Predicted Medal Tables
There are numerous systems for ranking the success of countries at the Olympics, these methods are usually based on the actual results at each Olympic Games. For upcoming Olympics, it is an interesting exercise to predict the number of medals to be won. There are two main categories of predictions. One is from scholars using economics and a range of factors to base their predictions. The other is predictions based on competition results leading up to the Olympics.
Predictors for 2024
Here are those who made predictions for the 2024 Paris Olympics. You can see each of their predictions listed side by side.
- Gracenote Sports (previously Infostrada Sports) Virtual Medal Table — This popular Olympic Games medal table prediction can be seen in many media outlets around the world. This predictor uses an algorithm to rank athletes and teams in each Olympic event based on recent competition results (taking into account the result, how recent it is and the level of competition), and is regularly updated leading up to the Games. These predictions have been compared for 2012, 2016, 2021, and 2024. See also changes over time of the Gracenote medal predictions for 2016 and 2021 and 2024.
- Totallympics - a simple ranking based on the current world champions for each event/sport. There will be changes as time progresses and more world championships are undertaken. See website. Prediction data is listed for 2021 and 2024
- Nagpal, P., Gupta, K., Verma, Y., Kirar, J.S. (2023). Paris Olympic (2024) Medal Tally Prediction. In: Sharma, N., Goje, A., Chakrabarti, A., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2023. Lecture Notes in Networks and Systems, vol 662. Springer, Singapore. Based on regression equations using data from the last few Olympics. Prediction data is listed for 2024.
Predictors from previous years
Here are those who made predictions for previous Olympic Games. 
- BEST Sports — predictions of sports results generated by experts using proprietary statistical models. These models are applied in their extensive database of sports results, with the aim of predicting future results. From the BESTSports website. Prediction data is listed for 2021.
- Olympicmedalspredictions — predictions were once made on the website www.olympicmedalspredictions.com (author unknown, data now removed), based on the latest results, taking into account the importance of them. They considered the best events of the year; world championships, World Cups and other major competitions. Prediction data is listed for 2016 and 2021.
- Towards Data Science - from the article "The Tokyo 2020 Olympic Champions, Predicting the Medal Table of the Summer Games" by Adam C Dick, published 28 July 2019. The features they included for predicting medal counts were athletes, events, athletes per event, summer games, outlier nations, host nation. Prediction data is listed for 2021.
- Financial Times - from the article "Tokyo Olympics Alternative medals table" Financial Times 2021. Data and analysis by John Burn-Murdoch, Martin Stabe and Kazuhiro Kida, using an ecconomic prediction model first developed by Julia Bredtmann, Carsten J. Crede, Sebastian Otten. The factors used included population size, GDP per capita and past results. Prediction data is listed for 2021.
- Bleacher Report - from the article "Olympics Predictions 2021: Predicting Tokyo's Final Medal Count: by Joe Tansey on the Bleacher Report website. Prediction data is listed for 2021.
- Luciano Barra - a former sports managing director of the Italian National Olympic Committee and deputy chief executive of the 2006 Winter Olympics and Paralympics in Turin, and has a reputation as being an accurate predictor of Olympic success. Barra calls his medal counts as "projections, not predictions". His projections are based on the results of recent world championships and will be updated as the Games near. His list has been compared for 2012 and 2016.
- Brian Cazeneuve - has published his predictions in Sports Illustrated, picking the actual winners of each event based on recent results. These predictions have been compared for 2012 and 2016.
- Wall Street Journal - using analysis which is part subjective reporting, part statistics and part computer simulation. The WSJ factors in opinions of athletes and experts and the results from recent performances. But rather than simply allocating each place finisher in every event, they use this information to come up with a probability that any Olympic contender will win. Their list has been compared for 2012 and 2016.
- Goldman Sachs - Analysts José Ursúa and Kamakshya Trivedi used econometric models to forecast country-by-country medal count based on previous Olympic performance and economic growth. These predictions have been compared for 2012 and 2016.
- Tuck School - a prediction model developed by Andrew B. Bernard of the Tuck School of Business at Dartmouth, Hanover, NH. This forecasting model incorporates four factors: measures of available resources, population and per capita income, as well as the share of medals in the most recent Summer Olympics and a host effect. These predictions have been compared for 2000, 2004, 2008, 2012 and 2016.
- Bernard's research publications include: Bernard A.B and Busse, M, "Who Wins the Olympic Games: Economics Resources and Medal Totals," The Review of Economics and Statistics, 2004, Vol 86. No. 1. More information is available on his website.
- For the 2012 Olympics, Emily Williams, a recent graduate of the Tuck School of Business, published online predictions based on the same model formula originally presented by Bernard and Busse.
- For 2016, predictions using the same model from Andrew B. Bernard, Emily Williams and Meghan Busse is taken from an article by Camila Gonzales "Going for the Gold in the Cidade Maravilhosa: Who Will Win the 2016 Olympic Games in Rio de Janeiro?" published in July, 2016.
- Noland & Kevin Stahler: this prediction is based on modelling using previous results. Reference: Marcus Noland and Kevin Stahler, Asian Participation and Performance at the Olympic Games, Innovation and Economic Growth Series, No. 4, May 2015. Prediction data is listed for 2016.
- Nielsen - Professor Klaus Nielsen is a Professor of Institutional Economics, at the Sport Business Centre and Department of Management at Birkbeck, University of London. His prediction is a combination of results from recent world championships in Olympic sport disciplines and world rankings. His predictions have been compared for 2016.
- Bredtmann et al. - Julia Bredtmann, Carsten J. Crede and Sebastian Otten, Chair for Empirical Economics, Dept. of Economics, Ruhr University Bochum RWI Essen - these economists at Ruhr-Universität Bochum in Germany have used economic models to predict the medal winners since 2004. Their predictions have been compared for 2004, 2008, 2012 and 2016.
- Price Waterhouse Coopers — PwC developed a medal prediction model based on the following factors: Population; Average income levels (measured by GDP per capita at PPP exchange rates); Whether the country was previously part of the former Soviet bloc (including Cuba in this case); Whether the country is the host nation; and Medal shares in the previous Olympic Games. An updated prediction model from 2016 factored in the size of economies (as measured by GDP at PPP exchange rates), performance in the previous two Olympic Games; and whether the country is the host nation. PwC only predicts total medal count, not gold medals as many of the other predictions have done. These predictions have been compared for 2012 and 2016.
- Kuper et al. - predictions by Gerard H. Kuper, Fabian ten Kate and Elmer Sterken of the University of Groningen in the Netherlands, using an "econometric" approach. They consider three factors: the results of the World Championships in the year before the Olympic Games, the number of athletes per country determined as a proportion of the total number of participants, and a factor considering home advantage. Details were published on their website http://ghkuper.nl/olympics.html (archived here). Their predictions have been compared for 2016.
- ATASS Sports (Advanced Training and Statistical Services) - a company which provides statistical forecasting of sports results. They describe their methods as a "probabilistic model". Each Olympic event was modelled separately, creating a probability distribution over the possible medal outcomes for each event, and generating an overall medal forecast by aggregating the outcome simulating the entirety of the Games hundreds of thousands of times. Their predictions have been compared for 2016.
- David Forrest and Juan De Dios Tena (University of Liverpool) and Ian McHale (University of Salford) - predictions using statistical modelling. Their predictions have been compared for 2008 and 2016.
- Benchmark Analysis — these results are from analysis by the Australian Olympic Committee which predicted Olympic Games medal rankings in 2012 and 2016 based on results in World Championships, World Cups and other major international events held in the previous years. In 2012, the actual number of Olympic medals were not predicted, just the medal rank order. These predictions have been compared for 2012 and 2016.
- Dan Johnson — a prediction model provided by this professor of economics at Colorado College. The model includes only non-athletic data. Historically, the prediction model included these five key variables: income per capita, population, political structure, climate, and a host nation advantage and using data from every participating nation since 1952. The model was updated for the 2012 predictions, removing political structure and climate factors and adding a host nation effect and a "nation-specific cultural effect". His predictions have been compared for 2004, 2008 and 2012.
- Meghan Busse — Busse is a co-author with Andrew Bernard who have provided previous prediction models. Busse is from the Kellogg School of Management at Northwestern University in the US, and this prediction of the total medals to be won at the London Olympics is based solely on population and GDP per head. Her predictions have been compared for 2012.
- Sports Myriad — another prediction based on predicting individual medalists like the Medal Tracker above, by Beau Dure. Sports Myriad shifts though each sport and projects the winners in London, based on past results. The list was regularly updated based on recent data such as results in World Cups and World Championships leading up to the Games. These predictions have been compared for 2012.
How Good are the Predictions?
Having predictions from previous Olympic Games leads to another method of ranking - you can rank countries based on actual results compared to that predicted. These would not necessarily be the most success countries, but those that performed much better than expected. We have used the prediction results of those listed below to compare to the actual lists from the last few Olympics.
See the predictions and how they compared side by side with actual results for the 2000, 2004, 2008, 2012 and 2016 and 2021 Olympic Games. We are also doing an analysis of the prediction after each Olympic Games to see which methods or people are best at predicting the medal table.
Additional Resources
- Scelles, Nicolas & Andreff, Wladimir & Bonnal, Liliane & Andreff, Madeleine & Favard, Pascal. (2020). Forecasting National Medal Totals at the Summer Olympic Games Reconsidered. Social Science Quarterly. 101. 10.1111/ssqu.12782.
- A. RAY GRIMES, JR., WILLIAM J. KELLY and PAUL H. RUBIN, A Socieconomic Model of National Olympic Performance. Social Science Quarterly Vol. 55, No. 3 (DECEMBER, 1974), pp. 777-783
- Bredtmann, J., Crede, C. J. and Otten, S. (2016), Olympic medals: Does the past predict the future?. Significance, 13: 22–25. doi: 10.1111/j.1740-9713.2016.00915.x
- Johnson D. & A. Ali (2004), A Tale of Two Seasons: Participation and Medal Counts at the Summer and Winter Olympic Games, Social Science Quarterly, 85 (4), 974-93
- Andrew B. Bernard and Meghan R. Busse, (2004) "Who Wins the Olympic Games: Economic Resources and Medal Totals", Review of Economics and Statistics, Vol. 86, no.1.
- Bian, X. 2005. Predicting Olympic medal counts: The effects of economic development on Olympic performance. The Park Place Economist, XIII: 37–44.
- CONCLUSION: Consistent with previous studies on national Olympic performance, this paper found that socioeconomic variables, including population size, economic resources, hosting advantage, and political structure have a significant impact on a country's Olympic performance. In general, population size and economic resources are positively correlated with medal counts. The larger the population size, the more likely a country is going to do better in the Olympics; the richer a country is, the more Olympic medals it will likely win. Being a hosting nation and having a communist background both have a favorable influence on a country's Olympic performance.
Related Pages
- Summer Olympics medal predictions for 2000, 2004, 2008, 2012, 2016, 2021 and 2024
- Analysis method for predicted medal tables
- My 2012 Gold Medal Table Prediction
- About Olympic Medal Ranking Systems
- Comparison of demographic and weighted ranking systems.
- Winter Olympics medal predictions
- Medal tables from all Olympic Games
- FIFA World Cup Predicitons
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