Keywords

Rowing, crew race strategy, drive model, probabilistically decisive lead, video analysis, visualization software

Abstract

Crew race strategy is typically formulated by coaches based on rowing tradition and years of experience. However, coaching strategies are not generally supported by empirical evidence and decision-support models. Previous models of crew race strategy have been constrained by the sparse information published on crew race performance (quarterly 500-meter splits). Empirical research has merely summarized which quarterly splits averaged the fastest and slowest relative to the other splits and relative to the average speed of the other competitors. Video records of crew race world championships provide a rich source of data for those capable and patient enough to mine this level of detail. This dissertation is based on a precise frame-by-frame video analysis of five world championship rowing finals. With six competing crews per race, a database of 75 race-pair duels was compiled that summarizes race positioning, competitive drives, and relative stroke rates at 10-meter intervals recorded with photo-finish precision (30 frames per second). The drive-based research pioneered in this dissertation makes several contributions to understanding the dynamics of crew race strategy and performance: 1) An 8-factor conceptual model of crew race performance. 2) A generic drive model that decomposes how pairs of crews duel in a race. 3) Graphical summaries of the rates and locations of successful and unsuccessful drives. 4) Contour lines of the margins that winning crews hold over the course of the race. 5) Trend lines for what constitutes a probabilistically decisive lead as a function of position along the course, seconds behind the leader, and whether the trailing crew is driving. This research defines a new drive-based vocabulary for evaluating crew race performance for use by coaches, competitors and race analysts. The research graphically illustrates situational parameters helpful in formulating race strategy and guiding real-time decision-making by competitors. This research also lays the foundation for future industrial engineering decision-support models and associated parameters as applied to race strategy and tactics.

Notes

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Graduation Date

2008

Advisor

Bush, Pamela

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0002428

URL

http://purl.fcla.edu/fcla/etd/CFE0002428

Language

English

Release Date

December 2008

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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