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server.py
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server.py
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from flask import Flask
from flask import request
from flask_cors import CORS
import statistics
import csv
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.linear_model import Ridge
app = Flask(__name__)
CORS(app)
@app.route('/goals-for-against', methods=['POST'])
def goals_for_against_avg():
teams = request.json
team_1 = teams['team_1']
team_2 = teams['team_2']
reader = csv.DictReader(open('./sample-data/teams1.csv'))
data = []
for row in reader:
data.append(row)
out_data = {}
for team in data:
if team['Team'] == team_1:
out_data[team_1] = team
if team['Team'] == team_2:
out_data[team_2] = team
t1_gf_ga = float(out_data[team_1]['xGF/60']) + float(out_data[team_1]['xGA/60'])
t2_gf_ga = float(out_data[team_2]['xGF/60']) + float(out_data[team_2]['xGA/60'])
return {
'average': str(statistics.mean([t1_gf_ga, t2_gf_ga])),
'team_1': t1_gf_ga,
'team_2': t2_gf_ga
}
@app.route('/adjusted-plus-minus', methods=['POST'])
def adjusted_plus_minus():
df = pd.read_csv('./sample-data/test.csv', skiprows=1, names=['date', 'visitor', 'visitor_goals', 'home', 'home_goals'])
df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
df['goal_difference'] = df['home_goals'] - df['visitor_goals']
df['home_win'] = np.where(df['goal_difference'] > 0, 1, 0)
df['home_loss'] = np.where(df['goal_difference'] < 0, 1, 0)
df_visitor = pd.get_dummies(df['visitor'], dtype=np.int64)
df_home = pd.get_dummies(df['home'], dtype=np.int64)
df_model = df_home.sub(df_visitor)
df_model['goal_difference'] = df['goal_difference']
lr = Ridge(alpha=0.001)
X = df_train.drop(['goal_difference'], axis=1)
y = df_train['goal_difference']
lr.fit(X, y)
df_ratings = pd.DataFrame(data={'team': X.columns, 'rating': lr.coef_})
if __name__ == '__main__':
app.run()