Lau Sze Yui 903124

## EPA_CFB.ipynb

      
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## llm_chess.py
import chess
import re
import outlines.text.generate as generate
import outlines.models as models
import chess.engine
import chessboard.display
import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

## tracking_data_sync.ipynb

      
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              August 7, 2022 10:10
            
          
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## easy-RAPM.py
import numpy as np
from sklearn import linear_model
import pandas as pd
import requests
from sklearn.feature_extraction import DictVectorizer

team_id_list = ['1610612737','1610612738','1610612751','1610612766','1610612741','1610612739','1610612742','1610612743','1610612765','1610612744','1610612745','1610612754','1610612746','1610612747','1610612763','1610612748','1610612749','1610612750','1610612740','1610612752','1610612760','1610612753','1610612755','1610612756','1610612757','1610612758','1610612759','1610612761','1610612762','1610612764']


headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'}

## time_decay.ipynb

      
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              September 13, 2020 17:19
            
          
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## hockey_benchmark.ipynb

      
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                903124
                / hockey_benchmark.ipynb
            
            
              Created
              August 17, 2020 16:34
            
          
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## EPA_calculation.ipynb

      
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              June 3, 2020 08:29
            
          
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## Interception_baye.ipynb

      
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                / Interception_baye.ipynb
            
            
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              September 2, 2019 16:35
            
          
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## Sampling_EP.ipynb

      
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              May 27, 2019 17:36
            
          
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## Spin_efficiency.py
query = '''
SELECT pitch_type,release_speed,release_pos_x,release_pos_z,pitcher,pfx_x,pfx_z,plate_x,plate_z,balls,strikes,
vx0,vy0,vz0,ax,ay,az,release_spin_rate,release_extension,description


FROM statcast_2018
    WHERE  release_speed >= 0

'''
	import chess
	import re
	import outlines.text.generate as generate
	import outlines.models as models
	import chess.engine
	import chessboard.display
	import os

	os.environ["TOKENIZERS_PARALLELISM"] = "false"
	import numpy as np
	from sklearn import linear_model
	import pandas as pd
	import requests
	from sklearn.feature_extraction import DictVectorizer

	team_id_list = ['1610612737','1610612738','1610612751','1610612766','1610612741','1610612739','1610612742','1610612743','1610612765','1610612744','1610612745','1610612754','1610612746','1610612747','1610612763','1610612748','1610612749','1610612750','1610612740','1610612752','1610612760','1610612753','1610612755','1610612756','1610612757','1610612758','1610612759','1610612761','1610612762','1610612764']


	headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'}
	query = '''
	SELECT pitch_type,release_speed,release_pos_x,release_pos_z,pitcher,pfx_x,pfx_z,plate_x,plate_z,balls,strikes,
	vx0,vy0,vz0,ax,ay,az,release_spin_rate,release_extension,description


	FROM statcast_2018
	WHERE release_speed >= 0

	'''