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!

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!

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.

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.

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.

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.