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Paper Abstract and Keywords
Presentation 2023-12-08 09:30
A Proposal of Losing Point Prediction Method Reflecting the Day's Conditions Based on Tracking Data
Sota Akamatsu, Eiichiro Kodama, Wang Jiahong, Bhed Bahadur Bista, Toyoo Takata (Iwate Prefectural University) SIS2023-33
Abstract (in Japanese) (See Japanese page) 
(in English) In baseball, the timing of pitcher substitutions is one of the most important factors influencing the winning percentage. Generally, the decision is made by the manager based on vague criteria such as the number of pitches, the condition of the starting pitcher, and the condition of the relieving pitchers, which may result in suboptimal decisions. On the other hand, recently, tracking data on pitch types and outcomes have been recorded for every game, and we believe that a predictive model can be constructed using the data to support the decision-making on when to substitute pitchers. This study proposes a method for predicting a pitcher's earned runs for the day based on tracking data reflecting the condition on the day of the game, with the aim of helping the manager’s decide-making when to substitute a pitcher.
Keyword (in Japanese) (See Japanese page) 
(in English) Big data analysis / Tracking data / Least-squares method / Run prediction method / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 296, SIS2023-33, pp. 49-54, Dec. 2023.
Paper # SIS2023-33 
Date of Issue 2023-11-30 (SIS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIS2023-33

Conference Information
Committee SIS  
Conference Date 2023-12-07 - 2023-12-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Sakurayama Campus, Nagoya City University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Smart Personal Sysytems, etc. 
Paper Information
Registration To SIS 
Conference Code 2023-12-SIS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Proposal of Losing Point Prediction Method Reflecting the Day's Conditions Based on Tracking Data 
Sub Title (in English)  
Keyword(1) Big data analysis  
Keyword(2) Tracking data  
Keyword(3) Least-squares method  
Keyword(4) Run prediction method  
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Keyword(6)  
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1st Author's Name Sota Akamatsu  
1st Author's Affiliation Graduate School of Software and Information Science, Iwate Prefectural University (Iwate Prefectural University)
2nd Author's Name Eiichiro Kodama  
2nd Author's Affiliation Graduate School of Software and Information Science, Iwate Prefectural University (Iwate Prefectural University)
3rd Author's Name Wang Jiahong  
3rd Author's Affiliation Graduate School of Software and Information Science, Iwate Prefectural University (Iwate Prefectural University)
4th Author's Name Bhed Bahadur Bista  
4th Author's Affiliation Graduate School of Software and Information Science, Iwate Prefectural University (Iwate Prefectural University)
5th Author's Name Toyoo Takata  
5th Author's Affiliation Graduate School of Software and Information Science, Iwate Prefectural University (Iwate Prefectural University)
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Speaker Author-1 
Date Time 2023-12-08 09:30:00 
Presentation Time 20 minutes 
Registration for SIS 
Paper # SIS2023-33 
Volume (vol) vol.123 
Number (no) no.296 
Page pp.49-54 
#Pages 6 
Date of Issue 2023-11-30 (SIS) 

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