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.


 AthleteEventPositionPosition correctly predictedPodium finish predicted
1Usain Bolt100mGold59.5%97.5%
2Justin Gatlin100mSilver57.0%97.5%
3Trayvon Bromell100mBronze7.5%8.4%
4Andre De Grasse100mBronze3.6%4.3%
5Asafa Powell100mNM46.4%
6Usain Bolt200mGold66.8%91.4%
7Justin Gatlin200mSilver61.1%95.9%
8Anaso Jobodwana200mBronze3.2%3.6%
9Rasheed Dwyer200mNM37.3%
10Wayde van Niekerk400mGold11.4%81.1%
11LaShawn Merritt400mSilver19.1%56.8%
12Kirani James400mBronze3.4%95.9%
13Isaac Makwala400mNM35.0%
14David Rudisha800mGold20.5%77.3%
15Adam Kszczot800mSilver2.7%12.7%
16Amel Tuka800mBronze21.4%45.2%
17Nijel Amos800mNM91.4%
18Asbel Kiprop1500mGold88.4%96.4%
19Elijah Manangoi1500mSilver0.7%2.5%
20Abdalaati Iguider1500mBronze13.6%19.8%
21Taoufik Makhloufi1500mNM65.7%
22Mo Farah5000mGold78.9%90.5%
23Caleb Ndiku5000mSilver7.3%13.6%
24Hagos Gebrhiwet5000mBronze29.5%57.5%
25Yomif Kejelcha5000mNM62.7%
26Mo Farah10,000mGold90.5%99.3%
27Geoffrey Kamworor10,000mSilver26.4%63.9%
28Paul Tanui10,000mBronze25.0%71.1%
29Galen Rupp10,000mNM21.1%
30Ghirmay GhebreslassieMarathonGold0.2%2.1%
31Yemane TsegayMarathonSilver0.9%3.4%
32Solomon MutaiMarathonBronze0.5%1.1%
33Wilson KipsangMarathonNM78.8%
34Ezekiel Kemboi3000m steeplechaseGold22.7%70.7%
35Conseslus Kipruto3000m steeplechaseSilver8.6%38.4%
36Brimin Kipruto3000m steeplechaseBronze7.5%13.0%
37Jairus Birech3000m steeplechaseNM88.4%
38Sergey Shubenkov110m hurdlesGold2.7%50.9%
39Hansle Parchment110m hurdlesSilver2.5%9.5%
40Aries Merritt110m hurdlesBronze15.7%25.5%
41David Oliver110m hurdlesNM95.2%
42Nicholas Bett400m hurdlesGold1.6%10.5%
43Denis Kudryavtsev400m hurdlesSilver0.2%0.5%
44Jeffery Gibson400m hurdlesBronze3.0%4.1%
45Bershawn Jackson400m hurdlesNM94.3%
46Derek DrouinHigh jumpGold4.3%38.6%
47Bogdan BondarenkoHigh jumpSilver43.6%72.7%
48Zhang GuoweiHigh jumpBronze19.8%58.0%
49Mutaz Essa BarshimHigh jumpNM94.5%
50Shawn BarberPole vaultGold0.2%54.3%
51Raphael HolzdeppePole vaultSilver61.4%83.4%
52Renaud LavilleniePole vaultBronze0.2%100.0%
53Piotr LisekPole vaultBronze0.9%1.1%
54Pawel WojciechowskiPole vaultBronze4.8%5.7%
55Konstadinos FilippidisPole vaultNM29.5%
56Greg RutherfordLong jumpGold29.8%85.7%
57Fabrice LapierreLong jumpSilver0.5%0.7%
58Wang JiananLong jumpBronze0.5%0.7%
59Jeffrey HendersonLong jumpNM88.0%
60Christian TaylorTriple jumpGold42.0%98.2%
61Pedro Pablo PichardoTriple jumpSilver41.4%98.0%
62Nelson EvoraTriple jumpBronze5.0%5.2%
63Will ClayeTriple jumpNM45.7%
64Joe KovacsShotGold44.5%94.7%
65David StorlShotSilver42.9%96.8%
66O'Dayne RichardsShotBronze8.2%9.1%
67Christian CantwellShotNM53.4%
68Piotr MalachowskiDiscusGold80.4%97.7%
69Philip MilanovDiscusSilver14.8%36.4%
70Robert UrbanekDiscusBronze13.9%27.8%
71Christoph HartingDiscusNM46.7%
72Pawel FajdekHammerGold93.4%96.6%
73Dilshod NazarovHammerSilver2.7%36.4%
74Wojciech NowickiHammerBronze9.8%10.9%
75Krisztian ParsHammerNM94.3%
76Julius YegoJavelinGold36.0%75.6%
77Ihab El-SayedJavelinSilver0.7%1.4%
78Tero PitkamakiJavelinBronze21.4%82.2%
79Vitezslav VeselyJavelinNM51.5%
80Ashton EatonDecathlonGold83.8%87.7%
81Damian WarnerDecathlonSilver25.8%77.9%
82Rico FreimuthDecathlonBronze1.4%1.8%
83Trey HardeeDecathlonNM87.9%
84Miguel Angel Lopez20km race walkGold3.9%24.4%
85Wang Zhen20km race walkSilver40.7%75.9%
86Ben Thorne20km race walkBronze0.0%0.0%
87Yusuke Suzuki20km race walkNM74.7%
88Matej Toth50km race walkGold70.1%85.3%
89Jared Tallent50km race walkSilver12.9%28.7%
90Takayuki Tanii50km race walkBronze25.7%39.1%
91Hirooki Arai50km race walkNM55.9%
97Trinidad and Tobago4x400mSilver16.4%41.8%
98Great Britain4x400mBronze12.0%18.0%
99The Bahamas4x400mNM75.9%


 AthleteEventPositionPosition correctly predictedPodium finish predicted
1Shelly-Ann Fraser-Pryce100mGold96.1%99.5%
2Dafne Schippers100mSilver11.5%38.5%
3Tori Bowie100mBronze18.7%41.9%
4Blessing Okagbare100mNM53.0%
5Dafne Schippers200mGold54.0%93.8%
6Elaine Thompson200mSilver21.5%49.9%
7Veronica Campbell-Brown200mBronze2.3%4.2%
8Candyce McGrone200mNM43.2%
9Allyson Felix400mGold68.3%91.7%
10Shaunae Miller400mSilver45.4%77.1%
11Shericka Jackson400mBronze1.2%1.6%
12Christine Ohuruogu400mNM24.5%
13Maryna Arzamasova800mGold0.0%0.2%
14Melissa Bishop800mSilver0.0%1.2%
15Eunice Sum800mBronze1.4%95.6%
16Rose-Mary Almanza800mNM45.6%
17Genzebe Dibaba1500mGold91.9%95.4%
18Faith Kipyegon1500mSilver0.2%3.0%
19Sifan Hassan1500mBronze10.9%93.3%
20Jenny Simpson1500mNM63.1%
21Almaz Ayana5000mGold11.4%91.9%
22Senberi Teferi5000mSilver0.7%6.7%
23Genzebe Dibaba5000mBronze0.7%98.1%
24Mercy Cherono5000mNM65.3%
25Vivian Cheruiyot10,000mGold23.7%53.3%
26Gelete Burka10,000mSilver23.0%89.3%
27Emily Infeld10,000mBronze0.0%0.0%
28Alemitu Heroye10,000mNM50.2%
29Mare DibabaMarathonGold51.4%78.4%
30Helah KipropMarathonSilver3.3%7.7%
31Eunice KirwaMarathonBronze8.5%29.3%
32Edna KiplagatMarathonNM58.9%
33Hyvin Jepkemoi3000m steeplechaseGold18.0%73.1%
34Habiba Ghribi3000m steeplechaseSilver9.8%77.5%
35Gesa Felicitas Krause3000m steeplechaseBronze0.2%0.2%
36Virginia Nyambura3000m steeplechaseNM59.5%
37Danielle Williams100m hurdlesGold0.0%0.5%
38Cindy Roleder100m hurdlesSilver0.0%0.0%
39Alina Talay100m hurdlesBronze0.9%0.9%
40Dawn Harper-Nelson100m hurdlesNM90.2%
41Zuzana Hejnova400m hurdlesGold68.1%96.5%
42Shamier Little400m hurdlesSilver32.6%73.5%
43Cassandra Tate400m hurdlesBronze16.5%18.8%
44Kaliese Spencer400m hurdlesNM51.9%
45Maria KuchinaHigh jumpGold4.4%67.4%
46Blanka VlasicHigh jumpSilver7.3%22.0%
47Anna ChicherovaHigh jumpBronze4.7%96.0%
48Ruth BeitiaHigh jumpNM82.4%
49Yarisley SilvaPole vaultGold81.5%95.1%
50Fabiana MurerPole vaultSilver18.3%52.5%
51Nikoleta KyriakopoulouPole vaultBronze26.7%70.3%
52Jenn SuhrPole vaultNM58.8%
53Tianna BartolettaLong jumpGold75.6%91.8%
54Shara ProctorLong jumpSilver20.1%55.3%
55Ivana SpanovicLong jumpBronze8.2%16.9%
56Christabel NetteyLong jumpNM56.4%
57Caterine IbarguenTriple jumpGold78.7%96.7%
58Hanna Knyazyeva-MinenkoTriple jumpSilver0.2%10.1%
59Olga RypakovaTriple jumpBronze8.0%11.9%
60Ekaterina KonevaTriple jumpNM92.3%
61Christina SchwanitzShotGold84.5%97.4%
62Gong LijiaoShotSilver75.2%93.0%
63Michelle CarterShotBronze82.4%94.1%
64Anita MartonShotNM2.3%
65Denia CaballeroDiscusGold14.3%88.8%
66Sandra PerkovicDiscusSilver13.8%97.9%
67Nadine MullerDiscusBronze8.2%10.3%
68Yaimi PerezDiscusNM64.2%
69Anita WlodarczykHammerGold97.2%97.7%
70Zhang WenxiuHammerSilver2.6%9.6%
71Alexandra TavernierHammerBronze11.9%13.8%
72Betty HeidlerHammerNM92.3%
73Katharina MolitorJavelinGold10.1%47.5%
74Lu HuihuiJavelinSilver0.2%2.1%
75Sunette ViljoenJavelinBronze18.0%62.3%
76Barbora SpotakovaJavelinNM85.0%
77Jessica Ennis-HillHeptathlonGold12.1%82.5%
78Brianne Theisen-EatonHeptathlonSilver15.2%96.3%
79Laura Ikauniece-AdmidinaHeptathlonBronze0.9%1.2%
80Katarina Johnson-ThompsonHeptathlonNM56.4%
81Liu Hong20km race walkGold81.5%91.3%
82Lu Xiuzhi20km race walkSilver40.8%69.2%
83Lyudmyla Olyanovska20km race walkBronze2.3%3.3%
84Eleonora Giorgi20km race walkNM49.3%
87Trinidad and Tobago4x100mBronze9.3%9.3%
88Great Britain4x100mNM37.8%
91Great Britain4x400mBronze31.5%37.5%

One Comment

  1. Eric Spence says: February 10, 2016 • 09:05:33

    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!

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