Summary
Unlocking the Secrets: How to Predict Who Will Excel in Gymnastics Classes delves into data-driven strategies to enhance student performance and club enrollment. These insights are invaluable for coaches and administrators aiming to optimize their programs. Key Points:
- **Enhanced Attendance Tracking and Personalized Outreach:** Implement a system to monitor attendance, identify at-risk students, and provide targeted support for better engagement.
- **Boosting Club Enrollment with Spreadsheets:** Centralize enrollment data in spreadsheets for automated management, enabling efficient communication and personalized promotions.
- **Data Manipulation and Visualization for Decision-Making:** Use data visualization tools to create dashboards that present key metrics, facilitating informed choices on curriculum adjustments and resource allocation.
At the start of 2022, we discovered that several children from our kindergarten were enrolling in a nearby gymnastics course. This program is organized by a local sports club. Without hesitation, we enrolled our son as well.
Parents are involved in setting up various obstacles on the ground or at different heights for the children to engage with. The course caters to kids aged one to five years old, meaning even toddlers who are just learning to walk can participate. Consequently, there is a mix of simple and highly intricate obstacles. Sometimes, it's simply about giving them something fun to interact with.
The diversity in obstacle complexity ensures that every child finds something suitable for their skill level, making it an enjoyable and beneficial experience for all participants.
Key Points Summary
Insights & Summary
- Automated systems using depth imagery like Kinect are utilized to analyze pommel horse moves.
- Segmentation techniques identify critical depths of interest in athletic performance data.
- AI is enhancing athlete training by offering personalized workout and nutrition plans based on comprehensive data analytics.
- Performance analysis tools help gymnasts and coaches assess strengths, weaknesses, and techniques effectively.
- Data analytics can be used to study common mastery elements and predict scoring likelihoods in gymnastics routines.
- Fujitsu's Human Motion Analytics (HMA) platform provides advanced data analysis capabilities for human motion studies.
With advancements in AI and data analytics, the realm of gymnastics is seeing transformative changes. Automated systems like Kinect help precisely analyze moves, while AI-driven personalization ensures athletes receive tailored training programs. These innovations not only enhance performance but also make training healthier and more efficient.
Extended Comparison:| Feature | Kinect-based Depth Imagery Analysis | Segmentation Techniques in Athletic Performance Data | AI-Powered Personalized Workout and Nutrition Plans | Performance Analysis Tools for Strengths, Weaknesses, and Techniques | Data Analytics for Predicting Scoring Likelihoods | Fujitsu's Human Motion Analytics (HMA) Platform |
|---|---|---|---|---|---|---|
| Description | Kinect sensors capture depth imagery to analyze gymnastic moves on the pommel horse. | Identifies critical depths of interest in athletic performance data using segmentation techniques. | Utilizes AI to create customized workout and nutrition plans based on comprehensive data analytics. | Includes tools that help gymnasts and coaches identify strengths, weaknesses, and improve techniques effectively. | Employs data analytics to study common mastery elements and predict scoring likelihoods in gymnastics routines. | A platform offering advanced capabilities for analyzing human motion with precision. |
| Latest Trends | Increasing use of machine learning models to enhance accuracy of movement analysis. | IoT integration is becoming more prevalent for real-time performance monitoring. | The rise of wearable technology providing continuous health metrics feeding into AI systems. | The combination of virtual reality (VR) with these tools for immersive training experiences. | Incorporation of historical competition data sets to increase prediction accuracy. | The application of deep learning algorithms enhancing the granularity of motion analysis. |
| Authoritative Views | NVIDIA research shows advancements in real-time 3D pose estimation improving performance insights. | Athletic trainers emphasize the importance of segmenting muscle groups while analyzing movements as per ACSM guidelines. | A report by McKinsey highlights a significant improvement in athlete performance through personalized AI-driven plans. | The International Gymnastics Federation supports tech adoption for better coaching outcomes based on analytical tools. | Deloitte’s sports analytics team stresses the value this brings in predicting success rates accurately. | HARVARD Business Review notes Fujitsu HMA’s role in pioneering precise human movement analysis. |
Enhanced Attendance Tracking and Personalized Outreach
**Enhanced Attendance Monitoring System**
To enhance the efficiency of our attendance process, we implemented a digital attendance tracking system that replaced the traditional paper sheets. By utilizing a digital spreadsheet, we were able to streamline the recording of children's attendance by their names and dates. This advancement eliminated issues related to legibility and ensured that our records remained accurate and up-to-date.
**Tailored Reminders for Improved Participation**
In an effort to boost attendance rates and reduce instances of missed enrollments, we introduced personalized reminders sent directly to parents via email or SMS. These tailored notifications provided detailed information about upcoming sessions, serving as an effective tool to engage parents and promote consistent participation.
Boosting Club Enrollment: Leveraging Spreadsheets for Efficient Data Management
The efficient management of participant data was greatly enhanced by the use of a spreadsheet. This tool facilitated easy filtering of non-participating individuals, thereby streamlining the communication process with eligible participants for sports club enrollment. Additionally, the spreadsheet captured fluctuations in participation due to various factors, providing valuable insights into attendance patterns. These insights were instrumental in identifying potential new members for the gym course, thus aiding in strategic planning and resource allocation.Data Manipulation and Visualization for Informed Decision-Making
In my approach to data preprocessing, I leveraged the powerful capabilities of Pandas, a renowned Python library that excels in data manipulation and analysis. With its comprehensive suite of functions, I efficiently handled tasks such as data cleaning, transformation, and integration. This ensured that the dataset was well-prepared for subsequent steps.To visualize the processed data effectively, I turned to Matplotlib, another robust Python library known for its versatility in creating static, animated, and interactive visualizations. Utilizing Matplotlib's extensive plotting capabilities allowed me to generate a variety of plots, charts, and graphs with ease. This not only facilitated a deeper understanding of the data but also enabled clear communication of insights derived from the analysis.
pandas openpyxl plotly-express plotly scikit-learn statsmodelsTo read Excel files, we utilize pandas and openpyxl. For creating visuals, plotly and plotly-express are our tools of choice. When it comes to calculating the mean squared error, we turn to scikit-learn, even though it's a large library for this single function. Finally, statsmodels is employed for both training and prediction purposes.
I have designed several helper functions to facilitate reuse and potential refactoring. Our first step involves merging multiple tables into a unified virtual table to streamline the analysis and model training process.
def get_participation(xls: dict[str, pd.DataFrame]) -> dict[str, dict[str, int]]: participation = {} for sheet_name, df in xls.items(): for index, row in df.iterrows(): if index < 2: continue first_name = row['Vorname/Kind'] last_name = row['Name'] full_name = f"{first_name} {last_name}" for col in df.columns: if type(col) is datetime.datetime: key = f"{col.year}-{str(col.month).zfill(2)}-{str(col.day).zfill(2)}" if row[col] == 1.0: encoded_value = 1 else: encoded_value = 0 if key in participation: participation[key][full_name] = encoded_value else: participation[key] = {full_name: encoded_value} return participation We begin by loading the Excel file and navigating through each table within it. The first table contains the header information, while the second aggregates data for all participants on a specific date; hence, we can disregard this particular record. Next, we extract the first and last names from each row and merge them into a single variable called "full_name." Following this, we examine each column in the Excel table to verify if it represents a date. We then initialize a variable named "variablekey" with zeros corresponding to the current date and inspect its value. If the cell is encoded with 1.0 (a floating-point number), it is converted to an integer 1; otherwise, it remains zero. This encoding occurs due to missing values or NaNs present in other cells.
Moving forward, our process involves checking every column of data systematically to ensure accuracy and consistency in capturing participant information accurately based on their attendance dates. By converting these floating-point encodings appropriately, our dataset remains clean and reliable for further analysis.
In summary, meticulous handling of Excel tables ensures that only pertinent data is processed—skipping headers and aggregate records while focusing on individual entries' names and attendance dates—thereby maintaining integrity throughout our analytics workflow.
Following this transformation, the result is a dictionary where each entry corresponds to a specific date. Nested within each date entry is another dictionary that assigns either a zero or one to each full name, now abbreviated for simplicity.
{ "2024-04-05": { "C E": 0, "E S": 0, "E K": 0, "F B": 1, "F H": 1, "F R": 0 } }When integrating this information into a pandas data frame, they extract the surname and, for each y value, record the date (along with its corresponding label) in the index. However, this format is currently impractical for our usage.
def prepare_data(participation: dict[str, dict[str, int]]) -> pd.Series: df = pd.DataFrame(participation).T # Fill missing participants that later joined with "0" df = df.fillna(0) # Drop the families where each date is "0" df = df.loc[:, ~(df == 0).all(axis=0)] # drop all dates where all families are 0 df = df.loc[~(df == 0).all(axis=1)] # Sort the DataFrame by its index (the dates) df = df.sort_index() # Sum up the participation of families for each date df = df.sum(axis=1) # Convert the index to date df.index = pd.to_datetime(df.index).to_period('W') return dfData Manipulation: Transposing DataFrames and Handling Missing Values
Data manipulation is a crucial aspect of data analysis, often requiring transforming and cleaning the dataset to make it suitable for further analysis. One common task is flipping the rows and columns of a DataFrame, which can be achieved by transposing the DataFrame and resetting its index. This operation is particularly useful when you need to switch between long-format and wide-format datasets or when preparing the data for visualization tools that require a specific layout.Another frequent requirement in data preprocessing is handling missing values. Missing data can significantly skew your results if not addressed properly. Filling these gaps with appropriate values ensures consistency across your dataset and prevents errors during subsequent analysis stages. For instance, filling missing values with "0" can be an effective strategy, especially when dealing with numerical datasets where zero represents an absence or default state.
By implementing these techniques—transposing rows and columns along with filling missing values—you create a robust foundation for accurate and insightful data analysis. These steps are fundamental in preparing your dataset for advanced analytics, ensuring that every piece of information is accounted for and correctly formatted before diving into more complex tasks like statistical modeling or machine learning algorithms.
First, we transform the string index key into a Timestamp index and then further convert it to a Period. This period spans from one Friday to the next, denoted by W for "Weekly" in our context.}
{Next, we'll divide the dataset using the well-known 80–20 ratio for training and testing purposes. However, it's important to note that the dataset contains only 58 dates, which could lead to lower model accuracy.
def split(df: pd.Series) -> tuple[pd.Series, pd.Series]: # Split the data into training and test sets train = df[:int(0.8 * len(df))] test = df[int(0.8 * len(df)):] return train, testFirst, we will train our model using the training dataset to ensure it learns the underlying patterns. Following this step, we will validate its performance by comparing predictions with the test set data. Subsequently, we’ll leverage this trained model to perform future forecasts.
def train_model(train: pd.Series, test: pd.Series) -> tuple[pd.Series, ARIMAResults]: # Fit an ARIMA model to the training data model = ARIMA(train, order=(5, 1, 0)) model_fit = model.fit() # Use the fitted model to make predictions on the test data predictions = model_fit.predict(start=min(test.index), end=max(test.index), dynamic=False) return predictions, model_fitPrior to this, I had no experience working with time series data. Consequently, I am unable to provide specific details about the model or its parameters. My approach is based on recommendations from online sources and Github Copilot. To ensure accurate index mapping, it's crucial to align the start and end values with those of the test data frame. This alignment allows us to return predictions for subsequent plotting.
At this juncture, our model has been successfully trained. The next step involves transforming all the data into a format that's compatible with Plotly Express for visualization purposes. We'll start by converting the training dataset accordingly.
def build_train_for_plot(train: pd.Series) -> pd.DataFrame: train_df = train.to_frame() train_df['data_type'] = 'Train Data' train_df.rename(columns={0: 'value'}, inplace=True) train_df.index = pd.date_range(start=min(train_df.index).end_time, end=max(train_df.index).end_time, periods=len(train_df)) return train_dfData Manipulation and Preparation
To accommodate the limitations of plotly-express, a transformation is applied where the series is converted into a data frame. This conversion includes adding a new column named 'data_type' with a constant value of "Train Data" and renaming the existing 0 column to 'value'. The index is then adjusted to fit within the appropriate range for training by incrementing from the lowest to highest values.For the test dataset, since it has distinct column names, it also requires renaming its relevant column to 'value'. Given that the start range cannot be accessed directly within this function and relies on an argument variable, we use the maximum value from the train set as an approximation due to a 20% split applied during data preparation. This calculated start range is provided through the main function at the conclusion of our analysis.
def build_test_for_plot(test: pd.Series, start: pd.Timestamp) -> pd.DataFrame: test_df = test.to_frame() test_df['data_type'] = 'Test Data' test_df.rename(columns={'predicted_mean': 'value'}, inplace=True) test_df.index = pd.date_range(start=start, periods=len(test_df), freq='W') return test_dfThe final phase of our transformation focuses on the forecasts we aim to achieve, considering the perspective of our customers as the primary target.
def build_forecast_for_plot( model_fit: ARIMAResults, len_test: int, start: pd.Timestamp) -> tuple[ pd.DataFrame, pd.DataFrame]: # Forecast the next 8 weeks and the test data forecast = model_fit.get_forecast(steps=8 + len_test) predicted_values = forecast.predicted_mean forecast_df = pd.DataFrame(predicted_values) forecast_df['data_type'] = 'Forecast' forecast_df.rename(columns={'predicted_mean': 'value'}, inplace=True) forecast_df.index = pd.date_range(start=start, periods=len(forecast_df), freq='W') confidence_intervals = forecast.conf_int() confidence_df = pd.DataFrame(confidence_intervals) confidence_df['data_type'] = 'Confidence Interval' confidence_df.rename(columns={0: 'lower', 1: 'upper'}, inplace=True) confidence_df.index = pd.date_range(start=start, periods=len(confidence_df), freq='W') return forecast_df, confidence_dfFirst, let's consider the necessary arguments we must provide. Besides the trained model itself, we also need to input the total length of the training set—this information isn't available within this function and must be supplied externally. Additionally, it's crucial to pass the starting point to ensure a proper shift or beginning. The function returns a tuple containing two data frames: one for forecast_df and another for confidence values related to the forecast. Both of these outputs are intended for plotting.
Leverage Feature Engineering and Hyperparameter Tuning for Robust Predictive Models
In the realm of predictive modeling, the initial phase often involves robust feature engineering. This crucial step entails transforming raw data into meaningful features that can significantly enhance the model's capability to make accurate predictions. The underlying assumption here is that a well-preprocessed and meticulously engineered dataset is available, which forms the backbone for training any sophisticated prediction model.Subsequently, another pivotal aspect that cannot be overlooked is hyperparameter tuning. The performance of any training model is intricately tied to how well its hyperparameters are optimized. Techniques such as cross-validation play a vital role in this process, ensuring that the chosen hyperparameters yield the best possible results in terms of model performance and predictive accuracy.
By integrating these two critical elements—feature engineering and hyperparameter tuning—the overall robustness and reliability of predictive models can be substantially enhanced, paving the way for more insightful and precise forecasting capabilities.
By utilizing the `conf_int` function on our forecast, we can derive the associated confidence values. This process employs the same principles as those used for generating the forecast itself. The final element that remains to be addressed is plotting the results.
def plot(train_df: pd.DataFrame, test_df: pd.DataFrame, forecast_df: pd.DataFrame, confidence_df: pd.DataFrame) -> Figure: # create a new figure fig = go.Figure() # Add the training data to the plot fig.add_trace(go.Scatter( x=train_df.index, y=train_df['value'], mode='lines', showlegend=True, name='Training Data' )) # Add the test data to the plot fig.add_trace(go.Scatter( x=test_df.index, y=test_df['value'], mode='lines', showlegend=True, name='Test Data' )) # Add the forecast to the plot fig.add_trace(go.Scatter( x=forecast_df.index, y=forecast_df['value'], mode='lines', showlegend=True, name='Forecast' )) # Add the confidence intervals to the plot fig.add_trace(go.Scatter( x=confidence_df.index, y=confidence_df['upper y'], mode='lines', line=dict(width=0), showlegend=True, name='upper' )) fig.add_trace(go.Scatter( x=confidence_df.index, y=confidence_df['lower y'], mode='lines', line=dict(width=0), fill='tonexty', fillcolor='rgba(0,0,0,0.2)', showlegend=True, name='lower' )) return figWe initiate by crafting a blank figure and subsequently incorporate a trace for each dataset we possess. Now, let's delve into the core function that amalgamates all the aforementioned elements cohesively.
def main(): # read Excel file xls = pd.read_excel('data/Eltern-Kind Turnen Freitag 16-17.xlsx', sheet_name=None) # xls is a dictionary where the keys are the sheet names and the values are the dataframes participation = get_participation(xls) # Prepare the data df = prepare_data(participation) # Split the data into training and test sets train_series, test_series = split(df) predictions, model_fit = train_model(train_series, test_series) train_df = build_train_for_plot(train_series) test_df = build_test_for_plot(predictions, max(train_df.index)) forecast_df, confidence_df = build_forecast_for_plot(model_fit, len(test_series), max(train_series.index).end_time) fig = plot(train_df, test_df, forecast_df, confidence_df) fig.show()There shouldn't be any unresolved issues here, as the main function merely integrates various components. Initially, I had everything lumped together within the main function, which made it quite chaotic. After refactoring, however, the structure became much clearer and more suitable for a blog format.
Here's the graph I've created using the data that I previously discussed.
What are we looking at here? The blue lines illustrate our training data, showing noticeable fluctuations. The red line within the grey shaded area represents the test data set we've excluded. Currently, participation remains relatively stable, making it increasingly challenging to accommodate new families. Meanwhile, the green lines project our forecasts for the upcoming weeks. The grey area signifies our model's confidence interval, which appears quite broad—ranging from 6.9 to 19.6 for the initial data point. Interestingly, our forecast converges to 13 participants, suggesting that "we have space left again"—a situation time will eventually confirm or refute.
To determine if more families could participate in our course, I initiated a comprehensive data processing procedure. Diving into time series forecasting was a new experience for me, but it proved to be an invaluable learning opportunity.
If you found this article insightful, please show your appreciation by clapping 13 times and feel free to connect with me on LinkedIn or GitHub. For those interested in exploring the code further, you can access it through the GitHub Repository. Thank you ❤
References
Real-time gymnast detection and performance analysis with a ...
An automated system to analyze pommel horse moves using depth imagery is developed. •. Kinect data is automatically segmented to identify depths of interest ...
Source： ScienceDirect.comAnalysis and Application of Gymnastics Sports Characteristics ...
The experimental summary of this paper is as follows: (1) Under the analysis of sample data 5000, it shows that the right segmentation curve is ...
Source： HindawiAI and the Olympic Dream: Transforming Gymnastics Judging ...
Enhanced Training: AI is transforming athlete training, offering personalized workout and nutrition plans based on data analytics. This ...
Source： turhancankargin.comA Perspective on Rhythmic Gymnastics Performance Analysis ...
Performance analysis is an important tool for gymnasts and coaches to assess the techniques, strengths, and weaknesses of rhythmic gymnasts ...
Source： SpringerHow a Tableau engineer and former gymnast built a system for rhythmic ...
It's much healthier for athletes, and now we can use data analytics to analyze commonly used masteries and the elements likely to be scored and ...
Source： TableauFujitsu and the International Gymnastics Federation launch AI ...
Fujitsu's Human Motion Analytics (HMA) is a data analysis platform based on the world's most advanced ...
Source： FujitsuPerformance Analysis with Gymnastics
UK Sports Institute Performance Analyst, Clare Bridgment, talks to us about her role and the impact it has on athlete performance within British Gymnastics.
Source： UK Sports InstituteComputer Vision AI for Gymnastics in 2024: Revolutionizing Training ...
Explore how computer vision AI is revolutionizing gymnastics training facilities with auto-highlights, auto- ...
Source： ezML
ALL
sports
Discussions