BraydenMoore commited on
Commit
e8d98e1
1 Parent(s): 1146624

Update with 2023 data

Browse files
Source/Data/gbg_and_odds_this_year.csv CHANGED
@@ -1,3 +1,3 @@
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  size 13267
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:534898308f03fb7f298241e00406faacb95b8a07322572f907f691dbc7fd2b89
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  size 13267
Source/Data/gbg_this_year.csv CHANGED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:411920a2c58045bf2dc885094f60a21fe1abece0e797c592110e6cfaaa26ad8f
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- size 21430
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:73ba311ba24b5c0a0d5514b9ecd560dc3b46eb21b99e0c6dce02700636add533
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+ size 22222
Source/Data/results.csv CHANGED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- size 1700
 
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+ oid sha256:493be4d5bb80ee44cdcf7c13c24ff7b462aaaa43cfba1fe933be548ac9668eea
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+ size 1739
get_record.py CHANGED
@@ -1,4 +1,4 @@
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- import datetime as dt
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  import numpy as np
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  import pandas as pd
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  pd.set_option('chained_assignment',None)
@@ -28,8 +28,9 @@ predictions_df = pd.DataFrame(predictions).T
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  predictions_df['predicted_winner'] = [i['Winner'][0] if type(i['Winner'])==list else None for i in predictions_df[1]]
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  predictions_df['predicted_winner'] = predictions_df['predicted_winner'].map(team_abbreviation_to_name)
30
  predictions_df['predicted_over_under'] = [i['Over/Under'][0] if type(i['Over/Under'])==list else None for i in predictions_df[2]]
31
- predictions_df = predictions_df.merge(results, left_index=True, right_on='game_id').merge(gbg_and_odds_this_year[['game_id','Total Score Close']]).dropna(subset=['predicted_winner'])
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  predictions_df['over_under'] = ['Over' if t>tsc else 'Under' if t<tsc else 'Push' for t,tsc in predictions_df[['total','Total Score Close']].values]
 
33
 
34
  predictions_df['winner_correct'] = (predictions_df['predicted_winner']==predictions_df['winner']).astype(int)
35
  predictions_df['winner_incorrect'] = (predictions_df['predicted_winner']!=predictions_df['winner']).astype(int)
@@ -40,11 +41,15 @@ winners_correct = predictions_df['winner_correct'].sum()
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  winners_incorrect = predictions_df['winner_incorrect'].sum()
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  over_unders_correct = predictions_df['over_under_correct'].sum()
42
  over_unders_incorrect = predictions_df['over_under_incorrect'].sum()
 
 
 
43
 
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  record = {"winners_correct":str(winners_correct),
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  "winners_incorrect":str(winners_incorrect),
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  "over_unders_correct":str(over_unders_correct),
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- "over_unders_incorrect":str(over_unders_incorrect)}
 
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  import json
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  with open('Source/Data/record.json', 'w') as f:
 
1
+ from datetime import datetime
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  import numpy as np
3
  import pandas as pd
4
  pd.set_option('chained_assignment',None)
 
28
  predictions_df['predicted_winner'] = [i['Winner'][0] if type(i['Winner'])==list else None for i in predictions_df[1]]
29
  predictions_df['predicted_winner'] = predictions_df['predicted_winner'].map(team_abbreviation_to_name)
30
  predictions_df['predicted_over_under'] = [i['Over/Under'][0] if type(i['Over/Under'])==list else None for i in predictions_df[2]]
31
+ predictions_df = predictions_df.merge(results, left_index=True, right_on='game_id').merge(gbg_and_odds_this_year[['game_id','Total Score Close','home_team','away_team','game_date']]).dropna(subset=['predicted_winner'])
32
  predictions_df['over_under'] = ['Over' if t>tsc else 'Under' if t<tsc else 'Push' for t,tsc in predictions_df[['total','Total Score Close']].values]
33
+ predictions_df['game_date'] = pd.to_datetime(predictions_df['game_date'])
34
 
35
  predictions_df['winner_correct'] = (predictions_df['predicted_winner']==predictions_df['winner']).astype(int)
36
  predictions_df['winner_incorrect'] = (predictions_df['predicted_winner']!=predictions_df['winner']).astype(int)
 
41
  winners_incorrect = predictions_df['winner_incorrect'].sum()
42
  over_unders_correct = predictions_df['over_under_correct'].sum()
43
  over_unders_incorrect = predictions_df['over_under_incorrect'].sum()
44
+ max_date = predictions_df['game_date'].max()
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+ date_obj = datetime.strptime(max_date, "%m/%d/%Y")
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+ latest_game = date_obj.strftime("%A, %m/%d")
47
 
48
  record = {"winners_correct":str(winners_correct),
49
  "winners_incorrect":str(winners_incorrect),
50
  "over_unders_correct":str(over_unders_correct),
51
+ "over_unders_incorrect":str(over_unders_incorrect),
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+ "latest_game":latest_game}
53
 
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  import json
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  with open('Source/Data/record.json', 'w') as f: