The analysis of sports forecasting : modeling parallels between sports gambling and financial markets /
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| Author / Creator: | Mallios, William S. (William Steve), 1935- |
|---|---|
| Imprint: | Boston : Kluwer Academic, c2000. |
| Description: | xviii, 294 p. : ill. ; 25 cm. |
| Language: | English |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/4270549 |
Table of Contents:
- Preface
- Introduction: A Variety of Betting Lines
- Part I. Models, Moralities, and Misconceptions
- 1. A New Information Age
- 1.1. Commentary on Television and the Information Age
- 1.2. Information Ages of Bygone Days
- 1.3. Data, Data Everywhere
- 1.4. Data, Data Analysis, Information, and Knowledge
- 1.5. Internet and Data Oversaturation
- 2. Data Generators
- 2.1. Experiments Versus Observational Studies
- 3. Gambling, Speculation, and Investment
- 3.1. Gambling Versus Speculation
- 3.2. Speculation Versus Investment
- 4. Commentaries
- 4.1. Modeling Persuasions
- Part II. Modeling Concepts
- 1. Expectations: Gambling, Rational and Statistical
- 1.1. Gambling Expectations as Defined by the Oddsmaker's Line
- 1.2. Inadequacies of Simple Extrapolation Models
- 1.3. The Rational Expectations Hypothesis in the Context of the Oddsmaker's Line
- 1.4. Known Versus Unknown Expectations
- 2. Shocks: Deviations From Expectations
- 2.1. Gambling Shocks
- 2.2. Statistical Shocks
- 2.3. Baseball Shocks
- 2.4. Effects of Lagged Shocks on Game Outcomes
- 3. Modeling Approaches
- 3.1. A Review of Related Literature
- 3.2. Posterior Probability of Beating the Line
- 3.3. An Exploratory Function for Model Building
- 3.4. On the Nature of Effects
- 3.5. Approximating the Exploratory Function
- 3.6. Team Specific Versus Game Specific Forecasting Equations
- Part III. Football
- 1. Modeling National Football League Games
- 1.1. Variables and Data Under Study
- 1.2. Modeling Procedure
- 1.3. Year of the 49er Repeat
- 1.4. Selected Team Profiles of 1989-90: The Bills, 49ers, Giants, and Vikings
- 1.5. Studies in Playoff Game Forecasts
- 1.6. Some Implications of NFL Modeling Results
- 1.7. The Jocks Speak: A Potpourri
- Part IV. Basketball
- 1. Modeling National Basketball Association Games
- 1.1. Variables and Data Under Study
- 1.2. Modeling Procedure
- 1.3. The Year 1989: The Pistons' First and Kareem's Last
- 1.4. A Contrast in Team Profiles: The Lakers and Pistons
- 1.5. Forecasting Results for Laker-Piston Playoff Games
- 1.6. Some implications of NBA Modeling Results
- Part V. Baseball
- 1. Evolution of a Ball and Stick Experiment
- 1.1. Mythology and Abner Doubleday
- 1.2. Ball, Stick, and Fertility Rites
- 1.3. Baseball in King Arthur's Court
- 1.4. Cartwright's Rules
- 1.5. The Demigod
- 1.6. Baseball and the Civil-Human Rights Movements
- 2. Modeling Major League Baseball Games
- 2.1. Variables and Data Under Study
- 2.2. Team Specific Versus Pitcher Specific Models
- 2.3. Modeling Procedure
- 2.4. The 1990 Season: According to Form--Except for Four Games
- 2.5. Team Profiles: Boston and Oakland
- 2.6. Pitcher Profiles: Clemens, Stewart, and Welch
- 2.7. Some Forecasting Results
- 2.8. Some implications of Major League Baseball Modeling Results
- Part VI. Selection of Athletes
- 1. The Belarussian Connection
- 1.1. Experiments in Transition
- 1.2. Factors Affecting Performance in the 500 Meter Run and Standing Long Jump
- 1.3. Discrimination Between Top Class Swimmers and Rowers
- Part VII. Financial Markets
- 1. On the Predictability of Short Term Currency Fluctuations
- 1.1. Modeling: To Forecast or to Induce Changes
- 1.2. Cracks in Random Walk Dogma
- 1.3. Modeling Parallels Between Sports Gambling and Currency Markets
- 1.4. Gambling Expectations From the Forward Market
- 1.5. Japanese Candlestick Configurations
- 1.6. Quantification of Candlestick Configurations for Use as Concomitant Variables in Modeling
- 1.7. The Weekly Yen/$U.S. Exchange Rate Through a Period of Volatility
- 1.8. The Yen/$U.S. Exchange Rate Analysis: Modeling Weekly Changes in Currency Rates
- 1.9. An Indication of Predictive Validity for the Yen Model
- 2. Modeling Short Term Fluctuations of Common Stock Issues
- 2.1. Estimating Gambling Expectation from the Options Markets
- 2.2. Volume Configurations as Concomitants
- 2.3. IBM Analysis: Modeling Weekly Price Changes
- 2.4. An Indication of Predictive Validity for the IBM Model
- 2.5. United Air Lines Time Series Analysis: Modeling Daily Price Changes
- 2.6. An Indication of Predictive Validity for the UAL Model
- 2.7. Multiple Time Series Analysis of United Air Lines and Delta Air Lines
- 2.8. Discriminant Analysis of Daily Price Changes: K Mart and Wal Mart Modeling
- 2.9. Modeling Mixtures of Time Series
- Appendix
- A.1. Time Series Analysis: Overview of Arma, Bilinear, and Higher Order Models
- A.1.1. Preliminary Comments
- A.1.2. Overview of Autoregressive Moving Average (ARMA) Models
- A.1.3. Overview of Bilinear Models
- A.1.4. Approaches to Modeling Heteroskedasticity Through Time Varying Coefficients
- A.1.5. Autoregressive Conditional Heteroskedasticity
- A.1.6. Generalized Autoregressive Conditional Heteroskedasticity
- A.1.7. ARMA Models with GARCH Errors
- A.1.8. Model Misspecification
- A.1.9. Least Squares Estimation for Non-Varying Coefficients
- A.1.10. Empirical Bayes Estimation for Time Varying Coefficients
- A.2. Multiple Time Series Equations
- A.2.1. Models Based on Wold's Decomposition Theorem
- A.2.2. Multiple, Higher-Order Systems of Time Series Equations
- A.2.3. Extensions to Rational Expectations
- A.2.4. Classification of Events According to Observed Outcomes and States of Nature in Currency Markets
- A.3. Quantification of Structural Effects in Regression Systems
- A.3.1. Preliminary Comments
- A.3.2. Structural and Reduced Systems: Exploratory Models and Assumptions
- A.3.3. Increasing Efficiency Through Restricted Systems: Adjustments for Intra Sample Biases
- A.3.4. Estimation in Structural Systems
- A.3.5. Examples of Model Ambiguity in Structural Systems
- A.3.6. Structural Experimental Design Reconsidered
- Index
- List of Figures