JAAM
@egrojIf you want to tip me for a comment or market please use this market: https://manifold.markets/egroj/2023-mana-tips-for-egroj-jaam-and-l
Trading profits
---
Balance
---
Portfolio
---
1D
1W
1M
ALL
Creator rank
#0
Total markets
0
Unique traders
6,100+2%
Posts
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)
0
comments
Comments
No comments yet