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 ======Creating and Comparing Pitching Release Metrics====== ======Creating and Comparing Pitching Release Metrics======
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 ====Abstract==== ====Abstract====
-One of the most highly documented and discussed areas of biomechanics in sports is a pitcher's throwing motion from the mound. The pitching motion is different from most others in sports in that it occurs from the same origin every time, and several repetitions with highly similar intent and mechanics occur during each outing. As such, the positions that a pitcher achieves at the release point of their throw are the subject of intense scrutiny by scouts, players, and coaches. The positions that a pitcher achieves for each of their pitch types can be crucial in determining the velocity, movement, and effectiveness of each pitch. These positions are often the product of a combination of the pitcher's unique musculoskeletal structure and coaching they have received [1].+One of the most highly documented and discussed areas of biomechanics in sports is a pitcher's throwing motion from the mound. The pitching motion is different from most others in sports in that it occurs from the same origin every time, and several repetitions with highly similar intent and mechanics occur during each outing. As such, the positions that a pitcher achieves at the release point of their throw are the subject of intense scrutiny by scouts, players, and coaches. The positions that a pitcher achieves for each of their pitch types can be crucial in determining the velocity, movement, and effectiveness of each pitch. These positions are often the product of a combination of the pitcher's unique musculoskeletal structure and coaching they have received [1].\\ 
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 +{{:baseballMetricTutImage.png?400}}\\
  
 This tutorial will describe how to build a pipeline in Visual3D to identify key release metrics (extension, stride length, release height, etc.) and compare them across different groups of pitchers in Sift. This tutorial will describe how to build a pipeline in Visual3D to identify key release metrics (extension, stride length, release height, etc.) and compare them across different groups of pitchers in Sift.
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 |**Stride Length** |The distance between the front of the rubber and the left heel marker (for a right handed pitcher) at lead leg plant.|  |**Stride Length** |The distance between the front of the rubber and the left heel marker (for a right handed pitcher) at lead leg plant.| 
 |**Release Height**| The vertical position of the ball relative to the ground at the time of release.| |**Release Height**| The vertical position of the ball relative to the ground at the time of release.|
-|**Arm Slot** |The angle between the throwing hand/ball and the shoulder joint center, relative to the ground, at the time of release (pictured below).|+|**Arm Slot** |The angle between the throwing hand/ball and the shoulder joint center, relative to the ground, at the time of release.|
  
-{{:arm_slot_clock.png?400|}}\\ //image source// [2] 
 ====Data==== ====Data====
 This tutorial will uses a public dataset made available by [[https://www.drivelinebaseball.com/|Driveline Baseball]]. The data set includes motion capture data for 101 different male college and high school pitchers throwing 2-5 pitches each. The dataset is available through Driveline Baseball's [[https://github.com/drivelineresearch/openbiomechanics/tree/main|Github repository]]. The OpenBiomechanics Project is an initiative started by Driveline Baseball Research & Development to provide raw (in the form of cleaned C3D files) and processed (full signal + point of interest) sports biomechanics data to the general public. This tutorial will uses a public dataset made available by [[https://www.drivelinebaseball.com/|Driveline Baseball]]. The data set includes motion capture data for 101 different male college and high school pitchers throwing 2-5 pitches each. The dataset is available through Driveline Baseball's [[https://github.com/drivelineresearch/openbiomechanics/tree/main|Github repository]]. The OpenBiomechanics Project is an initiative started by Driveline Baseball Research & Development to provide raw (in the form of cleaned C3D files) and processed (full signal + point of interest) sports biomechanics data to the general public.
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 ===Computing Release Metrics=== ===Computing Release Metrics===
-Now that we have defined the release time for each pitch we can use metric commands to identify several key metrics at the time of release that are frequently used to compare different elite pitching prospects [3]. +Now that we have defined the release time for each pitch we can use metric commands to identify several key metrics at the time of release that are frequently used to compare different elite pitching prospects [2]. 
  
 First, we will use **Metric Signal Value at Event** 4 times to identify the release height, release extension, release depth, and stride length for each pitch. First, we will use **Metric Signal Value at Event** 4 times to identify the release height, release extension, release depth, and stride length for each pitch.
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 **1.** In the explore tab select the 2 groups that you wish to compare. For this tutorial we'll start with 'Extension_Over_90' and 'Extension_Under_90'.\\ **1.** In the explore tab select the 2 groups that you wish to compare. For this tutorial we'll start with 'Extension_Over_90' and 'Extension_Under_90'.\\
-**2.** Select Metric Plot as the plot type and check Plot Group Mean, Plot Group Dispersion, and Select All Workspaces. Your plots should look similar to below.\\+**2.** Select Metric Plot as the plot type and check Plot Group Mean, Plot Group Dispersion, and Select All Workspaces.\\  
 +**3.** Select the General Options {{:sift_general_options.png?20x20}} button and increase the graph rows and columns to create a 2x2 grid.\\ 
 +**4.** Repeat steps 1 and 2 for release height, stride length, and arm slot metrics. Your plots should look similar to below.\\
  
-{{:extension_plot_1.png?nolink&600|}}{{:release_height_plot_2.png?nolink&600|}} +{{:plottingMetricsBaseball.png}}
-{{:extension_plot_1.png?nolink&600|}}{{:arm_slot_plot_1.png?nolink&600|}}+
  
 ===Analyzing Statistical Significance=== ===Analyzing Statistical Significance===
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 ===Bonferroni Correction=== ===Bonferroni Correction===
-When testing multiple null hypotheses in the same family the likelihood of a false positive increases with each additional test. To control for the probability of this error a **Bonferroni Correction** can be applied [[https://www.statology.org/bonferroni-correction/|[4]]]. This correction is done by dividing the original significance threshold (alpha value) by the number of tests being performed to determine a corrected significance threshold. +When testing multiple null hypotheses in the same family the likelihood of a false positive increases with each additional test. To control for the probability of this error a **Bonferroni Correction** can be applied [[https://www.statology.org/bonferroni-correction/|[3]]]. This correction is done by dividing the original significance threshold (alpha value) by the number of tests being performed to determine a corrected significance threshold. 
  
 Our original significance threshold was set at 0.05. In this tutorial we performed 4 t-tests, thus our now significance threshold is 0.0125. The python script shown below uses [[https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html|this function]] to take the degrees of freedom calculated by our t-tests and the new alpha value to determine the t score range necessary to reject the null hypothesis for each of our tests.  Our original significance threshold was set at 0.05. In this tutorial we performed 4 t-tests, thus our now significance threshold is 0.0125. The python script shown below uses [[https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html|this function]] to take the degrees of freedom calculated by our t-tests and the new alpha value to determine the t score range necessary to reject the null hypothesis for each of our tests. 
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 The following table presents the t-test results for each metric comparison we performed. The table includes the t-stat and degrees of freedom reported by Sift, and whether or not the null hypothesis was rejected before and after the Bonferroni Correction. In this case, the null hypothesis is that the mean value of the metric in question (release height, arm slot, stride length, or extension) is the same for pitches thrown above 90 mph and pitches thrown below 90 mph. The following table presents the t-test results for each metric comparison we performed. The table includes the t-stat and degrees of freedom reported by Sift, and whether or not the null hypothesis was rejected before and after the Bonferroni Correction. In this case, the null hypothesis is that the mean value of the metric in question (release height, arm slot, stride length, or extension) is the same for pitches thrown above 90 mph and pitches thrown below 90 mph.
 |**Metric**|**T Stat**|**DOF**|**Null Hypothesis State (Significance Level = 0.05)**|**Corrected Null Hypothesis State (Significance Level = 0.0125)**| |**Metric**|**T Stat**|**DOF**|**Null Hypothesis State (Significance Level = 0.05)**|**Corrected Null Hypothesis State (Significance Level = 0.0125)**|
-|Release Height|1.88|412|Rejected|Accepted| +|Release Height|1.88|412|Rejected|Failed to reject| 
-|Arm Slot|0.29|328|Accepted|Accepted| +|Arm Slot|0.29|328|Failed to reject|Failed to reject| 
-|Stride Length|2.23|308|Rejected|Accepted| +|Stride Length|2.23|308|Rejected|Failed to reject| 
-|Extension|1.86|412|Rejected|Accepted|+|Extension|1.86|412|Rejected|Failed to reject|
  
 ===Interpreting Results==== ===Interpreting Results====
-Through visual inspection of our plots noticeably higher values in all 4 metrics for faster pitchers were apparent. However, the results from our 4 t-tests told a different story. After applying the Bonferroni correction to our significance threshold to account for false positives, the null hypothesis was accepted for all 4 t-tests. These results tell us that there is likely not a statistically significant trend between any of these mechanical patterns and the resulting fastball velocity.+Through visual inspection of our plots noticeably higher values in all 4 metrics for faster pitchers were apparent. However, the results from our 4 t-tests told a different story. After applying the Bonferroni correction to our significance threshold to account for false positives, all 4 t-tests failed to reject the null hypothesis. These results tell us that there is likely not a statistically significant trend between any of these mechanical patterns and the resulting fastball velocity.
  
-One key observation that can be made is substantially higher t-stats and lower p-values associated with release height, extension, and stride length in comparison to the results for arm slot. All 3 of these metrics are correlated to pitcher height, suggesting that there may be a link between subject height and average fastball velocity. Some existing research shows that taller pitchers are more likely to throw harder [5]. Further analysis of the dataset could involve separating subjects into groups by height and including this as part of the queries, or creating metrics like extension divided by height to further isolate these variables and investigate this relationship.+One key observation that can be made is substantially higher t-stats and lower p-values associated with release height, extension, and stride length in comparison to the results for arm slot. All 3 of these metrics are correlated to pitcher height, suggesting that there may be a link between subject height and average fastball velocity. Some existing research shows that taller pitchers are more likely to throw harder [4]. Further analysis of the dataset could involve separating subjects into groups by height and including this as part of the queries, or creating metrics like extension divided by height to further isolate these variables and investigate this relationship.
 ====References==== ====References====
 [1]K. A. Giordano, A. Schmitt, A. Nebel, Y. Yanagita, and G. D. Oliver, “Normative In-Game Data for Collegiate Baseball Pitchers Using Markerless Tracking Technology,” Orthopaedic Journal of Sports Medicine, vol. 12, no. 10, Oct. 2024, doi: https://doi.org/10.1177/23259671241274137. [1]K. A. Giordano, A. Schmitt, A. Nebel, Y. Yanagita, and G. D. Oliver, “Normative In-Game Data for Collegiate Baseball Pitchers Using Markerless Tracking Technology,” Orthopaedic Journal of Sports Medicine, vol. 12, no. 10, Oct. 2024, doi: https://doi.org/10.1177/23259671241274137.
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-[2]BAID, “The Science of the Perfect Arm Slot - Motor Preferences in Baseball,” Motor Preferences in Baseball - 𝗙𝗼𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗜𝗻𝗷𝘂𝗿𝘆 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻!, Sep. 05, 2023. https://www.baseballactionid.com/the-science-of-the-perfect-arm-slot/. +[2]Y. Hashimoto, Tomoyuki Nagami, S. Yoshitake, and H. Nakata, “The relationship between pitching parameters and release points of different pitch types in major league baseball players,” Frontiers in sports and active living, vol. 5, Apr. 2023, doi: https://doi.org/10.3389/fspor.2023.1113069.
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-[3]Y. Hashimoto, Tomoyuki Nagami, S. Yoshitake, and H. Nakata, “The relationship between pitching parameters and release points of different pitch types in major league baseball players,” Frontiers in sports and active living, vol. 5, Apr. 2023, doi: https://doi.org/10.3389/fspor.2023.1113069.+
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-[4]Zach, “The Bonferroni Correction: Definition & Example,” Statology, Feb. 16, 2021. https://www.statology.org/bonferroni-correction/+[3]Zach, “The Bonferroni Correction: Definition & Example,” Statology, Feb. 16, 2021. https://www.statology.org/bonferroni-correction/
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-[5]J. H. Huang, S.-H. Chen, and C. H. Chiu, “Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings,” PLOS ONE, vol. 17, no. 3, p. e0265525, Mar. 2022, doi: https://doi.org/10.1371/journal.pone.0265525.+[4]J. H. Huang, S.-H. Chen, and C. H. Chiu, “Correlation of pitching velocity with anthropometric measurements for adult male baseball pitchers in tryout settings,” PLOS ONE, vol. 17, no. 3, p. e0265525, Mar. 2022, doi: https://doi.org/10.1371/journal.pone.0265525.
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