Data
Ipl_predictions2020

Ipl_predictions2020

active ARFF Database: Open Database, Contents: Original Authors Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context Indian Premier League (IPL) is a Twenty20 cricket format league in India. It is usually played in April and May every year. As of 2019, the title sponsor of the game is Vivo. The league was founded by Board of Control for Cricket India (BCCI) in 2008. Content Data till Season 11 (2008 - 2019) matches.csv - Match by match data Acknowledgements Data source from 2008-2017 - CricSheet.org and Manas - Kaggle Data source for 2018-2019 - IPL T20 - Official website Inspiration Draw analysis, player/team performance, apply and learn statistical methods on real data Kernels -Statistics Summarizing quantitative data (mean, median, std. deviation, percentile, box plots etc.) Distributions - Cumulative relative frequency, Normal distribution, PDF, Z-score, empirical rule, binomial distribution, Bernoulli distribution Bivariate data - Scatter plot, Correlation, Covariance, Least square regression, R-Squared, Root mean square error

18 features

idnumeric756 unique values
0 missing
seasonnumeric12 unique values
0 missing
citystring32 unique values
7 missing
datestring546 unique values
0 missing
team1string15 unique values
0 missing
team2string15 unique values
0 missing
toss_winnerstring15 unique values
0 missing
toss_decisionstring2 unique values
0 missing
resultstring3 unique values
0 missing
dl_appliednumeric2 unique values
0 missing
winnerstring15 unique values
4 missing
win_by_runsnumeric89 unique values
0 missing
win_by_wicketsnumeric11 unique values
0 missing
player_of_matchstring226 unique values
4 missing
venuestring41 unique values
0 missing
umpire1string61 unique values
2 missing
umpire2string65 unique values
2 missing
umpire3string25 unique values
637 missing
Show all 18 features

19 properties

NumberOfInstances
756
Number of instances (rows) of the dataset.
NumberOfFeatures
18
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
656
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
638
Number of instances with at least one value missing.
NumberOfNumericFeatures
5
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
Dimensionality
0.02
Number of attributes divided by the number of instances.
PercentageOfNumericFeatures
27.78
Percentage of numeric attributes.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
MajorityClassSize
Number of instances belonging to the most frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
84.39
Percentage of instances having missing values.
AutoCorrelation
Average class difference between consecutive instances.
PercentageOfMissingValues
4.82
Percentage of missing values.
Show all 19 properties

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