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generate_stats.py
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generate_stats.py
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from datetime import datetime
import numpy as np
import pandas as pd
###############################################################################
# League stats
##############################################################################
def generate_leauge_batting_stats(save=False):
main_df = pd.DataFrame()
s_df = pd.DataFrame()
current_year = datetime.now().year
print("Loading in NJCAA batting stats.")
for season in range(2013, current_year + 1):
print(f"\tLoading in {season} NJCAA batting stats.")
s_df = pd.read_parquet(
"game_stats/player/batting_game_stats/"
+ f"parquet/{season}_batting.parquet"
)
main_df = pd.concat([main_df, s_df], ignore_index=True)
finished_df = main_df.groupby(
["season", "njcaa_season", "njcaa_division"], axis=0, as_index=False
)[
[
"PA",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"K",
"TB",
"HBP",
"SF",
"SH",
"XBH",
"HDP",
"GO",
"FO",
]
].sum()
print("Adding Stats...")
# Groundouts/Flyouts ratio
finished_df["GO/FO"] = finished_df["GO"] / finished_df["FO"]
finished_df["GO/FO"] = finished_df["GO/FO"].round(3)
# Batting Average
finished_df["BA"] = finished_df["H"] / finished_df["AB"]
finished_df["BA"] = finished_df["BA"].round(3)
# On Base Percentage (OBP)
finished_df["OBP"] = (
finished_df["H"] + finished_df["BB"] + finished_df["HBP"]
) / finished_df["PA"]
finished_df["OBP"] = finished_df["OBP"].round(3)
# Slugging Percentae
finished_df["SLG"] = (
finished_df["H"]
+ (finished_df["2B"] * 2)
+ (finished_df["3B"] * 3)
+ (finished_df["HR"] * 4)
) / finished_df["AB"]
finished_df["SLG"] = finished_df["SLG"].round(3)
# On-Base + Slugging Percentages
finished_df["OPS"] = finished_df["OBP"] + finished_df["SLG"]
finished_df["OPS"].round(3)
# Batting Average on balls in play
finished_df["BAbip"] = (finished_df["H"] - finished_df["HR"]) / (
finished_df["AB"] - finished_df["K"] - finished_df["HR"] + finished_df["SF"]
)
finished_df["BAbip"] = finished_df["BAbip"].round(3)
# Isolated power
finished_df["ISO"] = (
finished_df["2B"] + (finished_df["3B"] * 2) + (finished_df["3B"])
) / finished_df["AB"]
finished_df["ISO"] = finished_df["ISO"].round(3)
# Runs scored percentage
finished_df["RS%"] = (finished_df["R"] - finished_df["HR"]) / (
finished_df["H"] + finished_df["HBP"] + finished_df["BB"] - finished_df["HR"]
)
finished_df["RS%"] = finished_df["RS%"].round(3)
# Home Run percentage
finished_df["HR%"] = finished_df["HR"] / finished_df["PA"]
finished_df["HR%"] = finished_df["HR%"].round(3)
# Strikeout percentage
finished_df["K%"] = finished_df["K"] / finished_df["PA"]
finished_df["K%"] = finished_df["K%"].round(3)
# Strikeout percentage
finished_df["BB%"] = finished_df["BB"] / finished_df["PA"]
finished_df["BB%"] = finished_df["BB%"].round(3)
# Walks to strikeouts ratio
finished_df["K-BB%"] = finished_df["K%"] - finished_df["BB%"]
finished_df["K-BB%"] = finished_df["K-BB%"].round(3)
finished_df["BB/K"] = finished_df["BB"] / finished_df["K"]
finished_df["BB/K"] = finished_df["BB/K"].round(3)
# Convert infinates into Null values
# finished_df = finished_df.mask(np.isinf(finished_df))
finished_df.replace([np.inf, -np.inf], np.nan, inplace=True)
print(finished_df)
if save == True:
finished_df.to_csv(
f"season_stats/leauge/batting_season_stats/csv/league_batting_stats.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/leauge/batting_season_stats/parquet/league_batting_stats.parquet",
index=False,
)
return finished_df
def generate_league_pitching_stats(save=False):
main_df = pd.DataFrame()
s_df = pd.DataFrame()
current_year = datetime.now().year
print("Loading in NJCAA pitching stats.")
for season in range(2013, current_year + 1):
print(f"\tLoading in {season} NJCAA pitching stats.")
s_df = pd.read_parquet(
f"game_stats/player/pitching_game_stats/parquet/{season}_pitching.parquet"
)
main_df = pd.concat([main_df, s_df], ignore_index=True)
main_df = main_df.dropna(subset=["IP"])
main_df = main_df.astype({"IP": "string"})
main_df[["whole_innings", "part_innings"]] = main_df["IP"].str.split(
".", expand=True
)
main_df = main_df.astype({"whole_innings": "int", "part_innings": "int"})
main_df["IP"] = round(main_df["whole_innings"] + (main_df["part_innings"] / 3), 3)
finished_df = pd.DataFrame(
main_df.groupby(["season", "njcaa_season", "njcaa_division"], as_index=False)[
["IP", "H", "R", "ER", "BB", "K", "HR"]
].sum()
)
# Earned Run Average (ERA)
finished_df["ERA"] = 9 * (finished_df["ER"] / finished_df["IP"])
finished_df["ERA"] = finished_df["ERA"].round(3)
# Walks and Hits per Inning Pitched (WHIP)
finished_df["WHIP"] = (finished_df["BB"] + finished_df["H"]) / finished_df["IP"]
finished_df["WHIP"] = finished_df["WHIP"].round(3)
# Hits per 9 innings
finished_df["H9"] = 9 * (finished_df["H"] / finished_df["IP"])
finished_df["H9"] = finished_df["H9"].round(3)
# Home Runs per 9 innings
finished_df["HR9"] = 9 * (finished_df["HR"] / finished_df["IP"])
finished_df["HR9"] = finished_df["HR9"].round(3)
# Walks per 9 innings
finished_df["BB9"] = 9 * (finished_df["BB"] / finished_df["IP"])
finished_df["BB9"] = finished_df["BB9"].round(3)
# Strikeouts per 9 innings
finished_df["K9"] = 9 * (finished_df["K"] / finished_df["IP"])
finished_df["K9"] = finished_df["K9"].round(3)
# Strikeouts/Walks ratio
finished_df["K/BB"] = finished_df["K"] / finished_df["BB"]
finished_df["K/BB"] = finished_df["K/BB"].round(3)
# Runs Allowed per 9 innings pitched (RA9)
finished_df["RA9"] = 9 * (finished_df["R"] / finished_df["IP"])
finished_df["RA9"] = finished_df["RA9"].round(3)
# Convert infinates into Null values
finished_df.replace([np.inf, -np.inf], np.nan, inplace=True)
print(finished_df)
if save == True:
finished_df.to_csv(
f"season_stats/leauge/pitching_season_stats/csv/leauge_pitching.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/leauge/pitching_season_stats/parquet/leauge_pitching.parquet",
index=False,
)
return finished_df
###############################################################################
# Season Player Stats
#
##############################################################################
def generate_season_player_batting_stats(season: int, save=False):
def parser(division: int, main_df: pd.DataFrame()):
print(f"\nParsing NJCAA division #{division}.")
njcaa_division = f"div{division}"
league_df = pd.read_csv(
"season_stats/leauge/batting_season_stats/csv/" + "league_batting_stats.csv"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["njcaa_division"] == njcaa_division]
if league_df.empty is True:
return pd.DataFrame()
lg_obs = league_df["OPS"].iloc[0]
main_df = main_df[main_df["njcaa_division"] == njcaa_division]
main_df = pd.DataFrame(
main_df.groupby(
[
"season",
"njcaa_division",
"team",
"team_id",
"player_id",
"player_name",
],
as_index=False,
)[
[
"G",
"PA",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"K",
"TB",
"HBP",
"SH",
"SF",
"XBH",
"HDP",
"GO",
"FO",
]
].sum()
)
# Groundouts/Flyouts ratio
main_df["GO/FO"] = main_df["GO"] / main_df["FO"]
main_df["GO/FO"] = main_df["GO/FO"].round(3)
# Batting Average
main_df["BA"] = main_df["H"] / main_df["AB"]
main_df["BA"] = main_df["BA"].round(3)
# On Base Percentage (OBP)
main_df["OBP"] = (main_df["H"] + main_df["BB"] + main_df["HBP"]) / main_df["PA"]
main_df["OBP"] = main_df["OBP"].round(3)
# Slugging Percentae
main_df["SLG"] = (
main_df["H"]
+ (main_df["2B"] * 2)
+ (main_df["3B"] * 3)
+ (main_df["HR"] * 4)
) / main_df["AB"]
main_df["SLG"] = main_df["SLG"].round(3)
# On-Base + Slugging Percentages
main_df["OPS"] = main_df["OBP"] + main_df["SLG"]
main_df["OPS"] = main_df["OPS"].round(3)
# OPS+
main_df["OPS+"] = 100 * (main_df["OPS"] / lg_obs)
main_df["OPS+"] = main_df["OPS+"].round(0)
# Isolated power
main_df["ISO"] = (
main_df["2B"] + (main_df["3B"] * 2) + (main_df["3B"])
) / main_df["AB"]
main_df["ISO"] = main_df["ISO"].round(3)
# Batting Average on balls in play
main_df["BAbip"] = (main_df["H"] - main_df["HR"]) / (
main_df["AB"] - main_df["K"] - main_df["HR"] + main_df["SF"]
)
main_df["BAbip"] = main_df["BAbip"].round(3)
# Runs scored percentage
main_df["RS%"] = (main_df["R"] - main_df["HR"]) / (
main_df["H"] + main_df["HBP"] + main_df["BB"] - main_df["HR"]
)
main_df["RS%"] = main_df["RS%"].round(3)
# Home Run percentage
main_df["HR%"] = main_df["HR"] / main_df["PA"]
main_df["HR%"] = main_df["HR%"].round(3)
# Strikeout percentage
main_df["K%"] = main_df["K"] / main_df["PA"]
main_df["K%"] = main_df["K%"].round(3)
# Strikeout percentage
main_df["BB%"] = main_df["BB"] / main_df["PA"]
main_df["BB%"] = main_df["BB%"].round(3)
# Walks to strikeouts ratio
main_df["K-BB%"] = main_df["K%"] - main_df["BB%"]
main_df["K-BB%"] = main_df["K-BB%"].round(3)
main_df["BB/K"] = main_df["BB"] / main_df["K"]
main_df["BB/K"] = main_df["BB/K"].round(3)
# Convert infinates into Null values
main_df.replace([np.inf, -np.inf], np.nan, inplace=True)
del league_df
return main_df
finished_df = pd.DataFrame()
div_df = pd.DataFrame()
main_df = pd.read_parquet(
f"game_stats/player/batting_game_stats/parquet/{season}_batting.parquet"
)
main_df["G"] = 1
for i in range(1, 4):
div_df = parser(i, main_df)
finished_df = pd.concat([finished_df, div_df], ignore_index=True)
print(finished_df)
if save == True:
finished_df.to_csv(
f"season_stats/player/batting_season_stats/csv/{season}_batting.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/player/batting_season_stats/parquet/{season}_batting.parquet",
index=False,
)
return finished_df
def generate_season_player_pitching_stats(season: int, save=False):
def parser(division: int, main_df: pd.DataFrame()):
print(f"\nParsing NJCAA division #{division}.")
njcaa_division = f"div{division}"
league_df = pd.read_csv(
f"season_stats/leauge/pitching_season_stats/csv/leauge_pitching.csv"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["njcaa_division"] == njcaa_division]
if league_df.empty == True:
return pd.DataFrame()
lg_era = league_df["ERA"].iloc[0]
main_df = pd.DataFrame(
main_df.groupby(
[
"season",
"njcaa_season",
"team",
"team_id",
"player_id",
"player_name",
],
as_index=False,
)[["GS", "W", "L", "SV", "IP", "H", "R", "ER", "BB", "K", "HR"]].sum()
)
# Win-loss percentage
main_df["W-L%"] = main_df["W"] / (main_df["W"] + main_df["L"])
main_df["W-L%"] = main_df["W-L%"].round(3)
# Earned Run Average (ERA)
main_df["ERA"] = 9 * (main_df["ER"] / main_df["IP"])
main_df["ERA"] = main_df["ERA"].round(3)
# ERA+
main_df["ERA+"] = 100 * (lg_era / main_df["ERA"])
main_df["ERA+"] = main_df["ERA+"].round(0)
# Walks and Hits per Inning Pitched (WHIP)
main_df["WHIP"] = (main_df["BB"] + main_df["H"]) / main_df["IP"]
main_df["WHIP"] = main_df["WHIP"].round(3)
# Hits per 9 innings
main_df["H9"] = 9 * (main_df["H"] / main_df["IP"])
main_df["H9"] = main_df["H9"].round(3)
# Home Runs per 9 innings
main_df["HR9"] = 9 * (main_df["HR"] / main_df["IP"])
main_df["HR9"] = main_df["HR9"].round(3)
# Walks per 9 innings
main_df["BB9"] = 9 * (main_df["BB"] / main_df["IP"])
main_df["BB9"] = main_df["BB9"].round(3)
# Strikeouts per 9 innings
main_df["K9"] = 9 * (main_df["K"] / main_df["IP"])
main_df["K9"] = main_df["K9"].round(3)
# Strikeouts/Walks ratio
main_df["K/BB"] = main_df["K"] / main_df["BB"]
main_df["K/BB"] = main_df["K/BB"].round(3)
# Runs Allowed per 9 innings pitched (RA9)
main_df["RA9"] = 9 * (main_df["R"] / main_df["IP"])
main_df["RA9"] = main_df["RA9"].round(3)
# Convert infinates into Null values
main_df.replace([np.inf, -np.inf], np.nan, inplace=True)
return main_df
finished_df = pd.DataFrame()
div_df = pd.DataFrame()
main_df = pd.read_parquet(
f"game_stats/player/pitching_game_stats/parquet/{season}_pitching.parquet"
)
main_df["G"] = 1
for i in range(1, 4):
div_df = parser(i, main_df)
finished_df = pd.concat([finished_df, div_df], ignore_index=True)
print(finished_df)
if save == True:
finished_df.to_csv(
f"season_stats/player/pitching_season_stats/csv/{season}_pitching.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/player/pitching_season_stats/parquet/{season}_pitching.parquet",
index=False,
)
return finished_df
def generate_season_player_fielding_stats(season: int, save=False):
main_df = pd.read_parquet(
f"game_stats/player/fielding_game_stats/parquet/{season}_fielding.parquet"
)
main_df["G"] = 1
finished_df = pd.DataFrame(
main_df.groupby(
["season", "njcaa_season", "team", "team_id", "player_id", "player_name"],
as_index=False,
)[
[
"G",
"TC",
"PO",
"A",
"E",
"FPCT",
"DP",
"SBA",
"RCS",
"RCS_PCT",
"PB",
"CI",
]
].sum()
)
# Fielding Percentage (Putouts + Assists) / (Putouts + Assists + Errors)
finished_df["FPCT"] = (finished_df["PO"] + finished_df["A"]) / (
finished_df["PO"] + finished_df["A"] + finished_df["E"]
)
finished_df["FPCT"] = finished_df["FPCT"].round(3)
# Runners caught stealing percentage
finished_df["RCS_PCT"] = finished_df["RCS"] / finished_df["SBA"]
finished_df["RCS_PCT"] = finished_df["RCS_PCT"].round(3)
if save == True:
finished_df.to_csv(
f"season_stats/player/fielding_season_stats/csv/{season}_fielding.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/player/fielding_season_stats/parquet/{season}_fielding.parquet",
index=False,
)
return finished_df
############################################################################### Team Season Stats
#
##############################################################################
def generate_season_team_batting_stats(season: int, save=False):
def parser(division: int, main_df: pd.DataFrame()):
print(f"\nParsing NJCAA division #{division}.")
njcaa_division = f"div{division}"
league_df = pd.read_csv(
f"season_stats/leauge/batting_season_stats/csv/league_batting_stats.csv"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["njcaa_division"] == njcaa_division]
if league_df.empty == True:
return pd.DataFrame()
lg_obs = league_df["OPS"].iloc[0]
main_df = main_df[main_df["njcaa_division"] == njcaa_division]
main_df = pd.DataFrame(
main_df.groupby(
["season", "njcaa_division", "team", "team_id"], as_index=False
)[
[
"G",
"PA",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"K",
"TB",
"HBP",
"SH",
"SF",
"XBH",
"HDP",
"GO",
"FO",
]
].sum()
)
# Groundouts/Flyouts ratio
main_df["GO/FO"] = main_df["GO"] / main_df["FO"]
main_df["GO/FO"] = main_df["GO/FO"].round(3)
# Batting Average
main_df["BA"] = main_df["H"] / main_df["AB"]
main_df["BA"] = main_df["BA"].round(3)
# On Base Percentage (OBP)
main_df["OBP"] = (main_df["H"] + main_df["BB"] + main_df["HBP"]) / main_df["PA"]
main_df["OBP"] = main_df["OBP"].round(3)
# Slugging Percentae
main_df["SLG"] = (
main_df["H"]
+ (main_df["2B"] * 2)
+ (main_df["3B"] * 3)
+ (main_df["HR"] * 4)
) / main_df["AB"]
main_df["SLG"] = main_df["SLG"].round(3)
# On-Base + Slugging Percentages
main_df["OPS"] = main_df["OBP"] + main_df["SLG"]
main_df["OPS"] = main_df["OPS"].round(3)
# OPS+
main_df["OPS+"] = 100 * (main_df["OPS"] / lg_obs)
main_df["OPS+"] = main_df["OPS+"].round(0)
# Batting Average on balls in play
main_df["BAbip"] = (main_df["H"] - main_df["HR"]) / (
main_df["AB"] - main_df["K"] - main_df["HR"] + main_df["SF"]
)
main_df["BAbip"] = main_df["BAbip"].round(3)
# Runs scored percentage
main_df["RS%"] = (main_df["R"] - main_df["HR"]) / (
main_df["H"] + main_df["HBP"] + main_df["BB"] - main_df["HR"]
)
main_df["RS%"] = main_df["RS%"].round(3)
# Home Run percentage
main_df["HR%"] = main_df["HR"] / main_df["PA"]
main_df["HR%"] = main_df["HR%"].round(3)
# Strikeout percentage
main_df["K%"] = main_df["K"] / main_df["PA"]
main_df["K%"] = main_df["K%"].round(3)
# Strikeout percentage
main_df["BB%"] = main_df["BB"] / main_df["PA"]
main_df["BB%"] = main_df["BB%"].round(3)
# Walks to strikeouts ratio
main_df["K-BB%"] = main_df["K%"] - main_df["BB%"]
main_df["K-BB%"] = main_df["K-BB%"].round(3)
main_df["BB/K"] = main_df["BB"] / main_df["K"]
main_df["BB/K"] = main_df["BB/K"].round(3)
# Convert infinates into Null values
main_df.replace([np.inf, -np.inf], np.nan, inplace=True)
del league_df
return main_df
finished_df = pd.DataFrame()
div_df = pd.DataFrame()
main_df = pd.read_parquet(
f"game_stats/player/batting_game_stats/parquet/{season}_batting.parquet"
)
main_df["G"] = 1
for i in range(1, 4):
div_df = parser(i, main_df)
finished_df = pd.concat([finished_df, div_df], ignore_index=True)
print(finished_df)
if save == True:
finished_df.to_csv(
f"season_stats/team/batting_season_stats/csv/{season}_batting.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/team/batting_season_stats/parquet/{season}_batting.parquet",
index=False,
)
return finished_df
def generate_season_team_pitching_stats(season: int, save=False):
def parser(division: int, main_df: pd.DataFrame()):
print(f"\nParsing NJCAA division #{division}.")
njcaa_division = f"div{division}"
league_df = pd.read_csv(
f"season_stats/leauge/pitching_season_stats/csv/leauge_pitching.csv"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["njcaa_division"] == njcaa_division]
if league_df.empty == True:
return pd.DataFrame()
lg_era = league_df["ERA"].iloc[0]
main_df = pd.DataFrame(
main_df.groupby(
["season", "njcaa_season", "team", "team_id"], as_index=False
)[["GS", "W", "L", "SV", "IP", "H", "R", "ER", "BB", "K", "HR"]].sum()
)
# Win-loss percentage
main_df["W-L%"] = main_df["W"] / (main_df["W"] + main_df["L"])
main_df["W-L%"] = main_df["W-L%"].round(3)
# Earned Run Average (ERA)
main_df["ERA"] = 9 * (main_df["ER"] / main_df["IP"])
main_df["ERA"] = main_df["ERA"].round(3)
# ERA+
main_df["ERA+"] = 100 * (lg_era / main_df["ERA"])
main_df["ERA+"] = main_df["ERA+"].round(0)
# Walks and Hits per Inning Pitched (WHIP)
main_df["WHIP"] = (main_df["BB"] + main_df["H"]) / main_df["IP"]
main_df["WHIP"] = main_df["WHIP"].round(3)
# Hits per 9 innings
main_df["H9"] = 9 * (main_df["H"] / main_df["IP"])
main_df["H9"] = main_df["H9"].round(3)
# Home Runs per 9 innings
main_df["HR9"] = 9 * (main_df["HR"] / main_df["IP"])
main_df["HR9"] = main_df["HR9"].round(3)
# Walks per 9 innings
main_df["BB9"] = 9 * (main_df["BB"] / main_df["IP"])
main_df["BB9"] = main_df["BB9"].round(3)
# Strikeouts per 9 innings
main_df["K9"] = 9 * (main_df["K"] / main_df["IP"])
main_df["K9"] = main_df["K9"].round(3)
# Strikeouts/Walks ratio
main_df["K/BB"] = main_df["K"] / main_df["BB"]
main_df["K/BB"] = main_df["K/BB"].round(3)
# Runs Allowed per 9 innings pitched (RA9)
main_df["RA9"] = 9 * (main_df["R"] / main_df["IP"])
main_df["RA9"] = main_df["RA9"].round(3)
# Convert infinates into Null values
main_df.replace([np.inf, -np.inf], np.nan, inplace=True)
return main_df
finished_df = pd.DataFrame()
div_df = pd.DataFrame()
main_df = pd.read_parquet(
f"game_stats/player/pitching_game_stats/parquet/{season}_pitching.parquet"
)
main_df["G"] = 1
for i in range(1, 4):
div_df = parser(i, main_df)
finished_df = pd.concat([finished_df, div_df], ignore_index=True)
print(finished_df)
if save == True:
finished_df.to_csv(
f"season_stats/team/pitching_season_stats/csv/{season}_pitching.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/team/pitching_season_stats/parquet/{season}_pitching.parquet",
index=False,
)
return finished_df
def generate_season_team_fielding_stats(season: int, save=False):
main_df = pd.read_parquet(
f"game_stats/player/fielding_game_stats/parquet/{season}_fielding.parquet"
)
main_df["G"] = 1
finished_df = pd.DataFrame(
main_df.groupby(["season", "njcaa_season", "team", "team_id"], as_index=False)[
[
"G",
"TC",
"PO",
"A",
"E",
"FPCT",
"DP",
"SBA",
"RCS",
"RCS_PCT",
"PB",
"CI",
]
].sum()
)
# Fielding Percentage (Putouts + Assists) / (Putouts + Assists + Errors)
finished_df["FPCT"] = (finished_df["PO"] + finished_df["A"]) / (
finished_df["PO"] + finished_df["A"] + finished_df["E"]
)
finished_df["FPCT"] = finished_df["FPCT"].round(3)
# Runners caught stealing percentage
finished_df["RCS_PCT"] = finished_df["RCS"] / finished_df["SBA"]
finished_df["RCS_PCT"] = finished_df["RCS_PCT"].round(3)
if save == True:
finished_df.to_csv(
f"season_stats/team/fielding_season_stats/csv/{season}_fielding.csv",
index=False,
)
finished_df.to_parquet(
f"season_stats/team/fielding_season_stats/parquet/{season}_fielding.parquet",
index=False,
)
return finished_df
############################################################################### Team Game Stats
#
##############################################################################
def generate_team_game_batting_stats(season: int, save=False):
def parser(division: int, main_df: pd.DataFrame()):
print(f"\nParsing NJCAA division #{division}.")
njcaa_division = f"div{division}"
league_df = pd.read_csv(
f"season_stats/leauge/batting_season_stats/csv/league_batting_stats.csv"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["njcaa_division"] == njcaa_division]
if league_df.empty == True:
return pd.DataFrame()
lg_obs = league_df["OPS"].iloc[0]
main_df = main_df[main_df["njcaa_division"] == njcaa_division]
main_df = pd.DataFrame(
main_df.groupby(
[
"season",
"njcaa_division",
"team",
"team_id",
"location",
"opponent",
"result",
"score",
"game_id",
],
as_index=False,
)[
[
"G",
"PA",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"K",
"TB",
"HBP",
"SH",
"SF",
"XBH",
"HDP",
"GO",
"FO",
]
].sum()
)
# Groundouts/Flyouts ratio
main_df["GO/FO"] = main_df["GO"] / main_df["FO"]
main_df["GO/FO"] = main_df["GO/FO"].round(3)
# Batting Average
main_df["BA"] = main_df["H"] / main_df["AB"]
main_df["BA"] = main_df["BA"].round(3)
# On Base Percentage (OBP)
main_df["OBP"] = (main_df["H"] + main_df["BB"] + main_df["HBP"]) / main_df["PA"]
main_df["OBP"] = main_df["OBP"].round(3)
# Slugging Percentae
main_df["SLG"] = (
main_df["H"]
+ (main_df["2B"] * 2)
+ (main_df["3B"] * 3)
+ (main_df["HR"] * 4)
) / main_df["AB"]
main_df["SLG"] = main_df["SLG"].round(3)
# On-Base + Slugging Percentages
main_df["OPS"] = main_df["OBP"] + main_df["SLG"]
main_df["OPS"] = main_df["OPS"].round(3)
# OPS+
main_df["OPS+"] = 100 * (main_df["OPS"] / lg_obs)
main_df["OPS+"] = main_df["OPS+"].round(0)
# Batting Average on balls in play
main_df["BAbip"] = (main_df["H"] - main_df["HR"]) / (
main_df["AB"] - main_df["K"] - main_df["HR"] + main_df["SF"]
)
main_df["BAbip"] = main_df["BAbip"].round(3)
# Runs scored percentage
main_df["RS%"] = (main_df["R"] - main_df["HR"]) / (
main_df["H"] + main_df["HBP"] + main_df["BB"] - main_df["HR"]
)
main_df["RS%"] = main_df["RS%"].round(3)
# Home Run percentage
main_df["HR%"] = main_df["HR"] / main_df["PA"]
main_df["HR%"] = main_df["HR%"].round(3)
# Strikeout percentage
main_df["K%"] = main_df["K"] / main_df["PA"]
main_df["K%"] = main_df["K%"].round(3)
# Strikeout percentage
main_df["BB%"] = main_df["BB"] / main_df["PA"]
main_df["BB%"] = main_df["BB%"].round(3)
# Walks to strikeouts ratio
main_df["K-BB%"] = main_df["K%"] - main_df["BB%"]
main_df["K-BB%"] = main_df["K-BB%"].round(3)
main_df["BB/K"] = main_df["BB"] / main_df["K"]
main_df["BB/K"] = main_df["BB/K"].round(3)
# Convert infinates into Null values
main_df.replace([np.inf, -np.inf], np.nan, inplace=True)
del league_df
return main_df
finished_df = pd.DataFrame()
div_df = pd.DataFrame()
main_df = pd.read_parquet(
f"game_stats/player/batting_game_stats/parquet/{season}_batting.parquet"
)
main_df["G"] = 1
for i in range(1, 4):
div_df = parser(i, main_df)
finished_df = pd.concat([finished_df, div_df], ignore_index=True)
print(finished_df)
if save == True:
finished_df.to_csv(
f"game_stats/team/batting_game_stats/csv/{season}_batting.csv", index=False
)
finished_df.to_parquet(
f"game_stats/team/batting_game_stats/parquet/{season}_batting.parquet",
index=False,
)
return finished_df
def generate_team_game_pitching_stats(season: int, save=False):
print("Starting up.")
def parser(division: int, main_df: pd.DataFrame()):
print(f"\nParsing NJCAA division #{division}.")
njcaa_division = f"div{division}"
league_df = pd.read_csv(
f"season_stats/leauge/pitching_season_stats/csv/leauge_pitching.csv"
)
league_df = league_df[league_df["season"] == season]
league_df = league_df[league_df["njcaa_division"] == njcaa_division]
if league_df.empty == True:
return pd.DataFrame()
lg_era = league_df["ERA"].iloc[0]
main_df = pd.DataFrame(
main_df.groupby(
[
"season",
"njcaa_division",
"team",
"team_id",
"location",
"opponent",
"result",
"score",
"game_id",
],
as_index=False,
)[["GS", "W", "L", "SV", "IP", "H", "R", "ER", "BB", "K", "HR"]].sum()
)
# Win-loss percentage
main_df["W-L%"] = main_df["W"] / (main_df["W"] + main_df["L"])
main_df["W-L%"] = main_df["W-L%"].round(3)
# Earned Run Average (ERA)
main_df["ERA"] = 9 * (main_df["ER"] / main_df["IP"])
main_df["ERA"] = main_df["ERA"].round(3)
# ERA+
main_df["ERA+"] = 100 * (lg_era / main_df["ERA"])
main_df["ERA+"] = main_df["ERA+"].round(0)
# Walks and Hits per Inning Pitched (WHIP)
main_df["WHIP"] = (main_df["BB"] + main_df["H"]) / main_df["IP"]