The sports analytics market was valued at USD 2.45 billion in 2023 and is projected to reach USD 6.73 billion by 2027, growing at an average annual rate of 22.13%. Sports analytics covers areas like player performance, injury prevention, scouting, and fan engagement.

This rapid growth indicates that more sports organizations are using data analytics to stay competitive, improve player performance, and connect with fans. Teams and clubs rely on data to make better decisions about player development, game strategies, and marketing efforts.

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About dataset :

The dataset contains information about the world cup 22 tournament, it has all the columns about the teams performance, about the score in each innings, the matches played in each and every venue and a lot more about the teams performance.

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How to Enhance Sports Performance using Analytics?

When initiating sports analytics to enhance performance and fan engagement, it's crucial to adhere to the 5 essential factors of Data Analysis:

1. Determine Stakeholders: Start by identifying the key users or stakeholders who will utilize the analytics.

2. Create Empathy Map: Develop an empathy map to understand users' goals, challenges, and pain points effectively.

3. Identify KPI’s Metrics: Pinpoint the most important metrics or Key Performance Indicators (KPIs) crucial for measuring success.

4. Define Objectives: Gain a clear understanding of the objectives and goals that the analysis aims to achieve.

5. Pose Business Questions: Ask pertinent business questions that will guide the analysis and provide valuable insights for decision-making.

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Step 1: Identifying the Users for This Analysis

Define the user or stakeholder who will use the sports dataset. In our case, we’ll use a Sports Performance Analyst. Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story.

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User Profile: Sports Performance Analyst

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Responsibilities:

1. Analyze player performance, health metrics, and game statistics to support coaching decisions.

2. Provide insights and recommendations based on thorough analysis of internal and external data.

3. Evaluate risks (e.g., injury risks) and opportunities to optimize player performance.

4. Develop and implement performance improvement plans.

5. Collaborate with coaching staff and medical teams to align performance objectives and ensure effective implementation.

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Needs:

1. Access to comprehensive and up-to-date performance data for informed decision-making.

2. User-friendly analytics tools and dashboards for efficient data analysis and visualization.

3. Advanced analytical capabilities to identify trends, patterns, and potential improvements.

4. Insights into player health, opponent strategies, and game dynamics.

5. Timely and relevant information to support proactive performance enhancement.

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Challenges:

1. Keeping pace with rapidly changing game dynamics and player conditions.

2. Balancing short-term performance objectives with long-term player development.

3. Managing and interpreting vast amounts of data to extract actionable insights.

4. Ensuring alignment and buy-in from coaching and medical staff.

5. Adapting strategies to address unforeseen challenges and game situations effectively.

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Step 2: Design Empathy Map

Understanding the Sports Performance Analyst is important, and that's where an empathy map comes in handy. This tool helps us grasp their feelings, goals, and challenges, giving us insights into their work beyond the usual tasks. By putting ourselves in their shoes, we can create a data story that not only works well but also connects with them on a personal level. It's about finding the right balance and designing our analysis with their perspective in mind.

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Step 3: Identify the Key Performance Indicators (KPIs)

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Step 4: Understand the Goals & Objectives of User

Based on the insights from the Empathy Map and KPIs, we need to define the goals and objectives of the Sports Performance Analyst user persona. This ensures that our data story aligns with their needs for effective decision-making.

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Objectives:

- Provide strategic insights and recommendations based on comprehensive performance analysis and data-driven insights.

- Identify emerging player trends and evaluate game opportunities.

- Support coaching decisions to drive team growth and competitive advantage.

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Goals:

- Enhance team performance and competitiveness.

- Deliver actionable insights and recommendations for game strategies.

- Analyze player trends and evaluate opponent dynamics.

- Identify growth opportunities to support performance improvement.

- Collaborate with coaching and medical teams to align performance objectives and drive successful implementation of strategies.

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Step 5: Pose Business Questions

1. What are the venues with count of matches played during the tournament?

Python Code :

# Group by 'venue' and count matches, then sort and reset index
matches_count_by_venue = df.groupby('venue').size().sort_values(ascending=False).reset_index()
matches_count_by_venue.columns =['venue', 'match_count']  # Rename columns

# Set figure size and style
plt.figure(figsize=(10, 6))
sns.set_style('darkgrid')

# Create and display the bar plot
sns.barplot(x='match_count', y='venue', data=matches_count_by_venue, orient='h')
plt.title('Matches Played at Venues')
plt.xlabel('Number of Matches')
plt.ylabel('Venue')
plt.show()

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Visualization :

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2. What is the total number of matches won by each country?

Python Code :

# Group by 'winner' and count matches won, then sort and reset index
matches_won_by_country = df.groupby('winner').size().sort_values(ascending=False).reset_index()
matches_won_by_country.columns = ['country', 'matches won']  # Rename columns

# Set figure size
plt.figure(figsize=(10, 8))

# Create and display the bar plot
sns.barplot(x='country', y='matches won', data=matches_won_by_country)
plt.title('Matches Won by Country')
plt.xlabel('Countries')
plt.ylabel('Matches Won')
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.show()

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Visualization :

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3. What is the frequency of decision taken after the toss ?

Python Code :

# Group by 'toss decision' and count occurrences, then sort and reset index
toss_decisions = df.groupby('toss decision').size().sort_values(ascending=False).reset_index()
toss_decisions.columns = ['decision', 'count']  # Rename columns

# Set figure size
plt.figure(figsize=(10, 6))

# Create and display the bar plot with a specific color palette
sns.barplot(x='decision', y='count', data=toss_decisions, palette='Dark2')
plt.title('Toss Decisions Frequency')
plt.xlabel('Decisions')
plt.ylabel('Count')
plt.show()

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Visualization :

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4. What does the comparison between first and second innings scores look like?

Python Code :

# Set figure size
plt.figure(figsize=(10, 8))

# Create and display the box plot for first and second innings scores
sns.boxplot(data=df[['first innings score', 'second innings score']])

# Label the axes and set the title
plt.xlabel('Innings')
plt.ylabel('Score')
plt.title('Comparison of First Innings Score and Second Innings Score')

# Set custom x-axis labels
plt.xticks([0, 1], ['First Innings', 'Second Innings'])

# Display the plot
plt.show()

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Visualization :

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5. How do the innings scores differ across venues?

Python Code :

# Creating a combined DataFrame with both innings scores
scores_by_venue = pd.melt(df, id_vars=['venue'], value_vars=['first innings score', 'second innings score'], 
                          var_name='innings', value_name='score')

# Set figure size
plt.figure(figsize=(12, 8))

# Plotting the boxplot
sns.boxplot(x='venue', y='score', data=scores_by_venue, hue='innings')
plt.xlabel('Venue')
plt.ylabel('Score')
plt.title('Comparison of First Innings Score and Second Innings Score by Venue')

# Rotate x-axis labels for better readability
plt.xticks(rotation=90)

# Show the plot
plt.show()

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Visualization :

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6. What are the count of winning methods across different venue?

Python Code :

# Create a new DataFrame with the required columns, dropping rows with missing values
won_by_venue = df[['venue', 'won by']].dropna()

# Set figure size
plt.figure(figsize=(12, 6))

# Plot the count plot with 'won by' method by venue, ordered by the count of venues
sns.countplot(x='venue', hue='won by', data=won_by_venue, order=won_by_venue['venue'].value_counts().index)

# Add labels and title
plt.xlabel('Venue')
plt.ylabel('Count')
plt.title('Won by Method by Venue')

# Rotate x-axis labels for better readability
plt.xticks(rotation=90)

# Show the plot
plt.show()

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Visualization :

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Conclusion:

This sports analytics use case outlines a structured approach to leveraging data analytics for enhancing team performance, optimizing game strategies, and engaging analytics effectively in the sports. By focusing on the needs and challenges of a Sports Performance Analyst, we can develop tailored analytics solutions that drive informed decision-making and achieve measurable outcomes.