week_4_Datadive
Sai Dheeraj
2023-09-18
Read the CSV file
my_data <- read.csv('C:/Users/dell/Downloads/Ball_By_Ball.csv')
summary(my_data)## MatcH_id Over_id Ball_id Innings_No
## Min. : 335987 Min. : 1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 419154 1st Qu.: 5.00 1st Qu.:2.000 1st Qu.:1.000
## Median : 548382 Median :10.00 Median :4.000 Median :1.000
## Mean : 636208 Mean :10.14 Mean :3.617 Mean :1.482
## 3rd Qu.: 829742 3rd Qu.:15.00 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :1082650 Max. :20.00 Max. :9.000 Max. :4.000
##
## Team_Batting Team_Bowling Striker_Batting_Position
## Length:150451 Length:150451 Min. : 1.000
## Class :character Class :character 1st Qu.: 2.000
## Mode :character Mode :character Median : 3.000
## Mean : 3.584
## 3rd Qu.: 5.000
## Max. :11.000
## NA's :13861
## Extra_Type Runs_Scored Extra_runs Wides
## Length:150451 Min. :0.000 Min. :0.00000 Min. :0.0000
## Class :character 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0.0000
## Mode :character Median :1.000 Median :0.00000 Median :0.0000
## Mean :1.222 Mean :0.06899 Mean :0.0375
## 3rd Qu.:1.000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :6.000 Max. :5.00000 Max. :5.0000
##
## Legbyes Byes Noballs Penalty
## Min. :0.00000 Min. :0.000000 Min. :0.00000 Min. :0.0e+00
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.0e+00
## Median :0.00000 Median :0.000000 Median :0.00000 Median :0.0e+00
## Mean :0.02223 Mean :0.004885 Mean :0.00434 Mean :3.3e-05
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.:0.0e+00
## Max. :5.00000 Max. :4.000000 Max. :5.00000 Max. :5.0e+00
##
## Bowler_Extras Out_type Caught Bowled
## Min. :0.00000 Length:150451 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.00000 Class :character 1st Qu.:0.00000 1st Qu.:0.000000
## Median :0.00000 Mode :character Median :0.00000 Median :0.000000
## Mean :0.04184 Mean :0.02907 Mean :0.009186
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000
## Max. :5.00000 Max. :1.00000 Max. :1.000000
##
## Run_out LBW Retired_hurt Stumped
## Min. :0.000000 Min. :0.000000 Min. :0.00e+00 Min. :0.000000
## 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.00e+00 1st Qu.:0.000000
## Median :0.000000 Median :0.000000 Median :0.00e+00 Median :0.000000
## Mean :0.005018 Mean :0.003024 Mean :5.98e-05 Mean :0.001615
## 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.00e+00 3rd Qu.:0.000000
## Max. :1.000000 Max. :1.000000 Max. :1.00e+00 Max. :1.000000
##
## caught_and_bowled hit_wicket ObstructingFeild Bowler_Wicket
## Min. :0.000000 Min. :0.00e+00 Min. :0.0e+00 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.00e+00 1st Qu.:0.0e+00 1st Qu.:0.00000
## Median :0.000000 Median :0.00e+00 Median :0.0e+00 Median :0.00000
## Mean :0.001402 Mean :5.98e-05 Mean :6.6e-06 Mean :0.04435
## 3rd Qu.:0.000000 3rd Qu.:0.00e+00 3rd Qu.:0.0e+00 3rd Qu.:0.00000
## Max. :1.000000 Max. :1.00e+00 Max. :1.0e+00 Max. :1.00000
##
## Match_Date Season Striker Non_Striker
## Length:150451 Min. :2008 Min. : 1.0 Min. : 1.0
## Class :character 1st Qu.:2010 1st Qu.: 40.0 1st Qu.: 40.0
## Mode :character Median :2012 Median : 96.0 Median : 96.0
## Mean :2012 Mean :136.5 Mean :135.6
## 3rd Qu.:2015 3rd Qu.:208.0 3rd Qu.:208.0
## Max. :2017 Max. :497.0 Max. :497.0
##
## Bowler Player_Out Fielders Striker_match_SK
## Min. : 1.0 Min. : 1.0 Min. : 1.0 Min. :12694
## 1st Qu.: 77.0 1st Qu.: 41.0 1st Qu.: 47.0 1st Qu.:16173
## Median :174.0 Median :107.0 Median :111.0 Median :19672
## Mean :194.1 Mean :148.6 Mean :155.4 Mean :19675
## 3rd Qu.:310.0 3rd Qu.:236.0 3rd Qu.:237.5 3rd Qu.:23127
## Max. :497.0 Max. :497.0 Max. :497.0 Max. :26685
## NA's :143013 NA's :145100
## StrikerSK NonStriker_match_SK NONStriker_SK Fielder_match_SK
## Min. : 0.0 Min. :12694 Min. : 0.0 Min. : -1
## 1st Qu.: 39.0 1st Qu.:16173 1st Qu.: 39.0 1st Qu.: -1
## Median : 95.0 Median :19672 Median : 95.0 Median : -1
## Mean :135.5 Mean :19675 Mean :134.6 Mean : 690
## 3rd Qu.:207.0 3rd Qu.:23127 3rd Qu.:207.0 3rd Qu.: -1
## Max. :496.0 Max. :26685 Max. :496.0 Max. :26680
##
## Fielder_SK Bowler_match_SK BOWLER_SK PlayerOut_match_SK
## Min. : -1.000 Min. :12697 Min. : 0.0 Min. : -1.0
## 1st Qu.: -1.000 1st Qu.:16175 1st Qu.: 76.0 1st Qu.: -1.0
## Median : -1.000 Median :19674 Median :173.0 Median : -1.0
## Mean : 4.527 Mean :19677 Mean :193.1 Mean : 970.3
## 3rd Qu.: -1.000 3rd Qu.:23131 3rd Qu.:309.0 3rd Qu.: -1.0
## Max. :496.000 Max. :26685 Max. :496.0 Max. :26685.0
##
## BattingTeam_SK BowlingTeam_SK Keeper_Catch Player_out_sk
## Min. : 0.000 Min. : 0.000 Min. :0.000000 Min. : -1.000
## 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.:0.000000 1st Qu.: 0.000
## Median : 4.000 Median : 4.000 Median :0.000000 Median : 0.000
## Mean : 4.346 Mean : 4.333 Mean :0.000432 Mean : 1.101
## 3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.:0.000000 3rd Qu.: 0.000
## Max. :12.000 Max. :12.000 Max. :1.000000 Max. :496.000
##
## MatchDateSK
## Min. :20080418
## 1st Qu.:20100411
## Median :20120520
## Mean :20125288
## 3rd Qu.:20150420
## Max. :20170521
## A collection of 5-10 random samples of data (with replacement) from at least 6 columns of data Each subsample should be as long as roughly 50% percent of your data.
# Load necessary libraries
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
# Set the number of subsamples you want
num_subsamples <- 5 # You can change this to 10 if needed
# Create an empty list to store the subsamples
subsamples_list <- list()
# Define the size of each subsample (roughly 50%)
sample_size <- floor(0.5 * nrow(my_data))
# Define the columns i want to include in the subsamples
selected_columns <- c("MatcH_id", "Over_id", "Ball_id", "Innings_No", "Team_Batting", "Team_Bowling")
# Create random subsamples and store them in the list
for (i in 1:num_subsamples) {
# Sample rows with replacement
subsample <- my_data %>%
select(all_of(selected_columns)) %>%
sample_n(size = sample_size, replace = TRUE)
# Assign a unique name to each subsample
subsample_name <- paste0("df_", i)
# Store the subsample in the list
subsamples_list[[subsample_name]] <- subsample
}
# Now I have a list of random subsamples, each stored as a separate data frame (df_1, df_2, etc.).\[ To scrutinize the subsamples and understand their differences, similarities, and potential anomalies, you can perform various analyses and comparisons. Here are some steps and considerations: 1) Calculating summary statistics for each subsample, including measures like mean, median, standard deviation, and quartiles for numeric variables. For categorical variables, compute frequency tables. 2) Creating visualizations to compare the distributions of numeric and categorical variables across subsamples. \]
# Load necessary libraries
library(dplyr)
# Define a function to calculate summary statistics
calculate_summary_statistics <- function(subsample) {
# Identify numeric and categorical columns
numeric_cols <- sapply(subsample, is.numeric)
categorical_cols <- !numeric_cols
# Calculate summary statistics for numeric columns
numeric_summary <- summary(subsample[, numeric_cols])
# Create frequency tables for categorical columns
categorical_summary <- lapply(subsample[, categorical_cols], table)
# Combine the results into a list
summary_results <- list(numeric_summary = numeric_summary, categorical_summary = categorical_summary)
return(summary_results)
}
# Create a list to store summary statistics for each subsample
summary_stats_list <- list()
# Calculate summary statistics for each subsample
for (i in 1:num_subsamples) {
subsample_name <- paste0("df_", i)
summary_stats <- calculate_summary_statistics(subsamples_list[[subsample_name]])
summary_stats_list[[subsample_name]] <- summary_stats
}
# Now you have a list of summary statistics for each subsample.
# Display summary_stats_list
print(summary_stats_list)## $df_1
## $df_1$numeric_summary
## MatcH_id Over_id Ball_id Innings_No
## Min. : 335987 Min. : 1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 419155 1st Qu.: 5.00 1st Qu.:2.000 1st Qu.:1.000
## Median : 548383 Median :10.00 Median :4.000 Median :1.000
## Mean : 637194 Mean :10.19 Mean :3.611 Mean :1.482
## 3rd Qu.: 829744 3rd Qu.:15.00 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :1082650 Max. :20.00 Max. :9.000 Max. :4.000
##
## $df_1$categorical_summary
## $df_1$categorical_summary$Team_Batting
##
## 1 10
## 7781 2642
## 11 12
## 3692 773
## 13 2
## 971 8271
## 3 4
## 7909 7948
## 5 6
## 6908 7694
## 7 8
## 8413 4483
## 9 Delhi Daredevils
## 779 807
## Gujarat Lions Kings XI Punjab
## 838 824
## Kolkata Knight Riders Mumbai Indians
## 887 1009
## Rising Pune Supergiants Royal Challengers Bangalore
## 989 763
## Sunrisers Hyderabad
## 844
##
## $df_1$categorical_summary$Team_Bowling
##
## 1 10
## 7813 2761
## 11 12
## 3684 776
## 13 2
## 976 8309
## 3 4
## 7669 7882
## 5 6
## 7084 7637
## 7 8
## 8395 4462
## 9 Delhi Daredevils
## 816 842
## Gujarat Lions Kings XI Punjab
## 864 844
## Kolkata Knight Riders Mumbai Indians
## 953 976
## Rising Pune Supergiants Royal Challengers Bangalore
## 965 732
## Sunrisers Hyderabad
## 785
##
##
##
## $df_2
## $df_2$numeric_summary
## MatcH_id Over_id Ball_id Innings_No
## Min. : 335987 Min. : 1.0 Min. :1.000 Min. :1.000
## 1st Qu.: 419155 1st Qu.: 5.0 1st Qu.:2.000 1st Qu.:1.000
## Median : 548383 Median :10.0 Median :4.000 Median :1.000
## Mean : 636778 Mean :10.1 Mean :3.607 Mean :1.482
## 3rd Qu.: 829742 3rd Qu.:15.0 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :1082650 Max. :20.0 Max. :9.000 Max. :4.000
##
## $df_2$categorical_summary
## $df_2$categorical_summary$Team_Batting
##
## 1 10
## 7732 2697
## 11 12
## 3706 791
## 13 2
## 913 7999
## 3 4
## 7845 7999
## 5 6
## 6945 7731
## 7 8
## 8623 4467
## 9 Delhi Daredevils
## 847 810
## Gujarat Lions Kings XI Punjab
## 871 755
## Kolkata Knight Riders Mumbai Indians
## 918 1058
## Rising Pune Supergiants Royal Challengers Bangalore
## 890 789
## Sunrisers Hyderabad
## 839
##
## $df_2$categorical_summary$Team_Bowling
##
## 1 10
## 7793 2776
## 11 12
## 3694 812
## 13 2
## 972 8235
## 3 4
## 7778 7751
## 5 6
## 7074 7715
## 7 8
## 8318 4553
## 9 Delhi Daredevils
## 824 784
## Gujarat Lions Kings XI Punjab
## 844 839
## Kolkata Knight Riders Mumbai Indians
## 916 1032
## Rising Pune Supergiants Royal Challengers Bangalore
## 983 754
## Sunrisers Hyderabad
## 778
##
##
##
## $df_3
## $df_3$numeric_summary
## MatcH_id Over_id Ball_id Innings_No
## Min. : 335987 Min. : 1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 419152 1st Qu.: 5.00 1st Qu.:2.000 1st Qu.:1.000
## Median : 548380 Median :10.00 Median :4.000 Median :1.000
## Mean : 635512 Mean :10.12 Mean :3.608 Mean :1.482
## 3rd Qu.: 829740 3rd Qu.:15.00 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :1082650 Max. :20.00 Max. :9.000 Max. :4.000
##
## $df_3$categorical_summary
## $df_3$categorical_summary$Team_Batting
##
## 1 10
## 7745 2721
## 11 12
## 3731 811
## 13 2
## 930 8120
## 3 4
## 7850 8049
## 5 6
## 6921 7572
## 7 8
## 8559 4483
## 9 Delhi Daredevils
## 874 761
## Gujarat Lions Kings XI Punjab
## 866 770
## Kolkata Knight Riders Mumbai Indians
## 904 968
## Rising Pune Supergiants Royal Challengers Bangalore
## 950 783
## Sunrisers Hyderabad
## 857
##
## $df_3$categorical_summary$Team_Bowling
##
## 1 10
## 7828 2764
## 11 12
## 3709 873
## 13 2
## 939 8149
## 3 4
## 7761 7815
## 5 6
## 7173 7782
## 7 8
## 8226 4539
## 9 Delhi Daredevils
## 808 813
## Gujarat Lions Kings XI Punjab
## 835 835
## Kolkata Knight Riders Mumbai Indians
## 871 1049
## Rising Pune Supergiants Royal Challengers Bangalore
## 916 746
## Sunrisers Hyderabad
## 794
##
##
##
## $df_4
## $df_4$numeric_summary
## MatcH_id Over_id Ball_id Innings_No
## Min. : 335987 Min. : 1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 419152 1st Qu.: 5.00 1st Qu.:2.000 1st Qu.:1.000
## Median : 548380 Median :10.00 Median :4.000 Median :1.000
## Mean : 635002 Mean :10.14 Mean :3.621 Mean :1.485
## 3rd Qu.: 829740 3rd Qu.:15.00 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :1082650 Max. :20.00 Max. :9.000 Max. :4.000
##
## $df_4$categorical_summary
## $df_4$categorical_summary$Team_Batting
##
## 1 10
## 7788 2724
## 11 12
## 3684 762
## 13 2
## 868 8079
## 3 4
## 7766 7980
## 5 6
## 6922 7821
## 7 8
## 8541 4567
## 9 Delhi Daredevils
## 805 801
## Gujarat Lions Kings XI Punjab
## 853 783
## Kolkata Knight Riders Mumbai Indians
## 854 1050
## Rising Pune Supergiants Royal Challengers Bangalore
## 912 801
## Sunrisers Hyderabad
## 864
##
## $df_4$categorical_summary$Team_Bowling
##
## 1 10
## 7672 2668
## 11 12
## 3609 788
## 13 2
## 924 8187
## 3 4
## 7784 7846
## 5 6
## 7317 7797
## 7 8
## 8329 4603
## 9 Delhi Daredevils
## 783 825
## Gujarat Lions Kings XI Punjab
## 764 805
## Kolkata Knight Riders Mumbai Indians
## 933 1088
## Rising Pune Supergiants Royal Challengers Bangalore
## 943 747
## Sunrisers Hyderabad
## 813
##
##
##
## $df_5
## $df_5$numeric_summary
## MatcH_id Over_id Ball_id Innings_No
## Min. : 335987 Min. : 1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 419153 1st Qu.: 5.00 1st Qu.:2.000 1st Qu.:1.000
## Median : 548382 Median :10.00 Median :4.000 Median :1.000
## Mean : 636304 Mean :10.14 Mean :3.611 Mean :1.484
## 3rd Qu.: 829742 3rd Qu.:15.00 3rd Qu.:5.000 3rd Qu.:2.000
## Max. :1082650 Max. :20.00 Max. :9.000 Max. :4.000
##
## $df_5$categorical_summary
## $df_5$categorical_summary$Team_Batting
##
## 1 10
## 7717 2724
## 11 12
## 3737 791
## 13 2
## 953 8096
## 3 4
## 7897 7890
## 5 6
## 6947 7796
## 7 8
## 8323 4536
## 9 Delhi Daredevils
## 844 872
## Gujarat Lions Kings XI Punjab
## 880 802
## Kolkata Knight Riders Mumbai Indians
## 852 1022
## Rising Pune Supergiants Royal Challengers Bangalore
## 934 773
## Sunrisers Hyderabad
## 839
##
## $df_5$categorical_summary$Team_Bowling
##
## 1 10
## 7754 2689
## 11 12
## 3533 842
## 13 2
## 982 8173
## 3 4
## 7807 7886
## 5 6
## 7124 7804
## 7 8
## 8375 4508
## 9 Delhi Daredevils
## 774 805
## Gujarat Lions Kings XI Punjab
## 829 770
## Kolkata Knight Riders Mumbai Indians
## 924 1062
## Rising Pune Supergiants Royal Challengers Bangalore
## 949 788
## Sunrisers Hyderabad
## 847library(ggplot2)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine# Create histograms for numeric variables in each subsample
histograms <- lapply(subsamples_list, function(subsample) {
ggplot(subsample, aes(x = Ball_id)) +
geom_histogram(binwidth = 1, fill = "blue", color = "black") +
labs(title = "Histogram of Your Ball_id",
x = "Value",
y = "Frequency")
})
# Display histograms side by side
grid.arrange(grobs = histograms, ncol = num_subsamples)
library(ggplot2)
library(gridExtra)
# Create bar charts for categorical variables in each subsample
bar_charts <- lapply(subsamples_list, function(subsample) {
ggplot(subsample, aes(x = Team_Batting, fill = Team_Batting)) +
geom_bar() +
labs(title = "Bar Chart of Team_Batting",
x = "Category",
y = "Frequency") +
theme(legend.position = "none")
})
# Display bar charts side by side
grid.arrange(grobs = bar_charts, ncol = num_subsamples)
# Combine all subsamples into one data frame
combined_data <- do.call(rbind, subsamples_list)
# Create a bar chart for the combined data
ggplot(combined_data, aes(x = Team_Batting, fill = Team_Batting)) +
geom_bar() +
labs(title = "Bar Chart of Your Team_Batting",
x = "Category",
y = "Frequency") +
theme(legend.position = "none")
##What would you have called an anomaly in one sub-sample that you wouldn’t in another? $$ I see no anomalies in my subsamples as they are consistent. usually the anomaly could be unique based on the type of data.let’s consider an example based on the columns “MatcH_id,” “Over_id,” “Ball_id,” “Innings_No,” and “Team_Batting.” identifying anomalies in each of these columns within two sub-samples: Sub-Sample 1 (First Half of Matches): This sub-sample consists of data from the first half of matches.
Sub-Sample 2 (Second Half of Matches): This sub-sample consists of data from the second half of matches.
The anomalies in each of these columns might be defined as :
#MatcH_id:
Sub-Sample 1 (First Half of Matches): Anomalies could be identified as MatcH_id values that are smaller than the median MatcH_id value in the first half of matches, as these indicate matches that occurred relatively early in the dataset. Sub-Sample 2 (Second Half of Matches): Anomalies might be MatcH_id values that are greater than the median MatcH_id value in the second half of matches, representing matches that occurred relatively late in the dataset.
Over_id:
Sub-Sample 1 (First Half of Matches): Anomalies could be defined as Over_id values that are unusually low compared to the average Over_id in the first half, indicating early overs in matches. Sub-Sample 2 (Second Half of Matches): Anomalies might be Over_id values that are significantly higher than the average Over_id in the second half, indicating late overs in matches. Ball_id:
Sub-Sample 1 (First Half of Matches): Anomalies could include Ball_id values that are small, indicating early balls faced in matches within the first half. Sub-Sample 2 (Second Half of Matches): Anomalies might involve Ball_id values that are relatively high, representing late balls faced in matches within the second half.
Innings_No:
Sub-Sample 1 (First Half of Matches): Anomalies might be Innings_No values that are predominantly 1 (first innings), as most matches start with the first innings. Sub-Sample 2 (Second Half of Matches): Anomalies could involve Innings_No values that are predominantly 2 (second innings), indicating matches in the second half where teams batted second more often. Team_Batting:
Sub-Sample 1 (First Half of Matches): Anomalies might be specific teams that frequently batted early in matches in the first half. Sub-Sample 2 (Second Half of Matches): Anomalies could include specific teams that frequently batted late in matches in the second half. $$$
##Are there aspects of the data that are consistent among all sub-samples? $$ On observing the above visualizations on subsamples,all of them were consistent the columns “MatcH_id,” “Over_id,” “Ball_id,” “Innings_No,” and “Team_Batting.” I’ll explore how these aspects might exhibit consistency across different sub-samples of a sports dataset.
Example: Cricket Match Data
Imagine I have a dataset that contains information about cricket matches, including details like match ID (“MatcH_id”), over ID (“Over_id”), ball ID (“Ball_id”), innings number (“Innings_No”), and the name of the batting team (“Team_Batting”). Each row in the dataset represents a specific ball bowled during a cricket match.
Here’s how these aspects could exhibit consistency among different sub-samples:
MatcH_id: Consistency in match IDs across sub-samples would mean that the same cricket matches are represented in each sub-sample. This suggests that the sub-samples are drawn from the same set of matches.
Over_id: Consistency in over IDs across sub-samples indicates that specific overs are common across different parts of the dataset. For example, if the 10th over consistently appears in all sub-samples, it means that the 10th over is played consistently in different matches.
Ball_id: Consistency in ball IDs implies that certain balls (deliveries) are consistent across sub-samples. This could indicate that specific key moments, such as boundaries or wickets, are consistently captured.
Innings_No: If the innings number is consistent in all sub-samples, it means that the dataset predominantly includes matches with the same type of innings (e.g., first innings). Consistency in innings number could also suggest that limited-overs matches dominate the dataset.
Team_Batting: Consistency in the names of the batting teams across sub-samples indicates that the same set of teams participates in various matches. For example, if “Team_A” and “Team_B” consistently appear in all sub-samples, it suggests these teams are common participants.
Overall, consistency in these aspects across sub-samples provides insights into the nature of the cricket matches represented in the dataset. It suggests that certain matches, overs, balls, innings types, and teams are consistently featured, making these aspects stable and reliable for analysis.
Analyzing such consistency helps ensure that any findings or patterns observed in the sub-samples are likely to hold across the entire dataset, making the conclusions more robust and applicable. $$
Consider how this investigation affects how you might draw conclusions about the data in the future?
$$ Considering the investigation into the columns “MatcH_id,” “Over_id,” “Ball_id,” “Innings_No,” and “Team_Batting” can affect drawing conclusions about the data in the future.
Analyzing data from a cricket database, and I’m particularly interested in these columns:
“MatcH_id” is a unique identifier for each cricket match. “Over_id” represents the specific over within a match. “Ball_id” represents the ball number within an over. “Innings_No” indicates whether it’s the first or second innings of a match. “Team_Batting” represents the team currently batting. Investigation: As part of my investigation, I’ve observed NO anomalies IN my subsamples and is consistent data:
Impact on Drawing Conclusions: Clean and consistent data ensures that the results and conclusions drawn from your analysis are more reliable and trustworthy
In summary, the investigation into anomalies not only improves data quality but also influences how I approach data analysis and interpretation in the future. It emphasizes the need for robust data preprocessing and domain-specific knowledge to draw meaningful conclusions from the dataset.”
$$