Visualization of Crew Race Performance: Drives and Duels
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Jeffrey L Cornett
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 paper 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 paper makes several contributions to visualizing the dynamics of crew race strategy and performance:1. A generic drive model that decomposes how pairs of crews duel in a race.2. Graphical summaries of the rates and locations of successful and unsuccessful drives.3. Contour lines of the margins that winning crews hold over the course of the race.4. 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.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Conference Paper
- Predicting Overtime with the Pythagorean Formula
- Using Game Theory to Optimize Performance in a Best-of-N Set Match
- A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity
- Anomalies in Tournament Design: The Madness of March Madness
- Skill Importance in Women's Volleyball
- Quantifying the Effect of Performance-Enhancing Drug Use on Fastball Velocity in Major League Baseball
- A New Iterative Method for Ranking College Football Teams
- Receiver Operating Characteristic (ROC) Curves for Measuring the Quality of Decisions in Cricket
- Visualization of Crew Race Performance: Drives and Duels
- Valuations of Soccer Players from Statistical Performance Data
- Measuring Risk in NFL Playcalling
- Using Random Forests and Simulated Annealing to Predict Probabilities of Election to the Baseball Hall of Fame
- The 2009 New England Symposium on Statistics in Sports
