07:49 34% +29% -37% (15) 242
17 12 Allister Caird AUS 09:17:18 09:33:19 00:54:17 05:04:36 03:13:25 53% +0% -47% (2) (233)
18 22 Matt Randall NZL 09:50:49 10:00:14 00:56:16 05:02:31 03:47:02 55% +0% -45% (5) 275
9 Josh Amberger AUS n/a unrated unrated unrated unrated (n/a)
10 Levi Maxwell AUS n/a unrated unrated unrated unrated (n/a)
20 Samuel Murphy AUS n/a unrated unrated unrated unrated (n/a)
21 Caleb Noble AUS n/a unrated unrated unrated unrated (n/a)
23 Leigh Stabryla AUS n/a unrated unrated unrated unrated (n/a)
24 Lindsey Wall AUS n/a unrated unrated unrated unrated (n/a)

Female Race Participants

This is the first time I’m including a “Consistency” in my pre-race post. It shows the fraction of races where an athlete has been about as expected (the first number), significantly faster (the number with the “+”) or significantly slower (including DNFs, the number with the “-“) and also the total number of IM starts. For more information have a look at my post on “Consistency of Athletes“.

Rank Bib Name Nation Expected Time Rating Exp. Swim Exp. Bike Exp. Run Consistency Overall
1 32 Gina Crawford NZL 09:23:30 09:25:14 00:50:11 05:17:22 03:10:56 78% +2% -20% (37) 20
2 33 Beth Gerdes USA 09:25:35 09:35:51 00:58:02 05:19:53 03:02:40 47% +40% -14% (12) 40
3 31 Michelle Bremer NZL 09:33:00 09:44:22 00:54:33 05:15:27 03:18:00 91% +0% -9% (10) 53
4 37 Lisa Marangon AUS 09:36:50 10:08:41 00:51:09 05:11:48 03:28:54 16% +13% -71% (13) (107)
5 35 Dimity-Lee Duke AUS 09:41:50 09:46:00 00:58:20 05:22:03 03:16:27 71% +29% -0% (8) 56
6 34 Melanie Burke NZL 09:44:52 09:51:11 01:01:46 05:19:03 03:19:02 74% +15% -11% (10) 69
7 41 Alison Fitch AUS 09:46:04 09:50:08 00:51:39 05:20:13 03:29:12 100% +0% -0% (7) (65)
8 40 Wendy Mcalpine AUS 09:54:24 10:11:29 00:55:54 05:22:35 03:30:55 100% +0% -0% (1) (112)
9 38 Tamsyn Hayes NZL 10:04:12 10:23:50 00:55:49 05:21:26 03:41:56 35% +15% -50% (9) (145)
10 36 Amy Javens USA 10:16:37 10:22:56 01:04:39 05:28:37 03:38:21 100% +0% -0% (4) 141
11 39 Kristy Hallett AUS 10:17:30 10:29:26 01:00:08 05:31:15 03:41:07 58% +28% -15% (6) 160
42 Lauren Parker AUS n/a unrated unrated unrated unrated (n/a)
43 Jessica Richards AUS n/a unrated unrated unrated unrated (n/a)

Winning Odds

Male Race Participants

Even if almost all the athletes are from Australia or New Zealand, it’ll be an interesting, wide open race: Most of the top-rated athletes have been struggling a bit lately. Brad Kahlefeldt has only finished one IM, followed by a DNF in Kona. David Dellow had a fantastic sub-8 in Roth but also a string of DNFs. Dougal Allan has already been racing a lot this year, posting bike records in Wanaka and at IM New Zealand, but it’s unclear if he’s recovered well for another fast race. Defending champion Paul Ambrose is seeded fourth in my data, if he has a good day he’ll be the one to watch. Luke Bell is another great athlete, but he’s been quite inconsistent lately. Tim Reed is returning to the IM distance after a DNF in Western Australia – as a strong swimmer he’ll be in the mix for a long time, but he’ll need a solid run for a solid Ironman finish.

In addition, with Josh Amberger and Levi Maxwell there are two Ironman-rookies that make predicting the race even harder.

  • Dougal Allan: 29% (2-1)
  • David Dellow: 27% (3-1)
  • Brad Kahlefeldt: 20% (4-1)
  • Paul Ambrose: 11% (8-1)
  • Luke Bell: 11% (8-1)

Female Race Participants

Gina Crawford is clearly the best rated athlete in the women’s field, but she hasn’t had many good results lately and seems to be dialing back her racing career this year. This would put US athlete (but currently Australian-based) Beth Gerdes into the favorite’s role. Beth will be eying Lisa Bentley’s run record (3:01:06) – and so far we haven’t had a sub-3 women’s run this year. Defending champion Michelle Bremer should be in front of Beth for the swim and bike, but Michelle will need another new run PR (she just ran 3:14 at IM New Zealand) to defend her title.

  • Gina Crawford: 35% (2-1)
  • Beth Gerdes: 33% (2-1)
  • Michelle Bremer: 19% (4-1)
  • Lisa Marangon: 6% (15-1)
  • Dimity-Lee Duke: 3% (29-1)

Ironman South Africa 2016 – Analyzing Results

Race Conditions

After a typically slow swim the new bike course proved to be much quicker than the old one. It remains to be seen if this was because most of the harder hills were cut from the course or because conditions were favorable this year – reportedly there wasn’t much wind. Even with the fast conditions we didn’t see any new course records – even if Ben Hoffman would probably have been able to find 3 seconds somewhere to break Gerrit Schellens run course record from 2007!

Male Race Results

After leading out of the swim, Marko Albert was at the front for the bike as well, even if he had some company with Bas Diederen and Ben Collins. Bas was the first into T2, but he struggled on the run, and Marko was leading the race until 15k. He then dropped back into fourth place, but rallied in the final stretch to pass Matt Trautmann and claimed the third spot on the podium. The quickest runners were Ben Hoffmann and Tim Van Berkel who had reached T2 just three minute behind the leaders, and Ben proved to be the stronger of the two. With 2:45:50 he posted the fastest marathon of the day, missing the run course record by only two seconds! Tim finished in second place.

Ben received an Automatic Qualifier slot for Kona, and both Tim and Matt should have enough points for Kona, as do fifth and sixth place finishers Boris Stein and Ruedi Wild (who had the best relative performance of the day, more than an hour quicker than expected!). Marko will still need a few more points to qualify for Kona.

Rank Name Nation Swim Bike Run Time Diff to exp. Prize Money
1 Ben Hoffman USA 00:52:58 04:29:36 02:45:50 08:12:37 -19:39 US$ 30000
2 Tim Van Berkel AUS 00:50:00 04:32:33 02:48:15 08:14:51 -15:28 US$ 15000
3 Marko Albert EST 00:49:37 04:30:53 02:54:19 08:18:52 -14:58 US$ 8000
4 Matt Trautman ZAF 00:54:18 04:31:28 02:49:39 08:19:25 -02:22 US$ 6500
5 Boris Stein GER 00:54:24 04:28:17 02:52:52 08:19:51 -07:34 US$ 5000
6 Ruedi Wild SUI 00:52:56 04:38:12 02:49:28 08:24:47 -1:07:53 US$ 3500
7 Alessandro Degasperi ITA 00:54:32 04:39:03 02:50:58 08:29:37 05:02 US$ 2500
8 Christian Kramer GER 00:49:59 04:33:13 03:01:47 08:29:57 -05:24 US$ 2000
9 Ronnie Schildknecht SUI 00:55:10 04:34:36 02:58:24 08:32:11 10:46 US$ 1500
10 Jens Petersen-Bach DEN 00:54:25 04:42:07 02:53:01 08:34:03 00:39 US$ 1000
11 Kyle Buckingham ZAF 00:51:20 04:31:39 03:07:20 08:34:48 00:01
12 Jan Van Berkel SUI 00:54:20 04:36:46 03:03:05 08:38:31 -06:02
13 Markus Thomschke GER 00:57:12 04:36:21 03:00:32 08:38:52 -10:38
14 Ivan Risti ITA 00:52:19 04:41:16 03:07:23 08:45:36 -16:15
15 Ben Collins USA 00:49:51 04:41:55 03:08:39 08:46:24 06:57
16 Mike Aigroz SUI 00:52:12 04:41:27 03:11:09 08:48:52 08:40
17 David Plese SLO 00:55:05 04:38:35 03:15:07 08:53:38 18:38
18 Tomas Mika CZE 00:57:53 04:51:52 03:00:17 08:55:47 -04:12
19 Frederic Limousin FRA 00:55:01 04:52:33 03:14:46 09:08:07 -17:52
20 Mark Oude Bennink NED 00:49:51 05:16:55 03:00:02 09:12:31 26:44
21 Olivier Godart LUX 01:00:26 04:53:56 03:19:53 09:19:02 21:34
22 Michael Davidson ZAF 01:01:34 04:51:55 03:24:14 09:22:57 30:54
23 Nick Baldwin SEY 00:54:27 04:34:49 03:50:23 09:24:28 36:13
24 Bas Diederen NED 00:49:47 04:29:42 04:06:33 09:30:33 1:11:20
25 Eric Watson AUS 00:49:38 05:21:11 03:17:28 09:34:46 -07:41
26 Felipe De Oliveira Manente BRA 00:57:16 04:50:45 03:46:27 09:39:51 28:26
27 Thomas De Schutter BEL 01:02:20 05:04:07 03:35:54 09:47:19 n/a
28 Gerhard De Bruin ZAF 01:00:14 04:55:17 03:49:26 09:51:18 12:35
29 Simon Brierley SEY 01:02:24 05:15:59 04:17:23 10:41:54 06:28
James Cunnama ZAF 00:51:55 04:35:43 DNF
Oliver Simon GBR 00:52:53 04:50:26 DNF
Darby Thomas FIN 01:02:23 04:43:23 DNF
Greg Close USA 01:00:18 04:49:26 DNF
Freddy Lampret ZAF 00:54:38 04:57:32 DNF
Andrej Vistica CRO 01:01:23 04:55:00 DNF
Frederic Garcia FRA 00:57:49 05:07:24 DNF
Marek Nemcik SVK 01:23:32 DNF

Female Race Results

As expected Jodie Swallow was the fastest swimmer and in the lead on the bike. But at about 110k into the ride she fell hard without being clear what caused the crash. She was able to continue on the bike but had hurt her elbow and was loosing time to Annabel Luxford who was leading in T2 by 6 minutes over Jodie. Jodie started the run but had to call it a day after 8k. (I’m wishing her a speedy recovery and look forward to see her again at the front of her next race!) Bella had the fastest bike split by more than six minutes, but she posted after the race that she might have ridden a bit too hard. After Jodie withdrew Bella had a gap of more than 12 minutes to the speedy runners behind her, but that was only good enough for 20k. Kaisa Lehtonen proved that her second place at IM Barcelona was just the start and claimed her first Ironman title with the fastest run split. The second and third from last year took repeated this year but traded places: Susie Cheetham again had the better run and claimed second place, but Lucy Gossage’s third place only four weeks after her second place at IM New Zealand is an amazing result. Asa Lundstroem in fourth place had a solid race all day, she also passed Bella who ended up in fifth place.

As a Regional Champion Kaisa receives an Automatic Qualifier slot. After their great results in Kona and in Port Elizabeth Susie, Lucy, Asa and Annabel have enough points to be safe for a July slot.

Rank Name Nation Swim Bike Run Time Diff to exp. Prize Money
1 Kaisa Lehtonen FIN 00:58:21 04:59:41 03:02:34 09:06:50 -11:46 US$ 30000
2 Susie Cheetham GBR 00:58:21 05:04:26 03:02:43 09:09:49 -02:39 US$ 15000
3 Lucy Gossage GBR 01:03:20 04:56:10 03:07:36 09:11:43 -01:23 US$ 8000
4 Asa Lundstroem SWE 01:03:30 04:58:18 03:08:33 09:15:34 -12:35 US$ 6500
5 Annabel Luxford AUS 00:56:07 04:49:17 03:37:50 09:28:32 11:16 US$ 5000
6 Verena Walter GER 01:03:24 05:08:18 03:18:02 09:35:36 -21:36 US$ 3500
7 Bianca Steurer AUT 01:02:53 05:09:13 03:22:31 09:39:25 -04:45 US$ 2500
8 Annah Watkinson ZAF 01:03:20 05:17:58 03:17:42 09:44:11 n/a US$ 2000
9 Katharina Grohmann GER 01:16:21 05:09:59 03:18:51 09:50:27 -04:59 US$ 1500
10 Dede Griesbauer USA 00:58:16 05:13:11 03:43:14 10:01:19 15:26 US$ 1000
11 Rahel Bellinga NED 01:09:44 05:06:54 03:38:34 10:02:08 -25:25
12 Annett Finger GER 01:04:34 05:21:11 03:41:51 10:13:08 19:37
13 Darbi Roberts USA 00:58:23 05:30:14 03:51:38 10:27:36 58:05
Jodie Swallow GBR 00:52:11 05:00:00 DNF
Lina-Kristin Schink GER 01:14:23 05:24:30 DNF
Claire Horner ZAF 01:03:25 DNF

Analyzing Consistency of Athletes

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.

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