get_milb_player_game_stats.py

import argparse
import json
import os
import platform
import random
import time
from datetime import datetime
from urllib.request import urlopen

import pandas as pd
from tqdm import tqdm

from get_milb_schedule import load_milb_schedule


def get_milb_player_game_stats(game_id: int, cache_data=False, cache_dir=""):
    """
    Retrieves and parses the player game box score stats from a valid
    MiLB game ID.

    Parameters
    ----------
    `game_id`: (int, mandatory):
        The MiLB game ID you want player game box score stats from.

    `cache_data`: (bool, optional) = `False`:
        Optional boolean flag.
        If set to `True`, data downloaded by this function will be cached to
        a folder named `./milb/`.
        This folder will either be located in the user's home directory,
        or in a existing directory specified
        by the optional argument `cache_dir`.

    `cache_dir`: (str, optional) = `""`:
        Optional string.
        If not set to `""` or `None`, this will be the directory used
        to cache data if `cache_data` is set to `True`.
        This directory must exist prior to running this function!

    Returns
    ----------
    A pandas `DataFrame` object containing player game box score stats from
    the MiLB game ID.
    """
    if cache_data is True and (cache_dir == "" or cache_dir is None):
        home_dir = os.path.expanduser("~")

        try:
            os.mkdir(f"{home_dir}/.milb/")
        except Exception:
            pass

        try:
            os.mkdir(f"{home_dir}/.milb/pbp/")
            os.mkdir(f"{home_dir}/.milb/lineups/")
        except Exception:
            pass

    elif cache_data is True and (cache_dir != "" or cache_dir is not None):
        try:
            os.mkdir(f"{cache_dir}/.milb/")
        except Exception:
            pass

        try:
            os.mkdir(f"{cache_dir}/.milb/pbp/")
            os.mkdir(f"{cache_dir}/.milb/lineups/")
        except Exception:
            pass

    game_df = pd.DataFrame()
    row_df = pd.DataFrame()
    game_url = f"https://statsapi.mlb.com/api/v1.1/game/{game_id}/feed/live?"

    if cache_data is True and (cache_dir == "" or cache_dir is None):
        # Cached files, default directory
        try:
            with open(f"{home_dir}/.milb/pbp/{game_id}.json", "r") as f:
                json_string = f.read()

            json_data = json.loads(json_string)
        except Exception:
            response = urlopen(game_url, timeout=30)
            time.sleep(1)

            if response.code == 200:
                pass
            elif response.code == 403:
                raise ConnectionRefusedError(
                    "The MiLB API is actively refusing your connection."
                    + "\nHTTP Error Code:\t403"
                )
            else:
                raise ConnectionError(
                    "Could not establish a connection to the MiLB API."
                    + f"\nHTTP Error Code:\t{response.code}"
                )

            json_data = json.loads(response.read())
            with open(f"{home_dir}/.milb/pbp/{game_id}.json", "w+") as f:
                f.write(json.dumps(json_data, indent=2))

    elif cache_data is True and (cache_dir != "" or cache_dir is not None):
        try:
            with open(f"{cache_dir}/.milb/pbp/{game_id}.json", "r") as f:
                json_string = f.read()

            json_data = json.loads(json_string)
        except Exception:
            response = urlopen(game_url)
            time.sleep(1)

            if response.code == 200:
                pass
            elif response.code == 403:
                raise ConnectionRefusedError(
                    "The MiLB API is actively refusing your connection."
                    + "\nHTTP Error Code:\t403"
                )
            else:
                raise ConnectionError(
                    "Could not establish a connection to the MiLB API."
                    + f"\nHTTP Error Code:\t{response.code}"
                )

            json_data = json.loads(response.read())
            with open(f"{cache_dir}/.milb/pbp/{game_id}.json", "w+") as f:
                f.write(json.dumps(json_data, indent=2))

    else:
        # No cached files used.
        response = urlopen(game_url)
        time.sleep(1)

        if response.code == 200:
            pass
        elif response.code == 403:
            raise ConnectionRefusedError(
                "The MiLB API is actively refusing your connection."
                + "\nHTTP Error Code:\t403"
            )
        else:
            raise ConnectionError(
                "Could not establish a connection to the MiLB API."
                + f"\nHTTP Error Code:\t{response.code}"
            )

        json_data = json.loads(response.read())

    # json_data = json.loads(response.read())
    if len(json_data) == 0:
        print(f"\nCould not get player game stats data for game ID {game_id}")
        return pd.DataFrame()

    game_type = json_data["gameData"]["game"]["type"]
    game_date = json_data["gameData"]["datetime"]["officialDate"]
    game_date = datetime.strptime(game_date, "%Y-%m-%d")
    # game_date_str = game_date.strftime("%Y-%m-%d")

    league_id = json_data["gameData"]["teams"]["away"]["league"]["id"]
    league_name = json_data["gameData"]["teams"]["away"]["league"]["name"]

    league_level_id = json_data["gameData"]["teams"]["away"]["sport"]["id"]
    league_level_name = json_data["gameData"]["teams"]["away"]["sport"]["name"]

    try:
        away_team_org_id = json_data[
            "gameData"]["teams"]["away"]["parentOrgId"]
    except Exception:
        away_team_org_id = None

    try:
        away_team_org_name = json_data[
            "gameData"]["teams"]["away"]["parentOrgName"]
    except Exception:
        away_team_org_name = None

    try:
        away_team_id = json_data["gameData"]["teams"]["away"]["id"]
    except Exception:
        away_team_id = None

    try:
        away_team_abv = json_data["gameData"]["teams"]["away"]["abbreviation"]
    except Exception:
        away_team_abv = None

    try:
        away_team_name = json_data["gameData"]["teams"]["away"]["name"]
    except Exception:
        away_team_name = None

    try:
        home_team_org_id = json_data[
            "gameData"]["teams"]["home"]["parentOrgId"]
    except Exception:
        home_team_org_id = None

    try:
        home_team_org_name = json_data[
            "gameData"]["teams"]["home"]["parentOrgName"]
    except Exception:
        home_team_org_name = None

    try:
        home_team_id = json_data["gameData"]["teams"]["home"]["id"]
    except Exception:
        home_team_id = None

    try:
        home_team_abv = json_data["gameData"]["teams"]["home"]["abbreviation"]
    except Exception:
        home_team_abv = None

    try:
        home_team_name = json_data["gameData"]["teams"]["home"]["name"]
    except Exception:
        home_team_name = None

    try:
        away_runs = json_data["gameData"]["linescore"]["teams"]["away"]["runs"]
    except Exception:
        away_runs = 0

    try:
        home_runs = json_data["gameData"]["linescore"]["teams"]["home"]["runs"]
    except Exception:
        home_runs = 0

    away_player_stats = json_data[
        "liveData"]["boxscore"]["teams"]["away"]["players"]
    home_player_stats = json_data[
        "liveData"]["boxscore"]["teams"]["home"]["players"]

    for key, value in away_player_stats.items():
        loc = "A"
        if home_runs == away_runs:
            final_score_str = f"T {away_runs}-{home_runs}"
        elif away_runs > home_runs:
            final_score_str = f"W {away_runs}-{home_runs}"
        elif away_runs < home_runs:
            final_score_str = f"L {away_runs}-{home_runs}"

        player_pos = ""
        player_pos_num = 0

        try:
            for i in value["allPositions"]:
                player_pos_num += 1

                if player_pos_num == 1:
                    player_pos = i["abbreviation"]
                else:
                    player_pos = f"{player_pos}/{i['abbreviation']}"
        except Exception:
            player_pos = None

        del player_pos_num

        player_id = value["person"]["id"]
        try:
            player_jersey_number = str(value["jerseyNumber"])
        except Exception:
            player_jersey_number = None
        try:
            player_full_name = value["person"]["fullName"]
        except Exception:
            player_full_name = None
        try:
            player_batting_order = str(value["battingOrder"])
        except Exception:
            player_batting_order = None

        row_df = pd.DataFrame(
            {
                "season": game_date.year,
                "game_id": game_id,
                "game_date": game_date,
                "game_type": game_type,
                "league_id": league_id,
                "league_name": league_name,
                "league_level_id": league_level_id,
                "league_level_name": league_level_name,
                "team_org_id": away_team_org_id,
                "team_org_name": away_team_org_name,
                "team_id": away_team_id,
                "team_abv": away_team_abv,
                "team_name": away_team_name,
                "loc": loc,
                "opp_org_id": home_team_org_id,
                "opp_org_name": home_team_org_name,
                "opp_id": home_team_id,
                "opp_abv": home_team_abv,
                "opp_name": home_team_name,
                "team_runs": away_runs,
                "opp_runs": home_runs,
                "score": final_score_str,
                "player_id": player_id,
                "player_jersey_number": player_jersey_number,
                "player_full_name": player_full_name,
                "player_position": player_pos,
                "player_batting_order": player_batting_order,
            },
            index=[0],
        )

        player_data = value["stats"]

        if len(player_data["batting"]) > 0:
            row_df["batting_G"] = player_data["batting"]["gamesPlayed"]
            row_df["batting_PA"] = player_data["batting"]["plateAppearances"]
            row_df["batting_AB"] = player_data["batting"]["atBats"]
            row_df["batting_R"] = player_data["batting"]["runs"]
            row_df["batting_H"] = player_data["batting"]["hits"]
            row_df["batting_2B"] = player_data["batting"]["doubles"]
            row_df["batting_3B"] = player_data["batting"]["triples"]
            row_df["batting_HR"] = player_data["batting"]["homeRuns"]
            row_df["batting_RBI"] = player_data["batting"]["rbi"]
            row_df["batting_SB"] = player_data["batting"]["stolenBases"]
            row_df["batting_CS"] = player_data["batting"]["caughtStealing"]
            row_df["batting_BB"] = player_data["batting"]["baseOnBalls"]
            row_df["batting_IBB"] = player_data["batting"]["intentionalWalks"]
            row_df["batting_SO"] = player_data["batting"]["strikeOuts"]
            row_df["batting_TB"] = player_data["batting"]["totalBases"]
            row_df["batting_GiDP"] = player_data[
                "batting"]["groundIntoDoublePlay"]
            row_df["batting_GiTP"] = player_data[
                "batting"]["groundIntoTriplePlay"]
            row_df["batting_HBP"] = player_data["batting"]["hitByPitch"]
            row_df["batting_SH"] = player_data["batting"]["sacBunts"]
            row_df["batting_SF"] = player_data["batting"]["sacFlies"]
            row_df["batting_CI"] = player_data[
                "batting"]["catchersInterference"]
            row_df["batting_FO"] = player_data["batting"]["flyOuts"]
            row_df["batting_GO"] = player_data["batting"]["groundOuts"]
            row_df["batting_LOB"] = player_data["batting"]["leftOnBase"]

        if len(player_data["pitching"]) > 0:
            row_df["batting_G"] = player_data["pitching"]["gamesPlayed"]
            row_df["pitching_G"] = player_data["pitching"]["gamesPitched"]
            row_df["pitching_GS"] = player_data["pitching"]["gamesStarted"]
            row_df["pitching_GF"] = player_data["pitching"]["gamesFinished"]
            row_df["pitching_CG"] = player_data["pitching"]["completeGames"]
            row_df["pitching_SHO"] = player_data["pitching"]["shutouts"]

            row_df["pitching_W"] = player_data["pitching"]["wins"]
            row_df["pitching_L"] = player_data["pitching"]["losses"]

            row_df["pitching_SVO"] = player_data[
                "pitching"]["saveOpportunities"]
            row_df["pitching_SV"] = player_data["pitching"]["saves"]
            row_df["pitching_BS"] = player_data["pitching"]["blownSaves"]
            row_df["pitching_HLD"] = player_data["pitching"]["holds"]

            row_df["pitching_IP"] = round(
                player_data["pitching"]["outs"] / 3, 3
            )
            row_df["pitching_IP_str"] = str(
                player_data["pitching"]["inningsPitched"]
            )

            row_df["pitching_R"] = player_data["pitching"]["runs"]
            row_df["pitching_ER"] = player_data["pitching"]["earnedRuns"]
            row_df["pitching_BF"] = player_data["pitching"]["battersFaced"]
            row_df["pitching_AB"] = player_data["pitching"]["atBats"]
            row_df["pitching_H"] = player_data["pitching"]["hits"]
            row_df["pitching_2B"] = player_data["pitching"]["doubles"]
            row_df["pitching_3B"] = player_data["pitching"]["triples"]
            row_df["pitching_HR"] = player_data["pitching"]["homeRuns"]
            row_df["pitching_RBI"] = player_data["pitching"]["rbi"]
            row_df["pitching_BB"] = player_data["pitching"]["baseOnBalls"]
            row_df["pitching_IBB"] = player_data[
                "pitching"]["intentionalWalks"]
            row_df["pitching_SO"] = player_data["pitching"]["strikeOuts"]
            row_df["pitching_HBP"] = player_data["pitching"]["hitByPitch"]
            row_df["pitching_BK"] = player_data["pitching"]["balks"]
            row_df["pitching_WP"] = player_data["pitching"]["wildPitches"]

            row_df["pitching_GO"] = player_data["pitching"]["groundOuts"]
            row_df["pitching_AO"] = player_data["pitching"]["airOuts"]
            row_df["pitching_SB"] = player_data["pitching"]["stolenBases"]
            row_df["pitching_CS"] = player_data["pitching"]["caughtStealing"]

            row_df["pitching_SH"] = player_data["pitching"]["sacBunts"]
            row_df["pitching_SF"] = player_data["pitching"]["sacFlies"]
            row_df["pitching_CI"] = player_data[
                "pitching"]["catchersInterference"]
            row_df["pitching_PB"] = player_data["pitching"]["passedBall"]
            row_df["pitching_PK"] = player_data["pitching"]["pickoffs"]

            row_df["pitching_IR"] = player_data["pitching"]["inheritedRunners"]
            row_df["pitching_IRS"] = player_data[
                "pitching"]["inheritedRunnersScored"]

            row_df["pitching_PI"] = player_data["pitching"]["numberOfPitches"]
            row_df["pitching_PI_strikes"] = player_data["pitching"]["strikes"]
            row_df["pitching_PI_balls"] = player_data["pitching"]["balls"]

        game_df = pd.concat([game_df, row_df], ignore_index=True)

    for key, value in home_player_stats.items():
        loc = "H"

        if home_runs == away_runs:
            final_score_str = f"T {home_runs}-{away_runs}"
        elif home_runs > away_runs:
            final_score_str = f"W {home_runs}-{away_runs}"
        elif home_runs < away_runs:
            final_score_str = f"L {home_runs}-{away_runs}"

        player_pos = ""
        player_pos_num = 0

        try:
            for i in value["allPositions"]:
                player_pos_num += 1

                if player_pos_num == 1:
                    player_pos = i["abbreviation"]
                else:
                    player_pos = f"{player_pos}/{i['abbreviation']}"
        except Exception:
            player_pos = None

        del player_pos_num

        player_id = value["person"]["id"]
        try:
            player_jersey_number = str(value["jerseyNumber"])
        except Exception:
            player_jersey_number = None

        try:
            player_full_name = value["person"]["fullName"]
        except Exception:
            player_full_name = None

        try:
            player_batting_order = str(value["battingOrder"])
        except Exception:
            player_batting_order = None

        row_df = pd.DataFrame(
            {
                "season": game_date.year,
                "game_id": game_id,
                "game_date": game_date,
                "game_type": game_type,
                "league_id": league_id,
                "league_name": league_name,
                "league_level_id": league_level_id,
                "league_level_name": league_level_name,
                "team_org_id": home_team_org_id,
                "team_org_name": home_team_org_name,
                "team_id": home_team_id,
                "team_abv": home_team_abv,
                "team_name": home_team_name,
                "loc": loc,
                "opp_org_id": away_team_org_id,
                "opp_org_name": away_team_org_name,
                "opp_id": away_team_id,
                "opp_abv": away_team_abv,
                "opp_name": away_team_name,
                "team_runs": home_runs,
                "opp_runs": away_runs,
                "score": final_score_str,
                "player_id": player_id,
                "player_jersey_number": player_jersey_number,
                "player_full_name": player_full_name,
                "player_position": player_pos,
                "player_batting_order": player_batting_order,
            },
            index=[0],
        )

        player_data = value["stats"]

        if len(player_data["batting"]) > 0:
            row_df["batting_G"] = player_data["batting"]["gamesPlayed"]
            row_df["batting_PA"] = player_data["batting"]["plateAppearances"]
            row_df["batting_AB"] = player_data["batting"]["atBats"]
            row_df["batting_R"] = player_data["batting"]["runs"]
            row_df["batting_H"] = player_data["batting"]["hits"]
            row_df["batting_2B"] = player_data["batting"]["doubles"]
            row_df["batting_3B"] = player_data["batting"]["triples"]
            row_df["batting_HR"] = player_data["batting"]["homeRuns"]
            row_df["batting_RBI"] = player_data["batting"]["rbi"]
            row_df["batting_SB"] = player_data["batting"]["stolenBases"]
            row_df["batting_CS"] = player_data["batting"]["caughtStealing"]
            row_df["batting_BB"] = player_data["batting"]["baseOnBalls"]
            row_df["batting_IBB"] = player_data["batting"]["intentionalWalks"]
            row_df["batting_SO"] = player_data["batting"]["strikeOuts"]
            row_df["batting_TB"] = player_data["batting"]["totalBases"]
            row_df["batting_GiDP"] = player_data[
                "batting"]["groundIntoDoublePlay"]
            row_df["batting_GiTP"] = player_data[
                "batting"]["groundIntoTriplePlay"]
            row_df["batting_HBP"] = player_data["batting"]["hitByPitch"]
            row_df["batting_SH"] = player_data["batting"]["sacBunts"]
            row_df["batting_SF"] = player_data["batting"]["sacFlies"]
            row_df["batting_CI"] = player_data[
                "batting"]["catchersInterference"]
            row_df["batting_FO"] = player_data["batting"]["flyOuts"]
            row_df["batting_GO"] = player_data["batting"]["groundOuts"]
            row_df["batting_LOB"] = player_data["batting"]["leftOnBase"]

        if len(player_data["pitching"]) > 0:
            row_df["batting_G"] = player_data["pitching"]["gamesPlayed"]
            row_df["pitching_G"] = player_data["pitching"]["gamesPitched"]
            row_df["pitching_GS"] = player_data["pitching"]["gamesStarted"]
            row_df["pitching_GF"] = player_data["pitching"]["gamesFinished"]
            row_df["pitching_CG"] = player_data["pitching"]["completeGames"]
            row_df["pitching_SHO"] = player_data["pitching"]["shutouts"]

            row_df["pitching_W"] = player_data["pitching"]["wins"]
            row_df["pitching_L"] = player_data["pitching"]["losses"]

            row_df["pitching_SVO"] = player_data[
                "pitching"]["saveOpportunities"]
            row_df["pitching_SV"] = player_data["pitching"]["saves"]
            row_df["pitching_BS"] = player_data["pitching"]["blownSaves"]
            row_df["pitching_HLD"] = player_data["pitching"]["holds"]

            row_df["pitching_IP"] = round(
                player_data["pitching"]["outs"] / 3, 3
            )
            row_df["pitching_IP_str"] = str(
                player_data["pitching"]["inningsPitched"]
            )

            row_df["pitching_R"] = player_data["pitching"]["runs"]
            row_df["pitching_ER"] = player_data["pitching"]["earnedRuns"]
            row_df["pitching_BF"] = player_data["pitching"]["battersFaced"]
            row_df["pitching_AB"] = player_data["pitching"]["atBats"]
            row_df["pitching_H"] = player_data["pitching"]["hits"]
            row_df["pitching_2B"] = player_data["pitching"]["doubles"]
            row_df["pitching_3B"] = player_data["pitching"]["triples"]
            row_df["pitching_HR"] = player_data["pitching"]["homeRuns"]
            row_df["pitching_RBI"] = player_data["pitching"]["rbi"]
            row_df["pitching_BB"] = player_data["pitching"]["baseOnBalls"]
            row_df["pitching_IBB"] = player_data[
                "pitching"]["intentionalWalks"]
            row_df["pitching_SO"] = player_data["pitching"]["strikeOuts"]
            row_df["pitching_HBP"] = player_data["pitching"]["hitByPitch"]
            row_df["pitching_BK"] = player_data["pitching"]["balks"]
            row_df["pitching_WP"] = player_data["pitching"]["wildPitches"]

            row_df["pitching_GO"] = player_data["pitching"]["groundOuts"]
            row_df["pitching_AO"] = player_data["pitching"]["airOuts"]
            row_df["pitching_SB"] = player_data["pitching"]["stolenBases"]
            row_df["pitching_CS"] = player_data["pitching"]["caughtStealing"]

            row_df["pitching_SH"] = player_data["pitching"]["sacBunts"]
            row_df["pitching_SF"] = player_data["pitching"]["sacFlies"]
            row_df["pitching_CI"] = player_data[
                "pitching"]["catchersInterference"]
            row_df["pitching_PB"] = player_data["pitching"]["passedBall"]
            row_df["pitching_PK"] = player_data["pitching"]["pickoffs"]

            row_df["pitching_IR"] = player_data["pitching"]["inheritedRunners"]
            row_df["pitching_IRS"] = player_data[
                "pitching"]["inheritedRunnersScored"]

            row_df["pitching_PI"] = player_data["pitching"]["numberOfPitches"]
            row_df["pitching_PI_strikes"] = player_data["pitching"]["strikes"]
            row_df["pitching_PI_balls"] = player_data["pitching"]["balls"]

        game_df = pd.concat([game_df, row_df], ignore_index=True)

    return game_df


def get_month_milb_player_game_stats(
    season: int,
    month: int,
    level: str = "AAA",
    cache_data: bool = False,
    cache_dir: str = "",
    save: bool = True,
):
    """ """

    game_df = pd.DataFrame()
    stats_df = pd.DataFrame()
    sched_df = pd.DataFrame()

    if (
        (level.lower() == "aaa")
        or (level.lower() == "triple-a")
        or (level.lower() == "triple a")
    ):
        sched_df = load_milb_schedule(season, "AAA")
    elif (
        (level.lower() == "aa")
        or (level.lower() == "double-a")
        or (level.lower() == "double a")
    ):
        sched_df = load_milb_schedule(season, "AA")
    elif (
        (level.lower() == "a+")
        or (level.lower() == "high-a")
        or (level.lower() == "high a")
    ):
        sched_df = load_milb_schedule(season, "A+")
    elif (
        (level.lower() == "a")
        or (level.lower() == "single-a")
        or (level.lower() == "single-a")
    ):
        sched_df = load_milb_schedule(season, "A")
    elif (
        (level.lower() == "a-")
        or (level.lower() == "short-a")
        or (level.lower() == "short a")
    ):
        sched_df = load_milb_schedule(season, "A-")
    elif (
        (level.lower() == "rk")
        or (level.lower() == "rok")
        or (level.lower() == "rookie")
    ):
        sched_df = load_milb_schedule(season, "rk")
    elif (
        (level.lower() == "win")
        or (level.lower() == "winter")
    ):
        sched_df = load_milb_schedule(season, "win")

    # print(sched_df)
    sched_df = sched_df[sched_df["status_abstract_game_state"] == "Final"]
    sched_df = sched_df[sched_df["game_month"] == month]
    # sched_df = sched_df.loc[
    #     (sched_df["game_month"] == month)
    #     & (sched_df["status_detailed_state"] != "Cancelled")
    #     & (sched_df["status_detailed_state"] != "Postponed")
    #     & (sched_df["status_detailed_state"] != "In Progress")
    #     & (sched_df["status_detailed_state"] != "Scheduled")
    # ]
    print(sched_df)
    game_ids_arr = sched_df["game_pk"].to_numpy()
    # game_years_arr = sched_df["game_year"].to_numpy()
    try:
        game_years_arr = sched_df["game_year"].to_list()
        game_year = game_years_arr[0]
    except Exception:
        game_year = season

    if len(game_ids_arr) > 30 and cache_data is False:
        print(
            "HEY!\nThat's a ton of data you want to access."
            + "\nPlease cache this data in the future " +
            "to avoid severe data loss!"
        )

    # for game_id in tqdm(game_ids_arr):
    for i in tqdm(range(0, len(game_ids_arr))):
        game_id = game_ids_arr[i]

        try:
            game_df = get_milb_player_game_stats(
                game_id=game_id, cache_data=cache_data, cache_dir=cache_dir
            )
            stats_df = pd.concat([stats_df, game_df], ignore_index=True)
        except Exception as e:
            print(f"Unhandled use case. Error Details:\n{e}")

        # game_df = get_milb_player_game_stats(
        #     game_id=game_id, cache_data=cache_data, cache_dir=cache_dir)
        stats_df = pd.concat([stats_df, game_df], ignore_index=True)

    if save is True and len(stats_df) > 0:
        stats_df.to_csv(
            f"game_stats/player/{game_year}_{month}_{level.lower()}" +
            "_player_game_stats.csv",
            index=False,
        )

    return stats_df


if __name__ == "__main__":
    print("starting up")

    now = datetime.now()
    c_dir = "D:/"
    start_month = 1
    # This is to ensure that any game played in December
    # for this level is downloaded.
    end_month = 13

    # parser = argparse.ArgumentParser()
    # parser.add_argument("--season", type=int, required=False)
    # parser.add_argument("--level", type=str, required=True)
    # args = parser.parse_args()

    # lg_level = args.level
    # season = args.season
    lg_level = "winter"
    season = 2024
    if season is None:
        season == now.year

    if (
        lg_level == "winter" and
        (
            now.month > 9 or
            now.month < 3
        )
    ):
        start_month = now.month - 2
        end_month = now.month + 1
    elif season == now.year and now.month >= 11:
        start_month = now.month - 7
        end_month = now.month - 6
    elif season == (now.year + 1) and now.month <= 3:
        start_month = now.month + 3
        end_month = now.month + 4
        season -= 1
    elif season == now.year and now.day <= 5:
        # This is here to ensure that a game being played
        # in between 2 months
        # (like a game starting on March 31st but ending on April 1st)
        # is not skipped,
        # and there's multiple opportunities
        # to make sure nothing is skipped.
        start_month = now.month - 1
        end_month = now.month + 1
    elif season == now.year:
        start_month = now.month
        end_month = now.month + 1

    if start_month < 1:
        start_month = 1

    if end_month > 13:
        end_month = 13

    for i in range(start_month, end_month):
        if platform.system() == "Windows":
            print(
                f"Getting {i}/{season} player game stats data " +
                f"in the {lg_level} level of MiLB."
            )
            df = get_month_milb_player_game_stats(
                season, i, level=lg_level, cache_data=True, cache_dir=c_dir
            )
        else:
            print(
                f"Getting {i}/{season} player game stats data " +
                f"in the {lg_level} level of MiLB."
            )
            df = get_month_milb_player_game_stats(season, i, level=lg_level)
        # get_month_milb_player_game_stats(season, i, level=lg_level)

    if len(df) == 0:
        season -= random.randint(1, 10)
        schedule_df = load_milb_schedule(
            season=season,
            level=lg_level
        )
        random_int = random.randint(0, len(schedule_df))
        game_date = schedule_df["official_date"].iloc[random_int]
        game_month = int(game_date.split("-")[1])

        if platform.system() == "Windows":
            print(
                f"Getting {i}/{season} player game stats data " +
                f"in the {lg_level} level of MiLB."
            )
            df = get_month_milb_player_game_stats(
                season, i, level=lg_level, cache_data=True, cache_dir=c_dir
            )
        else:
            print(
                f"Getting {i}/{season} player game stats data " +
                f"in the {lg_level} level of MiLB."
            )
            df = get_month_milb_player_game_stats(season, i, level=lg_level)

    # get_month_milb_player_game_stats(
    #     2023, 10, level="win", cache_data=True, cache_dir="D:/"
    # )