A few months have passed since the 2015 IAAF World Championships in Beijing, but even now I find myself looking back at some of the results and thinking, ‘did that really happen?!’
Of the four World Championships I have attended, it was my favourite one to date. World records, great contests, surprise medallists, it had it all.
Just like most major championships, some events very much went to the form book while others did not. Heck, for some events it was as though the form book never existed at all (women’s 100m hurdles anyone?!). So I set about trying to work out who were the most surprising medallists in Beijing.
To do that, my first step was to look at the atstat.org prediction contest, run by Dutch statistician Ronald van Weele. If you’re not familiar with it, firstly you have no right calling yourself a serious athletics fan. Essentially it’s a prediction contest whereby you have to predict the medallists in all events at the World Championships.
More than 400 people entered last year’s contest, and these are people who know their shizz when it comes to athletics, so it creates a pretty good data set for working out who the favourites are in each event.
The ‘chosen athletes’ section from the contest shows how many predictions each athlete received across the three medal positions. Using that information, I worked out two percentages: one for correct predictions, and one to show how many people predicted a medal of any colour for that particular athlete.
My findings are in the tables below. Using the first athlete, Usain Bolt, as an easy example to explain the table: 59.5% of people correctly predicted that he would win gold. 97.5% of all the entrants predicted that he would win a medal of some sort.
The really interesting stuff happens when you sort the table by the various columns; that way you see who was the most – and least – surprising medallists in Beijing.
There were three athletes who were not on anybody’s radar in Beijing (or at least not among the 430-ish people who entered this contest). They are: Ben Thorne (20km race walk bronze), Cindy Roleder (100m hurdles silver) and Emily Infeld (10,000m bronze).
The most surprising gold medallist, according to this data, was 800m runner Maryna Arzamasova. No one predicted she would win gold, and only 0.2% (basically one person) predicted she would make it on to the podium.
As alluded to above, the event which most disregarded the form book – and I’m talking full-on chuck-it-out-the-window-get-run-over-by-a-lorry-shred-it-up-and-burn-it here – was the women’s 100m hurdles. Of the combined 1290 predictions made for that event (430 for each medal), just four of them were correct. Four people predicted that Alina Talay would take bronze. No one predicted gold for Danielle Williams nor silver for Cindy Roleder.
Conversely, the most all-round predictable event was the women’s shot. The vast majority of people predicted gold for Christina Schwanitz, silver for Gong Lijiao and bronze for Michelle Carter.
The biggest gold-medal favourites to live up to expectations, on both the men’s and women’s side, were Polish hammer throwers. 97.2% of people correctly predicted gold for Anita Wlodarczyk and 93.2% did likewise for Pawel Fajdek.
Just one athlete featured on everyone’s competition entry. 100% of people predicted that Renaud Lavillenie would win a medal. However, only one person correctly predicted that he would take bronze. Five people thought he’d earn silver, while a whopping 434 people had him down for gold.
Within each event, I have also included the athlete who received the most medal predictions yet missed out on making the podium. Sticking with the 100m to use as an example, you can see that 46.4% of entrants predicted that Asafa Powell would win a medal.
Using that information, you can see which athletes did not live up to expectations. In many cases, the non-medallist with the most medal predictions wound up finishing just outside the medals anyway, so it is perhaps unfair to label those as ‘disappointments’. But in other instances, the athletes who received the most medal predictions in a given event finished outside of the medals.
Statistically speaking, the biggest disappointment was the US men’s 4x100m team. 97.7% of people predicted they would get a medal, but they failed to get the baton around safely in the final.
Men
| Athlete | Event | Position | Position correctly predicted | Podium finish predicted | |
|---|---|---|---|---|---|
| 1 | Usain Bolt | 100m | Gold | 59.5% | 97.5% |
| 2 | Justin Gatlin | 100m | Silver | 57.0% | 97.5% |
| 3 | Trayvon Bromell | 100m | Bronze | 7.5% | 8.4% |
| 4 | Andre De Grasse | 100m | Bronze | 3.6% | 4.3% |
| 5 | Asafa Powell | 100m | NM | 46.4% | |
| 6 | Usain Bolt | 200m | Gold | 66.8% | 91.4% |
| 7 | Justin Gatlin | 200m | Silver | 61.1% | 95.9% |
| 8 | Anaso Jobodwana | 200m | Bronze | 3.2% | 3.6% |
| 9 | Rasheed Dwyer | 200m | NM | 37.3% | |
| 10 | Wayde van Niekerk | 400m | Gold | 11.4% | 81.1% |
| 11 | LaShawn Merritt | 400m | Silver | 19.1% | 56.8% |
| 12 | Kirani James | 400m | Bronze | 3.4% | 95.9% |
| 13 | Isaac Makwala | 400m | NM | 35.0% | |
| 14 | David Rudisha | 800m | Gold | 20.5% | 77.3% |
| 15 | Adam Kszczot | 800m | Silver | 2.7% | 12.7% |
| 16 | Amel Tuka | 800m | Bronze | 21.4% | 45.2% |
| 17 | Nijel Amos | 800m | NM | 91.4% | |
| 18 | Asbel Kiprop | 1500m | Gold | 88.4% | 96.4% |
| 19 | Elijah Manangoi | 1500m | Silver | 0.7% | 2.5% |
| 20 | Abdalaati Iguider | 1500m | Bronze | 13.6% | 19.8% |
| 21 | Taoufik Makhloufi | 1500m | NM | 65.7% | |
| 22 | Mo Farah | 5000m | Gold | 78.9% | 90.5% |
| 23 | Caleb Ndiku | 5000m | Silver | 7.3% | 13.6% |
| 24 | Hagos Gebrhiwet | 5000m | Bronze | 29.5% | 57.5% |
| 25 | Yomif Kejelcha | 5000m | NM | 62.7% | |
| 26 | Mo Farah | 10,000m | Gold | 90.5% | 99.3% |
| 27 | Geoffrey Kamworor | 10,000m | Silver | 26.4% | 63.9% |
| 28 | Paul Tanui | 10,000m | Bronze | 25.0% | 71.1% |
| 29 | Galen Rupp | 10,000m | NM | 21.1% | |
| 30 | Ghirmay Ghebreslassie | Marathon | Gold | 0.2% | 2.1% |
| 31 | Yemane Tsegay | Marathon | Silver | 0.9% | 3.4% |
| 32 | Solomon Mutai | Marathon | Bronze | 0.5% | 1.1% |
| 33 | Wilson Kipsang | Marathon | NM | 78.8% | |
| 34 | Ezekiel Kemboi | 3000m steeplechase | Gold | 22.7% | 70.7% |
| 35 | Conseslus Kipruto | 3000m steeplechase | Silver | 8.6% | 38.4% |
| 36 | Brimin Kipruto | 3000m steeplechase | Bronze | 7.5% | 13.0% |
| 37 | Jairus Birech | 3000m steeplechase | NM | 88.4% | |
| 38 | Sergey Shubenkov | 110m hurdles | Gold | 2.7% | 50.9% |
| 39 | Hansle Parchment | 110m hurdles | Silver | 2.5% | 9.5% |
| 40 | Aries Merritt | 110m hurdles | Bronze | 15.7% | 25.5% |
| 41 | David Oliver | 110m hurdles | NM | 95.2% | |
| 42 | Nicholas Bett | 400m hurdles | Gold | 1.6% | 10.5% |
| 43 | Denis Kudryavtsev | 400m hurdles | Silver | 0.2% | 0.5% |
| 44 | Jeffery Gibson | 400m hurdles | Bronze | 3.0% | 4.1% |
| 45 | Bershawn Jackson | 400m hurdles | NM | 94.3% | |
| 46 | Derek Drouin | High jump | Gold | 4.3% | 38.6% |
| 47 | Bogdan Bondarenko | High jump | Silver | 43.6% | 72.7% |
| 48 | Zhang Guowei | High jump | Bronze | 19.8% | 58.0% |
| 49 | Mutaz Essa Barshim | High jump | NM | 94.5% | |
| 50 | Shawn Barber | Pole vault | Gold | 0.2% | 54.3% |
| 51 | Raphael Holzdeppe | Pole vault | Silver | 61.4% | 83.4% |
| 52 | Renaud Lavillenie | Pole vault | Bronze | 0.2% | 100.0% |
| 53 | Piotr Lisek | Pole vault | Bronze | 0.9% | 1.1% |
| 54 | Pawel Wojciechowski | Pole vault | Bronze | 4.8% | 5.7% |
| 55 | Konstadinos Filippidis | Pole vault | NM | 29.5% | |
| 56 | Greg Rutherford | Long jump | Gold | 29.8% | 85.7% |
| 57 | Fabrice Lapierre | Long jump | Silver | 0.5% | 0.7% |
| 58 | Wang Jianan | Long jump | Bronze | 0.5% | 0.7% |
| 59 | Jeffrey Henderson | Long jump | NM | 88.0% | |
| 60 | Christian Taylor | Triple jump | Gold | 42.0% | 98.2% |
| 61 | Pedro Pablo Pichardo | Triple jump | Silver | 41.4% | 98.0% |
| 62 | Nelson Evora | Triple jump | Bronze | 5.0% | 5.2% |
| 63 | Will Claye | Triple jump | NM | 45.7% | |
| 64 | Joe Kovacs | Shot | Gold | 44.5% | 94.7% |
| 65 | David Storl | Shot | Silver | 42.9% | 96.8% |
| 66 | O'Dayne Richards | Shot | Bronze | 8.2% | 9.1% |
| 67 | Christian Cantwell | Shot | NM | 53.4% | |
| 68 | Piotr Malachowski | Discus | Gold | 80.4% | 97.7% |
| 69 | Philip Milanov | Discus | Silver | 14.8% | 36.4% |
| 70 | Robert Urbanek | Discus | Bronze | 13.9% | 27.8% |
| 71 | Christoph Harting | Discus | NM | 46.7% | |
| 72 | Pawel Fajdek | Hammer | Gold | 93.4% | 96.6% |
| 73 | Dilshod Nazarov | Hammer | Silver | 2.7% | 36.4% |
| 74 | Wojciech Nowicki | Hammer | Bronze | 9.8% | 10.9% |
| 75 | Krisztian Pars | Hammer | NM | 94.3% | |
| 76 | Julius Yego | Javelin | Gold | 36.0% | 75.6% |
| 77 | Ihab El-Sayed | Javelin | Silver | 0.7% | 1.4% |
| 78 | Tero Pitkamaki | Javelin | Bronze | 21.4% | 82.2% |
| 79 | Vitezslav Vesely | Javelin | NM | 51.5% | |
| 80 | Ashton Eaton | Decathlon | Gold | 83.8% | 87.7% |
| 81 | Damian Warner | Decathlon | Silver | 25.8% | 77.9% |
| 82 | Rico Freimuth | Decathlon | Bronze | 1.4% | 1.8% |
| 83 | Trey Hardee | Decathlon | NM | 87.9% | |
| 84 | Miguel Angel Lopez | 20km race walk | Gold | 3.9% | 24.4% |
| 85 | Wang Zhen | 20km race walk | Silver | 40.7% | 75.9% |
| 86 | Ben Thorne | 20km race walk | Bronze | 0.0% | 0.0% |
| 87 | Yusuke Suzuki | 20km race walk | NM | 74.7% | |
| 88 | Matej Toth | 50km race walk | Gold | 70.1% | 85.3% |
| 89 | Jared Tallent | 50km race walk | Silver | 12.9% | 28.7% |
| 90 | Takayuki Tanii | 50km race walk | Bronze | 25.7% | 39.1% |
| 91 | Hirooki Arai | 50km race walk | NM | 55.9% | |
| 92 | Jamaica | 4x100m | Gold | 44.8% | 99.8% |
| 93 | China | 4x100m | Silver | 0.0% | 0.9% |
| 94 | Canada | 4x100m | Bronze | 6.8% | 6.8% |
| 95 | USA | 4x100m | NM | 97.7% | |
| 96 | USA | 4x400m | Gold | 91.4% | 98.6% |
| 97 | Trinidad and Tobago | 4x400m | Silver | 16.4% | 41.8% |
| 98 | Great Britain | 4x400m | Bronze | 12.0% | 18.0% |
| 99 | The Bahamas | 4x400m | NM | 75.9% |
Women
| Athlete | Event | Position | Position correctly predicted | Podium finish predicted | |
|---|---|---|---|---|---|
| 1 | Shelly-Ann Fraser-Pryce | 100m | Gold | 96.1% | 99.5% |
| 2 | Dafne Schippers | 100m | Silver | 11.5% | 38.5% |
| 3 | Tori Bowie | 100m | Bronze | 18.7% | 41.9% |
| 4 | Blessing Okagbare | 100m | NM | 53.0% | |
| 5 | Dafne Schippers | 200m | Gold | 54.0% | 93.8% |
| 6 | Elaine Thompson | 200m | Silver | 21.5% | 49.9% |
| 7 | Veronica Campbell-Brown | 200m | Bronze | 2.3% | 4.2% |
| 8 | Candyce McGrone | 200m | NM | 43.2% | |
| 9 | Allyson Felix | 400m | Gold | 68.3% | 91.7% |
| 10 | Shaunae Miller | 400m | Silver | 45.4% | 77.1% |
| 11 | Shericka Jackson | 400m | Bronze | 1.2% | 1.6% |
| 12 | Christine Ohuruogu | 400m | NM | 24.5% | |
| 13 | Maryna Arzamasova | 800m | Gold | 0.0% | 0.2% |
| 14 | Melissa Bishop | 800m | Silver | 0.0% | 1.2% |
| 15 | Eunice Sum | 800m | Bronze | 1.4% | 95.6% |
| 16 | Rose-Mary Almanza | 800m | NM | 45.6% | |
| 17 | Genzebe Dibaba | 1500m | Gold | 91.9% | 95.4% |
| 18 | Faith Kipyegon | 1500m | Silver | 0.2% | 3.0% |
| 19 | Sifan Hassan | 1500m | Bronze | 10.9% | 93.3% |
| 20 | Jenny Simpson | 1500m | NM | 63.1% | |
| 21 | Almaz Ayana | 5000m | Gold | 11.4% | 91.9% |
| 22 | Senberi Teferi | 5000m | Silver | 0.7% | 6.7% |
| 23 | Genzebe Dibaba | 5000m | Bronze | 0.7% | 98.1% |
| 24 | Mercy Cherono | 5000m | NM | 65.3% | |
| 25 | Vivian Cheruiyot | 10,000m | Gold | 23.7% | 53.3% |
| 26 | Gelete Burka | 10,000m | Silver | 23.0% | 89.3% |
| 27 | Emily Infeld | 10,000m | Bronze | 0.0% | 0.0% |
| 28 | Alemitu Heroye | 10,000m | NM | 50.2% | |
| 29 | Mare Dibaba | Marathon | Gold | 51.4% | 78.4% |
| 30 | Helah Kiprop | Marathon | Silver | 3.3% | 7.7% |
| 31 | Eunice Kirwa | Marathon | Bronze | 8.5% | 29.3% |
| 32 | Edna Kiplagat | Marathon | NM | 58.9% | |
| 33 | Hyvin Jepkemoi | 3000m steeplechase | Gold | 18.0% | 73.1% |
| 34 | Habiba Ghribi | 3000m steeplechase | Silver | 9.8% | 77.5% |
| 35 | Gesa Felicitas Krause | 3000m steeplechase | Bronze | 0.2% | 0.2% |
| 36 | Virginia Nyambura | 3000m steeplechase | NM | 59.5% | |
| 37 | Danielle Williams | 100m hurdles | Gold | 0.0% | 0.5% |
| 38 | Cindy Roleder | 100m hurdles | Silver | 0.0% | 0.0% |
| 39 | Alina Talay | 100m hurdles | Bronze | 0.9% | 0.9% |
| 40 | Dawn Harper-Nelson | 100m hurdles | NM | 90.2% | |
| 41 | Zuzana Hejnova | 400m hurdles | Gold | 68.1% | 96.5% |
| 42 | Shamier Little | 400m hurdles | Silver | 32.6% | 73.5% |
| 43 | Cassandra Tate | 400m hurdles | Bronze | 16.5% | 18.8% |
| 44 | Kaliese Spencer | 400m hurdles | NM | 51.9% | |
| 45 | Maria Kuchina | High jump | Gold | 4.4% | 67.4% |
| 46 | Blanka Vlasic | High jump | Silver | 7.3% | 22.0% |
| 47 | Anna Chicherova | High jump | Bronze | 4.7% | 96.0% |
| 48 | Ruth Beitia | High jump | NM | 82.4% | |
| 49 | Yarisley Silva | Pole vault | Gold | 81.5% | 95.1% |
| 50 | Fabiana Murer | Pole vault | Silver | 18.3% | 52.5% |
| 51 | Nikoleta Kyriakopoulou | Pole vault | Bronze | 26.7% | 70.3% |
| 52 | Jenn Suhr | Pole vault | NM | 58.8% | |
| 53 | Tianna Bartoletta | Long jump | Gold | 75.6% | 91.8% |
| 54 | Shara Proctor | Long jump | Silver | 20.1% | 55.3% |
| 55 | Ivana Spanovic | Long jump | Bronze | 8.2% | 16.9% |
| 56 | Christabel Nettey | Long jump | NM | 56.4% | |
| 57 | Caterine Ibarguen | Triple jump | Gold | 78.7% | 96.7% |
| 58 | Hanna Knyazyeva-Minenko | Triple jump | Silver | 0.2% | 10.1% |
| 59 | Olga Rypakova | Triple jump | Bronze | 8.0% | 11.9% |
| 60 | Ekaterina Koneva | Triple jump | NM | 92.3% | |
| 61 | Christina Schwanitz | Shot | Gold | 84.5% | 97.4% |
| 62 | Gong Lijiao | Shot | Silver | 75.2% | 93.0% |
| 63 | Michelle Carter | Shot | Bronze | 82.4% | 94.1% |
| 64 | Anita Marton | Shot | NM | 2.3% | |
| 65 | Denia Caballero | Discus | Gold | 14.3% | 88.8% |
| 66 | Sandra Perkovic | Discus | Silver | 13.8% | 97.9% |
| 67 | Nadine Muller | Discus | Bronze | 8.2% | 10.3% |
| 68 | Yaimi Perez | Discus | NM | 64.2% | |
| 69 | Anita Wlodarczyk | Hammer | Gold | 97.2% | 97.7% |
| 70 | Zhang Wenxiu | Hammer | Silver | 2.6% | 9.6% |
| 71 | Alexandra Tavernier | Hammer | Bronze | 11.9% | 13.8% |
| 72 | Betty Heidler | Hammer | NM | 92.3% | |
| 73 | Katharina Molitor | Javelin | Gold | 10.1% | 47.5% |
| 74 | Lu Huihui | Javelin | Silver | 0.2% | 2.1% |
| 75 | Sunette Viljoen | Javelin | Bronze | 18.0% | 62.3% |
| 76 | Barbora Spotakova | Javelin | NM | 85.0% | |
| 77 | Jessica Ennis-Hill | Heptathlon | Gold | 12.1% | 82.5% |
| 78 | Brianne Theisen-Eaton | Heptathlon | Silver | 15.2% | 96.3% |
| 79 | Laura Ikauniece-Admidina | Heptathlon | Bronze | 0.9% | 1.2% |
| 80 | Katarina Johnson-Thompson | Heptathlon | NM | 56.4% | |
| 81 | Liu Hong | 20km race walk | Gold | 81.5% | 91.3% |
| 82 | Lu Xiuzhi | 20km race walk | Silver | 40.8% | 69.2% |
| 83 | Lyudmyla Olyanovska | 20km race walk | Bronze | 2.3% | 3.3% |
| 84 | Eleonora Giorgi | 20km race walk | NM | 49.3% | |
| 85 | Jamaica | 4x100m | Gold | 32.6% | 99.1% |
| 86 | USA | 4x100m | Silver | 31.2% | 98.8% |
| 87 | Trinidad and Tobago | 4x100m | Bronze | 9.3% | 9.3% |
| 88 | Great Britain | 4x100m | NM | 37.8% | |
| 89 | Jamaica | 4x400m | Gold | 1.6% | 86.9% |
| 90 | USA | 4x400m | Silver | 95.8% | 99.5% |
| 91 | Great Britain | 4x400m | Bronze | 31.5% | 37.5% |
| 92 | Russia | 4x400m | NM | 52.9% |
This compilation of stats tells quite a tale, Jon. Thanks for taking the time to dig it up.
As someone who follows the sport closely, I personally live for the surprises. I knew Van Niekirk was on the rise but who could have predicted he’d go for broke and actually take the men’s 400m gold? Or that Jamaica would beat the U.S in the women’s 4x400m relay with such inspired sprinting? Or that Kaliese Spencer would have such a disastrous race?
These are the moments I live for… it’s why I love the sport so much. Talent on paper is not always the best predictor of performance but focus, resolve and strength of the human spirit at decisive moments are more of a factor than we can ever measure. Asafa Powell and Justin Gatlin are the perfect examples of that.
That’s why I called Hainsle Parchment for gold and Merritt for silver because both of these guys, despite having so-so seasons coming into the finals, have the heart of champions, but loved it when Shubenkov ran brilliantly to win. Now that exciting! Never would have predicted that!
Here’s an idea – I’m part of a small group here in Canada which includes an Australian resident as well, who really get into the stats and career paths of each athlete (via email), focusing mostly in the sprints, hurdles and relay events. (And I’m sure there are other enthusiasts as well besides us) Maybe you could put out a challenge to your readers like us to join in a contest that you run on this site during the Olympics, where these enthusiasts can predict the medal order and you publish the prediction/actual results, like you have here. Run your own prediction contest. Or maybe even have guest posts for certain events?
It’d be a great way to connect people from across the globe and increase engagement among your readers for the big one in Rio.
Waddya think?
BTW – great article!