Cheaters in a Sports Tracking App

Solution & Explanation

When tasked with detecting potential cheaters in a sports tracking app, it's crucial to focus on identifying metrics and statistical approaches that can effectively highlight unusual patterns indicative of dishonest behavior. Below is a detailed breakdown of the solution:

Key Metrics to Focus On:

1. Speed/Pace:

   • Definition: Measure of how fast a user is moving, typically expressed in minutes per kilometer or miles per hour.
   • Reasoning: Unusually high speed for a given activity (e.g., cycling speeds while claiming to run) could indicate cheating.

2. Heart Rate:

   • Definition: The number of heartbeats per minute.
   • Reasoning: A mismatch between expected heart rate ranges and reported activity can be a red flag. For example, a low heart rate during a supposedly intense run could suggest the user is not exercising.

3. Elevation Gain:

   • Definition: The total elevation climbed during an activity.
   • Reasoning: Unexpected elevation gain patterns, such as rapid ascents/descents inconsistent with the user's claimed activity, may indicate anomalous behavior.

4. Acceleration Patterns:

   • Definition: Changes in speed over time.
   • Reasoning: Human acceleration differs significantly from vehicle acceleration. Analyzing acceleration/deceleration patterns can help differentiate between genuine athletic activity and vehicular movement.

Statistical Approaches:

1. Z-score Analysis:

   • Description: Calculate the Z-score for key metrics to determine how many standard deviations a data point is from the mean.
   • Application: If a user's pace or heart rate Z-score is beyond a typical threshold (e.g., >3), flag it as potentially anomalous.

2. Hypothesis Testing:

   • Null Hypothesis (H0): The user's activity data is consistent with typical athletic behavior.
   • Alternative Hypothesis (H1): The user's data suggests cheating.
   • Approach: Use t-tests or ANOVA to compare user data against a baseline of verified athlete data. Reject the null hypothesis if significant differences are found.

3. Moving Average Baselines:

   • Description: Calculate moving averages for key metrics over a defined period (e.g., 7 or 14 days).
   • Application: Compare current user data against these baselines to spot deviations.

4. Box Plot Visualization:

   • Description: Visual representation of data distribution.
   • Application: Identify outliers in user data for various metrics like pace or heart rate.

Implementation Considerations:

• Data Collection: Ensure accurate and comprehensive data collection for all relevant metrics.
• Baseline Establishment: Gather data from verified users to establish realistic baselines for each activity.
• User Feedback: Allow users to contest flagged activities, providing an opportunity to verify genuine activities.
• Continuous Improvement: Regularly update baselines and detection algorithms to adapt to evolving user behaviors and technological advancements.

By focusing on these metrics and employing robust statistical methods, we can effectively identify and address potential cheating in sports tracking applications, ensuring fair play and accurate activity tracking for all users.