Adjusting Winning-Percentage Standard Deviations and a Measure of Competitive Balance for Home Advantage

Trandel, Gregory A.; Maxcy, Joel

doi:10.2202/1559-0410.1297

Cited by 24 publications

(28 citation statements)

References 18 publications

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“…RSD represents the competitive balance: the smaller the ratio the lesser the differences in team abilities and, therefore, greater competitive balance; the higher the ratio the greater the differences in team abilities and, hence, lesser competitive balance. This method was improved by Trandel and Maxcy (2011) , who realized that the calculation of ISD did not allow for home advantage, because it was based on the consideration that all the teams had the same ability, and, thus, had a probability of 0.5 (50%) to win any game against any team, either home or away. Trandel and Maxcy’s paper describes a formula that can be used to calculate a home-advantage-corrected ISD, and, therefore, a corrected measure of competitive balance.…”

Section: Methodsmentioning

confidence: 99%

Home Advantage in Men’s and Women’s Spanish First and Second Division Water Polo Leagues

Bermejo

Gómez

Pollard

2013

Journal of Human Kinetics

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The purpose of this study was to quantify the home advantage in both men’s and women’s First and Second Division water polo leagues, to compare the results obtained according to sex of participants and the level of competition, and to test for possible differences in home advantage when considering the interaction between these two factors. The sample comprised four seasons from 2007–2008 to 2010–2011 for a total of 1942 games analyzed. The results showed the existence of home advantage in both men’s and women’s First and Second Divisions. After controlling for the competitive balance of each league in each season, there was a significant difference between men’s and women’s leagues, with higher home advantage for men’s leagues (58.60% compared with 53.70% for women’s leagues). There was also a significant difference between the levels of competition, with greater home advantage for the Second Division (57.95% compared with 54.35% for First Division). No significant differences in home advantage were found when considering the interaction between sex of participants and the level of competition. The results in relation to sex of participants and the level of competition are consistent with previous studies in other sports such as football or handball.

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Section: Methodsmentioning

confidence: 99%

Home Advantage in Men’s and Women’s Spanish First and Second Division Water Polo Leagues

Bermejo

Gómez

Pollard

2013

Journal of Human Kinetics

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“…If draws are feasible, then we use the corresponding ISD =

\sqrt{(1 - d) / 4 G}

for a (2,1,0) points scheme, or

\sqrt{[(1 - d) (d + 9) / 4] / 9 G}

for a (3,1,0) points scheme, where d is the simulated probability of a draw in that season (Owen, , Equations (2′) and (3′), respectively). If H ≠ 0, we use the ISD expression derived by Trandel and Maxcy (, 10) that allows for home advantage…”

Section: Simulation Designmentioning

confidence: 99%

“…The home‐advantage‐corrected ISD calculated by Trandel and Maxcy () treats a draw, if feasible, as half a win. We therefore apply this form of ISD only to the case of home advantage with no draws or with draws treated as half a win.…”

mentioning

confidence: 99%

Competitive Balance Measures in Sports Leagues: The Effects of Variation in Season Length

Owen

King

2014

Economic Inquiry

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Appropriate measurement of competitive balance is central to the economic analysis of professional sports leagues. We examine the distributional properties of the ratio of standard deviations (RSD) of points percentages, the most widely used measure of competitive balance in the sports economics literature, in comparison with other standard-deviation-based measures. Simulation methods are used to evaluate the effects of changes in season length on the distributions of competitive balance measures for different distributions of the strengths of teams in a league. The popular RSD measure performs as expected only in cases of perfect balance; if there is imbalance in team strengths, its distribution is sensitive to changes in season length. It is therefore not recommended for comparisons of competitive balance for different sports leagues with different numbers of teams and/or games played. (JEL L83, D63, C63) * We are grateful to the co-editor, Jeff Borland, and an anonymous referee for their very helpful and constructive suggestions.

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“…N training and test sets are created and applied to the models 3. Draft Age interacting with Position Variable 1 x i, 10 Draft Age interacting with Position Variable 2 x i, 11 Draft Age interacting with Nationality Variable 3 x i, 12 Draft Age interacting with Nationality Variable 4 x i, 13 Overall Pick interacting with Position Variable 1 x i, 14 Overall Pick interacting with Position Variable 2 x i, 15 Overall Pick interacting with Nationality Variable 3 x i, 16 Overall Pick interacting with Nationality Variable 4…”

Section: Model Development: the Voting Methodsmentioning

confidence: 99%

“…The works of Pollard [11] in 2005, and more recently, Doyle and Leard [12] in 2012 confirm that home teams consistently win more games. Trandel and Maxcy [13] derived a balanced league standard deviation formula of winning percentages that takes into account the home advantage. They used this new formula to recompute the standard deviation ratios for major sports leagues and they considered the competitive balance in the various leagues.…”

Section: Introduction To Ice Hockeymentioning

confidence: 99%

Mining NHL Draft Data and A New Value Pick Chart

Wilson¹

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For every hockey player, getting drafted to the National Hockey League (NHL) is a dream come true, but the real goal is to reach 160 games played (GP) for pension reasons. First, a simple model was fit to NHL draft years 1998 to 2009 with an aim at predicting the proportion of players who will play a specified number of games in future years. For individual players drafted between 1998 and 2011, predictive models (Generalized Linear Model, Artificial Neural Network, Support Vector Machine and a LOESS) were created to predict a player's career GP. These models were combined to create a voting model to decide whether or not a player will reach 160 GP -a tool that can be used by any team, player or agent. Next, all players drafted in 1998 to 2008 were analysed using a non-linear multivariate model, with a modified weighted least squares, to predict a player's Time-On-Ice (TOI) for their first seven seasons. Position and Nationality dummy variables were used to distinguish between players. The seven seasons of TOI were then summed and smoothed using a LOESS. The graphs that resulted were analysed for positional advantage, then converted to a proportion and multiplied by a value of 1000 to create positional and Nationality value pick charts, which may be used by teams when choosing a draft pick or considering a trade, as well as player agents when negotiating player salaries.ii

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Adjusting Winning-Percentage Standard Deviations and a Measure of Competitive Balance for Home Advantage

Cited by 24 publications

References 18 publications

Home Advantage in Men’s and Women’s Spanish First and Second Division Water Polo Leagues

Home Advantage in Men’s and Women’s Spanish First and Second Division Water Polo Leagues

Competitive Balance Measures in Sports Leagues: The Effects of Variation in Season Length

Mining NHL Draft Data and A New Value Pick Chart

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