Code to compare a manifold market with 538 World Cup predictions

"""For resolution of market: https://manifold.markets/egroj/will-a-single-manifold-market-beat"""


import pandas as pd

from manifoldpy import api # available on pypi: pip install manifoldpy



## Get manifold market data

# markets = api.get_all_markets() # I think this is not working anymore,

#                                 # but it is how I got the market ID

#                                 # below

mm_id = 'aNHGE4mlVvSwIUhRZdgr'

mm = api.get_market(mm_id)

mm = mm.answers

mm = {entry['text']:entry['probability'] for entry in mm}

mm = {country:(value*16 if value*16 < 1 else 1) for country, value in mm.items()}

mm = dict(sorted(mm.items(), key=lambda x:x[1], reverse=True))


## Values from November 20, 2022 10 am ET

mm = {'England': 1,

      'Argentina': 1,

      'Brazil': 1,

      'Spain': 0.9451968360534888,

      'France': 0.8974907339737943,

      'Portugal': 0.849977244235089,

      'Germany': 0.8112415168267147,

      'Netherlands': 0.7971968295073112,

      'Belgium': 0.7595755431348061,

      'Senegal': 0.7426855519809036,

      'Croatia': 0.741065876527692,

      'Denmark': 0.6719708710471769,

      'Uruguay': 0.617516701467408,

      'Switzerland': 0.49974494455889923,

      'Mexico': 0.49965919586782787,

      'Poland': 0.4608358796614559,

      'United States': 0.45698850032681404,

      'Ecuador': 0.4202289531028565,

      'Wales': 0.36538907075213484,

      'Serbia': 0.3057351303302865,

      'Morocco': 0.27690844073942406,

      'Canada': 0.20328431337571382,

      'Australia': 0.18374603476500043,

      'Cameroon': 0.15410561203952933,

      'South Korea': 0.14789233306591248,

      'Iran': 0.09774239358780965,

      'Ghana': 0.09141480094632379,

      'Japan': 0.08992401674617002,

      'Tunisia': 0.08298078235641886,

      'Saudi Arabia': 0.06596123866028777,

      'Qatar': 0.028929836130672053,

      'Costa Rica': 0.02812887522247895}



## Parse 538 data

## Download the data from https://projects.fivethirtyeight.com/soccer-api/international/2022/wc_forecasts.csv

fivethirtyeight = pd.read_csv("fivethirtyeight/wc_forecasts.csv")

fivethirtyeight = fivethirtyeight.set_index('team')

fivethirtyeight = dict(fivethirtyeight['make_round_of_16'])

fivethirtyeight = {(country if country != 'USA' else 'United States'):value

                   for country, value in fivethirtyeight.items()}


## Values from November 20, 2022 10 am ET

fivethirtyeight = {'Brazil': 0.91045,

                   'Spain': 0.80686,

                   'France': 0.82686,

                   'Argentina': 0.83995,

                   'Portugal': 0.81087,

                   'Germany': 0.76424,

                   'England': 0.80426,

                   'Netherlands': 0.79006,

                   'Denmark': 0.64835,

                   'Uruguay': 0.65274,

                   'Belgium': 0.624,

                   'Croatia': 0.54419,

                   'Switzerland': 0.47861,

                   'United States': 0.52953,

                   'Mexico': 0.53636,

                   'Senegal': 0.51129,

                   'Ecuador': 0.48209,

                   'Morocco': 0.46473,

                   'Serbia': 0.41295,

                   'Japan': 0.34492,

                   'Canada': 0.36708,

                   'Poland': 0.3792,

                   'South Korea': 0.35636,

                   'Tunisia': 0.30862,

                   'Iran': 0.34207,

                   'Wales': 0.32414,

                   'Cameroon': 0.19799,

                   'Saudi Arabia': 0.24449,

                   'Australia': 0.21617,

                   'Qatar': 0.21656,

                   'Ghana': 0.18003,

                   'Costa Rica': 0.08398}



## Final outcomes (who qualified for Round of 16)

outcomes = {"Qatar":0,

            "Ecuador":0,

            "Senegal":1,

            "Netherlands":1,

            "England":1,

            "Iran":0,

            "United States":1,

            "Wales":0,

            "Argentina":1,

            "Saudi Arabia":0,

            "Mexico":0,

            "Poland":1,

            "France":1,

            "Australia":1,

            "Denmark":0,

            "Tunisia":0,

            "Spain":1,

            "Costa Rica":0,

            "Germany":0,

            "Japan":1,

            "Belgium":0,

            "Canada":0,

            "Morocco":1,

            "Croatia":1,

            "Brazil":1,

            "Serbia":0,

            "Switzerland":1,

            "Cameroon":0,

            "Portugal":1,

            "Ghana":0,

            "Uruguay":0,

            "South Korea":1}



## Compute Brier scores

print("Brier fivethirtyeight:", sum([(fivethirtyeight[country] - outcome)**2 for country, outcome in outcomes.items()])/32)

print("Brier manifold market:", sum([(mm[country] - outcome)**2 for country, outcome in outcomes.items()])/32)