Aprenda Generating Performance Reports | Automating Coaching Tasks

Regular performance reports are essential for tracking athlete progress, identifying areas for improvement, and communicating results to both athletes and stakeholders. Creating these reports manually can be time-consuming and prone to error, especially as the amount of data grows. By using Python, you can automate the process, ensuring consistency, accuracy, and efficiency in generating detailed, customized reports for each athlete.


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def generate_athlete_report(name, speeds, attendance, improvements):
    avg_speed = sum(speeds) / len(speeds) if speeds else 0
    attendance_rate = (sum(attendance) / len(attendance)) * 100 if attendance else 0
    improvement = improvements[-1] - improvements[0] if len(improvements) > 1 else 0
    report = (
        f"Athlete: {name}\n"
        f"Average Speed: {avg_speed:.2f} m/s\n"
        f"Attendance Rate: {attendance_rate:.1f}%\n"
        f"Improvement: {improvement:.2f} units\n"
    )
    return report

# Example usage:
speeds = [5.2, 5.5, 5.7, 5.8]
attendance = [1, 1, 0, 1, 1]  # 1 = attended, 0 = missed
improvements = [10, 12, 13, 15]
print(generate_athlete_report("Jordan Lee", speeds, attendance, improvements))

When you generate performance reports, you often want to include several key metrics such as average speed, attendance rate, and improvement over time. Combining these metrics into a single, clear report provides a comprehensive overview that is easy to interpret. You can also tailor the report's content and formatting depending on the audience—for example, focusing on technical details for coaches or using more visual summaries for parents and athletes. Python allows you to automate not only the calculation of these metrics but also the customization of the report output to fit different communication needs.


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athletes = [
    {
        "name": "Jordan Lee",
        "speeds": [5.2, 5.5, 5.7, 5.8],
        "attendance": [1, 1, 0, 1, 1],
        "improvements": [10, 12, 13, 15]
    },
    {
        "name": "Morgan Smith",
        "speeds": [4.8, 5.0, 5.1, 5.3],
        "attendance": [1, 1, 1, 1, 0],
        "improvements": [9, 10, 12, 13]
    }
]

def generate_athlete_report(name, speeds, attendance, improvements):
    avg_speed = sum(speeds) / len(speeds) if speeds else 0
    attendance_rate = (sum(attendance) / len(attendance)) * 100 if attendance else 0
    improvement = improvements[-1] - improvements[0] if len(improvements) > 1 else 0
    report = (
        f"Athlete: {name}\n"
        f"Average Speed: {avg_speed:.2f} m/s\n"
        f"Attendance Rate: {attendance_rate:.1f}%\n"
        f"Improvement: {improvement:.2f} units\n"
    )
    return report

for athlete in athletes:
    report = generate_athlete_report(
        athlete["name"],
        athlete["speeds"],
        athlete["attendance"],
        athlete["improvements"]
    )
    print(report)

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 2

Pergunte à IA

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Suggested prompts:

Can you explain how the attendance rate is calculated in the report?

How can I customize the report to include additional metrics?

Can you show how to generate reports for a larger group of athletes?

Deslize para mostrar o menu


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def generate_athlete_report(name, speeds, attendance, improvements):
    avg_speed = sum(speeds) / len(speeds) if speeds else 0
    attendance_rate = (sum(attendance) / len(attendance)) * 100 if attendance else 0
    improvement = improvements[-1] - improvements[0] if len(improvements) > 1 else 0
    report = (
        f"Athlete: {name}\n"
        f"Average Speed: {avg_speed:.2f} m/s\n"
        f"Attendance Rate: {attendance_rate:.1f}%\n"
        f"Improvement: {improvement:.2f} units\n"
    )
    return report

# Example usage:
speeds = [5.2, 5.5, 5.7, 5.8]
attendance = [1, 1, 0, 1, 1]  # 1 = attended, 0 = missed
improvements = [10, 12, 13, 15]
print(generate_athlete_report("Jordan Lee", speeds, attendance, improvements))


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athletes = [
    {
        "name": "Jordan Lee",
        "speeds": [5.2, 5.5, 5.7, 5.8],
        "attendance": [1, 1, 0, 1, 1],
        "improvements": [10, 12, 13, 15]
    },
    {
        "name": "Morgan Smith",
        "speeds": [4.8, 5.0, 5.1, 5.3],
        "attendance": [1, 1, 1, 1, 0],
        "improvements": [9, 10, 12, 13]
    }
]

def generate_athlete_report(name, speeds, attendance, improvements):
    avg_speed = sum(speeds) / len(speeds) if speeds else 0
    attendance_rate = (sum(attendance) / len(attendance)) * 100 if attendance else 0
    improvement = improvements[-1] - improvements[0] if len(improvements) > 1 else 0
    report = (
        f"Athlete: {name}\n"
        f"Average Speed: {avg_speed:.2f} m/s\n"
        f"Attendance Rate: {attendance_rate:.1f}%\n"
        f"Improvement: {improvement:.2f} units\n"
    )
    return report

for athlete in athletes:
    report = generate_athlete_report(
        athlete["name"],
        athlete["speeds"],
        athlete["attendance"],
        athlete["improvements"]
    )
    print(report)

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 3. Capítulo 2

Generating Performance Reports

1. What information is typically included in an athlete performance report?

2. How can Python help customize reports for different audiences?

3. What is the advantage of automating report generation?

Generating Performance Reports

1. What information is typically included in an athlete performance report?

2. How can Python help customize reports for different audiences?

3. What is the advantage of automating report generation?