Siméon Poisson and the National Hockey League

Mullet, Gary M.

doi:10.1080/00031305.1977.10479184

Cited by 12 publications

(6 citation statements)

References 7 publications

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“…For hockey, this literature would include Mullet (1977), Hurley (1995), Danehy and Lock (1995), Anderson-Cook and Thornton (1998), Berry (2000), and Gill (2000), and an interesting series of papers on when to pull the goalie (see Morrison 1976;Morrison and Wheat 1986;Erkut 1987;Nydick and Weiss 1989;Washburn 1991;Zaman 2002). Research on times for soccer goals includes Chu (2003), Dixon and Coles (1997), and Lee (1997).…”

Section: The Sample Datamentioning

confidence: 97%

Is the Overtime Period in an NHL Game Long Enough? An Example for Teaching Estimation and Hypothesis Testing in the Presence of Censored Data

Brimberg

Hurley

2008

The American Statistician

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This note outlines an approach for introducing students to estimation and hypothesis testing with censored data using a National Hockey League example. We consider the effects of extending the overtime period in a regular season game beyond its current length of five minutes. The rationale for this change is that more games would be decided on the basis of four-on-four play rather than on a shootout. In order to make this assessment, we must estimate the parameter of the exponential distribution. We used data from the 281 NHL games that went to overtime during the 2005-2006 season. Of these, more than half went to a shootout and hence these potential observations of the time to a goal are censored and must be taken into account in the estimation. For more advanced students, we offer the mechanics of a likelihood ratio test to confirm that four-on-four play in overtime is a much different game than five-on-five in regulation play.

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Section: The Sample Datamentioning

confidence: 97%

Is the Overtime Period in an NHL Game Long Enough? An Example for Teaching Estimation and Hypothesis Testing in the Presence of Censored Data

Brimberg

Hurley

2008

The American Statistician

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“…The number of goals scored by each game during a fixed time interval can be modeled using independent Poisson processes (Mullet, 1977). During the course of a game, each team makes, on average, about 25 shots at the goal with about 10% success rate.…”

Section: Icehockeymentioning

confidence: 99%

“…Poisson distribution is an appropriate model for low scoring sports like ice hockey and soccer. Mullet (1977) Dixon and Coles (1997) and Lee (1997) used the Poisson model for the soccer scores in English leagues.…”

Section: Introductionmentioning

confidence: 99%

Late-Game Reversals in Professional Basketball, Football, and Hockey

Gill

2000

The American Statistician

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Empirical evidence suggests that in .basketball, football, and hockey, the leader at the beginning of the final period (quarter or period) wins the game about 80% of the time. We discuss modeling of late-game reversals in NBA, NFL, and NHL sports. The models are built around the assumptions that basketball scores and football scores are normally distributed and hockey scores vary according to a Poisson distribution. The models also accommodate the proverbial home field advantage. We use data from the 1997-1998 regular seasons of the leagues to estimate the parameters for the models. Predictions from the probabilistic models are in excellent agreement with the actual outcomes.

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“…Operations research (OR)-focused research in hockey has considered topics such as modeling the goal-scoring process (Mullet 1977), scheduling (Costa 1995, Ferland and Fleurent 1991, Fleurent and Ferland 1993, calculating the probability of making the playoffs (Ingolfsson 2004, Russell andvanBeek 2009), modeling different game states to determine successful team strategies (Thomas 2006), and modeling the win-loss percentage using a function of goals scored or allowed (Cochran and Blackstock 2009). Ingolfsson (2010) presents a comprehensive review of OR analyses in hockey.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Contribution of NHL Player Types to Team Performance

2012

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In this paper, we use k-means clustering to define distinct player types for each of the three positions on a National Hockey League (NHL) team and then use regression to determine a quantitative relationship between team performance and the player types identified in the clustering. Using NHL regular-season data from 2005-2010, we identify four forward types, four defensemen types, and three goalie types. Goalies tend to contribute the most to team performance, followed by forwards and then defensemen. We also show that once we account for salary cap and playing-time information, the value of different player types may become similar. Lastly, we illustrate how to use the regression results to analyze trades and their impact on team performance.

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Siméon Poisson and the National Hockey League

Cited by 12 publications

References 7 publications

Is the Overtime Period in an NHL Game Long Enough? An Example for Teaching Estimation and Hypothesis Testing in the Presence of Censored Data

Is the Overtime Period in an NHL Game Long Enough? An Example for Teaching Estimation and Hypothesis Testing in the Presence of Censored Data

Late-Game Reversals in Professional Basketball, Football, and Hockey

Quantifying the Contribution of NHL Player Types to Team Performance

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