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    Factors Affecting Performance of Kabaddi Players Inprogress (Autosaved)

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    h20230845
    The document describes developing multiple linear regression models to predict the tackle points scored by defenders in professional kabaddi matches. Four models were created and compared: 1) a correlation model, 2) a backward elimination model that dropped irrelevant variables, 3) a p-value model that dropped variables with high p-values, and 4) a full model. Both ANOVA and AIC model comparison tests were used, with AIC selecting the backward elimination model as the best model for its ability to handle more complexities. The backward elimination model was thus concluded to most accurately predict a defender's tackle points based on variables like total tackles, high 5s, and super tackles.

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    Factors Affecting Performance of Kabaddi Players Inprogress (Autosaved)

    Uploaded by

    h20230845
    31 views29 pages
    The document describes developing multiple linear regression models to predict the tackle points scored by defenders in professional kabaddi matches. Four models were created and compared: 1) a correlation model, 2) a backward elimination model that dropped irrelevant variables, 3) a p-value model that dropped variables with high p-values, and 4) a full model. Both ANOVA and AIC model comparison tests were used, with AIC selecting the backward elimination model as the best model for its ability to handle more complexities. The backward elimination model was thus concluded to most accurately predict a defender's tackle points based on variables like total tackles, high 5s, and super tackles.

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    Factors Affecting Performance of Kabaddi players Inprogress [Autosaved]

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    The document describes developing multiple linear regression models to predict the tackle points scored by defenders in professional kabaddi matches. Four models were created and compared: 1) a correlation model, 2) a backward elimination model that dropped irrelevant variables, 3) a p-value model that dropped variables with high p-values, and 4) a full model. Both ANOVA and AIC model comparison tests were used, with AIC selecting the backward elimination model as the best model for its ability to handle more complexities. The backward elimination model was thus concluded to most accurately predict a defender's tackle points based on variables like total tackles, high 5s, and super tackles.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    31 views29 pages

    Factors Affecting Performance of Kabaddi Players Inprogress (Autosaved)

    Uploaded by

    h20230845
    The document describes developing multiple linear regression models to predict the tackle points scored by defenders in professional kabaddi matches. Four models were created and compared: 1) a correlation model, 2) a backward elimination model that dropped irrelevant variables, 3) a p-value model that dropped variables with high p-values, and 4) a full model. Both ANOVA and AIC model comparison tests were used, with AIC selecting the backward elimination model as the best model for its ability to handle more complexities. The backward elimination model was thus concluded to most accurately predict a defender's tackle points based on variables like total tackles, high 5s, and super tackles.

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    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 29

    Optimal Model to Predict

    Defender’s Tackle Points


    -Submitted to Dr. Achint Nigam by Group 6

    Aigal Chetas Manjunath - 2023H1540802P


    Kannaiahgari Sahith - 2023H1540832P
    Sunil Kumar Behera - 2023H1540843P
    Naveen Kumar - 2023H1540845P
    Vudayagiri Sai Shiva Kumar - 2023H1540859P
    M V S Sri Sathvika - 2023H1540864P
    Bodempudi Siri - 2023H1540871P
    1
    Problem Statement
    2
    Objective
    3
    Data Set Snapshot
    4
    Methodology

    5 6 7
    Correlation Matrix
    8
    Multiple Linear
    Libraries Used Linearity Check

    OVERVIEW
    Plot Regression

    9
    Model - 1
    10
    Model - 2
    11
    Model -3
    12
    Model 4

    13
    Comparison
    14
    Conclusion
    between models
    Problem:
    In Professional Kabaddi League, defenders play a pivotal role in
    preventing the opposite team’s raiders from scoring a point.
    Analysis and estimation of defenders ability to accumulate tackle
    Problem points can provide the team management insights due to which
    then can make a strategic decision
    Statement Solution:
    In order to solve the above problem a predictive model using Multi
    Linear Regression is developed. It can forecast the tackle points that
    a defender can likely score in kabaddi matches
    Predict the total tackle points scored by a defender by taking following
    parameters into consideration:
     Total Tackles
     Height
     Weight
     Age
     High 5s
    Objective  Super Tackles
     Matches Played
     Auction Price
     Position
     Average time on mat

    Upon completion of this project, a predictive model will be developed which


    can estimate/ predict a defender’s tackle points in kabaddi matches accurately.
    Data Set
    Snapshot
    Methodology
     Linearity Check
     Correlation Matrix
     Model Building
     Model Evaluation
    Methodology  Model Comparison
     Picking the best model
     library(tidyverse)
     library(ggplot2)
     library(coefplot)
    Libraries Used  library(car)
     library(corrplot)
     library(caret)
    Linearity
    Check
    A Correlation matrix is a statistical technique to evaluate the relationship between
    two variables in a data set. For our project it is as plotted below

    Correlation
    Matrix Plot
     Multi Linear Regression or simply Multiple Regression is a study of
    how a dependent variable is related to two or more independent
    variables
    Multi linear  In order to Carry out Multi Linear Regression there are several
    Regression models. But for this project we have chosen 3 models which are
    1. Correlation Model
    2. Backward Elimination Model
    3. P Value Model
    MODEL – 1 : Correlation Model
    Model – 1
    Correlation
    Model Residuals v/s Fitted Values
    Model Evaluation

    Model – 1
    Correlation Sample vs Theoretial Values

    Model
    Model Evaluation

    Model – 1
    Correlation
    Model
    MODEL – 2 : Backward Elimination Model
    Dropped Variables are:
    • Age
    • Auction Price
    Model – 2 • Avg Time on Mat

    Backward
    Elimination
    Model
    Model – 2
    Backward
    Elimination
    Model
    Model Evaluation

    Model – 2
    Backward
    Elimination Sample vs Theoretical Values

    Model
    MODEL – 3 : P Value Model
    Dropped Variables are:
    Model – 3 • Matches
    • Height
    P- Value • Weight
    • Age
    Model • Auction Price
    • Avg Time on Mat
    • Position
    Model – 3
    P- Value
    Model
    Model Evaluation

    Model – 3
    P Value
    Model
    Model Evaluation

    Model – 3
    P Value
    Model
    Comparison Between Models
    Comparison
    Between
    Models
    Comparison
    Between
    Models :
    Using Anova
    Comparison
    Between
    Models :
    Using AIC • As per AIC test, Backward Elimination is the best model
     All the 3 models were tested using ANOVA and AIC for the
    comparison
     Both the tests gave different results

    Conclusion  But, considering the nature of dealing with more complexities we


    preferred AIC over ANOVA because AIC considers more
    complexities compared to ANOVA
     So, as per AIC test, we conclude that we choose Backward
    Elimination Model as the best model for the prediction.
    Thank You

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