##PYTHON FINAL PROJECT

• Introduction about data

• Pre-processing steps

• Analysis 1 : Season wise wins for each team at different venue

• Result : Stacked Bar Chart

• Analysis 2 : Toss Impact on different teams across seasons

• Result : Grouped Bar Plot

• Analysis 3a : Teams handling their nerves successfully

• Result : Bar Plot

• Analysis 3b : Teams dominating their opposition

• Result : Bar Plot

• Analysis 4 : Top 5 batsman performances across seasons

• Result : Point Plot Chart

• Analysis 5 : Dynamic Player Comparison against runs scored, strike rate

• Result : Grouped Bar Chart

• Raw data is collected from http://cricsheet.org/
• Raw data consists of 2 files matches.csv & deliveries.csv
• Matches.csv consists of following columns & data for all 577 matches held till date:

####Matches.csv

• Deliveries.csv consists of following columns & ball by ball data for each match held till date:

####Deliveries.csv

• Sample code to read csv data into a data frame.

sample code:
path = "C:/PYTHON/pythonFinalProject/rawDataPythonIPL"
all_matches_df = pd.read_csv(path+"\matches.csv")
all_matches_df.head(2)

• Aggregate Total scores, team extras for each match

####Total Team Score

####Total Team Extras

• Merge the team scores for each match with the matches, so adding new columns to the all matches data

• Adding new column which identifies the match type for each match as a Pre-qualifier, Qualifier, Eliminator & Final

sample code:
for year in range(2008,2017):
    fourth_last_match_in_each_season = all_matches_df[all_matches_df["season"] == year][-4:].index.values[0]
    third_last_match_in_each_season = fourth_last_match_in_each_season + 1
    second_last_match_in_each_season = third_last_match_in_each_season + 1
    last_match_in_each_season = second_last_match_in_each_season + 1
    
    all_matches_df = all_matches_df.set_value(fourth_last_match_in_each_season, "match-type" , "Qualifier-1")
    all_matches_df = all_matches_df.set_value(third_last_match_in_each_season, "match-type" , "Eliminator")
    all_matches_df = all_matches_df.set_value(second_last_match_in_each_season, "match-type" , "Qualifier-2")
    all_matches_df = all_matches_df.set_value(last_match_in_each_season, "match-type" , "Final")

####Code to get team wins per city data:

####Graph:

####Code to get toss winners data:

####Graph 1:

####Graph 2:

####Code to get teams which won close matches:

####Graph:

####Code to get teams winning with big margins

####Graph:

####Code to get top 5 batsman:

####Graph:

• Calculated all the batsman aggregates like runs scored per match, balls face, 4s scored, 6s scored, strike rate, dismissal-kind

####Graph 1: Player Comparison By Runs

####Graph 2: Player Comparison By Balls Faced

####Graph 3: Player Comparison By Strike_Rate