# Olympic Odds: Each Swimmer’s Probability Of Winning Gold In Tokyo

February 24th, 2020

By Daniel Takata Gomes

In this article, we present each swimmer’s chances of winning the gold medal at the Tokyo Olympic Games.

Statistical methods based on computational simulations were used in order to estimate the probability of winning the gold medal for each swimmer. Results from 2018 were considered in the calculations. The more recent the result is, more impact it has in the probabilities.

The method was determined by observing results from 2014 to 2016 and comparing them to what in fact occurred at the 2016 Olympics, in order to find the most suitable probability distribution.

Some remarks: Adam Peaty has a 97.09% chance of winning the 100 breast. Caeleb Dressel, 93.24% in the 100 fly. Regan Smith, 86.43% in the 200 back. Kristof Milak, 83.54% in the 200 fly. Katie Ledecky, 72.3% in the 800 and 1500 free.

The most disputed races are the women’s 100 free (Simone Manuel 35.48%, Cate Campbell 33.34% and Sarah Sjostrom 21.96%) and the men’s 1500 free (Florian Wellbrock 31.31%, Gregorio Paltrinieri 30.81% and Mykhailo Romanchuk 25.99%).

Of course, these probabilities will change, depending on how swimmers will swim until the Olympics. The probabilities will be updated on a regular basis.

### METHODOLOGY EXPLAINED

The approach here is to determine the probabilities in an empirical fashion. If you have a coin and you don’t know the chances of tails and heads, you can toss the coin, let’s say, 1,000 times. If you get 512 tails and 488 heads, the estimated probabilities are 51.2% and 48.8%, respectively.

So we don’t know what’s the chance of, let’s say, Simone Manuel winning the 50 freestyle. Manuel could swim the event 1,000 times, as well as her adversaries, and we could count how many times she would win to estimate her winning probability. Obviously this can’t be done. In this situation, we simulate the possible outcomes in a computer program using a statistical method.

How do we do that? Let’s go back to 2016. Manuel arrived in Rio with a 24.33 from the US Olympic Trials. In Rio, she managed to a 24.09. Cate Campbell came in with a 23.84, and in Rio, she went 24.15. And so on. I conducted the calculations and realized that, comparing to the times registered from 2014 until the Olympics, the times of the top swimmers in the Olympics present a specific pattern of variability, well explained by the so-called normal distribution of probability.

Using the very same pattern of variability, it is possible to simulate the possible outcomes for Tokyo. Manuel has a 23.97 from 2019. So, her time for Tokyo is simulated according to that pattern of variability. In 1,000 trials, let’s say that her simulated times are 24.08, 23.90, 24.18, 24.01, 23.79, 23.89, etc. Her times will float around 23.97 with some variability. We do the same for every other swimmer. Let’s say that, in 1,000 trials, Manuel has the best time of all swimmers in 800, so she has an 8% chance of winning.

The same procedure was conducted in all events, according to the respective pattern of variation. In relays, the sum of the times of the fastest swimmers of each country in each round of simulation was considered.

### CURRENT PROBABILITIES

#### Women’s events

50m freestyle

1. Sarah Sjostrom (SWE) – 52.31%
2. Cate Campbell (AUS) – 25.15%
3. Pernille Blume (DEN) – 9.87%
4. Simone Manuel (USA) – 8.49%
5. Liu Xiang (CHN) – 2.32%
6. Bronte Campbell (AUS) – 0.58%
7. Emma McKeon (AUS) – 0.48%
8. Ranomi Kromowidjojo (NED) – 0.30%

100m freestyle

1. Simone Manuel (USA) – 35.48%
2. Cate Campbell (AUS) – 33.34%
3. Sarah Sjostrom (SWE) – 21.96%
4. Emma McKeon (AUS) – 5.58%
5. Bronte Campbell (AUS) – 1.86%
6. Taylor Ruck (CAN) – 0.34%
7. Pernille Blume (DEN) – 0.24%
8. Mallory Comerford (USA) – 0.17%

200m freestyle

1. Federica Pellegrini (ITA) – 33.65%
2. Ariarne Titmus (AUS) – 23.77%
3. Sarah Sjostrom (SWE) – 16.55%
4. Emma McKeon (AUS) – 10.97%
5. Katie Ledecky (USA) – 4.16%
6. Siobhan Haughey (HKG) – 3.59%
7. Yang Junxuan (CHN) – 2.83%
8. Charlotte Bonnet (FRA) – 1.09%

400m freestyle

1. Katie Ledecky (USA) – 51.43%
2. Ariarne Titmus (AUS) – 34.51%
3. Leah Smith (USA) – 10.25%
4. Ajna Kesely (HUN) – 2.62%
5. Wang Jianjiahe (CHN) – 0.41%
6. Li Bingjie (CHN) – 0.39%
7. Katinka Hosszu (HUN) – 0.11%
8. Simona Quadarella (ITA) – 0.09%

800m freestyle

1. Katie Ledecky (USA) – 72.30%
2. Simona Quadarella (ITA) – 9.45%
3. Ariarne Titmus (AUS) – 7.71%
4. Wang Jianjiahe (CHN) – 4.54%
5. Leah Smith (USA) – 3.66%
6. Sarah Koehler (GER) – 1.95%
7. Kiah Melverton (AUS) – 0.11%
8. Mireia Belmonte (ESP) – 0.08%

1500m freestyle

1. Katie Ledecky (USA) – 72.25%
2. Simona Quadarella (ITA) – 16.21%
3. Wang Jianjiahe (CHN) – 5.96%
4. Sarah Koehler (GER) – 3.37%
5. Delfina Pignatiello (ARG) – 0.75%
6. Erica Sullivan (USA) – 0.30%
7. Ashley Twichell (USA) – 0.29%
8. Ajna Kesely (HUN) – 0.27%

100m backstroke

1. Regan Smith (USA) – 47.14%
2. Kylie Masse (CAN) – 30.49%
3. Minna Atherton (AUS) – 5.81%
4. Kathleen Baker (USA) – 5.26%
5. Olivia Smoliga (USA) – 4.25%
6. Taylor Ruck (CAN) – 2.67%
7. Phoebe Bacon (USA) – 1.79%
8. Emily Seebohm (AUS) – 0.89%

200m backstroke

1. Regan Smith (USA) – 86.43%
2. Kylie Masse (CAN) – 6.75%
3. Kaylee McKeown (AUS) – 2.76%
4. Margherita Panziera (ITA) – 2.26%
5. Taylor Ruck (CAN) – 0.51%
6. Katinka Hosszu (HUN) – 0.41%
7. Minna Atherton (AUS) – 0.40%
8. Kathleen Baker (USA) – 0.27%

100m breaststroke

1. Lilly King (USA) – 60.06%
2. Yuliya Efimova (RUS) – 34.23%
3. Reona Aoki (JPN) – 1.18%
4. Martina Carraro (ITA) – 1.17%
5. Annie Lazor (USA) – 1.01%
6. Tatjana Schoenmaker (RSA) – 0.43%
7. Arianna Castiglioni (ITA) – 0.23%
8. Molly Hannis (USA) – 0.22%

200m breaststroke

1. Yuliya Efimova (RUS) – 57.62%
2. Annie Lazor (USA) – 10.62%
3. Lilly King (USA) – 7.54%
4. Evgeniia Chikunova (RUS) – 7.52%
5. Tatjana Schoenmaker (RSA) – 7.07%
6. Bethany Galat (USA) – 2.57%
7. Sydney Pickrem (CAN) – 2.27%
8. Emily Escobedo (USA) – 1.72%

100m butterfly

1. Maggie Macneil (CAN) – 61.83%
2. Sarah Sjostrom (SWE) – 25.98%
3. Emma McKeon (AUS) – 9.02%
4. Kelsi Worrell (USA) – 1.43%
5. Marie Wattel (FRA) – 0.39%
6. Elena Di Liddo (ITA) – 0.35%
7. Brianna Throssell (AUS) – 0.26%
8. Louise Hansson (SWE) – 0.22%

200m butterfly

1. Hali Flickinger (USA) – 31.15%
2. Boglarka Kapas (HUN) – 16.06%
3. Katherine Drabot (USA) – 13.79%
4. Katinka Hosszu (HUN) – 8.70%
5. Alys Margaret Thomas (GBR) – 7.65%
6. Franziska Hentke (GER) – 4.06%
7. Zhang Yufei (CHN) – 2.70%
8. Regan Smith (USA) – 1.88%

200m ind. medley

1. Katinka Hosszu (HUN) – 52.63%
2. Shiho Matsumoto (JPN) – 26.10%
3. Ye Shiwen (CHN) – 7.50%
4. Sydney Pickrem (CAN) – 5.33%
5. Yui Ohashi (JPN) – 3.00%
6. Rika Omoyo (JPN) – 1.77%
7. Melanie Margalis (USA) – 1.11%
8. Alex Walsh (USA) – 0.93%

400m ind. medley

1. Katinka Hosszu (HUN) – 47.47%
2. Ye Shiwen (CHN) – 21.10%
3. Yui Ohashi (JPN) – 18.76%
4. Shiho Matsumoto (JPN) – 8.62%
5. Mireia Belmonte (ESP) – 1.60%
6. Emma Weyant (USA) – 0.48%
7. Sydney Pickrem (CAN) – 0.47%
8. Brooke Forde (USA) – 0.29%

4x100m freestyle relay

1. Australia – 63.87%
2. United States – 35.24%
4. France – 0.17%
5. Netherlands – 0.08%
6. Great Britain – 0.05%
7. Japan – 0.04%
8. China – 0.04%

4x200m freestyle relay

1. Australia – 48.16%
2. United States – 37.67%
3. China – 8.91%
5. Japan – 1.09%
6. Russia – 0.56%
7. Italy – 0.40%
8. Great Britain – 0.08%

4x100m medley relay

1. United States – 80.31%
2. Australia – 9.78%
4. Russia – 0.67%
5. Italy – 0.32%
6. Japan – 0.32%
7. Sweden – 0.06%
8. Great Britain – 0.03%

#### Men’s events

50m freestyle

1. Caeleb Dressel (USA) – 68.38%
2. Bruno Fratus (BRA) – 11.76%
3. Vladimir Morozov (RUS) – 7.41%
4. Benjamin Proud (GBR) – 6.02%
5. Kristian Gkolomeev (GRE) – 3.63%
6. Andrea Vergani (ITA) – 1.22%
7. Florent Manaudou (FRA) – 0.68%
8. Michael Andrew (USA) – 0.45%

100m freestyle

1. Caeleb Dressel (USA) – 41.03%
2. Kyle Chalmers (AUS) – 39.59%
3. Vladislav Grinev (RUS) – 8.56%
4. Ryan Held (USA) – 4.47%
5. Maxime Rooney (USA) – 1.53%
6. Marcelo Chierighini (BRA) – 1.20%
7. Zach Apple (USA) – 1.04%
8. Vladimir Morozov (RUS) – 0.42%

200m freestyle

1. Danas Rapsys (LTU) – 33.37%
2. Duncan Scott (GBR) – 16.10%
3. Sun Yang (CHN) – 10.85%
4. Katsuhiro Matsumoto (JPN) – 9.47%
5. Clyde Lewis (AUS) – 6.81%
6. Martin Malyutin (RUS) – 6.18%
7. Kyle Chalmers (AUS) – 2.73%
8. Ji Xinjie (CHN) – 2.30%

400m freestyle

1. Sun Yang (CHN) – 37.10%
2. Mack Horton (AUS) – 24.04%
3. Gabriele Detti (ITA) – 22.41%
4. Danas Rapsys (LTU) – 5.99%
5. Jack McLoughlin (AUS) – 2.64%
6. Elijah Winnington (AUS) – 1.69%
7. Marco De Tullio (ITA) – 1.61%
8. Alexander Krasnykh (RUS) – 0.68%

800m freestyle

1. Gregorio Paltrinieri (ITA) – 45.87%
2. Henrik Christiansen (NOR) – 17.98%
3. Gabriele Detti (ITA) – 10.65%
4. David Aubry (FRA) – 5.74%
5. Mykhailo Romanchuk (UCR) – 4.60%
6. Jack McLoughlin (AUS) – 4.46%
7. Florian Wellbrock (GER) – 3.62%
8. Sun Yang (CHN) – 2.84%

1500m freestyle

1. Florian Wellbrock (GER) – 31.31%
2. Gregorio Paltrinieri (ITA) – 30.81%
3. Mykhaulo Romanchuk (UKR) – 25.99%
4. David Aubry (FRA) – 2.27%
5. Henrik Christiansen (NOR) – 2.05%
6. Franko Grgic (CRO) – 1.45%
7. Daniel Jervis (GBR) – 1.39%
8. Alexander Norgaard (DEN) – 1.03%

100m backstroke

1. Evgeny Rylov (RUS) – 43.43%
2. Xu Jiayu (CHN) – 25.59%
3. Mitchell Larkin (AUS) – 14.28%
4. Ryan Murphy (USA) – 11.86%
5. Matt Grevers (USA) – 1.81%
6. Ryosuke Irie (JPN) – 0.86%
7. Shaine Casas (USA) – 0.79%
8. Kliment Kolesnikov (RUS) – 0.73%

200m backstroke

1. Evgeny Rylov (RUS) – 54.55%
2. Ryan Murphy (USA) – 33.90%
3. Xu Jiayu (CHN) – 5.41%
4. Mitchell Larkin (AUS) – 2.57%
5. Ryosuke Irie (JPN) – 1.03%
6. Luke Greenbank (GBR) – 0.98%
7. Austin Katz (USA) – 0.35%
8. Keita Sunama (JPN) – 0.24%

100m breaststroke

1. Adam Peaty (GBR) – 97.09%
2. James Wilby (GBR) – 1.02%
3. Ilya Shymanovich (BLR) – 0.70%
4. Yan Zibei (CHN) – 0.56%
5. Arno Kamminga (NED) – 0.22%
6. Anton Chupkov (RUS) – 0.11%
7. Nicolo Martinenghi (ITA) – 0.08%
8. Yasuhiro Koseki (JPN) – 0.05%

200m breaststroke

1. Anton Chupkov (RUS) – 46.71%
2. Matthew Wilson (AUS) – 17.69%
3. Ippei Watanabe (JPN) – 16.62%
4. Zac Stubblety-Cook (AUS) – 3.40%
5. James Wilby (GBR) – 2.04%
6. Marco Koch (GER) – 1.87%
7. Will Licon (USA) – 1.61%
8. Josh Prenot (USA) – 1.39%

100m butterfly

1. Caeleb Dressel (USA) – 93.24%
2. Chad Le Clos (RSA) – 2.09%
3. Andrei Minakov (RUS) – 1.79%
4. Maxime Rooney (USA) – 1.33%
5. Mehdy Metella (FRA) – 0.54%
6. Kristof Milak (HUN) – 0.25%
7. Jack Conger (USA) – 0.15%
8. Grant Irvine (AUS) – 0.12%

200m butterfly

1. Kristof Milak (HUN) – 83.54%
2. Daiya Seto (JPN) – 13.54%
3. Tamas Kenderesi (HUN) – 1.32%
4. Chad Le Clos (RSA) – 1.00%
5. Luca Urlando (USA) – 0.41%
6. Nao Horomura (JPN) – 0.06%
7. Federico Burdisso (ITA) – 0.05%
8. Denys Kesyl (UKR) – 0.04%

200m ind. medley

1. Daiya Seto (JPN) – 41.24%
2. Chase Kalisz (USA) – 19.81%
3. Mitchell Larkin (AUS) – 15.77%
4. Wang Shun (CHN) – 7.88%
5. Jeremy Desplanches (SUI) – 6.61%
6. Duncan Scott (GBR) – 2.21%
7. Philip Heintz (GER) – 1.88%
8. Qin Haiyang (CHN) – 1.73%

400m ind. medley

1. Daiya Seto (JPN) – 53.21%
2. Jay Litherland (USA) – 19.05%
3. Wang Shun (CHN) – 7.04%
4. Chase Kalisz (USA) – 5.24%
5. Qin Haiyang (CHN) – 3.80%
6. Lewis Clareburt (NZL) – 2.55%
7. Max Litchfield (GBR) – 2.12%
8. David Verraszto (HUN) – 1.59%

4x100m freestyle relay

1. United States – 86.00%
2. Russia – 7.48%
3. Australia – 4.04%
4. Brazil – 1.75%
5. Italy – 0.24%
6. Japan – 0.23%
7. Great Britain – 0.13%
8. China – 0.05%

4x200m freestyle relay

1. Australia – 43.47%
2. United States – 27.87%
3. Russia – 17.77%
4. Great Britain – 5.69%
5. China – 4.27%
6. Brazil – 0.54%
7. Japan – 0.25%
8. Italy – 0.09%

4x100m medley relay

1. United States – 76.70%
2. Russia – 12.77%
3. Australia – 3.74%
4. Great Britain – 3.50%
5. China – 1.26%
6. Japan – 1.08%
7. Italy – 0.47%
8. Brazil – 0.46%

#### Mixed Events

4x100m medley relay

1. United States – 86.61%
2. Australia – 8.25%
3. Great Britain – 2.71%
4. Russia – 1.22%
5. China – 0.37%
6. Italy – 0.27%
7. Japan – 0.27%

You can keep up to date with the probabilities here.

Daniel Takata is editor of Swim Channel Magazine from Brazil, has a PhD in Statistics and is a college professor. He also works as a swimming TV commentator on SporTV and holds a website dedicated to sports analytics, Esportístico (www.esportistico.com.br).

### In This Story

Subscribe
Notify of

Inline Feedbacks
Kenneth Kaya
3 years ago

Stefan
4 years ago

Sjöström will not lose two 100 fly finals in a row. I’d give her a 70% chance of winning the gold in Tokyo, stats be damned.
56.71 in Luxembourg is Sarah’s second best January time ever. I’m guessing that her training for the 200 free last year was mostly done to increase her endurance, in preparation for the 100 fly at the upcoming Olympics. She’s not swimming the 200 free in Tokyo.

Gold in 2013, 2015, 2016, 2017. It will be business as usual again in 2020. 🙂

Octopus
4 years ago

I would not leave out Baker from the 200 IM. She had a very unlucky year in 2019. Both in 2018 and this year she had sub 2:09 and has the potential to further improve if she focuses on IM. Go Kathleen 🙂

Octopus
4 years ago

The statistics deems to take the best and worst times equally important and ignore that swimmers prepare specially for the olympics. In most cases the winners produce close to their lifetime best or better. For instance Hosszu had a few poor times lately in Luxemburg, but IMHO this is totally irrelevant for her chances in Tokyo

Mike Peyrebrune
4 years ago

A great piece of research and very interesting. It would be good to run a similar analysis on likelihood of a medal (not just take the top 3 out of the Gold medal predictions). I will be watching this with interest as we track through the season and the various country’s Trials Meets.

DutchDolphin
4 years ago

Let’s say that, in 1,000 trials, Manuel has the best time of all swimmers in 800, so she has an 8% chance of winning.

How does she has an 8% chance of winning if she wins 800 out of 1000?

Jen
4 years ago

Poor Cody.

Yeet
4 years ago

M Mede