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IPM 45: Studying Variability through Sports Phenomena

IPM 45: Studying Variability through Sports Phenomena. Discussion of papers by Steve Clarke - Swinburne, Australia Tim Swartz - Simon Fraser, Canada Phil Everson - Swarthmore, U.S.A. Yamaguchi, Sakaori, Watanabe - Rikko, Chuo, Toyo , Japan.

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IPM 45: Studying Variability through Sports Phenomena

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  1. IPM 45: Studying Variability through Sports Phenomena Discussion of papers by Steve Clarke - Swinburne, Australia Tim Swartz - Simon Fraser, Canada Phil Everson - Swarthmore, U.S.A. Yamaguchi, Sakaori, Watanabe - Rikko, Chuo, Toyo, Japan Discussant: Larry Weldon, Simon Fraser, Canada

  2. Clarke: Studying Variability in Statistics via Performance Measures in Sport • Variability - why is it important? • Reproducibility of Apparent Effects • Estimation, Hypothesis Testing (not Description) • Inherently of Interest to Context • Golf: 12th hole at Augusta Masters - par 3 key hole because of variability • Triathlon: Equal Weighted Components? SD • Sports Interest Motivates • Interest in Variability • Interest in Descriptive Stats

  3. Clarke: Simulation Example • The Karrie Webb golf example:estimating chance of record being broken, unprecedented event … • Data + Simulation is an underutilized method for data analysis - we should teach it in early stat courses.

  4. Clarke: Studying Variability in Statistics via Performance Measures in Sport Overall … Sports Examples for Interest in Variability Importance of Descriptive Statistics Power of Simulation for Data Analaysis

  5. Swartz: A graduate course in Statistics in Sport Overall … Sports Contexts make cases interesting to many students Case Study Course is particularly suited to graduate courses Complexity can be designed to suit student level • Advanced Statistical Methods • But context is fairly straightforward (in sport) • Unfamiliar (to some) Sports Contexts • Like typical consulting experience • Case Study Approach • “process” course • Discussion/Communication Emphasis • Seminar discussion and student presentations • Active Student Involvement • Best practice for useful learning

  6. Everson: Teaching Regression Using American Football Scores Overall … Real Data Set Demonstrating Utility of Regression Illustrating Subjective Probability In Widely-Followed Sports Context • Motivation of Real Data (768 games) • Effects of Randomness in Sport • “lucky winner” • Regression for Prediction • Not just “curve fitting” • Subjective Probabilities Justified • Bookie’s probability guesses accurate • Nice Use of Graphics (Spread N(0,13))

  7. Yamaguchi, Sakaori, Watanabe:"A Trial of Statistical Education using Sports Data in Japan" Overall … Use Audience Interest in Sport to Motivate Stats Ed. • Social Science students math-phobic • Use interest in baseball to motivate • Show pitch types can be counted, and be studied numerically (fast ball, slider,..) • Distributions and Mixtures of Distr’ns • Causality Lesson: Home Run rate and Strike out rate correlated, obviously not causal

  8. Session Summary Sports provide examples of • Focus on variability & simulation • Case studies for graduate education • Gamblers need regression • Distributions exist without math

  9. Summary Summary Use Sports Examples to Motivate! Thank you

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