On the Probability of Winning a Football Game

Stern, Hal

doi:10.1080/00031305.1991.10475798

Cited by 71 publications

(51 citation statements)

References 10 publications

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“…So on a volatility scale the 4 point advantage assessed for the 49ers is under a 1 2 σ favorite. Another finding is that the inferred σ = 10.6 is historically low, as the typical volatility of an NFL game is approximately 14 points (see Stern, 1991). One possible explanation is that for a competitive game like this, one might expect a lower than usual volatility.…”

Section: Our Model Allows Us To Provide Answers For a Number Of Impormentioning

confidence: 97%

The implied volatility of a sports game

Polson¹

2015

Journal of Quantitative Analysis in Sports

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In this paper we provide a method for calculating the implied volatility of the outcome of a sports game. We base our analysis on Stern's stochastic model for the evolution of sports scores (Stern, 1994). Using bettors' point spread and moneyline odds, we extend the model to calculate the market-implied volatility of the game's score. The model can also be used to calculate the time-varying implied volatility during the game using inputs from real-time, online betting and to identify betting opportunities. We illustrate our methodology on data from Super Bowl XLVII between the Baltimore Ravens and the San Francisco 49ers and show how the market-implied volatility of the outcome varied as the game progressed.

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Section: Our Model Allows Us To Provide Answers For a Number Of Impormentioning

confidence: 97%

The implied volatility of a sports game

Polson¹

2015

Journal of Quantitative Analysis in Sports

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“…A few landmark studies, including Harville (1980) and Stern (1991), used data from National Football League (NFL) games to argue that, in general, point spreads should act as the standards on which to judge any pre-game predictions. While recent work has looked at gambling markets within, for example, European soccer (Constantinou, Fenton, and Neil, 2013), the Women's National Basketball Association , the NFL (Nichols, 2014), and NCAA men's football (Linna, Moore, Paul, and Weinbach, 2014), most research into the efficiency of men's college basketball markets was produced several years ago.…”

Section: The Las Vegas Point Spreadmentioning

confidence: 99%

Building an NCAA men’s basketball predictive model and quantifying its success

Lopez

Matthews

2015

Journal of Quantitative Analysis in Sports

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Computing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts than traditional statistical tools. In this manuscript, we provide evidence that the combination of modest statistical methods with informative data can meet or exceed the accuracy of more complex models when it comes to predicting the NCAA men’s basketball tournament. First, we describe a prediction model that merges the point spreads set by Las Vegas sportsbooks with possession based team efficiency metrics by using logistic regressions. The set of probabilities generated from this model most accurately predicted the 2014 tournament, relative to approximately 400 competing submissions, as judged by the log loss function. Next, we attempt to quantify the degree to which luck played a role in the success of this model by simulating tournament outcomes under different sets of true underlying game probabilities. We estimate that under the most optimistic of game probability scenarios, our entry had roughly a 12% chance of outscoring all competing submissions and just less than a 50% chance of finishing with one of the ten best scores.

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“…We have not attempted to verify whether the actual points scored in basketball games follow the Poisson distribution, as we are less interested in the statistical truths of point spread distributions than in improved performance in office pools. However, empirical support for the use of the normal distribution (albeit with a constant standard deviation) as a model for point spreads in football is presented in Stern (1991).…”

Section: An Expert Rating Modelmentioning

confidence: 99%

“…For example, a reviewer suggested that by slowing the offensive tempo of the game via use of the shot clock, a team could both reduce the number of points it scores as well as those scored by its opponent (by denying them time with the ball), inducing a positive correlation. Citing Stern's (1991) football study, Carlin (1996) has argued that the actual point spreads in NCAA tournament games roughly follow a normal distribution, but with a constant standard deviation. Such a model was also employed by Breiter and Carlin (1997).…”

Section: An Expert Rating Modelmentioning

confidence: 99%

March Madness and the Office Pool

Kaplan

Garstka

2001

Management Science

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March brings March Madness, the annual conclusion to the U.S. men's college basketball season with two single elimination basketball tournaments showcasing the best college teams in the country. Almost as mad is the plethora of office pools across the country where the object is to pick a priori as many game winners as possible in the tournament. More generally, the object in an office pool is to maximize total pool points, where different points are awarded for different correct winning predictions. We consider the structure of single elimination tournaments, and show how to efficiently calculate the mean and the variance of the number of correctly predicted wins (or more generally the total points earned in an office pool) for a given slate of predicted winners. We apply these results to both random and Markov tournaments. We then show how to determine optimal office pool predictions that maximize the expected number of points earned in the pool. Considering various Markov probability models for predicting game winners based on regular season performance, professional sports rankings, and Las Vegas betting odds, we compare our predictions with what actually happened in past NCAA and NIT tournaments. These models perform similarly, achieving overall prediction accuracies of about 58%, but do not surpass the simple strategy of picking the seeds when the goal is to pick as many game winners as possible. For a more sophisticated point structure, however, our models do outperform the strategy of picking the seeds.

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On the Probability of Winning a Football Game

Cited by 71 publications

References 10 publications

The implied volatility of a sports game

The implied volatility of a sports game

Building an NCAA men’s basketball predictive model and quantifying its success

March Madness and the Office Pool

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