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Badminton match outcome prediction model using Naïve Bayes and Feature Weighting technique

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Abstract

The recent growth in the field of data mining and machine learning has remitted into more recognition of outcome prediction and classification. However, the application of these techniques in the field of sports is still unexplored. This paper presents the implementation of data mining and machine learning in sports particularly. Here, machine learning based algorithm to predict the outcome of the badminton tournament has been proposed. We have employed three classifiers, Naïve Bayes with Correlation Based Feature Weighting (NB-CBFW), Composite Hypercubes on Iterated Random Projections (CHIRP) and Hyper Pipes to predict the outcome of Australian Open 2019, Malaysian Open 2019, German Open 2019 and Singapore Open 2019 Badminton tournaments. The outcome prediction is measured in terms of Accuracy, Root Mean Square Error (RMSE), True Positive Rate (TPR), True Negative Rate (TNR), Positive Predicted Value (PPV), Negative Predicted Value (NPV) and Receiver Operating Characteristics (ROC). After implementing the classifiers, it has been observed that NB-CBFW shows excellent accuracy in match outcome prediction as compared to CHIRP and Hyper Pipes.

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Abbreviations

\({F}_{i}; {F}_{j}\) :

Two different feature variables/attributes

T:

Target/class variable

\({\mathrm{f}}_{\mathrm{i}}, {\mathrm{f}}_{\mathrm{j}}\) and t:

The values of \({\mathrm{F}}_{\mathrm{i}}, {\mathrm{F}}_{\mathrm{j}}\) and T respectively

m:

Total number of feature variables

\(I \left({F}_{i};T\right)\) :

The mutual significance or feature-class/target correlative significance

\(I \left({F}_{i};{F}_{j}\right)\) :

Average mutual redundancy or average feature-feature correlative significance

\({Q}_{i}\) :

Difference between the feature-class correlation and the average feature-feature intercorrelation

\(\mathrm{NI}\left({\mathrm{F}}_{\mathrm{i}};\mathrm{T}\right)\) :

Normalized mutual significance

N\(\mathrm{I }\left({\mathrm{F}}_{\mathrm{i}};{\mathrm{F}}_{\mathrm{j}}\right)\) :

Normalized average mutual redundancy

\(F{W}_{i}\) :

The final weight to the attribute

Fi :

M feature variables with feature values as f1, f2, f3……fm

\(P(t)\) :

Prior probability

\(P{(f}_{i}|t)\) :

Conditional probability

b:

Marginal number of bins

q:

Number of instances

\({B}_{k}\) :

Purity Measure

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Giani Zail Singh Campus College of Engineering and Technology, MRSPTU, Bathinda, India

    Manoj Sharma

  2. Department of Computer Science, Banasthali Vidyapith, Jaipur, India

    Monika

  3. Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh, India

    Naresh Kumar

  4. Department of Electronics, Banasthali Vidyapith, Jaipur, India

    Pardeep Kumar

Authors
  1. Manoj Sharma
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  2. Monika
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  3. Naresh Kumar
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  4. Pardeep Kumar
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Contributions

Data collection: MS, NK; data analysis: Monika, MS; technical writing: PL, MS, NK.

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Correspondence to Naresh Kumar.

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Sharma, M., Monika, Kumar, N. et al. Badminton match outcome prediction model using Naïve Bayes and Feature Weighting technique. J Ambient Intell Human Comput 12, 8441–8455 (2021). https://doi.org/10.1007/s12652-020-02578-8

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  • DOI: https://doi.org/10.1007/s12652-020-02578-8

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