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The Financial Times’ alternative medal table ranks countries not by their total medal haul but by the difference to the tally they are expected to achieve, acco

The Financial Times’ alternative medal table ranks countries not by their total medal haul but by the difference to the tally they are expected to achieve, according to an economic model that takes into account their economic, social and political characteristics: 1. Russian Olympic Committee 2. Australia 3. China 4. Italy 5. Netherlands 6. Taiwan 7. Germany 8. Cuba 9. Austria 10. New Zealand https://ig.ft.com/tokyo-olympics-alternative-medal-table/ https://ig.ft.com/tokyo-olympics-alternative-medal-table/

foota 5y ago
That's a little silly, since in reality athletic skill is something acquired over time, but doesn't really require much economic input, relative to the overall economy.
- Overton-Window 5y ago
  > To predict the baseline total number of medals each country might be expected to win in any given summer Olympics, we updated a linear regression model produced by Julia Bredtmann, Carsten J Crede and Sebastian Otten ahead of the Rio Olympics. > Mimicking the original work by Bredtmann, Crede and Otten, we built the same model and fitted it on data for each Summer Olympic year between 1992 and 2008, and then used it to predict the medals table at London 2012. The full model outperforms a naive model that uses only the number of medals a country won and the year of the Games. > We then fitted the same model on data up to and including the 2016 Rio Games, and used this to predict medal counts for Tokyo 2020 (using the latest available data for each input, i.e forecasts for GDP per capita and population in 2021). Model: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00915.x https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-...
- Jorge1o1 5y ago
  So they don’t have any tables describing their regression coefficients, but one of the params they use is lag-1 medals. For example, predicting 2020 medals using 2016 medals. It wouldn’t surprise me if the vast majority of the model’s predictive power came from that, as opposed to GDP per capita, whether the country is Muslim (yes this is actually a predictor too!)
  - nayaketo 5y ago
    How do they determine what factor is a predictor and what isn't?
mbg721 5y ago
I'm a little surprised they don't get wild outliers, a small country having one athlete who gets four medals or something.
- abraae 5y ago
  [New Zealand has entered the chat]
lern_too_spel 5y ago
The whole point of a country medal table is to rank countries, not the countries' athletes. If a country is able to produce a better outcome by having a stronger economy and more social equity, that should be rewarded. Ranking outliers relative to predictions is only useful for figuring out which countries' doping programs to investigate.

foota 5y ago

That's a little silly, since in reality athletic skill is something acquired over time, but doesn't really require much economic input, relative to the overall economy.

Overton-Window 5y ago
> To predict the baseline total number of medals each country might be expected to win in any given summer Olympics, we updated a linear regression model produced by Julia Bredtmann, Carsten J Crede and Sebastian Otten ahead of the Rio Olympics. > Mimicking the original work by Bredtmann, Crede and Otten, we built the same model and fitted it on data for each Summer Olympic year between 1992 and 2008, and then used it to predict the medals table at London 2012. The full model outperforms a naive model that uses only the number of medals a country won and the year of the Games. > We then fitted the same model on data up to and including the 2016 Rio Games, and used this to predict medal counts for Tokyo 2020 (using the latest available data for each input, i.e forecasts for GDP per capita and population in 2021). Model: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00915.x https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-...
Jorge1o1 5y ago
So they don’t have any tables describing their regression coefficients, but one of the params they use is lag-1 medals. For example, predicting 2020 medals using 2016 medals. It wouldn’t surprise me if the vast majority of the model’s predictive power came from that, as opposed to GDP per capita, whether the country is Muslim (yes this is actually a predictor too!)
- nayaketo 5y ago
  How do they determine what factor is a predictor and what isn't?

mbg721 5y ago

I'm a little surprised they don't get wild outliers, a small country having one athlete who gets four medals or something.

abraae 5y ago
[New Zealand has entered the chat]

lern_too_spel 5y ago

The whole point of a country medal table is to rank countries, not the countries' athletes. If a country is able to produce a better outcome by having a stronger economy and more social equity, that should be rewarded. Ranking outliers relative to predictions is only useful for figuring out which countries' doping programs to investigate.