This paper considers an extension of the Kelly criterion used in sports wagering. By recognizing that the probabilitypof placing a correct wager is unknown, modified Kelly criteria are obtained that take the uncertainty into account. Estimators are proposed that are developed from a decision theoretic framework. We observe that the resultant betting fractions can differ markedly based on the choice of loss function. In the cases that we study, the modified Kelly fractions are smaller than original Kelly.
This paper investigates the fouling time distribution of players in the National Basketball Association. A Bayesian analysis is presented based on the assumption that fouling time distributions follow a gamma distribution. Various insights are obtained including the observation that players accumulate fouls at a rate that increases with the current number of fouls. We demonstrate possible ways to incorporate the fouling time distributions to provide decision support to coaches in the management of playing time.
Purpose The purpose of this paper is to investigate National Hockey League (NHL) expansion draft decisions to measure divestment aversion and endowment effects, and analyze bias and its affect on presumed rational analytic decision making. Design/methodology/approach A natural experiment with three variables (age, minutes played and presence of a prior relationship with a team’s management), filtered athletes that were exposed or protected to selection. A machine learning algorithm trained on a group of 17 teams was applied to the remaining 13 teams. Findings Athletes with pre-existing management relationships were 1.7 times more likely to be protected. Athletes playing fewer relative position minutes were less likely to be protected, as were older athletes. Athlete selection was predominantly determined by time on ice. Research limitations/implications This represents a single set of independent decisions using publicly available data absent of context. The results may not be generalizable beyond the NHL or sport. Practical implications The research confirms the affect of prior relationships on decision making and provides further evidence of measurable sub-optimal decision making. Social implications Decision making has implications throughout human resources and impacts competitiveness and productivity. This adds to the need for managers to recognize and implement de-biasing in areas such as hiring, performance appraisal and downsizing. Originality/value This natural experiment involving high-stakes decision makers confirms bias in a setting that has been dominated by students, low stakes or artificial settings.
The pacing strategy adopted by athletes is a major determinants of success during timed competition. Various pacing profiles are reported in the literature and its importance depends on the mode of sport. However, in 2000 metre rowing, the definition of these pacing profiles has been limited by the minimal availability of data. Purpose: Our aim is to objectively identify pacing profiles used in World Championship 2000 metre rowing races using reproducible methods. Methods: We use the average speed for each 50 metre split for each available boat in every race of the Rowing World Championships from 2010-2017. This data was scraped from www.worldrowing.com. This data set is publicly available (https://github.com/danichusfu/rowing_pacing_profiles) to help the field of rowing research. Pacing profiles are determined by using k-shape clustering, a time series clustering method. A multinomial logistic regression is then fit to test whether variables such as boat size, gender, round, or rank are associated with pacing profiles. Results: Four pacing strategies (Even, Positive, Reverse J-Shaped, and U-Shaped) are identified from the clustering process. Boat size, round (Heat vs Finals), rank, gender, and weight class are all found to affect pacing profiles. Conclusion: We use an objective methodology with more granular data to identify four pacing strategies. We identify important associations between these pacing profiles and race factors. Finally, we make the full data set public to further rowing research and to replicate our results.
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