Greg Potter

Author: Greg Potter, Business Development Manager, Elixirr Digital

Published: 4th July 2024

Football is often considered a passionate sport, evoking a wide range of reactions including enthusiasm, excitement and disappointment. It’s fair to say that football can provoke both positive and negative emotions, but what happens if we try to measure these emotions?

We hooked up a member of our team to our biometrics kit while they watched a couple of matches, including England, and analysed the results.

What did we do?

While we primarily use our biometrics kit to test user responses to website and user journeys, its applications are much broader. We decided to use it to test the Galvanic Skin Response (GSR)— a measure of perspiration levels linked to emotional arousal—of someone watching a live football match.

First, they watched the last group stage match of the 2024 Euros between England vs. Slovenia. Then, they watched another match (between Georgia and Portugal). Incidentally, anyone who watched that particular match may very well agree it was much more engaging and interesting than the England game!

For context, our subject isn’t a huge football fan but supports the England national team. Therefore, they were invested in the result of the England vs. Slovenia game but not concerned about the outcome of the Georgia vs. Portugal game.

In other words, they wanted England to win but didn’t care about the result of the other game. Would their GSR response be different simply because they were invested in the result, regardless of the match quality?

How did we do it?

All humans produce GSR readings at all times, and these vary from person to person. To determine what’s “normal” for each person, we establish a “resting” state by taking readings while the subject is relaxed and not doing anything. Any readings above this level can then be attributed to the activity.

We established a baseline “resting” state by averaging three separate readings. This gave us a control figure. We then tested the subject during both games, analysing each half of each game individually. This allowed us to compare the changes in values from all halves to the resting state and to each other.

What did we discover?

We observed an increase in GSR metrics across all halves compared to the resting state.

One key finding is an increase in average peaks per minute. A peak occurs anytime the detection algorithm identifies a spike above the baseline output, indicating a rise in arousal or intensity. This could represent stress, excitement, happiness, or fear. GSR alone doesn’t indicate whether a peak is positive or negative; it must be combined with other data sources or by asking the subject.

Peaks per minute refer to the average number of peaks in any given minute, averaged across the entire activity. This metric is less affected by outliers and anomalies, providing a good indication of overall increases in arousal over time.

In this graph, 0% represents resting state, and the bars show the percentage increase in peaks per minute for each half. Although all halves show an increase, the England game displays much higher increases, suggesting higher stress levels.

Another observation was an increase in average peak amplitude, which represents the intensity or “height” of the peaks. To get a comprehensive understanding, it’s essential to consider a range of metrics, as one metric alone may not tell the full story. The average peak amplitude indicates the average arousal of the recorded peaks and is less affected by outliers. This metric provides a good indication of arousal levels over a period of time.

In this graph, 0% represents the resting state, and the bars show the percentage increase in average peak amplitude for each half. Although all halves show an increase, the England game displays significantly greater increases, reinforcing the view that watching the England game was a more stressful experience.

We also saw an increase in both games, from first half to second half, across a range of metrics, including:

  • Total number of peaks – The total number recorder (regardless of amplitude).
  • Average peaks per minute (PPM) – Average in any given minute.
  • Average peak amplitude – Average peak “height”.
  • Highest peak – Single highest instance / peak amplitude.
  • Lowest peak – GSR shows positive and negative values, with negative peaks being below the baseline output – so this is the opposite to the highest peak and shows the lowest peak value.

In this graph, 0% represents the baseline figure for the first half of each game. The bars show the percentage increase in various metrics for the second half of each game. We observe that all metrics show notable increases in the second halves, suggesting an increase in intensity as the games progress.

Additionally, we observed a significant decrease in GSR metrics for the Georgia vs. Portugal game compared to the England vs. Slovenia game.

In this graph, 0% represents the figures for the England vs. Slovenia game, and the bars show the percentage decrease in those figures for the Georgia vs. Portugal game. We can see that all metrics consistently showed significant decreases for the Georgia vs. Portugal game, indicating significantly less arousal and a less stressful experience.

Overall, we observed a 34.21% increase in the total number of peaks for the England vs. Slovenia game compared to the Georgia vs. Portugal game. This reflects the total number of peaks recorded for both games.

This graph shows the total number of peaks for each game. The England vs. Slovenia game displays a notable increase in the total number of peaks compared to the Georgia vs. Portugal game, further supporting the view that it was a more intense and stressful experience for the subject.

So, what did we learn?

The data gathered supports the concept that the subject was more interested in the result of the England vs. Slovenia game, showing much higher arousal, stress, and excitement levels. We observed that tension seems to build as the games progress, causing increases in GSR levels during the second halves. All the GSR metrics we analysed were consistent, with all either increasing or decreasing together.

The data suggests that the amount of time remaining in a game impacts GSR intensity. As the game nears its end, the “jeopardy” increases, and the opportunity for significant events decreases, thus increasing the intensity as the games progress.

Key insights

  • Engagement and Intensity: All game halves showed significant increases in GSR metrics compared to the resting state, indicating that watching the games was engaging. The England game, in particular, showed substantially more intensity. There were significantly more peaks in total, more average peaks per minute, and higher average peak amplitudes, suggesting sustained intensity. This game also had the highest individual peaks, indicating the highest moments of arousal, stress, or excitement. This supports the view that the subject was more emotionally invested in the result of the England game.
  • Time-Related Intensity: The data indicates that as the game gets closer to the end, the intensity increases. The “jeopardy” factor—where the outcome of the game becomes more critical—heightens emotional responses and physiological arousal.

In conclusion, watching football can indeed be a stressful experience!

Broader applications

The data collected shows that it’s possible to measure the subconscious response to everyday and high-intensity activities and analyse the story it tells us. While this was a social activity, the technology and processes can be equally applied to business use cases, such as website UX user testing.

Interested in biometric testing?

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