📚 Handling of Data in Information Systems | 信息系统中的数据处理
Modern sport depends on information systems that collect, store, process and present data about athletes, competitions and organisations. Whether a coach tracks split times, a physiotherapist monitors training load, or a league manages player registrations, data handling follows a structured cycle. This article explains how data is handled in sports information systems, with a focus on data types, collection, storage, quality, security and practical applications for Cambridge Physical Education.
现代体育依赖信息系统来收集、存储、处理和呈现有关运动员、比赛和组织的各类数据。无论是教练追踪分段计时、理疗师监控训练负荷，还是联赛管理球员注册，数据处理都遵循一个结构化的周期。本文解释体育信息系统中数据的处理方式，重点关注数据类型、收集、存储、质量、安全以及在剑桥体育课程中的实际应用。
1. Information Systems in Sport | 体育中的信息系统
An information system combines people, hardware, software, data and procedures to turn raw facts into useful information. In sport, examples include electronic timing systems, athlete management platforms, video analysis software and fan engagement apps. The key purpose is to support decision-making, whether for coaching, injury prevention, officiating or administration.
信息系统由人员、硬件、软件、数据和流程共同组成，将原始事实转化为有用信息。在体育中，例子包括电子计时系统、运动员管理平台、视频分析软件和球迷互动应用。其关键目的是支持决策，无论是用于教练指导、伤病预防、裁判工作还是行政管理。
Information systems must handle data accurately and efficiently because decisions made from poor data can harm performance, health or fairness. For example, a false start detected by a timing system must be recorded within milliseconds to ensure a fair race.
信息系统必须准确高效地处理数据，因为基于不良数据做出的决策可能损害运动表现、健康或公平性。例如，计时系统检测到的抢跑必须在毫秒内被记录，以确保比赛公平。
2. The Data Handling Cycle | 数据处理周期
Data handling in information systems follows a continuous cycle with four main stages: input, processing, output and storage. Input involves collecting raw data, such as a heart rate reading from a chest strap. Processing transforms the data, for example by converting beats per minute into a training zone percentage. Output presents the result to a user, such as a graph on a coach's tablet, and storage saves the data for future use.
信息系统中的数据处理遵循一个连续的周期，包括四个主要阶段：输入、处理、输出和存储。输入涉及收集原始数据，例如胸带记录的心率读数。处理将数据转化，例如把每分钟心跳次数转换为训练区间百分比。输出将结果呈现给用户，例如教练平板电脑上的图表，存储则将数据保存以供将来使用。
This cycle is iterative rather than linear. Data stored after one event can become input for a later analysis. For instance, a season's worth of sprint times stored in a database can be processed again to identify long-term trends.
这个周期是循环往复的，而不是线性的。一次事件后存储的数据可以成为后续分析的输入。例如，存储在数据库中的整个赛季短跑成绩可以被再次处理，以识别长期趋势。
Input → Processing → Output → Storage → Input
3. Types of Data in Sport | 体育中的数据类型
Sports data can be quantitative or qualitative. Quantitative data are numerical and can be measured objectively. They are divided into discrete data, such as the number of goals scored, and continuous data, such as a runner's finish time of 9.58 s. Qualitative data are non-numerical and describe qualities or categories, such as a coach's rating of technique or a player's preferred foot.
体育数据可以是定量的或定性的。定量数据是数值型的，可以客观测量。它们分为离散数据，例如进球数，和连续数据，例如跑步者 9.58 秒的完赛时间。定性数据是非数值型的，描述性质或类别，例如教练对技术的评分或球员的惯用脚。
Understanding data type is essential because it determines which processing and analysis methods are appropriate. Discrete data are often counted, continuous data are measured, and qualitative data are usually coded into categories before statistical analysis.
理解数据类型至关重要，因为它决定了哪些处理和分析方法是合适的。离散数据通常被计数，连续数据被测量，定性数据通常在进行统计分析前先编码为类别。
| Data type | Example in sport | Processing method |
|---|---|---|
| Quantitative discrete | Number of passes completed | Counting, frequency tables |
| Quantitative continuous | 400 m split time | Mean, standard deviation |
| Qualitative nominal | Playing position | Coding, bar charts |
| Qualitative ordinal | Effort rating: low, medium, high | Ranking, median |
4. Data Collection Methods | 数据收集方法
Data collection in sport can be manual or automated. Manual methods include observation checklists, notational analysis by a scout, and questionnaires completed by athletes. Automated methods include global positioning system (GPS) trackers, accelerometers, force plates, heart rate monitors and optical tracking cameras.
体育中的数据收集可以是手动或自动的。手动方法包括观察检查表、球探的符号分析以及运动员填写的问卷。自动方法包括全球定位系统 (GPS) 追踪器、加速度计、测力台、心率监测器和光学追踪摄像机。
Each method has strengths and limitations. Manual methods are flexible and low-cost but may be subjective and time-consuming. Automated methods produce large volumes of objective data quickly, but require calibration, reliable hardware and trained operators. Choosing the right method depends on the research question or performance objective.
每种方法都有优点和局限。手动方法灵活且成本低，但可能主观且耗时。自动方法能快速产生大量客观数据，但需要校准、可靠的硬件和经过培训的操作人员。选择正确的方法取决于研究问题或表现目标。
- Wearable GPS vest: measures total distance, sprint distance and accelerations.
- Force plate: measures jump height and ground reaction force.
- Video analysis: records joint angles and movement patterns.
- Questionnaire: records perceived exertion or recovery status.
可穿戴 GPS 背心：测量总距离、冲刺距离和加速度。测力台：测量跳跃高度和地面反作用力。视频分析：记录关节角度和动作模式。问卷：记录主观用力感或恢复状态。
5. Data Storage and Organisation | 数据存储与组织
Sports organisations store data in structured databases, often using relational database management systems (RDBMS). A database organises data into tables made of records (rows) and fields (columns). Each record represents one entity, such as one athlete, and each field stores one attribute, such as name, date of birth or test result.
体育组织将数据存储在结构化数据库中，通常使用关系数据库管理系统 (RDBMS)。数据库将数据组织成由记录（行）和字段（列）组成的表格。每条记录代表一个实体，例如一名运动员，每个字段存储一个属性，例如姓名、出生日期或测试结果。
A primary key uniquely identifies each record, such as an athlete ID number. A foreign key links tables together, for example linking an athlete table to a performance test table. This structure reduces duplication and makes retrieval efficient. Cloud storage is increasingly used so that coaches, medical staff and analysts can access the same data from different locations.
主键唯一标识每条记录，例如运动员 ID 号。外键将不同的表连接起来，例如将运动员表与表现测试表关联。这种结构减少了重复，使检索更高效。云存储的使用越来越多，使教练、医疗人员和分析师可以从不同地点访问相同的数据。
| Athlete ID (Primary key) | Name | Date of birth | Team ID (Foreign key) |
|---|---|---|---|
| A001 | Sarah Chen | 2006-03-12 | T07 |
| A002 | Liam Patel | 2005-11-02 | T07 |
6. Data Processing and Analysis | 数据处理与分析
Processing converts raw data into meaningful information through sorting, filtering, calculating and summarising. In sport, common calculations include averages, ranges, percentages and rates. For example, a coach may calculate mean heart rate over a training session or filter sprint times below a threshold to identify high-intensity efforts.
处理通过排序、筛选、计算和汇总将原始数据转化为有意义的信息。在体育中，常见的计算包括平均值、极差、百分比和速率。例如，教练可以计算一次训练课的平均心率，或筛选出低于某阈值的短跑成绩以识别高强度努力。
Statistical analysis helps to identify patterns and make comparisons. Measures of central tendency, such as mean, median and mode, summarise typical performance. Measures of dispersion, such as range and standard deviation, show consistency. A lower standard deviation in a swimmer's lap times indicates more consistent pacing.
统计分析有助于识别模式并进行比较。集中趋势的度量，如平均数、中位数和众数，总结典型表现。离散程度的度量，如极差和标准差，显示一致性。游泳运动员单圈时间的标准差越低，说明配速越稳定。
Mean = (Sum of all values) ÷ Number of values
Data processing can also involve mathematical modelling. For example, maximum heart rate is often estimated using the formula 220 − age. Training intensity zones are then calculated as percentages of this estimated maximum, allowing coaches to prescribe aerobic or anaerobic work.
数据处理还可以涉及数学建模。例如，最大心率通常使用公式 220 − 年龄 来估算。训练强度区间随后按此估算最大值的百分比计算，使教练能够规定有氧或无氧训练。
7. Data Quality and Validation | 数据质量与验证
Data quality refers to accuracy, completeness, validity, reliability and timeliness. Accurate data reflect the true value, complete data have no missing fields, valid data follow accepted rules, reliable data are consistent when measured again, and timely data are available when needed. Poor data quality leads to incorrect conclusions.
数据质量指准确性、完整性、有效性、可靠性和及时性。准确的数据反映真实值，完整的数据没有缺失字段，有效的数据遵循公认规则，可靠的数据在再次测量时保持一致，及时的数据在需要时可用。数据质量差会导致错误结论。
Validation checks prevent incorrect data from entering a system. Examples include range checks, such as rejecting a heart rate above 250 bpm, format checks, such as requiring a date in YYYY-MM-DD form, and presence checks, which ensure that a required field is not left blank. Verification, such as double entry of competition results, confirms that data have been entered correctly.
验证检查防止错误数据进入系统。例子包括范围检查，例如拒绝超过 250 次/分的心率；格式检查，例如要求日期为 YYYY-MM-DD 格式；以及存在性检查，确保必填字段不为空。验证，例如竞赛成绩的二次录入，则确认数据已正确输入。
- Range check: 0 ≤ score ≤ 10 in gymnastics.
- Format check: time entered as HH:MM:SS.ss.
- Presence check: athlete ID cannot be empty.
范围检查：体操中 0 ≤ 分数 ≤ 10。格式检查：时间以 HH:MM:SS.ss 形式输入。存在性检查：运动员 ID 不能为空。
8. Data Security and Privacy | 数据安全与隐私
Sports information systems hold sensitive personal data, including health records, biometric measurements and contact details. Security measures protect data from unauthorised access, loss or corruption. Common methods include user authentication with passwords or biometric scans, access levels that restrict who can view or edit data, encryption of stored and transmitted data, and regular backups.
体育信息系统保存着敏感的个人数据，包括健康记录、生物特征测量和联系方式。安全措施保护数据免受未经授权的访问、丢失或损坏。常见方法包括使用密码或生物识别扫描进行用户身份验证、限制谁可以查看或编辑数据的访问级别、对存储和传输的数据进行加密以及定期备份。
Privacy is governed by regulations such as the UK General Data Protection Regulation (GDPR). Athletes must give informed consent before their data are collected, and organisations must state the purpose, storage period and who has access. Data should be anonymised for research where possible to protect identity.
隐私受英国《通用数据保护条例》(GDPR) 等法规的约束。在收集运动员数据前必须获得知情同意，组织必须说明目的、存储期限以及谁有权访问。在研究中应尽可能对数据进行匿名化处理，以保护身份。
A data breach in a sports club could expose medical histories or contract details. Therefore, access control is not just technical; staff training and clear policies are equally important for maintaining confidentiality and trust.
体育俱乐部中的数据泄露可能暴露病史或合同细节。因此，访问控制不仅是技术问题；员工培训和明确的政策对于维护保密性和信任同样重要。
9. Databases in Sports Organisations | 体育组织中的数据库
Relational databases allow different departments within a sports organisation to share consistent data. The medical team may record injury history, coaches record training attendance and performance results, and administrators record membership payments. All tables can be linked through common keys, reducing duplication and data entry errors.
关系数据库允许体育组织内不同部门共享一致的数据。医疗团队可以记录伤病史，教练记录训练出勤和表现结果，行政人员记录会员缴费。所有表格都可以通过公共键连接，减少重复和数据录入错误。
A well-designed database supports queries. For example, a query can identify all under-18 athletes who missed more than two training sessions in a month, or list players whose hamstring strength score fell below a set threshold. Such queries help staff act quickly on targeted information.
设计良好的数据库支持查询。例如，某查询可以找出一个月内缺席两次以上训练的 18 岁以下运动员，或列出腘绳肌力量得分低于设定阈值的球员。此类查询帮助工作人员根据针对性信息快速行动。
Cloud-based athlete management systems often combine database features with dashboards. This allows a coach to view current fitness levels, injury status and availability in one interface, and helps ensure that decisions are based on up-to-date records rather than memory.
基于云的运动员管理系统通常将数据库功能与仪表板结合起来。这使教练可以在一个界面中查看当前的体能水平、伤病状况和可用性，并有助于确保决策基于最新记录而不是记忆。
10. Output and Reporting | 输出与报告
Output is how processed data are communicated to users. Common outputs in sport include printed reports, on-screen dashboards, graphs, tables, heat maps and video overlays. The choice of output depends on the audience: an athlete may need simple visual feedback, while a sport scientist may require detailed statistical tables.
输出是处理后数据传达给用户的方式。体育中常见的输出包括打印报告、屏幕仪表板、图表、表格、热图和视频叠加。输出的选择取决于受众：运动员可能需要简单的视觉反馈，而运动科学家可能需要详细的统计表。
Good reporting highlights key performance indicators (KPIs) and trends rather than overwhelming users with raw data. For example, a weekly training report might show total distance, average intensity and injury risk score, with a short written summary. Visual formats such as line graphs for speed progression or pie charts for time in heart rate zones make data easier to interpret.
好的报告突出关键绩效指标 (KPI) 和趋势，而不是用原始数据淹没用户。例如，周训练报告可以显示总距离、平均强度和损伤风险评分，并附有简要的文字总结。折线图显示速度进展或饼图显示心率区间时间等可视化格式使数据更易于解读。
Reports should also be accurate and clearly labelled, with units of measurement, date of collection and athlete name. Misleading scales or omitted context can distort interpretation. Ethical reporting means presenting both positive and negative changes honestly.
报告还应准确并清晰标注，包括测量单位、收集日期和运动员姓名。误导性的刻度或遗漏背景会扭曲解读。符合伦理的报告意味着诚实地呈现积极和消极的变化。
11. Applications in Performance Analysis | 表现分析中的应用
Information systems are central to modern performance analysis. In team sports, analysts code match events such as passes, tackles, shots and turnovers. These data are processed to calculate possession percentage, passing accuracy and defensive effectiveness. Coaches use the output to adjust tactics and set training objectives.
信息系统是现代表现分析的核心。在团队运动中，分析师对传球、抢断、射门和失误等比赛事件进行编码。这些数据经过处理，用于计算控球率、传球准确率和防守效率。教练利用输出调整战术并设定训练目标。
In individual sports, data handling supports biomechanical feedback. For example, a sprinter's reaction time, step frequency and ground contact time are collected by sensors and processed into a technique report. A swimmer's stroke rate and distance per stroke can be compared with race video to improve efficiency.
在个人项目中，数据处理支持生物力学反馈。例如，短跑运动员的反应时间、步频和触地时间由传感器收集，并处理成技术报告。游泳运动员的划频和每次划水距离可以与比赛视频比较，以提高效率。
Injury prevention programmes use training load data. The acute:chronic workload ratio compares this week's load to the rolling average of the previous four weeks. A sudden spike is processed as an alert, prompting the coach to reduce intensity or add recovery. This is a practical example of data input, processing and output protecting athlete welfare.
损伤预防计划使用训练负荷数据。急慢性工作负荷比将本周负荷与前四周滚动平均值进行比较。突然的峰值被处理为警报，提示教练降低强度或增加恢复。这是数据输入、处理和输出保护运动员健康的一个实际例子。
12. Limitations and Ethical Considerations | 局限性与伦理考量
Despite their benefits, information systems have limitations. Equipment can fail, sensors may be worn incorrectly, and data can be misinterpreted if the context is ignored. Data alone do not replace coaching judgement; they are a tool that must be combined with experience and observation.
尽管信息系统有诸多好处，但也有局限。设备可能故障，传感器可能佩戴不当，如果忽略背景，数据可能被误解。数据本身不能取代教练判断；它是一种工具，必须与经验和观察相结合。
Ethical considerations include informed consent, transparency, data minimisation and fairness. Athletes should know what data are collected and why. Data should not be used to discriminate unfairly, such as denying selection solely based on a single biometric field. Young athletes need additional protection because their data are especially sensitive.
伦理考量包括知情同意、透明度、数据最小化和公平性。运动员应知道收集了哪些数据以及为什么收集。数据不应被用于不公平歧视，例如仅仅根据单一生物特征字段就拒绝入选。年轻运动员需要额外保护，因为他们的数据尤其敏感。
Finally, over-reliance on technology can reduce coach–athlete interaction. An effective information system supports human decisions; it does not make them. A balance between objective data and subjective feel remains essential in sport.
最后，过度依赖技术会减少教练与运动员之间的互动。有效的信息系统支持人的决策，而不是代替人做决策。客观数据与主观感觉之间的平衡在体育中仍然至关重要。
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