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PNation.sublime-workspace
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PNation.sublime-workspace
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{
"auto_complete":
{
"selected_items":
[
[
"2B",
"2B"
],
[
"Two",
"TwoYear\tstatement"
],
[
"add_",
"add_suffix\tfunction"
],
[
"cur",
"currentYear"
]
]
},
"buffers":
[
{
"file": "GoogleSheetTest.py",
"settings":
{
"buffer_size": 1765,
"encoding": "UTF-8",
"line_ending": "Unix"
}
},
{
"contents": "# Projected Rankings System\n\n# last Update : 9.2.19\n\n\n\n#### points(currentSeason)\n# Import data from pybaseball to create point totals for the currentSeason\n# Complete\n\n#### Rosters_To_Team_Files()\n# This seperates full_roster into each team\n# Complete\n\n#### Roster_lahman_tag()\n# This applies lahman, retro, bbref tags to players\n# Complete (some players skipped?)\n\n\nimport pandas as pd\nimport numpy as np\n\n#################################################\n\n#### Create Batting and Pitching Point totals ###\n\n#################################################\n\n# Creates files for batting and pitching point totals\n\n\n\ndef points(currentSeason):\n from pybaseball import batting_stats\n\n\n data = batting_stats(currentSeason, qual=1)\n\n\n\n # Procrastination Points\n\n # ESPN Settings\n\n ## Batting Scoring\n\n R = 1\n # Runs Scored\n\n Single = 0\n # Singles\n\n Double = 0\n # Doubles\n\n Triple = 0\n # Triples\n\n HR = 2\n # Home Runs\n\n TB = 1\n # Total Bases\n\n RBI = 1\n # Runs Batted In\n\n BB = 1\n # Walks\n\n K = -1\n # Strikeouts\n\n SB = 1\n # Stolen Bases\n\n\n AB = 0\n # At Bats\n\n Hits = 0\n # Hits\n\n XBH = 0\n # Extra Base Hits\n\n GWRBI = 0\n # Game Winning RBI\n\n IBB = 1\n # Intentional Walks\n\n HBP = 0\n # Hit by Pitch\n\n SAC = 0\n # Sacrifices\n\n CS = -1\n # Caught Stealing\n\n SBN = 0\n # Net Stolen Bases\n\n GIDP = 0\n # Ground into Double Plays\n\n CYC = 20\n # Hitting for the Cycle\n\n GSHR = 4\n # Grand Slam Home Runs\n\n BTW = 0\n # Batter Team Win\n\n BTL = 0\n # Batter Team Loss\n\n\n\n points = ( ( R * data['R'] )\n + ( Single * data['1B'] )\n + ( Double * data['2B'] )\n + ( Triple * data['3B'] )\n + ( HR * data['HR'] )\n + ( TB * ( data['1B'] + (2 * data['2B']) + (3 * data['3B']) + (4 * data['HR']) ) )\n + ( RBI * data['RBI'] )\n + ( BB * data['BB'] )\n + ( K * data['SO'] )\n + ( SB * data['SB'] )\n + ( AB * data['AB'] )\n + ( Hits * data['H'])\n + ( XBH * ( data['2B'] + data['3B'] + data['HR']) )\n + ( IBB * data['IBB'] )\n + ( HBP * data['HBP'] )\n + ( CS * data['CS'] ) )\n\n\n # missing Sac and GWRBI and everything past CS\n\n data['Points'] = points\n\n BattingStats = data[['Season', 'Name', 'Team', 'Age', 'Points', 'G', 'PA', 'AB', 'AVG', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SB', 'CS', 'HBP', 'SO', 'Pitches']]\n #BattingStats.sort_values(\"Points\", inplace=True)\n # print(BattingStats)\n\n\n BattingStats.to_csv('data/bstats.csv')\n\n ## Creating a Lg Total document for Marcel Projections\n lgAVG()\n\n ## Fielding\n\n FC = 0\n # Fielding Chances\n\n PO = 0\n # Put Outs\n\n AST = 0\n # Assists\n\n OFAST = 0\n # Outfield Assists\n\n BatE = -1\n # Errors\n\n DPT = 0\n # Double Plays Turned\n\n\n\n\n\n\n\n from pybaseball import pitching_stats\n\n pdata = pitching_stats(currentSeason)\n\n ### Pitching\n\n IP = 3\n # Innings Pitched\n # INNINGS PITCHED / OUTS RECORDED\n # Although Innings Pitched are typically displayed throughout the game, pitchers will accrue points for each out they record. The point value entered here applies to outs recorded. For example, if you choose a value of 2 points, a pitcher that pitches 1 inning will earn 6 points (2 points * 3 outs).\n\n ER = -2\n # Earned Runs\n\n K = 1\n # Strikeouts\n\n SO = 5\n # Shutouts\n\n W = 5\n # Wins\n\n L = -5\n # Losses\n\n SV = 5\n # Saves\n\n BS = -5\n # Blown Saves\n\n\n G = 0\n # Appearances\n\n GS = 0\n # Games Started\n\n H = -1\n # Hits Allowed\n\n RA = 0\n # Runs Allowed\n\n HR = 0\n # Home Runs Allowed\n\n BB = -1\n # Walks Issued\n\n HB = -1\n # Hit Batsmen\n\n WP = -1\n # Wild Pitches\n\n B = -1\n # Balks\n\n PKO = 2\n # Pick Offs\n\n QS = 5\n # Quality Starts\n\n CG = 10\n # Complete Games\n\n NH = 15\n # No Hitters\n\n PG = 20\n # Perfect Games\n\n BF = 0\n # Batters Faced\n\n PC = 0\n # Pitch Count\n\n SOP = 0\n # Save Opportunities\n\n HD = 0\n # Holds\n\n PTW = 0\n # Pitcher Team Win\n\n PTL = 0\n # Pitcher Team Loss\n\n SVHD = 0\n # Saves Plus Holds\n\n\n ppoints = ( ( IP * pdata['IP'] )\n + ( ER * pdata['ER'] )\n + ( K * pdata['SO'] )\n + ( SO * pdata['ShO'] )\n + ( W * pdata['W'] )\n + ( L * pdata['L'] )\n + ( SV * pdata['SV'] )\n + ( BS * pdata['BS'] )\n + ( G * pdata['G'] )\n + ( GS * pdata['GS'] )\n + ( BS * pdata['BS'] )\n + ( H * pdata['H'] )\n + ( RA * pdata['R'] )\n + ( HR * pdata['HR'] )\n + ( BB * pdata['BB'] )\n + ( HB * pdata['HBP'] )\n + ( IBB * pdata['IBB'] )\n + ( B * pdata['BK'] )\n #+ ( PKO * pdata['PKO'] )\n + ( QS * pdata['BK'] )\n + ( CG * pdata['CG'] )\n #+ ( NH * pdata['NH'] )\n #+ ( PG * pdata['PG'] )\n #+ ( BF * pdata['BF'] )\n + ( PC * pdata['Pitches'] )\n #+ ( SOP * pdata['SOP'] )\n #+ ( HD * pdata['HD'] )\n )\n\n pdata['Points'] = ppoints\n\n PitchingStats = pdata[['Season', 'Name', 'Team', 'Age', 'Points', 'W', 'L', 'ERA', 'WAR', 'G', 'GS', 'CG', 'ShO', 'SV', 'BS', 'IP', 'H', 'R', 'HR', 'BB', 'IBB', 'HBP', 'BK', 'SO', 'Pitches']]\n # PitchingStats.sort_values(\"Points\", inplace=True)\n # print(PitchingStats)\n PitchingStats.to_csv('data/pstats.csv')\n\n\n#####################################\n\n#### Combine Batting and Pitching ###\n\n#####################################\n\n# Combines Batting and Pitching csv files into one\n# ranks players by points and assigns a rank\n\ndef combinePoints():\n\n bstats = pd.read_csv('data/bstats.csv')\n pstats = pd.read_csv('data/pstats.csv')\n\n #Combine CSV\n Rankings = pd.merge(bstats, pstats, left_on=['Name'], right_on=['Name'], how='outer', suffixes=('_bat', '_pit'))\n Rankings.drop(labels=['Unnamed: 0_bat', 'Unnamed: 0_pit'], axis=1,inplace = True)\n Rankings = Rankings.fillna(0)\n Total_Points = Rankings['Points_bat'] + Rankings['Points_pit']\n Rankings.insert(1, 'Total_Points', Total_Points)\n Rankings = Rankings.sort_values('Total_Points', ascending=False)\n Rankings = Rankings.reset_index(drop=True)\n Rank = Rankings.index\n Rankings.insert(0, 'Rank', Rank)\n\n Rankings.to_csv('data/Rankings.csv', sep=',', index=False, encoding='utf-8')\n\n####################################\n\n#### Creating Marcel Projections ###\n\n####################################\n\n# runs points on current and past 2 season (3 total)\n# merges all files into one document\n# ranks by projection for the upcoming year\n\n\"\"\"\n\n# loop players and stats through function\n# have results sent to a file\n# have function then regear and run function again to create a 2, 3, 4? year outlook\n\ndef marcelCalculations(Mstat):\n\n #Import Files\n MarcelTable = pd.read_csv('data/marcel/MarcelTable.csv')\n lgAVG = pd.read_csv('data/marcel/lgAVG.csv')\n\n # What Year are we using?\n Year1 = MarcelTable['Season_Year1'][1]\n Year2 = MarcelTable['Season_Year2'][1]\n Year3 = MarcelTable['Season_Year3'][1]\n\n # Label the Stat for each year of Data\n MS_Year1 = (str(Mstat) + \"_Year1\")\n MS_Year2 = (str(Mstat) + \"_Year2\")\n MS_Year3 = (str(Mstat) + \"_Year3\")\n\n # Weighted total of individual players stat and Plate Apperances\n MStateTotal = (MS_Year1 * 5) + (MS_Year2 * 4) + (MS_Year3 * 3)\n PA = (MarcelTable['PA_Year1']*.5) + (MarcelTable['PA_Year2']*.1) + (200)\n\n # Calculating the Weighted Mean of state per plate apperances for that individual player\n MS_Y1 = MarcelTable[MS_Year1][Year1]/MarcelTable['PA'][Year1] * MarcelTable['PA_Year1'] * 5\n MS_Y2 = MarcelTable[MS_Year2][Year2]/MarcelTable['PA'][Year2] * MarcelTable['PA_Year2'] * 4\n MS_Y3 = MarcelTable[MS_Year3][Year3]/MarcelTable['PA'][Year3] * MarcelTable['PA_Year3'] * 3\n\n # Players total Weighted Plate Apperances\n Total_PAS = ( MarcelTable['PA_Year1'] * 5 ) + ( MarcelTable['PA_Year2'] * 4 ) + ( MarcelTable['PA_Year3'] * 3 )\n\n # Adjusting ratio to match 1200 apperances\n MS_Ratio = ( (MS_Y1 + MS_Y2 + MS_Y3) * 1200 ) / Total_PAS\n\n # taking league ratio out of 1200 and adding it to the actual players result to get that players ratio of stat per PA\n MS_Perct = ( MS_Ratio + MStateTotal ) / ( 1200 + Total_PAS )\n\n # Take the player's ratio and multiply it by the expected number of plate apperances\n MS_Expected = MS_perct * PA\n\n # Adjust for players age\n Age_Reg = ( ( 29 - MarcelTable['Age_Year1'] ) * 0.5 )\n\n # Final Result\n Result = MS_Expected * ( 1 + Age_Reg )\n\n ## Batting Required Stats\n\n PA = (MarcelTable['PA_Year1']*.5) + (MarcelTable['PA_Year2']*.1) + (200)\n\n R = (MarcelTable['R_Year1']*.5) + (MarcelTable['R_Year2']*.4) + (MarcelTable['R_Year3']*.3)\n 1b =\n 2b =\n 3b =\n HR =\n RBI =\n BB =\n SO =\n SB =\n AB =\n H =\n IBB =\n HBP =\n CS =\n\n BattingStats = data[['Season', 'Name', 'Team', 'Age', 'Points', 'G', 'PA', 'AB', 'AVG', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB', 'SO', 'HBP', 'SB', 'CS', 'HBP', 'SO', 'Pitches']]\n\n\n ## Pitching Required Stats\n\n IP =\n ER =\n SO =\n ShO =\n W =\n L =\n SV =\n BS =\n G =\n GS =\n BS =\n H =\n R =\n HR =\n BB =\n HBP =\n IBB =\n BK =\n CG =\n Pitches =\n\n PitchingStats = pdata[['Season', 'Name', 'Team', 'Age', 'Points', 'W', 'L', 'ERA', 'WAR', 'G', 'GS', 'CG', 'ShO', 'SV', 'BS', 'IP', 'H', 'R', 'HR', 'BB', 'IBB', 'HBP', 'BK', 'SO', 'Pitches']]\n\n\"\"\"\n\ndef lgAVGBat():\n # Import batting Stats\n BattingStats = pd.read_csv('data/bstats.csv')\n # find year of data\n Year = BattingStats['Season'][1]\n NumPlayers = len(BattingStats.index)\n\n\n # read lgAVG file\n lgAVG = pd.read_csv('data/marcel/lgAVGBat.csv')\n\n # locate correct year on file, remove previous entry, and add sum\n lgAVG = lgAVG[lgAVG.Season != Year]\n lgAVG.loc[Year] = BattingStats.sum()\n\n # delete unneeded Information columns\n lgAVG['Name'] = 'X'\n lgAVG['Team'] = 'X'\n\n # Change Season Calue to the correct year rather than the Sum\n lgAVG['Season'][Year] = Year\n # add number of players\n lgAVG['NumPlayers'][Year] = NumPlayers\n\n # drop unneeded column\n lgAVG.drop(labels=['Unnamed: 0'], axis=1,inplace = True)\n\n # Sort by Season and reset Index\n lgAVG = lgAVG.sort_values('Season', ascending=False)\n lgAVG = lgAVG.reset_index(drop=True)\n\n # Export Data to File\n lgAVG.to_csv('data/marcel/lgAVGBat.csv')\n\n\ndef lgAVGPit():\n # Import batting Stats\n PitchingStats = pd.read_csv('data/pstats.csv')\n # find year of data\n Year = PitchingStats['Season'][1]\n NumPlayers = len(PitchingStats.index)\n\n # read lgAVG file\n lgAVG = pd.read_csv('data/marcel/lgAVGPit.csv')\n\n # locate correct year on file, remove previous entry, and add sum\n lgAVG = lgAVG[lgAVG.Season != Year]\n lgAVG.loc[Year] = PitchingStats.sum()\n\n # delete unneeded Information columns\n lgAVG['Name'] = 'X'\n lgAVG['Team'] = 'X'\n\n # Change Season Calue to the correct year rather than the Sum\n lgAVG['Season'][Year] = Year\n # add number of players\n lgAVG['NumPlayers'][Year] = NumPlayers\n\n # drop unneeded column\n lgAVG.drop(labels=['Unnamed: 0'], axis=1,inplace = True)\n\n # Sort by Season and reset Index\n lgAVG = lgAVG.sort_values('Season', ascending=False)\n lgAVG = lgAVG.reset_index(drop=True)\n\n # Export Data to FIle\n lgAVG.to_csv('data/marcel/lgAVGPit.csv')\n\n\ndef lgAVG():\n # Run LGAVG for Batting and Pitching\n lgAVGBat()\n lgAVGPit()\n # Retrieve Data and add Suffix\n lgBat = pd.read_csv('data/marcel/lgAVGBat.csv')\n lgBat = lgBat.add_suffix('_Bat')\n lgPit = pd.read_csv('data/marcel/lgAVGPit.csv')\n lgPit = lgPit.add_suffix('_Pit')\n # 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