MLM2PRO vs. Foresight GCQuad: A Data-Driven Comparison of Club Path and Attack Angle

This report aims to evaluate the performance of Rapsodo MLM2PRO in measuring two important club data metrics: club path and attack angle. These metrics play a key role in understanding swing dynamics and ball flight behavior. 

Scope

We compare the Rapsodo MLM2PRO to the Foresight GCQuad and their sticker-based club data feature. The analysis includes visual explanations of the two metrics, a brief description of our calculation method, and a quantitative comparison using statistical metrics and shot-by-shot analysis.

Data

The evaluation is based on a dataset of 1021 shots, collected from golfers with varying skill levels and swing types. This diverse dataset helps ensure a thorough and meaningful comparison across different shot scenarios.

Explanation Of Metrics

Club Path

Club path describes the horizontal direction club head is moving at the moment of impact, relative to the target line. In other words, it indicates whether the club is moving in-to-out, out-to-in, or straight. Club Path plays a key role in determining the starting direction and curvature of the golf ball, making it essential for analyzing swing patterns and shot shapes.

Attack Angle

Attack angle refers to the vertical direction of the club head's movement at impact. A downward strike results in a negative attack angle, often seen with irons, while an upward strike creates a positive angle, typical with drivers. Attack Angle influences key launch conditions such as spin, trajectory, and distance, making it essential for optimizing shot performance.

Comparison Methodology Overview

Our comparative analysis of the MLM2PRO against the reference Foresight GCQuad launch monitor employs a multi-faceted approach combining statistical metrics, distribution visualization, time-series analysis, and random sampling techniques. This comprehensive methodology allows for both broad performance assessment and detailed error characterization across the dataset.

Statistical Performance Assessment

This section presents the core quantitative metrics that define the agreement between MLM2PRO and Foresight GCQuad measurements. The analysis includes:

·       Mean Absolute Error (MAE): Quantifies the average magnitude of errors

·       Root Mean Square Error (RMSE): Highlights the impact of larger errors

·       Pearson Correlation: Measures linear relationship strength between measurements (values closer to 1 indicate strong positive correlation; > 0.8 is typically considered strong)

·       Mean Error (Bias): Identifies systematic over/under-estimation

·       Standard Deviation of Error: Characterizes error consistency

Error Characterization & Distribution Analysis

In this section, you can see the distribution of attack angle and club path together with scatter plots.

The distribution analysis reveals that the MLM2PRO captures the same fundamental swing characteristics as the reference Foresight GCQuad system, with distributions showing similar shapes and substantial overlap. The correlation scatter plots demonstrate a notably strong linear relationship between MLM2PRO and Foresight GCQuad measurements for club path parameters. Attack angle appears more scattered in the plots, particularly on the positive side corresponding to high-speed swings, where slightly larger deviations are observed. On the other hand, Club Path errors are more randomly distributed and show no clear correlation with swing speed.

The error histograms for both attack angle and club path measurements exhibit approximately Gaussian distributions, indicating predominantly random measurement deviations between the MLM2PRO and reference Foresight GCQuad systems. The attack angle error distribution displays a mean deviation of 0.13° with a standard deviation of 1.42°, while the club path error distribution demonstrates a mean of 0.33° with a standard deviation of 1.42°. These parameters reveal two critical performance characteristics: a small positive systematic bias exists in both measurements, with the MLM2PRO slightly overestimating both parameters relative to the reference system.

Temporal Error Analysis

The time series visualization below shows the potential patterns in measurement errors across the full dataset.

The sequential error visualization indicates temporal patterns in measurement accuracy between the MLM2PRO and Foresight GCQuad systems. Most notably, the error distribution demonstrates consistency across the entire dataset, with no significant long-term drift or deterioration in measurement agreement—suggesting robust stability in the MLM2PRO's performance over extended use. The rolling error trend line oscillates within the ±1σ band, indicating that temporary deviations tend to self-correct rather than compound. The color-gradient visualization effectively highlights that extreme outliers (>2σ) appear randomly distributed throughout the sequence rather than concentrated in specific regions, suggesting these larger deviations stem from shot-specific challenges (primarily bad swings/shot attempts) rather than systematic measurement failures during particular sessions. Importantly, several brief periods where both parameters simultaneously show increased error magnitude likely indicate specific swing patterns or testing conditions that challenge the system's measurement capabilities, providing valuable insights for potential algorithm refinements targeting these particular scenarios.

Key Conclusions from Launch Monitor Comparative Analysis

1.     System Correlation and Accuracy: The MLM2PRO demonstrates a strong positive correlation with the reference Foresight GCQuad system, with overall MAE values of 1.05 ° for attack angle and 1.19 ° for club path, supporting its validity for professional golf swing analysis applications.

2.     Parameter-Specific Performance: Club Path and Attack Angle measurements from the MLM2PRO demonstrate strong and consistent agreement with the reference system across both evaluation metrics. Both parameters exhibit tight error distributions, high correlation coefficients, and stable shot-by-shot tracking performance. The results indicate that the system reliably captures key swing characteristics with comparable accuracy across different parameter types, reinforcing the robustness and consistency of the overall measurement pipeline.

3.     Error Distribution Properties: The predominantly Gaussian error distributions with relatively small standard deviations (1.42° and 1.42°) indicate that measurement deviations are primarily random rather than systematic, supporting fundamental measurement validity across the operating range.

4.     Temporal Stability: The shot-by-shot analysis reveals consistent performance without significant drift across the dataset, with randomly distributed outliers rather than sustained periods of degraded performance, demonstrating reliable operation over extended usage.