1. 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. 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. 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. 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. 5 Swartz: A graduate course in Statistics in Sport 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 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

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

7. 7 Yamaguchi, Sakaori, Watanabe: "A Trial of Statistical Education using Sports Data in Japan" 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 Distrns Causality Lesson: Home Run rate and Strike out rate correlated, obviously not causal Overall … Use Audience Interest in Sport to Motivate Stats Ed.

8. 8 Session Summary Sports provide examples of 1.Focus on variability & simulation 2.Case studies for graduate education 3.Gamblers need regression 4.Distributions exist without math

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