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WO2021225007A1 - Système de prédiction de résultat - Google Patents

Système de prédiction de résultat Download PDF

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Publication number
WO2021225007A1
WO2021225007A1 PCT/JP2020/027422 JP2020027422W WO2021225007A1 WO 2021225007 A1 WO2021225007 A1 WO 2021225007A1 JP 2020027422 W JP2020027422 W JP 2020027422W WO 2021225007 A1 WO2021225007 A1 WO 2021225007A1
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WIPO (PCT)
Prior art keywords
game
predetermined scene
analyzed
machine learning
video
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2020/027422
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English (en)
Japanese (ja)
Inventor
卓也 武本
誠也 宮崎
佳菜 根岸
ミャホライ・バトビルグーン
初実 鈴木
パク・ジョンソン
キム・ドンヒョン
オ・ソンテク
イ・ヨンソク
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Data Artist Inc
Dentsu Group Inc
Team Twelve Inc
Original Assignee
Data Artist Inc
Dentsu Inc
Team Twelve Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Data Artist Inc, Dentsu Inc, Team Twelve Inc filed Critical Data Artist Inc
Priority to KR1020207032988A priority Critical patent/KR102443007B1/ko
Priority to CN202080002630.6A priority patent/CN113939837B/zh
Publication of WO2021225007A1 publication Critical patent/WO2021225007A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/041—Abduction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content

Definitions

  • the present invention relates to a win / loss prediction system that can predict the outcome of a match.
  • a winning / losing result code as a reference index reflecting the qualitative strength of each soccer team divided into home and away and other related data items of each soccer team are collected from an existing database of soccer teams.
  • a qualitative force rating is performed by sum calculation (see, for example, Patent Document 1).
  • the present invention has been made in view of the above problems, and an object of the present invention is to provide a winning / losing prediction system capable of predicting the winning / losing result of a match.
  • the win / loss prediction system of the present invention includes an image acquisition unit that acquires an image during a match to be analyzed, a first system that estimates the ball possession during the match, and a second system that estimates an event that occurs during the match.
  • the first system includes a third system that predicts the winning / losing result of the game from the detection result of the first system and the detection result of the second system, and the first system includes images of predetermined scenes of past games.
  • the video acquisition unit is based on the relationship between the first machine learning unit that analyzes the relationship between the player and the position of the ball in the predetermined scene of the past game by machine learning and the relationship analyzed by the first machine learning unit.
  • the second system includes a first determination unit that determines and outputs a ball possession in a predetermined scene of the game to be analyzed based on the output positions of the player and the ball, and the second system is a predetermined game in the past. Based on the relationship between the second machine learning unit that analyzes the relationship between the video of the scene and the information about the event that occurred in the predetermined scene during the past game by machine learning, and the relationship analyzed by the second machine learning unit.
  • a second estimation unit that estimates and outputs an event that occurred in the predetermined scene during the analysis target game by inputting the video of the predetermined scene during the analysis target game acquired by the video acquisition unit.
  • the third system includes information on ball possession in a predetermined scene of a past game, an event generated in the predetermined scene, an elapsed time from the start of the game to the predetermined scene, information on goals and goals at the time of the predetermined scene, and the past. Based on the relationship analyzed by the third machine learning unit that analyzes the relationship between the winning and losing results of the game by machine learning and the third machine learning unit, the game to be analyzed that is output from the first determination unit.
  • the relationship analyzed by machine learning (the image of the predetermined scene of the past game and the player in the predetermined scene of the past game).
  • the position of the player and the ball in the predetermined scene is estimated by using (the relationship between the information and the information about the position of the ball).
  • the ball possession in the predetermined scene is determined.
  • the relationship analyzed by machine learning (the video of the predetermined scene of the past game and the information about the event that occurred in the predetermined scene during the past game).
  • Relationship is used to estimate the event that occurred in the predetermined scene (for example, attacking to the front of the goal and hitting a shot). Then, information on the ball possession (determined ball possession) in the predetermined scene of the game to be analyzed, the event that occurred in the predetermined scene (estimated event), the elapsed time to the predetermined scene, and the goal score in the predetermined scene.
  • the relationship analyzed by machine learning ball possession in the predetermined scene of the past game, the event that occurred in the predetermined scene, the elapsed time to the predetermined scene, the information on the goal score at the time of the predetermined scene and the information , Relationship with the winning / losing result of the past game), the winning / losing result of the game expected in real time at the time of the predetermined scene is estimated. In this way, the outcome of the match can be predicted in real time from the video of the match being analyzed.
  • the third system analyzes the image of the predetermined scene during the game to be analyzed by image analysis, and the process from the start of the game to the analysis target in the predetermined scene to the predetermined scene.
  • An image analysis unit may be provided for acquiring information on the time and points scored at the time of a predetermined scene of the game to be analyzed.
  • the image analysis by performing image analysis of the video of a predetermined scene during the game to be analyzed, it is possible to acquire information on the elapsed time until the predetermined scene and the points scored at the time of the predetermined scene.
  • the information acquired by the image analysis (the elapsed time to the predetermined scene, the information on the score at the time of the predetermined scene) can be input to the third estimation unit and used for estimating the winning / losing result of the match. ..
  • the third estimation unit determines the probability of winning, the probability of losing, and the probability of drawing as the winning / losing result predicted in real time at the time of a predetermined scene of the game to be analyzed. Each may be estimated and output.
  • the probability of winning, the probability of losing, and the probability of drawing are output as the expected winning / losing result, so that the winning / losing result can be easily understood at a glance.
  • the winning / losing prediction method of the present invention includes an image acquisition unit that acquires an image during a match to be analyzed, a first system that estimates the ball possession during the match, and a second system that estimates an event that occurs during the match.
  • a winning / losing prediction method executed by a winning / losing prediction system including the detection result of the first system and the third system for predicting the winning / losing result of the game from the detection result of the second system.
  • the first machine learning step for analyzing the relationship between the image of the predetermined scene of the past game and the information on the positions of the player and the ball in the predetermined scene of the past game by machine learning, and the first machine learning.
  • the video of the predetermined scene during the analysis target game acquired by the video acquisition unit is input, and the positions of the player and the ball in the predetermined scene of the analysis target game are estimated and output.
  • the video of the predetermined scene in the game to be analyzed acquired by the video acquisition unit is input, and it occurs in the predetermined scene in the game to be analyzed.
  • a third machine learning step that analyzes the relationship between the elapsed time of Based on, the ball possession in the predetermined scene of the game to be analyzed output from the first determination step, the event generated in the predetermined scene of the game to be analyzed output from the second estimation step, the analysis target.
  • the third estimation step of estimating and outputting the expected victory / defeat result of the game at the time of the scene is executed.
  • the relationship analyzed by machine learning (the image of the predetermined scene of the past game and the past game).
  • the position of the player and the ball in the predetermined scene is estimated by using the relation between the information about the position of the player and the ball in the predetermined scene).
  • the ball possession in the predetermined scene is determined.
  • the relationship analyzed by machine learning (the video of the predetermined scene of the past game and the information about the event that occurred in the predetermined scene during the past game).
  • Relationship is used to estimate the event that occurred in the predetermined scene (for example, attacking to the front of the goal and hitting a shot). Then, information on the ball possession (determined ball possession) in the predetermined scene of the game to be analyzed, the event that occurred in the predetermined scene (estimated event), the elapsed time to the predetermined scene, and the goal score in the predetermined scene.
  • the relationship analyzed by machine learning ball possession in the predetermined scene of the past game, the event that occurred in the predetermined scene, the elapsed time to the predetermined scene, the information on the goal score at the time of the predetermined scene and the information , Relationship with the winning / losing result of the past game), the winning / losing result of the game expected in real time at the time of the predetermined scene is estimated. In this way, the outcome of the match can be predicted in real time from the video of the match being analyzed.
  • FIG. 1 is a diagram showing a configuration of a win / loss prediction system according to the present embodiment.
  • the win / loss prediction system 1 is connected to the game shooting system 10 via the network N.
  • the game shooting system 10 includes a shooting unit 11 that shoots a video during a game such as soccer, and a video distribution unit 12 that distributes the shot video via the network N.
  • the win / loss prediction system 1 estimates the ball possession during the match and the video acquisition unit 2 that acquires the video during the match (the video during the match to be analyzed) distributed from the match shooting system 10.
  • First system 4 second system 5 that estimates events that occur during the match, and third system 6 that predicts the outcome of the match from the detection results of the first system 4 and the detection results of the second system 5.
  • the win / loss prediction system 1 includes a video storage unit 3 that stores video data of past games.
  • the video storage unit 3 may store the video during the game (the video during the game to be analyzed) distributed from the game shooting system 10.
  • FIG. 2 is a block diagram of the first system 4. As shown in FIG. 2, the first system 4 includes a first input / output unit 40, a first machine learning unit 41, a first estimation unit 42, and a first determination unit 43.
  • the video of the past game stored in the video storage unit 3 and the video of the game to be analyzed acquired by the video acquisition unit 2 are input to the first input / output unit 40.
  • the first input / output unit 40 outputs information on the determination result of the first determination unit 43 (information on the ball possession in a predetermined scene of the game to be analyzed).
  • the first machine learning unit 41 analyzes the relationship between the video of the predetermined scene of the past game stored in the video storage unit 3 and the information regarding the positions of the player and the ball in the predetermined scene of the past game by machine learning. do.
  • machine learning an arbitrary method such as deep learning by a neural network is used.
  • the image of a predetermined scene of a past game is input to the input layer, and information on the positions of the player and the ball in the predetermined scene of the past game is output from the output layer.
  • the weighting coefficient between the neurons of the neural network is optimized by supervised learning using the analysis data in which the data input to the input layer and the data output from the output layer are linked.
  • the first estimation unit 42 takes the video of the predetermined scene during the analysis target game acquired by the video acquisition unit 2 as input, and inputs the predetermined scene of the analysis target game. Estimates and outputs the positions of the player and the ball in.
  • the video of the predetermined scene during the analysis target game acquired by the video acquisition unit 2 is input to the input layer, and the information regarding the positions of the player and the ball in the predetermined scene of the analysis target game is input. Is output from the output layer to make an estimation.
  • the first determination unit 43 determines the ball possession (which team possesses the ball) in a predetermined scene of the game to be analyzed based on the positions of the player and the ball output from the first estimation unit 42. And output. For example, the first determination unit 43 determines that the team on the player side closest to the ball is the team in possession of the ball, based on the distance between the ball and the player.
  • FIG. 3 is a diagram showing an example of determination of ball possession.
  • the players of the team (Team A) attacking from left to right are shown by “triangles with diagonal lines", and the players of the team (Team B) attacking from right to left are shown by “white triangles”. ..
  • the ball is illustrated by a "black circle”.
  • the player closest to the ball is the player of team A, it is determined that team A is the team in possession of the ball.
  • FIG. 4 is a block diagram of the second system 5. As shown in FIG. 4, the second system 5 includes a second input / output unit 50, a second machine learning unit 51, and a second estimation unit 52.
  • the video of the past game stored in the video storage unit 3 is input to the second input / output unit 50.
  • the second input / output unit 50 outputs information on the estimation result of the second estimation unit 52 (information on an event that occurred in a predetermined scene of the game to be analyzed).
  • the second machine learning unit 51 analyzes the relationship between the video of the predetermined scene of the past game and the information about the event that occurred in the predetermined scene during the past game by machine learning.
  • machine learning an arbitrary method such as deep learning by a neural network is used.
  • the video of a predetermined scene of a past game is input to the input layer, and information about an event occurring in the predetermined scene of the past game is output from the output layer.
  • the weighting coefficient between the neurons of the neural network is optimized by supervised learning using the analysis data in which the data input to the input layer and the data output from the output layer are linked.
  • the second estimation unit 52 receives the video of the predetermined scene during the analysis target game acquired by the video acquisition unit 2 as input, and determines the predetermined scene during the analysis target game. Estimate and output the event that occurred in the scene. For example, in the case of the above neural network, the video of the predetermined scene during the game to be analyzed acquired by the video acquisition unit 2 is input to the input layer, and the information regarding the event occurring in the predetermined scene during the game to be analyzed is input. The estimation is performed by outputting from the output layer.
  • FIG. 5 is a diagram showing an example of an event that occurs in a predetermined scene during a match.
  • FIG. 5 shows an example of an event that occurs for a team in possession of the ball.
  • examples of events when an attack is successful include “successful dribble breakthrough", “successful pass”, and “successful shoot”.
  • examples of events when the attack fails include “dribble breakthrough failure", “pass failure”, and "shoot failure”.
  • FIG. 6 is a block diagram of the third system 6. As shown in FIG. 6, the third system 6 includes a third input / output unit 60, a third machine learning unit 61, and a third estimation unit 62.
  • the third input / output unit 60 includes information on the ball possession in the predetermined scene of the past game, the event that occurred in the predetermined scene, the elapsed time from the start of the game to the predetermined scene, the goal score at the time of the predetermined scene, and the first.
  • Information about the time and the goal score at the time of the predetermined scene of the game to be analyzed is input.
  • the third input / output unit 60 outputs information on the estimation result of the third estimation unit 62 (information on the winning / losing result of the game expected at the time of a predetermined scene of the game to be analyzed).
  • the third machine learning unit 61 includes information on the ball possession in the predetermined scene of the past game, the event that occurred in the predetermined scene, the elapsed time from the start of the game to the predetermined scene, the goal score at the time of the predetermined scene, and the past game.
  • the relationship with the winning and losing results is analyzed by machine learning.
  • machine learning an arbitrary method such as deep learning by a neural network is used.
  • information on the ball possession in a predetermined scene of a past game, the event that occurred in the predetermined scene, the elapsed time from the start of the game to the predetermined scene, and the goal score at the time of the predetermined scene is input to the input layer.
  • the weighting coefficient between the neurons of the neural network is optimized by supervised learning using the analysis data in which the data input to the input layer and the data output from the output layer are linked.
  • the third estimation unit 62 was output from the second estimation unit 52, which is the ball possession in the predetermined scene of the game to be analyzed, which was output from the first determination unit 43 based on the relationship analyzed by the third machine learning unit 61.
  • Predetermined of the game to be analyzed by inputting information on the event that occurred in the predetermined scene of the game to be analyzed, the elapsed time from the start of the game to be analyzed to the predetermined scene, and the points scored at the time of the predetermined scene of the game to be analyzed. Estimate and output the expected outcome of the match at the time of the scene.
  • the ball possession in the predetermined scene of the analysis target game output from the first determination unit 43, and the event generated in the predetermined scene of the analysis target game output from the second estimation unit 52 Elapsed time from the start of the game to be analyzed to the predetermined scene, and information on the points scored at the time of the predetermined scene of the game to be analyzed is input to the input layer, and the game expected at the time of the predetermined scene of the game to be analyzed.
  • the estimation is performed by outputting the information on the winning / losing result of the above from the output layer.
  • the third system 6 image-analyzes the image of the predetermined scene during the analysis target game, the elapsed time from the start of the analysis target game of the predetermined scene to the predetermined scene, and the time point of the predetermined scene of the analysis target game.
  • An image analysis unit (not shown) may be provided to acquire information on the points scored in. A known technique can be used for this image analysis.
  • the third estimation unit 62 may estimate and output the probability of winning, the probability of losing, and the probability of drawing as the expected winning / losing results at the time of a predetermined scene of the game to be analyzed.
  • the past results of the participating members (starting, mid-game) of each team, the attack stats of each team (dribble distance, pass success rate, shooting success rate, etc.), and the defensive stats of each team. (Clearing the ball, number of balls captured, etc.) Physical stats of each team (number of duels, etc.), number of fouls of each team (number of fouls, number of yellow / red cards, etc.), past match results between your team and the opponent team, your team And the latest results of the opponent team (win / loss / goal / goal), the ranking of the own team and the opponent team at the time of the match, the difference in the ranking of the own team and the opponent team at the time of the match, league / region / tournament information, weather (sunny) , Cloudy, rain, etc.), temperature / humidity, number of stadium spectators, kickoff time, kickoff time (year / month), etc. may be entered.
  • the final score of the match, the score in the middle of the match, the team / player to score next, the elapsed time until the next score, and the next play action (shoot, dribble, through pass, throwing).
  • Information such as the team / player (which takes an action such as), the elapsed time until the next play action, and the next player to be replaced may be output.
  • the win / loss prediction system 1 of the present embodiment first, as a preliminary preparation, in the first system 4, the video of the predetermined scene of the past game and the information on the positions of the player and the ball in the predetermined scene of the past game are The relationship is analyzed by machine learning (first machine learning step). Further, in the second system 5, the relationship between the image of the predetermined scene of the past game and the information about the event generated in the predetermined scene during the past game is analyzed by machine learning (second machine learning step). Further, in the third system 6, information on the ball possession in the predetermined scene of the past game, the event that occurred in the predetermined scene, the elapsed time from the start of the game to the predetermined scene, the goal score at the time of the predetermined scene, and the past game. The relationship with the winning and losing results is analyzed by machine learning (third machine learning step).
  • the image taken by the game shooting system 10 (the image during the game to be analyzed) is acquired (S1), and the first machine
  • the video of the predetermined scene during the analysis target game acquired by the video acquisition unit 2 is input, and the positions of the player and the ball in the predetermined scene of the analysis target game are estimated and output.
  • the ball possession in a predetermined scene of the game to be analyzed is determined and output (S3).
  • the video of the predetermined scene in the game to be analyzed acquired by the video acquisition unit 2 is input, and the video of the predetermined scene in the game to be analyzed is input.
  • the event that occurred in the predetermined scene is estimated and output (S4).
  • the ball possession in the predetermined scene of the analysis target game output from the first determination step, the event generated in the predetermined scene of the analysis target game output from the second estimation step, and the analysis target By inputting the elapsed time from the start of the game to the predetermined scene and the information on the points scored at the time of the predetermined scene of the game to be analyzed, the expected victory or defeat result of the game at the time of the predetermined scene of the game to be analyzed is estimated.
  • Output (S5) the expected victory or defeat result of the game at the time of the predetermined scene of the game to be analyzed.
  • the win / loss prediction system 1 of this embodiment it is possible to predict the win / loss result of a soccer match.
  • the relationship analyzed by machine learning (the image of the predetermined scene of the past game and the predetermined scene of the past game).
  • the position of the player and the ball in the predetermined scene is estimated by using the relation between the information about the position of the player and the ball in the above. Then, based on the estimated player and ball position, the ball possession in the predetermined scene is determined.
  • the relationship analyzed by machine learning (the video of the predetermined scene of the past game and the information about the event that occurred in the predetermined scene during the past game). (Relationship) is used to estimate the event that occurred in the predetermined scene (for example, attacking to the front of the goal and hitting a shot).
  • the relationship analyzed by machine learning ball possession in the predetermined scene of the past game, the event that occurred in the predetermined scene, the elapsed time to the predetermined scene, the information on the goal score at the time of the predetermined scene and the information , Relationship with the winning / losing result of the past game), the winning / losing result of the game expected in real time at the time of the predetermined scene is estimated. In this way, the outcome of the match can be predicted in real time from the video of the match being analyzed.
  • the present embodiment by performing image analysis of the video of the predetermined scene during the game to be analyzed, it is possible to acquire information on the elapsed time until the predetermined scene and the score and goal at the time of the predetermined scene.
  • the information acquired by the image analysis (the elapsed time to the predetermined scene, the information on the score at the time of the predetermined scene) can be input to the third estimation unit 62 and used for estimating the winning / losing result of the match. can.
  • the probability of winning, the probability of losing, and the probability of drawing are output as the expected winning / losing result, so that the winning / losing result can be easily understood at a glance.
  • the win / loss prediction system has the effect of being able to predict the win / loss result of the match from the video during the match to be analyzed, and is useful as a win / loss prediction system for a match such as soccer. Is.

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Abstract

L'invention concerne un système de prédiction de résultat avec lequel il est possible de prédire le résultat d'un jeu à partir d'une image d'un objet d'analyse telle que capturée pendant le jeu.　Ce système de prédiction de résultat (1) comprend : une unité d'acquisition d'image (2) qui acquiert une image vidéo d'un objet d'analyse telle que capturée pendant un jeu ; un premier système (4) qui estime une position de balle pendant le jeu à partir de l'image vidéo de l'objet d'analyse telle que capturée pendant le jeu ; un deuxième système (5) qui estime un événement qui se produit pendant le jeu à partir de l'image vidéo de l'objet d'analyse telle que capturée pendant le jeu ; et un troisième système (6) qui prédit le résultat du jeu à partir du résultat de détection obtenu à partir du premier système (4) et du résultat de détection obtenu à partir du deuxième système (5).
PCT/JP2020/027422 2020-05-08 2020-07-15 Système de prédiction de résultat Ceased WO2021225007A1 (fr)

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CN202080002630.6A CN113939837B (zh) 2020-05-08 2020-07-15 胜负预测系统

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JP2020082460A JP7078667B2 (ja) 2020-05-08 2020-05-08 勝敗予測システム

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Publication number Priority date Publication date Assignee Title
WO2018122956A1 (fr) * 2016-12-27 2018-07-05 日本電気株式会社 Système, procédé et programme de support d'analyse de mouvement sportif
US20190228290A1 (en) * 2018-01-21 2019-07-25 Stats Llc Method and System for Interactive, Interpretable, and Improved Match and Player Performance Predictions in Team Sports
JP2019136383A (ja) * 2018-02-14 2019-08-22 田中　成典 戦術分析装置

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