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发布时间:2021-10-14 03:55:01

Driven by modern technology, big data has gradually penetrated into the field of sports, and big data technology has been widely used in football, the world's largest sport. After the previous match between the national football team and the Maldives, it was revealed on the Internet that Luneng had shared the information of the referee through its own big data system with the national football team so that the players could understand the referee's enforcement style. Of course, the scope of application of big data is not just to understand the referee's law enforcement style. Player introduction, tactical layout, player training, technical and tactical arrangements and other fields are getting closer and closer to big data. However, unlike the first team that pays more attention to the use of big data to improve the players’ on-the-spot performance and the results of the game, the application of big data in the field of youth training needs to pay more attention to the long-term development of the players. Next, let us understand the Luneng Football School through several aspects. The role of big data in the growth of young players.


The first thing we introduce is the training aspect. There is an old saying that goes well: One minute on stage, ten years off stage. Both players and actors want to show their best to the audience when they are on stage, but training performance often determines a player's chance of playing, and even his performance. Training and learning are the two parts that take up the most time in the learning and life of young players. The arrangement of training and learning needs to be completed with the cooperation of the teaching department and the competition training department. The training and learning schedule of players is also placed in the big data system. The place.


Entering the big data system, we can see the training situation of any of the 22 youth training teams in the football academy, as well as the training situation of everyone. The statistical pie chart of the training name and attendance is displayed on the page after the click-in training. They record the training status of the players and the team throughout the season and the players' attendance.


In terms of attendance, a player’s season performance has a lot to do with the player’s attendance time. Most of the player’s attendance time statistics are affected by injuries. The lack of training attendance time will naturally affect the playing time the coach gives players. Attendance time is divided into rehabilitation time and regular training time. Rehabilitation training time includes not only the time for players to get recovery exercises during the recovery period from injuries and illnesses, but also the stretching and relaxation time for players after intense games and training.


The training name mainly includes 3 kinds of basic training and 2 kinds of supplementary training. Supplementary training includes technical and physical goals, as well as multi-player confrontation training; and the types of basic training are relatively more including tactical exercises and part of the overall team competition. Practice, as well as partial offensive and defensive drills and so on. Before each training session, the coaching staff will formulate an RPE (training intensity self-assessed by the coaching team) in the big data system based on the team’s recent matches and training situation and the players’ personal performance. Training class. The big data backend will summarize and analyze each training plan made by the coaching staff to form a training statistics for the team throughout the year. The coaching staff will then use big data to compare the training statistics of the players for a certain period of time with the actual game. , To find out the physical or technical deficiencies of the players at the current stage, and to design exercises for the players’ shortcomings on the basis of meeting the players’ ability to withstand the players through consultations with the physical trainer and the medical team.

培训名称主要包括3种基础培训和2种补充培训。补充训练包括技术和身体目标,以及多人对抗训练;基本训练的类型相对来说更多,包括战术练习和整个团队比赛的一部分。练习,以及部分进攻和防守演习等。在每次培训之前,教练组将根据团亚搏手机版APP下载官网队的最近比赛和训练情况以及运动员的个人表现,在大数据系统中制定RPE(由教练组自行评估的训练强度)。培训班。大数据后端将汇总并分析教练组制定的每项培训计划,以形成全年针对团队的培训统计数据。然后,教练组将使用大数据将特定时间段内球员的训练统计数据与实际比赛进行比较。 ,以找出球员现阶段的身体或技术缺陷,并在与体育教练和医疗队进行磋商的基础上,通过满足球员承受球员承受能力的能力,为球员的缺点设计锻炼方法。

In fact, big data analysis relies on not only training videos taken by HD cameras covering the entire venue and summary statistics of coaches’ subjective training plans. Wearable devices on players are also one of the important data sources, as well as auxiliary equipment. The data is more quantitative and accurate.


In 2018, Luneng foot correction introduced the first set of Catapult wearable devices, which was also the first set of wearable devices in the domestic youth training field at that time. The main purpose is to integrate it with the school’s big data system and analyze players through wearable devices. Through specific data analysis to help coaches assess the team’s training intensity and training quality. At this stage, the main function of the wearable device is to collect data such as the running distance, sprint distance, explosive exercise performance, blood oxygen and other body load indexes of the players in each training session and upload them to the big data system simultaneously to upload the exercise performance data and video images Combine. In this way, what the coach gets is not just the data on paper, but a more intuitive and vivid multimedia report, which will also help the team's tactical analysis and training summary. In the past, the only way to understand the exercise load of players was through RPE, which is a method of using subjective feelings to estimate the intensity of exercise load. But now the team can compare the difference between RPE and real-time feedback data, and at the same time can better control the training load.


In fact, sports big data is not a new thing, but the development and improvement of traditional artificial statistical data methods. The core of the application of big data in sports training is prediction. The essence is to find patterns in data, improve cognitive ability, and make predictions and guide decision-making. The traditional manual recording of player training performance has disadvantages such as susceptibility to subjective factors, excessive workload, inaccurate statistics, and difficult data storage. The big data system we use now can generate training and guidance value in real time and comprehensively. Sufficient data, and effectively applied to sports training practice, so as to effectively improve the level of training.


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