TEAM_PLAYER
docx
keyboard_arrow_up
School
Dr. MGR Educational and Research university *
*We aren’t endorsed by this school
Course
123A
Subject
Information Systems
Date
Nov 24, 2024
Type
docx
Pages
6
Uploaded by MISHANTH0308
""" cricketstats is a script for getting team and player statistics from the cricsheet.org database for data analysis.
Copyright (C) 2021 Saranga Sudarshan
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>. """
# This is an example script of how you can use cricketstats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# If you installed cricketstats by pip import the module with line below
import cricketstats as cks
# If you cloned the github repo uncomment the block below.
"""
import os
import sys
module_path = os.path.abspath(os.path.join("./cricketstats/src/"))
if module_path not in sys.path:
sys.path.append(module_path)
"""
""" 1. Create a search object """
# For ease of use first declare a list of players or teams to search stats for and then call that variable
Ozbatters = ["DA Warner", "SPD Smith", "M Labuschagne", "TM Head", "MS Harris", "C Green", "AT Carey", "UT Khawaja", "MR Marsh", "GJ Maxwell", "MP Stoinis", "MS Wade", "JP Inglis"]
bblteams = ["Sydney Thunder", "Melbourne Renegades", "Brisbane Heat", "Sydney Sixers", "Melbourne Stars", "Perth Scorchers", "Adelaide Strikers", "Hobart Hurricanes"]
# create a search object with "cricketstats.search" based on the inputs above. Input arguments: players=[] or teams=[] You can create multiple objects at the same time.
search1 = cks.cricketstats.search(players=Ozbatters)
# If you want to find stats for all players ot teams use the "allplayers=True" or "allteams=True" option in the class.
# search2 = cricketstats.search(allplayers=True)
# You can see below another search object setup for BBL teams. Just uncomment the line to use it.
# search3 = cricketstats.search(teams=bblteams)
""" 2. Apply the "stats()" method to the search object with the necessary arguments. """
# Arguments are mostly lists, with items separated by commas.
# First we can setup our arguments.
# Required input arguments:
database = "/home/saranga/Downloads/all_json.zip" # Path of cricsheet.org's all matches json zip file on your system. You can download the file at https://cricsheet.org/downloads/
from_date = (2018, 10, 1) # The (Year, Month, Day) from which to start search
to_date = (2021, 12, 31) # The (Year, Month, Day) from which to end search
matchtype = ["Test"] # Options: ["Test", "MDM", "ODI", "ODM", "T20", "IT20"] Explanation of what these
mean is found at https://cricsheet.org/downloads/
# Optional Arguments:
# First we can set overs and innings related arguments:
betweenovers=[] # Search only these overs. eg. betweenovers = [1, 20]. Or if you only want to search for powerplays that are mandatory and option input "powerplays" eg. betweenovers =["powerplays"]
innings=[] # Search these innings. Options: 1, 2, 3, 4 eg. innings = [1,3]
fielders=[] # Search bowling stats involves these fielders.
oppositionbatters=[] # Search overs where players have bowled against certain batters. Options: batter names. You can also put in a list of batters by handedness. eg. oppositionbatters=["Left hand", "Right Hand"]
oppositionbowlers=[] # Search overs where players have batted against certain bowlers Options: bowler names You can also put in list of bowlers by type, eg. oppositionbowlers=["Right arm pace","Left arm pace","Right arm Off break","Right arm Leg break","Left arm orthodox","Left arm wrist spin"]
superover=None # Search normal innings or superover innings. Options: True, False eg. superover=True
battingposition=[] # Search stats at certain position in batting order. eg. battingposition=[1,2,3]
bowlingposition=[] # Search stats at certain position in bowling order. eg. bowlingposition=[1,2]
# Second we have match related arguments
sex=[] # Search only matches of certain sex. Options: "male", "female" eg. sex = ["female"] playerteams=[] # Search matches where players have played in certain teams. Options: team names eg. playerteams=["Australia", "England"]
teammates=[] # Search matches where certain teammates play. For this to work playerteams must be populated.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help
oppositionteams=[] # Search matches where opposition is only certain teams. Options: team names eg. oppositionteams=["India", "England"]
venue=[] # Search matches played only at these venues Options: Cricket Grounds eg. venue=["Sydney Cricket Ground", "Melbourne Cricket Ground", ""Brisbane Cricket Ground, Woolloongabba""]
teamtype=[] # Search only for particualr type of teams. eg. "international", "club".
event=[] # Search matches played as part of these Leagues or Tournaments Options: Name of League or Tournament eg. event=["Sheffield Shield", "ICC World Cup", "Big Bash League"] matchresult=None # Search matches where players or teams have these results. When looking at players, this option **must** be used with at one team in playersteams variable. Options either "won" or "loss" or "draw" or "tie" eg. matchresult="won"
sumstats=False # When switched to True, it adds an "all players" or "all teams" row at end of result that sums all players or teams stats that are searched for.
# Apply stats() method applied on search object. You have to apply the method to every search object if you want the script to actually do teh search.
search1.stats(database, from_date, to_date, matchtype, betweenovers=betweenovers, innings=innings, sex=sex, playersteams=playerteams, oppositionbatters=oppositionbatters, oppositionbowlers=oppositionbowlers, oppositionteams=oppositionteams, venue=venue, event=event, matchresult=matchresult, superover=superover, battingposition=battingposition, bowlingposition=bowlingposition, fielders=fielders, sumstats=sumstats)
# You can use the above template if you want or put all the values inside the brackets of the "stats()" method like I've done below for the search2 object.
# search2.stats(database="/home/saranga/Downloads/all_json.zip", from_date=(2018, 10, 1), to_date=(2021, 12, 31), matchtype=["T20"], betweenovers=[], innings=[], sex=[], playersteams=[], oppositionbatters=[], oppositionbowlers=[], oppositionteams=[], venue=[], event=["Big Bash League"], matchresult=[], superover=None, battingposition=[], bowlingposition=[], fielders=[], sumstats=False)
""" 3. Print result. Output is a pandas dataframe. """
# The output of the search is a pandas dataframe.
# print(search1.result.columns) # Use this line to print all the stats that are recorded and can be displayed.
# The line below prints the columns of the stats. There are way more stats than just the four used in the line below.
print(search1.result[["Games", "Batting Avg", "Score MeanAD", "Balls Faced"]]) # To get the long form data of each innings the players/team have played use the line below. This is best displayed as dataframe in jupyter notebook.
print(search1.inningsresult)
# If you want to save the search result as a csv file that you can open in excel you can use the line below.
# search1.result.to_csv(./YOUR FILE LOCATION)
""" 4. Plotting """
# You can use the plotting methods from the pandas package or matplotlib.pyplot for plotting.
# Handy tip, use the following list compression to extract all scores from multiple players or teams: [x for sublist in dataframe for x in sublist]
#search1.result.sort_values(by="Balls Faced", ascending=False)[["Balls Faced", "Batting Avg", "Score MeanAD"]].plot(kind="bar", rot=15, fontsize=8, title="Tests since 2018/2019 (data: cricsheet.org)", secondary_y = "Balls Faced")
# plt.legend()
# plt.show()
""" 5. Bonus Data Analysis Example """
# Pythagorean Expected Win Percentage for a team: This is a formula for calculating expected win percentage given runs scored and conceded by team
# Formula: Expected Win % = (Runs scored)^exponent / ((Runs scored)^exponent + (Runs conceded)^exponent)
# Exponent Options: Vine2016: 7.41, SenevirathneaManage2021: maximum likelihood method, 4.71 for ODI matches and 6.06 for Twenty20. The least squares method, 5.01 and 6.56 respectively for ODI and Twenty20.
#search2.result["Exp Win %"] = ((pow(search2.result["Runs"], 7.41) / (pow(search2.result["Runs"], 7.41) + pow(search2.result["Runsgiven"], 7.41)))*100)
#print(search2.result[["Win %", "Exp Win %"]])
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help