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campanella.py
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import pandas as pd
from datetime import datetime
from pathlib import Path
import sqlite3
def main():
project_all("2022-04-01")
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
TOP_PA = 725
MIN_PA = 400
TOP_BF_SP = 850
MIN_BF_SP = 500
TOP_BF_RP = 330
MIN_BF_RP = 200
# Compared to the max appearances, what percentage does a player need to get a projection?
APPEARANCE_THRESHOLD = 0.11
# MLB's "GameTypes"
SPRING_TRAINING = "S"
EXHIBITION = "E"
REGULAR_SEASON = "R"
# POST_SEASON = "P"
# MLB's "SportIDs" for various leagues
MLB = 1
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["game_date"] = pd.to_datetime(df["game_date"], infer_datetime_format=True)
df["days_ago"] = (projection_date - df["game_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]
# Setup some initially calculated columns
df["UIBB"] = df["BB"] - df["IBB"]
if is_batting:
df["1B"] = df["H"] - df["HR"] - df["3B"] - df["2B"]
df["PA"] = df["AB"] + df["BB"] + df["HBP"] + df["SH"] + df["SF"]
appearances = "PA"
else:
appearances = "BFP"
# Calculate league average before applying weights
lg_avg = df[df["league_id"] == MLB]
lg_avg = lg_avg[lg_avg["game_type"] == REGULAR_SEASON]
if is_batting:
lg_avg = lg_avg.groupby(["player_id"]).sum()
max_pa = lg_avg[appearances].max()
lg_avg = lg_avg[lg_avg[appearances] > (max_pa * APPEARANCE_THRESHOLD)]
lg_avg = lg_avg.mean()
else:
lg_avg_sp = lg_avg[lg_avg["GS"] == 1]
lg_avg_sp = lg_avg_sp.groupby(["player_id"]).sum()
max_pa = lg_avg_sp[appearances].max()
lg_avg_sp = lg_avg_sp[lg_avg_sp[appearances] > (max_pa * APPEARANCE_THRESHOLD)]
lg_avg_sp = lg_avg_sp.mean()
lg_avg_rp = lg_avg[lg_avg["GS"] == 0]
lg_avg_rp = lg_avg_rp.groupby(["player_id"]).sum()
max_pa = lg_avg_rp[appearances].max()
lg_avg_rp = lg_avg_rp[lg_avg_rp[appearances] > (max_pa * APPEARANCE_THRESHOLD)]
lg_avg_rp = lg_avg_rp.mean()
# Weight stats by decay rate
for stat in settings["base_stats"]:
df[stat + "_denom"] = df[appearances]
df[stat] *= (settings["decay_rates"][stat] ** df["days_ago"])
df[stat + "_denom"] *= (settings["decay_rates"][stat] ** df["days_ago"])
# Reduce spring training and exhibition games
df.loc[df["game_type"] == SPRING_TRAINING, stat] *= 0.45
df.loc[df["game_type"] == SPRING_TRAINING, stat + "_denom"] *= 0.45
df.loc[df["game_type"] == EXHIBITION, stat] *= 0.45
df.loc[df["game_type"] == EXHIBITION, stat + "_denom"] *= 0.45
# Reduce minor league stats
df.loc[df["league_id"] == AAA, stat] *= settings["aaa_factors"][stat]
df.loc[df["league_id"] == AA, stat] *= settings["aa_factors"][stat]
df.loc[df["league_id"] == HIGH_A, stat] *= settings["high_a_factors"][stat]
df.loc[df["league_id"] == LOW_A, stat] *= settings["low_a_factors"][stat]
df.loc[df["league_id"] == ROOKIE, stat] *= settings["rookie_factors"][stat]
df.loc[df["league_id"] == FALL, stat] *= settings["fall_factors"][stat]
# For projected PA/BF, we're only considering MLB regular season
df[appearances] *= (settings["decay_rates"][appearances] ** df["days_ago"])
df["proj_app"] = df[appearances]
df.loc[df["game_type"] != REGULAR_SEASON, "proj_app"] *= 0
df.loc[df["league_id"] != MLB, "proj_app"] *= 0
# Combine a player's daily data into a single row
pr = df.groupby(["player_id"]).sum()
# Cull out players that don't have enough playing time
max_pa = pr[appearances].max()
pr = pr[pr[appearances] > (max_pa * APPEARANCE_THRESHOLD)]
if not is_batting:
pr["start_pct"] = (pr["GS"] * 5) / ((pr["GS"] * 5) + (pr["G"] - pr["GS"]))
# Add league average
for stat in settings["base_stats"]:
if is_batting:
pr[stat] += (lg_avg[stat] / lg_avg[appearances]) * settings["regression_pa"][stat]
pr[stat + "_denom"] += settings["regression_pa"][stat]
else:
pr[stat] += pr["start_pct"] * (lg_avg_sp[stat] / lg_avg_sp[appearances]) * settings["regression_pa"][stat]
pr[stat] += (1 - pr["start_pct"]) * (lg_avg_rp[stat] / lg_avg_rp[appearances]) * settings["regression_pa"][stat]
pr[stat + "_denom"] += settings["regression_pa"][stat]
# Scale everyone's PA/BF in relation to the top player
if is_batting:
pa_factor = (TOP_PA - MIN_PA) / pr["proj_app"].max()
pr[appearances] = pr["proj_app"] * pa_factor + MIN_PA
else:
pa_factor_sp = (TOP_BF_SP - MIN_BF_SP) / pr["proj_app"].max()
pa_factor_rp = (TOP_BF_RP - MIN_BF_RP) / pr.loc[pr["start_pct"] == 0, "proj_app"].max()
pr[appearances] = (pr["proj_app"] * pa_factor_sp + MIN_BF_SP) * pr["start_pct"] + (pr["proj_app"] * pa_factor_rp + MIN_BF_RP) * (1 - pr["start_pct"])
# Apply projected rates out to the projected playing time
for stat in settings["base_stats"]:
pr[stat] = pr[stat] / pr[stat + "_denom"] * pr[appearances]
# Recalculate traditional stats based on components
pr["BB"] = pr["IBB"] + pr["UIBB"]
if is_batting:
pr["H"] = pr["HR"] + pr["3B"] + pr["2B"] + pr["1B"]
pr["AB"] = pr["PA"] - pr["BB"] - pr["HBP"] - pr["SF"]
else:
pr["IP"] = (pr["BFP"] - pr["H"] - pr["BB"] - pr["HBP"]) / 3
# Rework ER/R with kwERA
if not is_batting:
pr["kwERA"] = 5.40 - 12 * (pr["SO"] - pr["BB"]) / pr["BFP"]
pr["kwER"] = pr["kwERA"] / 9 * pr["IP"]
pr["ER"] = (pr["kwER"] + pr["ER"]) / 2
pr["R"] = (pr["kwER"] + pr["R"]) / 2
pr = pr[settings["display_cols"]]
# Add names to the data for easy readability
pr = pr.join(load_names(), how="left")
save_projection(projection_date_str, pr, is_batting)
def load_gamelogs(is_batting):
df = pd.DataFrame()
filepath = Path(__file__).parent
con = sqlite3.connect(filepath / "gamelogs.db")
if is_batting:
df = pd.read_sql_query("SELECT * FROM batting", con)
else:
df = pd.read_sql_query("SELECT * FROM pitching", con)
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" / "v3" / "batting"
else:
filepath = Path(__file__).parent / "projections" / "v3" / "pitching"
df.round().to_csv(filepath / (date + ".csv"), index_label="mlbam_id")
def load_projection(date, is_batting):
if is_batting:
filepath = Path(__file__).parent / "projections" / "v3" / "batting"
else:
filepath = Path(__file__).parent / "projections" / "v3" / "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" / "v3" / "batting"
else:
filedir = Path(__file__).parent / "projections" / "v3" / "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","QS","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"] <= datetime.today().strftime('%Y-%m-%d')]
player = player[(player["Date"] == player["Date"].max()) | (pd.DatetimeIndex(player["Date"]).day == 1)]
return player
BATTING = {
"display_cols": [
"PA",
"AB",
"R",
"H",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"SO",
"BB",
"IBB",
"HBP",
"SH",
"SF",
"GIDP",
],
"base_stats": [
"R",
"1B",
"2B",
"3B",
"HR",
"RBI",
"SB",
"CS",
"SO",
"UIBB",
"IBB",
"HBP",
"SH",
"SF",
"GIDP",
],
"decay_rates": {
"PA": 0.9955,
"R": 0.9994,
"1B": 0.9994,
"2B": 0.9994,
"3B": 0.9994,
"HR": 0.9985,
"RBI": 0.9994,
"SB": 0.9974,
"CS": 0.9974,
"BB": 0.9974,
"SO": 0.9965,
"UIBB": 0.9974,
"IBB": 0.9974,
"HBP": 0.9994,
"SH": 0.9994,
"SF": 0.9994,
"GIDP": 0.9994,
},
"regression_pa": {
"R": 281,
"1B": 141,
"2B": 354,
"3B": 213,
"HR": 175,
"RBI": 214,
"SB": 81,
"CS": 81,
"SO": 92,
"UIBB": 117,
"IBB": 117,
"HBP": 120,
"SH": 200,
"SF": 200,
"GIDP": 200,
},
"aaa_factors": {
"R": 0.79,
"1B": 0.95,
"2B": 0.80,
"3B": 0.84,
"HR": 0.66,
"RBI": 0.79,
"SB": 0.72,
"CS": 1.01,
"SO": 0.90,
"UIBB": 0.78,
"IBB": 0.78,
"HBP": 0.78,
"SH": 0.78,
"SF": 0.78,
"GIDP": 0.78,
},
"aa_factors": {
"R": 0.82,
"1B": 0.99,
"2B": 0.85,
"3B": 0.95,
"HR": 0.67,
"RBI": 0.83,
"SB": 0.70,
"CS": 0.91,
"SO": 0.80,
"UIBB": 0.79,
"IBB": 0.79,
"HBP": 0.79,
"SH": 0.79,
"SF": 0.79,
"GIDP": 0.79,
},
"high_a_factors": {
"R": 0.78,
"1B": 0.95,
"2B": 0.79,
"3B": 0.81,
"HR": 0.61,
"RBI": 0.75,
"SB": 0.51,
"CS": 0.93,
"SO": 0.83,
"UIBB": 0.72,
"IBB": 0.72,
"HBP": 0.72,
"SH": 0.72,
"SF": 0.72,
"GIDP": 0.72,
},
"low_a_factors": {
"R": 0.69,
"1B": 0.92,
"2B": 0.76,
"3B": 0.69,
"HR": 0.65,
"RBI": 0.68,
"SB": 0.46,
"CS": 0.84,
"SO": 0.89,
"UIBB": 0.64,
"IBB": 0.64,
"HBP": 0.64,
"SH": 0.64,
"SF": 0.64,
"GIDP": 0.64,
},
"rookie_factors": {
"R": 0.51,
"1B": 0.77,
"2B": 0.63,
"3B": 0.26,
"HR": 0.39,
"RBI": 0.48,
"SB": 0.27,
"CS": 0.48,
"SO": 1.06,
"UIBB": 0.54,
"IBB": 0.54,
"HBP": 0.54,
"SH": 0.54,
"SF": 0.54,
"GIDP": 0.54,
},
"fall_factors": {
"R": 0.82,
"1B": 0.99,
"2B": 0.85,
"3B": 0.95,
"HR": 0.67,
"RBI": 0.83,
"SB": 0.70,
"CS": 0.91,
"SO": 0.80,
"UIBB": 0.79,
"IBB": 0.79,
"HBP": 0.79,
"SH": 0.79,
"SF": 0.79,
"GIDP": 0.79,
},
}
PITCHING = {
"display_cols": [
"W",
"L",
"QS",
"G",
"GS",
"CG",
"SHO",
"SV",
"HLD",
"BFP",
"IP",
"H",
"ER",
"R",
"HR",
"SO",
"BB",
"IBB",
"HBP",
"WP",
"BK",
],
"base_stats": [
"W",
"L",
"QS",
"G",
"GS",
"CG",
"SHO",
"SV",
"HLD",
"H",
"ER",
"R",
"HR",
"SO",
"UIBB",
"IBB",
"HBP",
"WP",
"BK",
],
"decay_rates": {
"BFP": 0.9955,
"W": 0.998,
"L": 0.999,
"QS": 0.998,
"G": 0.999,
"GS": 0.999,
"CG": 0.999,
"SHO": 0.999,
"SV": 0.997,
"HLD": 0.997,
"H": 0.998,
"ER": 0.999,
"R": 0.999,
"HR": 0.998,
"SO": 0.993,
"UIBB": 0.998,
"IBB": 1,
"HBP": 0.9985,
"WP": 0.999,
"BK": 0.999,
},
"regression_pa": {
"W": 200,
"L": 200,
"QS": 200,
"G": 200,
"GS": 200,
"CG": 200,
"SHO": 200,
"SV": 100,
"HLD": 100,
"H": 200,
"ER": 1200,
"R": 1200,
"HR": 1200,
"SO": 70,
"UIBB": 170,
"IBB": 200,
"HBP": 200,
"WP": 200,
"BK": 200,
},
"aaa_factors": {
"W": 0.92,
"L": 1.14,
"QS": 0.92,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.96,
"HLD": 0.96,
"H": 1.13,
"ER": 0.92,
"R": 0.92,
"HR": 0.88,
"SO": 0.56,
"UIBB": 0.87,
"IBB": 0.87,
"HBP": 0.87,
"WP": 0.87,
"BK": 0.87,
},
"aa_factors": {
"W": 0.92,
"L": 1.24,
"QS": 0.92,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.99,
"HLD": 0.99,
"H": 1.25,
"ER": 1.05,
"R": 1.05,
"HR": 1.02,
"SO": 0.50,
"UIBB": 0.95,
"IBB": 0.95,
"HBP": 0.95,
"WP": 0.95,
"BK": 0.95,
},
"high_a_factors": {
"W": 0.92,
"L": 1.31,
"QS": 0.92,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.96,
"HLD": 0.96,
"H": 1.28,
"ER": 1.03,
"R": 1.03,
"HR": 1.28,
"SO": 0.46,
"UIBB": 0.92,
"IBB": 0.92,
"HBP": 0.92,
"WP": 0.92,
"BK": 0.92,
},
"low_a_factors": {
"W": 0.86,
"L": 1.27,
"QS": 0.86,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 1.04,
"HLD": 1.04,
"H": 1.30,
"ER": 1.04,
"R": 1.04,
"HR": 1.57,
"SO": 0.45,
"UIBB": 0.89,
"IBB": 0.89,
"HBP": 0.89,
"WP": 0.89,
"BK": 0.89,
},
"rookie_factors": {
"W": 0.68,
"L": 1.10,
"QS": 0.68,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 1.04,
"HLD": 1.04,
"H": 1.30,
"ER": 1.08,
"R": 1.08,
"HR": 2.01,
"SO": 0.38,
"UIBB": 0.95,
"IBB": 0.95,
"HBP": 0.95,
"WP": 0.95,
"BK": 0.95,
},
"fall_factors": {
"W": 0.92,
"L": 1.24,
"QS": 0.92,
"G": 1.00,
"GS": 1.00,
"CG": 1.00,
"SHO": 1.00,
"SV": 0.99,
"HLD": 0.99,
"H": 1.25,
"ER": 1.05,
"R": 1.05,
"HR": 1.02,
"SO": 0.50,
"UIBB": 0.95,
"IBB": 0.95,
"HBP": 0.95,
"WP": 0.95,
"BK": 0.95,
},
}
if __name__ == "__main__":
main()