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This article discusses the properties of various knockout tournament designs and presents theoretical results. Potential playoff schemes for Division I-A football are examined via simulation studies. Several metrics are used to assess the relative merits of playoff scenarios, which differ in number, selection, and seeding of playoff teams. Most suggest that college football would benefit from a limited playoff system. Interestingly, for the class of playoff systems examined, the number of teams influences the performance far more than does the seeding procedure.

Box's paper helicopter has been used to teach experimental design for more than a decade. It is simple, inexpensive, and provides real data for an involved, multifactor experiment. Unfortunately it can also further an all-too-common practice that Professor Box himself has repeatedly cautioned against, namely ignoring the fundamental science while rushing to solve problems that may not be sufficiently understood. Often this slighting of the science so as to get on with the statistics is justified by referring to Box's oft-quoted maxim that “All models are wrong, however some are useful.” Nevertheless, what is equally true, to paraphrase both Professor Box and George Orwell, is that “All models are wrong, but some are more wrong than others.” To experiment effectively it is necessary to understand the relevant science so as to distinguish between what is usefully wrong, and what is dangerously wrong.This article presents an improved analysis of Box's helicopter problem relying on statistical and engineering...

In this paper, we propose a team rating system that accounts for differences in strength of schedule by computing an additive correction factor which adjusts team winning percentage. This approach permits teams to be ranked appropriately despite disparate schedules. Our method is easy to compute as it only requires a simple matrix inversion, and we prove that the needed inverse exists for all reasonable classes of schedules. We also prove some mathematical properties of the ratings system and apply our method to data from the 2007 Major League Baseball season.

Rating -- and subsequently ranking -- college football teams requires making sense of sometimes conflicting pair-wise comparisons. Classical statistical techniques fall into one of two classes: win/loss models, which focus on binary outcomes, and point-scoring models, which consider the distribution of component scores. Annis and Craig (2005) illustrate deficiencies of both, and propose a hybrid method that considers both sources of data. Their method, while providing satisfactory results in many circumstances, can be difficult to implement numerically. This paper presents a refinement of their hybrid rating algorithm which preserves their original intent but simplifies greatly its implementation. Like its predecessor, the new model enjoys robustness to model misspecification, while offering substantial simplification.

Existing paired comparison models used for ranking football teams primarily focus on either wins and losses or points scored (either via each team's total or a margin of victory). While reasonable, each approach fails to produce satisfactory rankings in frequently arising situations due to its ignorance of additional data. We propose a new, hybrid model incorporating both wins and constituent scores and show that it outperforms its competitors and is robust against model mis-specification based on a series of simulation studies. We conclude by illustrating the method using the 2003-04 and 2004-05 college football seasons.

(2006). Baseball's All-Time Best Hitters: How Statistics Can Level the Playing Field and Baseball's All-Time Best Sluggers: Adjusted Batting Performance from Strikeouts to Home Runs. The American Statistician: Vol. 60, No. 1, pp. 97-97.