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aparicio.py
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import pandas as pd
import numpy as np
from datetime import datetime
from pathlib import Path
def main():
# project(545361, "2021-04-01")
project_all("2021-07-17")
def project_all(projection_date_str):
project(projection_date_str, BATTING, True)
project(projection_date_str, PITCHING, False)
def project(projection_date_str, settings, is_batting):
MAX_DAYS_AGO = 2000
LG_AVG_PCT = 0.15
PROJECTED_PA = 650
PROJECTED_BF = 800
MIN_RP_BF = 250
# Compared to the max appearances, what percentage does a player need to get a projection?
APPEARANCE_THRESHOLD = 0.10
# MLB's "GameTypes"
SPRING_TRAINING = "S"
EXHIBITION = "E"
# REGULAR_SEASON = "R"
# POST_SEASON = "P"
# MLB's "SportIDs" for various leagues
AAA = 11
AA = 12
HIGH_A = 13
LOW_A = 14
ROOKIE = 16
FALL = 17
# Load all player stats
df = load_gamelogs(is_batting)
# Calculate days ago
projection_date = datetime.strptime(projection_date_str, "%Y-%m-%d")
df["date"] = pd.to_datetime(df["date"], infer_datetime_format=True)
df["days_ago"] = (projection_date - df["date"]).dt.days
# Remove data before and after the projection window
df = df[df["days_ago"] <= MAX_DAYS_AGO]
df = df[df["days_ago"] > 0]
# Weight stats by decay rate
for stat in settings["stat_cols"]:
df[stat] = df[stat] * (settings["decay_rates"][stat] ** df["days_ago"])
# Reduce spring training and exhibition games
df[stat] = np.where(
df["game_type"] == SPRING_TRAINING, df[stat] * 0.45, df[stat]
)
df[stat] = np.where(df["game_type"] == EXHIBITION, df[stat] * 0.45, df[stat])
# Reduce minor league stats
df[stat] = np.where(
df["league_id"] == AAA, df[stat] * settings["aaa_factors"][stat], df[stat]
)
df[stat] = np.where(
df["league_id"] == AA, df[stat] * settings["aa_factors"][stat], df[stat]
)
df[stat] = np.where(
df["league_id"] == HIGH_A,
df[stat] * settings["high_a_factors"][stat],
df[stat],
)
df[stat] = np.where(
df["league_id"] == LOW_A,
df[stat] * settings["low_a_factors"][stat],
df[stat],
)
df[stat] = np.where(
df["league_id"] == ROOKIE,
df[stat] * settings["rookie_factors"][stat],
df[stat],
)
df[stat] = np.where(
df["league_id"] == FALL, df[stat] * settings["fall_factors"][stat], df[stat]
)
pr = df.groupby(["player_id"]).sum()
pr = pr[settings["stat_cols"]]
if is_batting:
appearances = "PA"
pr[appearances] = pr["AB"] + pr["BB"] + pr["HBP"] + pr["SH"] + pr["SF"]
else:
appearances = "BFP"
pr["start_pct"] = pr["GS"] / pr["G"]
# Our regression to the mean will be 15% of the top player's PA
max_pa = pr[appearances].max()
regression_pa = LG_AVG_PCT * max_pa
# Clear out players with too few appearances (long-retired, pitchers batting, etc.)
pr = pr[pr[appearances] > (max_pa * APPEARANCE_THRESHOLD)]
# This should only be league average of MLB players
lg_avg = pr.mean()
factor = regression_pa / lg_avg[appearances]
lg_avg = lg_avg * factor
lg_avg["start_pct"] = 0
pr = pr + lg_avg
# Scale everyone to the same PA
if is_batting:
pr["projected_pa"] = PROJECTED_PA
else:
pr["projected_pa"] = MIN_RP_BF + pr["start_pct"] * (PROJECTED_BF - MIN_RP_BF)
pr = pr.multiply(pr["projected_pa"] / pr[appearances], axis="index")
# Clean up calc columns
pr = pr.drop("projected_pa", 1)
pr = pr.drop("start_pct", 1)
# Add names to the data for easy readability
pr = pr.join(load_names(), how="left")
print(pr.head())
save_projection(projection_date_str, pr, is_batting)
def load_gamelogs(is_batting):
df = pd.DataFrame()
filepath = Path(__file__).parent
if is_batting:
df = pd.read_csv(filepath / "stats" / "batting.csv")
else:
df = pd.read_csv(filepath / "stats" / "pitching.csv")
df["IP"] = df["IP"].map(convert_ip)
return df
# Convert IP fractional notation (.1 .2) to true decimals (.33 .67)
def convert_ip(ip):
return int(ip) + (ip % 1 * 10 / 3)
def load_names():
names = pd.DataFrame()
names = pd.read_csv("https://raw.githubusercontent.com/chadwickbureau/register/master/data/people.csv", low_memory=False)
names = names[["key_mlbam", "name_last", "name_first"]]
names.set_index("key_mlbam", inplace=True)
return names
def save_projection(date, df, is_batting):
if is_batting:
filepath = Path(__file__).parent / "projections" / "batting"
else:
filepath = Path(__file__).parent / "projections" / "pitching"
df.round().to_csv(filepath / (date + ".csv"), index_label="player_id")
def load_projection(date, is_batting):
if is_batting:
filepath = Path(__file__).parent / "projections" / "batting"
else:
filepath = Path(__file__).parent / "projections" / "pitching"
return pd.read_csv(filepath / (date + ".csv"))
def load_player_projections(player_id, is_batting):
player = pd.DataFrame()
if is_batting:
filedir = Path(__file__).parent / "projections" / "batting"
else:
filedir = Path(__file__).parent / "projections" / "pitching"
files = [x for x in filedir.iterdir() if x.is_file()]
for file in files:
df = pd.read_csv(file, index_col=0)
df["Date"] = file.stem
if player_id in df.index:
player = player.append(df.loc[[player_id]])
if not player.empty:
if is_batting:
int_cols = ["PA","AB","H","2B","3B","HR","R","RBI","SB","CS","BB","SO","GIDP","HBP","SH","SF","IBB"]
else:
int_cols = ["W","L","G","GS","CG","SHO","SV","HLD","IP","H","R","ER","HR","BB","IBB","SO","HBP","BK","WP","BFP"]
player[int_cols] = player[int_cols].astype(int)
player.sort_values(by=["Date"], inplace=True, ascending=False)
# Only keep the most recent projection
# and 1 projection for each of the previous months.
player = player[(player["Date"] == player["Date"].max()) | (pd.DatetimeIndex(player["Date"]).day == 1)]
return player
def search_players(search_string):
return True
BATTING = {
"stat_cols": [
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"BB",
"SO",
"IBB",
"HBP",
"SH",
"SF",
"GIDP",
],
"decay_rates": {
"AB": 0.9994,
"R": 0.9994,
"H": 0.9994,
"2B": 0.9994,
"3B": 0.9994,
"HR": 0.9994,
"RBI": 0.9994,
"SB": 0.9994,
"CS": 0.9994,
"BB": 0.9994,
"SO": 0.9994,
"IBB": 0.9994,
"HBP": 0.9994,
"SH": 0.9994,
"SF": 0.9994,
"GIDP": 0.9994,
},
"aaa_factors": {
"AB": 1.00,
"R": 0.79,
"H": 0.95,
"2B": 0.80,
"3B": 0.84,
"HR": 0.66,
"RBI": 0.79,
"SB": 0.72,
"CS": 1.01,
"BB": 0.78,
"SO": 0.90,
"IBB": 0.78,
"HBP": 0.78,
"SH": 0.78,
"SF": 0.78,
"GIDP": 0.78,
},
"aa_factors": {
"AB": 1.02,
"R": 0.82,
"H": 0.99,
"2B": 0.85,
"3B": 0.95,
"HR": 0.67,
"RBI": 0.83,
"SB": 0.70,
"CS": 0.91,
"BB": 0.79,
"SO": 0.90,
"IBB": 0.79,
"HBP": 0.79,
"SH": 0.79,
"SF": 0.79,
"GIDP": 0.79,
},
"high_a_factors": {
"AB": 1.03,
"R": 0.78,
"H": 0.95,
"2B": 0.79,
"3B": 0.81,
"HR": 0.61,
"RBI": 0.75,
"SB": 0.51,
"CS": 0.93,
"BB": 0.72,
"SO": 0.93,
"IBB": 0.72,
"HBP": 0.72,
"SH": 0.72,
"SF": 0.72,
"GIDP": 0.72,
},
"low_a_factors": {
"AB": 1.05,
"R": 0.69,
"H": 0.92,
"2B": 0.76,
"3B": 0.69,
"HR": 0.65,
"RBI": 0.68,
"SB": 0.46,
"CS": 0.84,
"BB": 0.64,
"SO": 0.89,
"IBB": 0.64,
"HBP": 0.64,
"SH": 0.64,
"SF": 0.64,
"GIDP": 0.64,
},
"rookie_factors": {
"AB": 1.08,
"R": 0.51,
"H": 0.77,
"2B": 0.63,
"3B": 0.26,
"HR": 0.39,
"RBI": 0.48,
"SB": 0.27,
"CS": 0.48,
"BB": 0.54,
"SO": 1.06,
"IBB": 0.54,
"HBP": 0.54,
"SH": 0.54,
"SF": 0.54,
"GIDP": 0.54,
},
"fall_factors": {
"AB": 1.02,
"R": 0.82,
"H": 0.99,
"2B": 0.85,
"3B": 0.95,
"HR": 0.67,
"RBI": 0.83,
"SB": 0.70,
"CS": 0.91,
"BB": 0.79,
"SO": 0.90,
"IBB": 0.79,
"HBP": 0.79,
"SH": 0.79,
"SF": 0.79,
"GIDP": 0.79,
},
}
PITCHING = {
"stat_cols": [
"W",
"L",
"G",
"GS",
"CG",
"SHO",
"SV",
"HLD",
"BFP",
"IP",
"H",
"ER",
"R",
"HR",
"SO",
"BB",
"IBB",
"HBP",
"WP",
"BK",
],
"decay_rates": {
"W": 0.999,
"L": 0.999,
"G": 0.999,
"GS": 0.999,
"CG": 0.999,
"SHO": 0.999,
"SV": 0.999,
"HLD": 0.999,
"BFP": 0.999,
"IP": 0.999,
"H": 0.999,
"ER": 0.999,
"R": 0.999,
"HR": 0.999,
"SO": 0.999,
"BB": 0.999,
"IBB": 0.999,
"HBP": 0.999,
"WP": 0.999,
"BK": 0.999,
},
"aaa_factors": {
"W": 0.92,
"L": 1.14,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.96,
"HLD": 0.96,
"BFP": 1.00,
"IP": 1.00,
"H": 1.13,
"ER": 0.92,
"R": 0.92,
"HR": 0.88,
"SO": 0.56,
"BB": 0.87,
"IBB": 0.87,
"HBP": 0.87,
"WP": 0.87,
"BK": 0.87,
},
"aa_factors": {
"W": 0.92,
"L": 1.24,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.99,
"HLD": 0.99,
"BFP": 0.99,
"IP": 0.99,
"H": 1.25,
"ER": 1.05,
"R": 1.05,
"HR": 1.02,
"SO": 0.50,
"BB": 0.95,
"IBB": 0.95,
"HBP": 0.95,
"WP": 0.95,
"BK": 0.95,
},
"high_a_factors": {
"W": 0.92,
"L": 1.31,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.96,
"HLD": 0.96,
"BFP": 0.98,
"IP": 0.98,
"H": 1.28,
"ER": 1.03,
"R": 1.03,
"HR": 1.28,
"SO": 0.46,
"BB": 0.92,
"IBB": 0.92,
"HBP": 0.92,
"WP": 0.92,
"BK": 0.92,
},
"low_a_factors": {
"W": 0.86,
"L": 1.27,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 1.04,
"HLD": 1.04,
"BFP": 1.00,
"IP": 1.00,
"H": 1.30,
"ER": 1.04,
"R": 1.04,
"HR": 1.57,
"SO": 0.45,
"BB": 0.89,
"IBB": 0.89,
"HBP": 0.89,
"WP": 0.89,
"BK": 0.89,
},
"rookie_factors": {
"W": 0.68,
"L": 1.10,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 1.04,
"HLD": 1.04,
"BFP": 0.96,
"IP": 0.96,
"H": 1.30,
"ER": 1.08,
"R": 1.08,
"HR": 2.01,
"SO": 0.38,
"BB": 0.95,
"IBB": 0.95,
"HBP": 0.95,
"WP": 0.95,
"BK": 0.95,
},
"fall_factors": {
"W": 0.92,
"L": 1.24,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.99,
"HLD": 0.99,
"BFP": 0.99,
"IP": 0.99,
"H": 1.25,
"ER": 1.05,
"R": 1.05,
"HR": 1.02,
"SO": 0.50,
"BB": 0.95,
"IBB": 0.95,
"HBP": 0.95,
"WP": 0.95,
"BK": 0.95,
},
}
if __name__ == "__main__":
main()