forked from mynameisjohnn/nfl_dashboard
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run_models.py
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run_models.py
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import pandas as pd
import numpy as np
from keras.models import load_model
from keras import backend
from statsmodels.iolib.smpickle import load_pickle
def get_default_df():
df = pd.read_csv("data/nfl.csv")
# Grab dummy variables
dummy_vars = pd.get_dummies(df[["opp", "team"]])
# Add our dummy columns into the features df
df[list(dummy_vars.columns)] = dummy_vars
# Define the features
feature_df = df.drop(["result", "date", "opp", "team", "margin",
"points", "points_allowed", "total_points"], axis=1)
# Grab column names
columns = list(feature_df.columns)
# Make row of all zeros
zeros = np.zeros(shape=(1, 82))
default_df = pd.DataFrame(zeros, columns=columns)
return default_df
form_names = ["team", "opp", "third_per", "third_per_allowed", "TOP", "first_downs",
"first_downs_allowed", "pass_yards", "pass_yards_allowed", "penalty_yards",
"plays", "rush_yards", "rush_yards_allowed", "sacked", "sacks", "takeaways",
"total_yards", "total_yards_allowed", "turnovers"]
form_select = {
"default": "Choose...",
"ARI": "Arizona Cardinals",
"ATL": "Atlanta Falcons",
"BAL": "Baltimore Ravens",
"BUF": "Buffalo Bills",
"CAR": "Carolina Panthers",
"CHI": "Chicago Bears",
"CIN": "Cincinnati Bengals",
"CLE": "Cleveland Browns",
"DAL": "Dallas Cowboys",
"DEN": "Denver Broncos",
"DET": "Detroit Lions",
"GBP": "Green Bay Packers",
"HOU": "Houston Texans",
"IND": "Indianapolis Colts",
"JAX": "Jacksonville Jaguars",
"KCC": "Kansas City Chiefs",
"LAC": "Los Angeles Chargers",
"LAR": "Los Angeles Rams",
"MIA": "Miami Dolphins",
"MIN": "Minnesota Vikings",
"NEP": "New England Patriots",
"NOS": "New Orleans Saints",
"NYG": "New York Giants",
"NYJ": "New York Jets",
"OAK": "Oakland Raiders",
"PHI": "Philadelphia Eagles",
"PIT": "Pittsburgh Steelers",
"SFO": "San Francisco 49ers",
"SEA": "Seattle Seahawks",
"TBB": "Tampa Bay Buccaneers",
"TEN": "Tennessee Titans",
"WAS": "Washington Redskins"
}
team_logos = {
"ARI": "arizona-cardinals-helmet-logo.png",
"ATL": "atlanta-falcons-helmet-logo.png",
"BAL": "baltimore-ravens-helmet-logo.png",
"BUF": "buffalo-bills-helmet-logo.png",
"CAR": "carolina-panthers-helmet-logo.png",
"CHI": "chicago-bears-helmet-logo.png",
"CIN": "cincinnati-bengals-helmet-logo.png",
"CLE": "cleveland-browns-helmet-logo.png",
"DAL": "dallas-cowboys-helmet-logo.png",
"DEN": "denver-broncos-helmet-logo.png",
"DET": "detroit-lions-helmet-logo.png",
"GBP": "green-bay-packers-helmet-logo.png",
"HOU": "houston-texans-helmet-logo.png",
"IND": "indianapolis-colts-helmet.png",
"JAX": "jacksonville-jaguars-helmet-logo.png",
"KCC": "kansas-city-chiefs-helmet-logo.png",
"LAC": "los-angeles-chargers-helmet-logo.png",
"LAR": "los-angeles-rams-helmet-logo.png",
"MIA": "miami-dolphins-helmet-logo.png",
"MIN": "minnesota-vikings-helmet-logo.png",
"NEP": "new-england-patriots-helmet-logo.png",
"NOS": "new-orleans-saints-helmet-logo.png",
"NYG": "new-york-giants-helmet-logo.png",
"NYJ": "new-york-jets-helmet-logo.png",
"OAK": "oakland-raiders-helmet-logo.png",
"PHI": "philadelphia-eagles-helmet-logo.png",
"PIT": "pittsburgh-steelers-helmet-logo.png",
"SFO": "san-francisco-49ers-helmet-logo.png",
"SEA": "seattle-seahawks-helmet-logo.png",
"TBB": "tampa-bay-buccaneers-helmet-logo.png",
"TEN": "tennessee-titans-helmet-logo.png",
"WAS": "washington-redskins-helmet-logo.png"
}
def run_win_loss_model(model_input_df, team, opponent):
deep_model = load_model("models/deep_neural_model_trained.h5")
encoded_prediction = deep_model.predict_classes(model_input_df)
data = {}
if encoded_prediction[0] == 0:
for key, value in form_select.items():
if key == opponent:
data["winner"] = value
elif key == team:
data["loser"] = value
for key, value in team_logos.items():
if key == opponent:
data["winner_logo"] = value
elif key == team:
data["loser_logo"] = value
elif encoded_prediction[0] == 2:
for key, value in form_select.items():
if key == team:
data["winner"] = value
elif key == opponent:
data["loser"] = value
for key, value in team_logos.items():
if key == team:
data["winner_logo"] = value
elif key == opponent:
data["loser_logo"] = value
# Clear the model session
backend.clear_session()
return data
def predict_margins(nfl):
margin_res = load_pickle("models/margin_res.pickle")
margin_ari_score = 0
margin_ari_opp = 0
margins = []
for key, row in nfl.iterrows():
if row.team == "ARI":
team_coeff = margin_ari_score
else:
res_team = "team[T." + row.team + "]"
team_coeff = margin_res.params[res_team]
if row.opp == "ARI":
opp_coeff = margin_ari_opp
else:
res_opp = "team[T." + row.opp + "]"
opp_coeff = margin_res.params[res_opp]
if row.ha == "away":
ha_coeff = margin_res.params["ha[T.home]"]*-1
else:
ha_coeff = margin_res.params["ha[T.home]"]*1
margin_predict = margin_res.params.Intercept + margin_res.params.third_per*row['third_per'] + \
margin_res.params.third_per_allowed*row['third_per_allowed'] + margin_res.params.TOP*row['TOP'] + \
margin_res.params.first_downs * row['first_downs'] + margin_res.params.first_downs_allowed * \
row['first_downs_allowed'] + margin_res.params.pass_yards*row['pass_yards'] + \
margin_res.params.pass_yards_allowed*row['pass_yards_allowed'] + margin_res.params.penalty_yards * \
row['penalty_yards'] + margin_res.params.plays*row['plays'] + margin_res.params.rush_yards * \
row['rush_yards'] + margin_res.params.rush_yards_allowed*row['rush_yards_allowed'] + \
margin_res.params.sacked*row['sacked'] + margin_res.params.sacks*row['sacks'] + \
margin_res.params.takeaways*row['takeaways'] + margin_res.params.total_yards*row['total_yards'] + \
margin_res.params.total_yards_allowed*row['total_yards_allowed'] + margin_res.params.turnovers * \
row['turnovers'] + ha_coeff + team_coeff + opp_coeff
margins.append(margin_predict)
away_margin = margins[0] + margins[1]
home_margin = -1*away_margin
pred_margins = [home_margin, away_margin]
return pred_margins
def predict_totals(nfl):
total_res = load_pickle("models/total_res.pickle")
total_ari_score = 0
total_ari_opp = 0
totals = []
for key, row in nfl.iterrows():
if row.team == "ARI":
team_coeff = total_ari_score
else:
res_team = "team[T." + row.team + "]"
team_coeff = total_res.params[res_team]
if row.opp == "ARI":
opp_coeff = total_ari_opp
else:
res_opp = "team[T." + row.opp + "]"
opp_coeff = total_res.params[res_opp]
if row.ha == "away":
ha_coeff = total_res.params["ha[T.home]"]*-1
else:
ha_coeff = total_res.params["ha[T.home]"]*1
total_predict = total_res.params.Intercept + total_res.params.third_per*row['third_per'] + \
total_res.params.third_per_allowed*row['third_per_allowed'] + total_res.params.TOP*row['TOP'] + \
total_res.params.first_downs*row['first_downs'] + total_res.params.first_downs_allowed*row['first_downs_allowed'] + \
total_res.params.pass_yards*row['pass_yards'] + total_res.params.pass_yards_allowed*row['pass_yards_allowed'] + \
total_res.params.penalty_yards*row['penalty_yards'] + total_res.params.plays*row['plays'] + \
total_res.params.rush_yards*row['rush_yards'] + total_res.params.rush_yards_allowed*row['rush_yards_allowed'] + \
total_res.params.sacked*row['sacked'] + total_res.params.sacks*row['sacks'] + total_res.params.takeaways * \
row['takeaways'] + total_res.params.total_yards*row['total_yards'] + total_res.params.total_yards_allowed * \
row['total_yards_allowed'] + total_res.params.turnovers * \
row['turnovers'] + ha_coeff + team_coeff + opp_coeff
totals.append(total_predict)
total_predicted = (totals[0] + totals[1])/2
pred_totals = [total_predicted, total_predicted]
return totals, pred_totals
def run_score_model(nfl, team, opponent):
pred_margins = predict_margins(nfl)
totals, pred_totals = predict_totals(nfl)
pred_pfs = []
pred_pas = []
for x in np.arange(len(totals)):
pred_margin = pred_margins[x]
pred_total = pred_totals[x]
a = np.array([[1, 1], [1, -1]])
b = np.array([[pred_total], [pred_margin]])
points = np.linalg.solve(a, b)
pf = (points[0][0])
pa = (points[1][0])
pred_pfs.append(pf)
pred_pas.append(pa)
data = {}
team_points = int(round(pred_pfs[0]))
opponent_points = int(round(pred_pas[0]))
margin = abs(team_points - opponent_points)
data["team"] = team
data["team_points"] = team_points
data["opponent"] = opponent
data["opponent_points"] = opponent_points
data["margin"] = margin
for key, value in team_logos.items():
if key == team:
data["team_logo"] = value
elif key == opponent:
data["opponent_logo"] = value
# print(f'{team} {team_points} @ {opponent} {opponent_points}')
return data