College application research evidence · understanding and original contribution · Seoul and online
Project courseThe student keeps the work and the record they made.
AI + Sports: Analysis and Pose Lab
Grades 6-8: match event coordinate data, player salaries...
- Free consultation and level check
- Apgujeong classroom or online
- One-to-one or small group
- Registered academy no. 11882
Is AI + Sports: Analysis and Pose Lab a good fit for middle school students in Grades 6-8?
AI + Sports: Analysis and Pose Lab is a good fit for students in grades 6-8 who are interested in match event coordinate data and player salaries versus performance. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make event and salary data cards with checks, a pose baseline and model evaluation, and a reproducible sports project with an error report.
Students flatten StatsBomb match-event JSON into tables, count one match's shot and pass coordinates, and join the Lahman salary table to player records, noting in a data card that salaries are nominal dollars. They compute sample sizes, means, and medians by position and season and fit a simple salary regression, using residuals to check the effect of outliers. In the pose unit, joint angles and speeds from MediaPipe sample coordinates feed an angle rule and a small decision tree compared by clip, with face coordinates excluded and no personal injury risk or training advice produced. The student sets their own question, then designs and tests a feature, model, interface, or solution of their own. In an application or interview, the student separates the material they drew on from their own decisions, results, failed attempts, revisions, and limits.
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My child found the AI classes interesting and stayed with them. It made for a worthwhile school break. Thank you for teaching so attentively.
- Age group
- Grades 6-8
- Academic subject
- AI & DataBrowse subject
- Course type
- Course
- Format
- Online or Apgujeong in person · one-to-one or small group
- Teaching language
- Korean by default, with complete English materials
- Curriculum status
- Lesson-readyEvery learning item includes the evidence, worked example, practical work, and tutor guidance required by CIT's lesson-ready standard.
From open research to student-owned work
Students inspect a relevant public source, reproduce the idea, then add an original question, feature, or test. Every source is public, and each lesson names the exact page it opens.
How can I explain this course to my child?
If questions about match event coordinate data or player salaries versus performance keep making you ask why, AI + Sports: Analysis and Pose Lab lets you investigate the question with evidence, then build and defend an extension of your own.
Which interests suggest this course?
- match event coordinate data
- player salaries versus performance
- movement read from joint angles
What does the student finish?Event and salary data cards with checks, a pose baseline and model evaluation, and a reproducible sports project with an error report.
Is this a good fit?
A strong fit for students who want to understand, build, test, or responsibly use AI systems.
Beginners can use guided templates.
Comfort with ratios, tables, or simple graphs is helpful.
Students should be ready to revise work after feedback.
What will my child learn?
- Flatten nested StatsBomb event JSON into tables and reconcile row counts with the raw file.
- Join Lahman salaries to player records and document version, currency, nominal dollars, and missing values in a data card.
- Report sample sizes with means and medians instead of ranking groups with different games or seasons by a single number.
- Normalize pose coordinates into joint-angle and speed features and split by clip so frames never leak between training and testing.
How does the course progress?
- 1Auditing Public Sports Data
Compare units and terms of use for four data types, then load StatsBomb events and Lahman salaries and verify row counts.
- 2Salary and Injury Analysis Baselines
Organize injury report categories and missing values, then build group comparisons with sample sizes and a simple salary regression.
- 3The Pose Vision Pipeline
Normalize MediaPipe coordinates, compute joint angles and speeds, and compare an angle rule with a decision tree by clip.
- 4Integrated Sports Analysis
Tally errors by distance, lighting, and occlusion, and present a capstone linking data analysis and pose in a README, graphs, and demo.
What counts as useful evidence?
Event and salary data cards with checks, a pose baseline and model evaluation, and a reproducible sports project with an error report.
What should an admissions reader be able to see?
The student sets their own question, then designs and tests a feature, model, interface, or solution of their own. In an application or interview, the student separates the material they drew on from their own decisions, results, failed attempts, revisions, and limits.
Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
The evidence an application reads is what the student understood, completed, and can explain.
Which sources were reviewed?
Catalog reviewed: 2026-09-30
Questions parents search before choosing this course
Is AI + Sports: Analysis and Pose Lab a good fit for middle school students in Grades 6-8?
AI + Sports: Analysis and Pose Lab is a good fit for students in grades 6-8 who are interested in match event coordinate data and player salaries versus performance. CIT offers online or in-person lessons in Apgujeong, Gangnam, Seoul, in one-to-one or small-group formats. Students make event and salary data cards with checks, a pose baseline and model evaluation, and a reproducible sports project with an error report.
Can my child take AI + Sports: Analysis and Pose Lab online or in person, one-to-one or in a small group?
Yes. CIT offers online and in-person lessons at its Apgujeong academy in Gangnam, Seoul, with one-to-one and small-group options. A readiness consultation confirms the available format and starting point for the course.
Does my child need prior subject knowledge or coding experience for AI + Sports: Analysis and Pose Lab?
Beginners can use guided templates. Comfort with ratios, tables, or simple graphs is helpful. Students should be ready to revise work after feedback.
What will my child make or practice in AI + Sports: Analysis and Pose Lab?
The main evidence is event and salary data cards with checks, a pose baseline and model evaluation, and a reproducible sports project with an error report. Students also document decisions, tests, feedback, and limits in age-appropriate language.
How can a student describe work from AI + Sports: Analysis and Pose Lab in a college application?
The student sets their own question, then designs and tests a feature, model, interface, or solution of their own. In an application or interview, the student separates the material they drew on from their own decisions, results, failed attempts, revisions, and limits. Useful evidence may include a documented dataset, baseline comparison, model evaluation, error analysis, and a clear record of the student's own decisions.
How are the schedule and tuition for AI + Sports: Analysis and Pose Lab determined?
CIT confirms the student's readiness, goal, location, class size, and current availability before recommending a course plan. The consultation and level check are free; tuition is explained before enrollment.
When should a student start?
There is no fixed intake month. CIT reviews the student's current school term, readiness, and available hours, then names the point in the course where they should begin.
I appreciated that a simple early idea was extended into an 'AI debate tool'. My child has always been interested in debate and social issues, so seeing that interest connect to the project makes me expect a more complete and distinctive result.
Where else parents check CIT
Choose the course after a readiness check
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