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The Effect of Varying Paneling Characteristics on Soccer Ball Flight

Name: Daniel Halbing
Major: Physics, Philosophy
Advisors: Dr. Susan Lehman, Dr. Niklas Manz
The predictability of flight of the Adidas Conext15, Adidas Jabulani, Adidas UEFA Nations League, Nike Incyte, Nike Flight, Nike Ordem, and Wilson NCAA Forte match balls were analyzed. Six to nine shots of each match ball were recorded from two angles, a rear angle and a side angle, for this investigation. Each of the videos were uploaded to Tracker in order to collect data on the spin, velocity, initial position, and final position of each of the trials in both the x-and y-directions. The data from the initial 20 frames of each trial was then used to create a theoretical final position of the ball using a radius of curvature of the flight path equation for the x-direction and a differential projectile motion equation for the y-direction. These theoretical final positions were then compared to the measured final positions of the ball in order to see how much the ball had deviated from the theoretical flight path based on the initial flight data. This deviation was then used as the metric for unpredictability in the scope of this investigation. For a soccer ball in general, it was found that a higher spin rate in the x-direction made the ball less predictable when compared to other low spin curved shots. However, in the y-direction, it was found that there was no relationship between the predictability of a ball and the spin rate. As the initial velocity in the y-direction increased, the predictability of the shot decreases. Of the seven balls that were tested, the most predictable ball in the x-direction was the adidas UEFA Nations League which deviated from the theoretical final x-position by an average of 0.78±0.67 m. The most predictable ball in the y-direction was the Wilson NCAA Forte which deviated by an average of 0.27±0.25 m. The most predictable ball overall was the Adidas UEFA Nations League as it deviated from the theoretical final x-position by an average of 0.78±0.67 m and deviated from the theoretical final y-position by an average of 0.29±0.15 m.


Daniel will be online to field comments on April 16:
2-4 pm EDT (PST 11am-1pm, Africa/Europe: evening)

Posted in Comments Enabled, I.S. Symposium 2021, Independent Study on April 16, 2021.


14 responses to “The Effect of Varying Paneling Characteristics on Soccer Ball Flight”

  1. Daniel Halbing says:

    It seems that there is an error with my audio recording, so I will clarify a few slides in this comment.
    The tracker animation slide demonstrates how the ball is tracked frame by frame using the tracker software to extract velocity and spin data.
    In the first “Results” slide, a graphic of six goals is shown. This graphic demonstrates the predictability of the ball if the theoretical final position of a shot were in the middle of the goal, as indicated by the soccer ball in the graphic. The colored rectangles represent the possible actual (measured) final positions of the shot if the shot were to theoretically land in the center of the goal. There are four possible final position rectangles since the ball could deviate in the x-axis in two different ways (right and left) and in the y-axis in two different ways (up and down). The teal space represents the space a goalkeeper would have to cover to make up for the unpredictability of the ball if they were to position themselves to save a shot in the center of the goal. In general, a larger teal space means a less predictable ball.
    The table on slide 15 shows the average predictability data of each of the balls in both the x-direction and y-direction.
    Slide 16 shows the surfaces of each of the balls. This is useful when considering the conclusions of this project, as it was found that certain panel characteristics of each of the balls yielded better or worse results for various shot situations (i.e. high spin, low spin, high velocity, etc.)
    Please let me know if you have any further questions and thank you for attending!

  2. Daniel Halbing says:

    It seems that there is an error with my audio recording, so I will clarify a few slides in this comment.
    The tracker animation slide demonstrates how the ball is tracked frame by frame using the tracker software to extract velocity and spin data.
    In the first “Results” slide, a graphic of six goals is shown. This graphic demonstrates the predictability of the ball if the theoretical final position of a shot were in the middle of the goal, as indicated by the soccer ball in the graphic. The colored rectangles represent the possible actual (measured) final positions of the shot if the shot were to theoretically land in the center of the goal. There are four possible final position rectangles since the ball could deviate in the x-axis in two different ways (right and left) and in the y-axis in two different ways (up and down). The teal space represents the space a goalkeeper would have to cover to make up for the unpredictability of the ball if they were to position themselves to save a shot in the center of the goal. In general, a larger teal space means a less predictable ball.
    The table on slide 15 shows the average predictability data of each of the balls in both the x-direction and y-direction.
    Slide 16 shows the surfaces of each of the balls. This is useful when considering the conclusions of this project, as it was found that certain panel characteristics of each of the balls yielded better or worse results for various shot situations (i.e. high spin, low spin, high velocity, etc.)
    Please let me know if you have any further questions and thank you for attending!

  3. Cody Leary says:

    Nice project, Dani! Can you elaborate on how you determined which ball was most/least predictable? What were your criteria and methods?

    • Daniel Halbing says:

      Thank you, Dr. Leary!
      My criteria was based on which ball deviated the least from its theoretical final position. More specifically, the real final position of the ball from the initial starting point was measured using tracker and this displacement was compared to the theoretical displacement that I calculate using the relevant initial shot data from the first 20 frames. The greater the difference in these two, the less predictable a ball was considered.
      It is important to note that this is not the only way to characterize predictability. Goalkeepers are of course mobile and reactive, and would not attempt a save solely off of positioning themselves after their first glance of the ball after a shot is taken. However, a big part of a goalkeeper’s positioning to stop shots does come from that initial part of the shot, and therefore that is why my project focused on initial data to characterize predictability. My project did not consider sudden changes during the flight path, as it was based off of the initial data, so this would be another interesting way of characterizing predictability to explore.

  4. Cody Leary says:

    Nice project, Dani! Can you elaborate on how you determined which ball was most/least predictable? What were your criteria and methods?

    • Daniel Halbing says:

      Thank you, Dr. Leary!
      My criteria was based on which ball deviated the least from its theoretical final position. More specifically, the real final position of the ball from the initial starting point was measured using tracker and this displacement was compared to the theoretical displacement that I calculate using the relevant initial shot data from the first 20 frames. The greater the difference in these two, the less predictable a ball was considered.
      It is important to note that this is not the only way to characterize predictability. Goalkeepers are of course mobile and reactive, and would not attempt a save solely off of positioning themselves after their first glance of the ball after a shot is taken. However, a big part of a goalkeeper’s positioning to stop shots does come from that initial part of the shot, and therefore that is why my project focused on initial data to characterize predictability. My project did not consider sudden changes during the flight path, as it was based off of the initial data, so this would be another interesting way of characterizing predictability to explore.

  5. Scot Stacho says:

    Great job and congratulations, Dani! Did you consider the weight of each ball and the weather conditions throughout your study? I would be interested to know if different soccer ball types/surfaces would react differently to heavy wind, for example.

    • Daniel Halbing says:

      Thank you, Scot! The balls were actually not considered by weight, but rather by their air pressure. Each manufacturer has a different ball weight, within the FIFA standard of 0.430 – 0.450 kg of course, however FIFA states that the ideal air pressure for a match ball is 12 PSI. Therefore, all balls were inflated to 12 PSI and their weights at this pressure were noted. Nike balls tended to be around 0.430 kg whereas Adidas balls were approximately 0.440 kg.
      The weather conditions were actually a pivotal part of this study. Rather than taking all of the shots for one ball in one session, each of the balls were used in each of the sessions equally to ensure that the balls experienced the same conditions. The balls did not seem to perform better or worse based off of weather conditions compared to each other, but the balls were as a whole less predictable when it was windy.

  6. Scot Stacho says:

    Great job and congratulations, Dani! Did you consider the weight of each ball and the weather conditions throughout your study? I would be interested to know if different soccer ball types/surfaces would react differently to heavy wind, for example.

    • Daniel Halbing says:

      Thank you, Scot! The balls were actually not considered by weight, but rather by their air pressure. Each manufacturer has a different ball weight, within the FIFA standard of 0.430 – 0.450 kg of course, however FIFA states that the ideal air pressure for a match ball is 12 PSI. Therefore, all balls were inflated to 12 PSI and their weights at this pressure were noted. Nike balls tended to be around 0.430 kg whereas Adidas balls were approximately 0.440 kg.
      The weather conditions were actually a pivotal part of this study. Rather than taking all of the shots for one ball in one session, each of the balls were used in each of the sessions equally to ensure that the balls experienced the same conditions. The balls did not seem to perform better or worse based off of weather conditions compared to each other, but the balls were as a whole less predictable when it was windy.

  7. Ron says:

    Interesting study. Based on your research, might you expect a soccer ball’s deviation from theoretical to be influenced by left-footed vs right-footed kicks? Or by the size or shape of the shoe that kicked it?
    Thanks

    • Daniel Halbing says:

      Great question! Participants with both feet were selected for this study, and there was actually no difference found between the two in terms of predictability. However, as expected, left footed shots tend to curl towards the right where as right footed shots tend to curl towards the left.
      The type of shoe is an interesting idea. I did not take note on the type of shoes that the participants were wearing, but there are many different kinds of cleats that even boast attributes such as increased grip to create better curved shots, such as the Adidas Predator cleat line. The effect of the cleat would be an interesting idea to explore for further research!

  8. Ron says:

    Interesting study. Based on your research, might you expect a soccer ball’s deviation from theoretical to be influenced by left-footed vs right-footed kicks? Or by the size or shape of the shoe that kicked it?
    Thanks

    • Daniel Halbing says:

      Great question! Participants with both feet were selected for this study, and there was actually no difference found between the two in terms of predictability. However, as expected, left footed shots tend to curl towards the right where as right footed shots tend to curl towards the left.
      The type of shoe is an interesting idea. I did not take note on the type of shoes that the participants were wearing, but there are many different kinds of cleats that even boast attributes such as increased grip to create better curved shots, such as the Adidas Predator cleat line. The effect of the cleat would be an interesting idea to explore for further research!

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