📚 6 Databases in Sport | 体育中的数据库
In modern sport, decisions that were once made by intuition are increasingly supported by structured evidence. A database is a central tool for collecting, storing, querying and sharing this evidence, from training loads to match statistics. This chapter explains how databases work and why they matter in physical education and sports science.
在现代体育中，过去依靠直觉做出的决策越来越受到结构化证据的支持。数据库是收集、存储、查询和共享这些证据的核心工具，从训练负荷到比赛统计都离不开它。本章将解释数据库的工作原理，以及它们在体育教育和运动科学中的重要性。
1. What Is a Database? | 什么是数据库？
A database is an organised collection of related data stored electronically, usually managed by a Database Management System (DBMS). In sport, a database might hold athlete profiles, training sessions, competition results and medical records, allowing users to retrieve specific information quickly.
数据库是以电子方式存储的有组织的相关数据集合，通常由数据库管理系统 (DBMS) 管理。在体育中，数据库可以保存运动员档案、训练课、比赛成绩和医疗记录，使用户能够快速检索特定信息。
Data in a database are structured into tables made up of records (rows) and fields (columns). For example, each row may represent one athlete, while each column stores one attribute such as name, age or 100 m time.
数据库中的数据被组织成表，表由记录（行）和字段（列）组成。例如，每一行可以代表一名运动员，而每一列存储一个属性，如姓名、年龄或 100 米成绩。
The main advantage over a spreadsheet is that a DBMS can enforce relationships, reduce duplication and handle large volumes of multi-user data with security controls.
与电子表格相比，数据库的主要优势在于 DBMS 可以强制关系、减少重复，并通过安全控制处理大量多用户数据。
2. Why Sport Needs Databases | 体育为什么需要数据库？
Sports organisations generate data at high speed during every session, match and medical check. A database allows coaches, analysts and physiotherapists to store this data centrally and avoid fragmented files across laptops, phones and paper forms.
体育组织在每次训练、比赛和体检中都会高速产生数据。数据库使教练、分析师和物理治疗师能够集中存储这些数据，避免数据分散在笔记本电脑、手机和纸质表格中。
Databases also support evidence-based decisions. A coach can compare an athlete’s current sprint times with historical averages, while a medical team can check injury history before clearing a player to return.
数据库还支持基于证据的决策。教练可以将运动员当前的短跑成绩与历史平均值进行比较，而医疗团队可以在允许球员复出前检查其伤病史。
Without a central database, important patterns may be missed because the data are incomplete, inconsistent or inaccessible.
如果没有中央数据库，重要的规律可能会因为数据不完整、不一致或无法访问而被忽略。
3. Types of Sporting Data | 体育数据的类型
Sporting databases store several types of data. Quantitative data include measurable values such as distance covered, heart rate, jump height and reaction time. Qualitative data include subjective ratings such as perceived exertion, skill execution notes and coach feedback.
体育数据库存储多种类型的数据。定量数据包括可测量的数值，如跑动距离、心率、跳跃高度和反应时间。定性数据包括主观评分，如自觉用力程度、技术执行记录和教练反馈。
Time-series data record the same variable repeatedly over time, such as daily sleep quality or weekly training load. Spatial data capture position and movement, often collected through GPS or video tracking.
时间序列数据记录同一变量随时间的重复变化，如每日睡眠质量或每周训练负荷。空间数据捕捉位置和移动，通常通过 GPS 或视频跟踪采集。
A well-designed database marks the data type, unit and collection method for every field, which prevents confusion when different practitioners use the same system.
设计良好的数据库会为每个字段标记数据类型、单位和采集方法，从而防止不同使用者使用同一系统时产生混淆。
4. Relational Database Design for Sport | 体育关系型数据库设计
Most sports databases are relational. This means data are divided into logical tables linked by unique identifiers. A primary key uniquely identifies each record, while a foreign key creates a relationship to another table.
大多数体育数据库是关系型的。这意味着数据被划分到逻辑表中，并通过唯一标识符链接。主键唯一标识每条记录，而外键则建立到另一个表的关系。
For instance, an Athlete table may have AthleteID as the primary key. A TrainingLog table can then include AthleteID as a foreign key, linking each training session to the correct athlete without repeating the athlete’s name and date of birth.
例如，运动员表可以将 AthleteID 作为主键。训练日志表则可以将 AthleteID 作为外键，将每次训练课与正确的运动员关联起来，而无需重复运动员的姓名和出生日期。
Normalisation is the process of removing duplication by splitting data into smaller related tables. This improves accuracy, saves storage and makes updates easier.
规范化是通过将数据拆分为更小的相关表来消除重复的过程。这可以提高准确性、节省存储空间并使更新更容易。
5. Common Tables and Fields in Sport | 体育中常见的数据表和字段
A typical performance database includes tables such as Athlete, Competition, TrainingSession, Injury, TestResult and Team. The table below shows example fields.
一个典型的运动表现数据库包括运动员、比赛、训练课、伤病、测试结果和团队等表。下表显示了示例字段。
| Table | Example Fields | 中文示例字段 |
|---|---|---|
| Athlete | AthleteID, Name, DOB, Sex, Sport, TeamID | 编号、姓名、出生日期、性别、项目、团队 |
| TrainingSession | SessionID, AthleteID, Date, Duration, Load, RPE | 课次编号、运动员编号、日期、时长、负荷、RPE |
| Injury | InjuryID, AthleteID, Date, Type, Severity, ReturnDate | 伤病编号、运动员编号、日期、类型、严重程度、复出日期 |
| TestResult | TestID, AthleteID, Date, VO2 max, 30 m time | 测试编号、运动员编号、日期、最大摄氧量、30 米成绩 |
These tables can be linked by IDs to answer questions such as ‘How many training sessions did athlete X complete before injury?’ without duplicating personal details.
这些表可以通过编号关联，以回答诸如 “运动员 X 在受伤前完成了多少次训练课？” 等问题，而无需重复个人信息。
6. Queries and Performance Insights | 查询与表现洞察
A query is a request for data that meets specific conditions. In SQL, a common language for relational databases, a coach could ask for all sprint times under 3.10 seconds in the last 90 days.
查询是对符合特定条件的数据提出请求。在关系型数据库的常用语言 SQL 中，教练可以查询过去 90 天内所有低于 3.10 秒的短跑成绩。
Aggregate functions such as COUNT, AVERAGE, MIN, MAX and SUM summarise large datasets. For example, AVG(RPE) can show the average session intensity of a squad, while MAX(heart rate) identifies peak cardiovascular stress.
COUNT、AVERAGE、MIN、MAX 和 SUM 等聚合函数可以汇总大型数据集。例如，AVG(RPE) 可以显示一个队伍的平均课次强度，而 MAX(heart rate) 可以识别峰值心血管压力。
Joins combine data from multiple tables through foreign keys, allowing analysts to link injury records with training loads and match minutes in one output.
连接通过外键将多个表中的数据结合起来，使分析师能够将伤病记录与训练负荷和比赛时间合并到一个输出中。
7. Data Collection Methods | 数据收集方法
Data enter a sports database through many routes. Wearable sensors record heart rate, accelerometry and skin temperature, while GPS units measure distance, speed and sprint count.
数据通过多种途径进入体育数据库。可穿戴传感器记录心率、加速度计数据和皮肤温度，而 GPS 单元测量距离、速度以及冲刺次数。
Video analysis and optical tracking systems produce spatial data such as player coordinates, pass locations and defensive shape. Manual input by coaches, such as RPE and technique comments, adds the qualitative layer.
视频分析和光学跟踪系统产生空间数据，如球员坐标、传球位置和防守阵型。教练手动输入的数据，如 RPE 和技术评价，则增加了定性层面。
To ensure consistency, every collection method should have a protocol: when, how, by whom and with what device the data are recorded.
为确保一致性，每种采集方法都应有规范：何时、如何、由谁以及使用什么设备记录数据。
8. Analysing Athlete Performance | 分析运动员表现
Once data are stored, database queries allow coaches to monitor progress. A database can plot an athlete’s 30 m time across a season to determine whether speed is improving or whether fatigue is reducing performance.
数据存储后，数据库查询使教练能够监测进展。数据库可以绘制运动员整个赛季的 30 米成绩，以判断速度是否在提高，或者疲劳是否在降低表现。
Load management uses training load data alongside acute and chronic averages. A database can calculate the acute:chronic workload ratio and flag sudden spikes that may increase injury risk.
Published by TutorHao | 体育 Revision Series | aleveler.com
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