CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games

Kyeongjin Mun; Bonggeon Cha; Jongwuk Lee; Jihyeon Kim; Hyuncheol Jo

doi:10.1109/BigComp57234.2023.00010

CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games

Kyeongjin Mun
, Bonggeon Cha
, Jongwuk Lee
, Jihyeon Kim
, Hyuncheol Jo

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Sports game prediction using machine learning techniques has gained much attention in the sports industry. Notably, this task is vital for sports-related fields, such as sports marketing, sports betting, in-game strategy establishment, etc. To improve prediction accuracy, it is necessary to consider various game factors, such as past match results and player features. In this paper, we formulate win-lose outcome prediction in baseball games as a binary classification problem. We first collect various team and player features from 2012-2021 Korean Baseball organization (KBO) data. Then, we propose a novel prediction model using Siamese neural networks, CompeteNet, to learn a competitive relationship between a pitcher and a batter player for two teams. Despite the simplicity, extensive experimental results show that CompeteNet outperforms existing machine learning models and achieves the best accuracy of 60.00%.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Editors	Hyeran Byun, Beng Chin Ooi, Katsumi Tanaka, Sang-Won Lee, Zhixu Li, Akiyo Nadamoto, Giltae Song, Young-guk Ha, Kazutoshi Sumiya, Wu Yuncheng, Hyuk-Yoon Kwon, Takehiro Yamamoto
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-8
Number of pages	8
ISBN (Electronic)	9781665475785
DOIs	https://doi.org/10.1109/BigComp57234.2023.00010
State	Published - 2023
Event	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 - Jeju, Korea, Republic of Duration: 13 Feb 2023 → 16 Feb 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

Conference

Conference	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Country/Territory	Korea, Republic of
City	Jeju
Period	13/02/23 → 16/02/23

Keywords

Baseball Analysis
Logistic Regression
Sabermetrics
Siamese Network
Sports Prediction

Access to Document

10.1109/BigComp57234.2023.00010

Cite this

Mun, K., Cha, B., Lee, J., Kim, J., & Jo, H. (2023). CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games. In H. Byun, B. C. Ooi, K. Tanaka, S.-W. Lee, Z. Li, A. Nadamoto, G. Song, Y. Ha, K. Sumiya, W. Yuncheng, H.-Y. Kwon, & T. Yamamoto (Eds.), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 (pp. 1-8). (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp57234.2023.00010

Mun, Kyeongjin ; Cha, Bonggeon ; Lee, Jongwuk et al. / CompeteNet : Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games. Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. editor / Hyeran Byun ; Beng Chin Ooi ; Katsumi Tanaka ; Sang-Won Lee ; Zhixu Li ; Akiyo Nadamoto ; Giltae Song ; Young-guk Ha ; Kazutoshi Sumiya ; Wu Yuncheng ; Hyuk-Yoon Kwon ; Takehiro Yamamoto. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1-8 (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023).

@inproceedings{aa070f1ed60b41da98c5a884cf230430,

title = "CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games",

abstract = "Sports game prediction using machine learning techniques has gained much attention in the sports industry. Notably, this task is vital for sports-related fields, such as sports marketing, sports betting, in-game strategy establishment, etc. To improve prediction accuracy, it is necessary to consider various game factors, such as past match results and player features. In this paper, we formulate win-lose outcome prediction in baseball games as a binary classification problem. We first collect various team and player features from 2012-2021 Korean Baseball organization (KBO) data. Then, we propose a novel prediction model using Siamese neural networks, CompeteNet, to learn a competitive relationship between a pitcher and a batter player for two teams. Despite the simplicity, extensive experimental results show that CompeteNet outperforms existing machine learning models and achieves the best accuracy of 60.00\%.",

keywords = "Baseball Analysis, Logistic Regression, Sabermetrics, Siamese Network, Sports Prediction",

author = "Kyeongjin Mun and Bonggeon Cha and Jongwuk Lee and Jihyeon Kim and Hyuncheol Jo",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 ; Conference date: 13-02-2023 Through 16-02-2023",

year = "2023",

doi = "10.1109/BigComp57234.2023.00010",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--8",

editor = "Hyeran Byun and Ooi, \{Beng Chin\} and Katsumi Tanaka and Sang-Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young-guk Ha and Kazutoshi Sumiya and Wu Yuncheng and Hyuk-Yoon Kwon and Takehiro Yamamoto",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",

}

Mun, K, Cha, B, Lee, J, Kim, J & Jo, H 2023, CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games. in H Byun, BC Ooi, K Tanaka, S-W Lee, Z Li, A Nadamoto, G Song, Y Ha, K Sumiya, W Yuncheng, H-Y Kwon & T Yamamoto (eds), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, Jeju, Korea, Republic of, 13/02/23. https://doi.org/10.1109/BigComp57234.2023.00010

CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games. / Mun, Kyeongjin; Cha, Bonggeon; Lee, Jongwuk et al.
Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. ed. / Hyeran Byun; Beng Chin Ooi; Katsumi Tanaka; Sang-Won Lee; Zhixu Li; Akiyo Nadamoto; Giltae Song; Young-guk Ha; Kazutoshi Sumiya; Wu Yuncheng; Hyuk-Yoon Kwon; Takehiro Yamamoto. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1-8 (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - CompeteNet

T2 - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

AU - Mun, Kyeongjin

AU - Cha, Bonggeon

AU - Lee, Jongwuk

AU - Kim, Jihyeon

AU - Jo, Hyuncheol

PY - 2023

Y1 - 2023

N2 - Sports game prediction using machine learning techniques has gained much attention in the sports industry. Notably, this task is vital for sports-related fields, such as sports marketing, sports betting, in-game strategy establishment, etc. To improve prediction accuracy, it is necessary to consider various game factors, such as past match results and player features. In this paper, we formulate win-lose outcome prediction in baseball games as a binary classification problem. We first collect various team and player features from 2012-2021 Korean Baseball organization (KBO) data. Then, we propose a novel prediction model using Siamese neural networks, CompeteNet, to learn a competitive relationship between a pitcher and a batter player for two teams. Despite the simplicity, extensive experimental results show that CompeteNet outperforms existing machine learning models and achieves the best accuracy of 60.00%.

AB - Sports game prediction using machine learning techniques has gained much attention in the sports industry. Notably, this task is vital for sports-related fields, such as sports marketing, sports betting, in-game strategy establishment, etc. To improve prediction accuracy, it is necessary to consider various game factors, such as past match results and player features. In this paper, we formulate win-lose outcome prediction in baseball games as a binary classification problem. We first collect various team and player features from 2012-2021 Korean Baseball organization (KBO) data. Then, we propose a novel prediction model using Siamese neural networks, CompeteNet, to learn a competitive relationship between a pitcher and a batter player for two teams. Despite the simplicity, extensive experimental results show that CompeteNet outperforms existing machine learning models and achieves the best accuracy of 60.00%.

KW - Baseball Analysis

KW - Logistic Regression

KW - Sabermetrics

KW - Siamese Network

KW - Sports Prediction

UR - https://www.scopus.com/pages/publications/85151528247

U2 - 10.1109/BigComp57234.2023.00010

DO - 10.1109/BigComp57234.2023.00010

M3 - Conference contribution

AN - SCOPUS:85151528247

T3 - Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

SP - 1

EP - 8

BT - Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

A2 - Byun, Hyeran

A2 - Ooi, Beng Chin

A2 - Tanaka, Katsumi

A2 - Lee, Sang-Won

A2 - Li, Zhixu

A2 - Nadamoto, Akiyo

A2 - Song, Giltae

A2 - Ha, Young-guk

A2 - Sumiya, Kazutoshi

A2 - Yuncheng, Wu

A2 - Kwon, Hyuk-Yoon

A2 - Yamamoto, Takehiro

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 February 2023 through 16 February 2023

ER -

Mun K, Cha B, Lee J, Kim J, Jo H. CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games. In Byun H, Ooi BC, Tanaka K, Lee SW, Li Z, Nadamoto A, Song G, Ha Y, Sumiya K, Yuncheng W, Kwon HY, Yamamoto T, editors, Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1-8. (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023). doi: 10.1109/BigComp57234.2023.00010

CompeteNet: Siamese Networks for Predicting Win-Loss Outcomes in Baseball Games

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