scholarly journals Ranking soccer teams on the basis of their current strength: A comparison of maximum likelihood approaches

2019 ◽  
Vol 19 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Christophe Ley ◽  
Tom Van de Wiele ◽  
Hans Van Eetvelde
Keyword(s):  
Maximum Likelihood ◽  
Statistical Models ◽  
Predictive Performance ◽  
Current Strength ◽  
Soccer Match ◽  
Poisson Models ◽  
Strength Based ◽  
Long Time ◽  
Importance Factor ◽  
Soccer Teams

We present 10 different strength-based statistical models that we use to model soccer match outcomes with the aim of producing a new ranking. The models are of four main types: Thurstone–Mosteller, Bradley–Terry, independent Poisson and bivariate Poisson, and their common aspect is that the parameters are estimated via weighted maximum likelihood, the weights being a match importance factor and a time depreciation factor giving less weight to matches that are played a long time ago. Since our goal is to build a ranking reflecting the teams’ current strengths, we compare the 10 models on the basis of their predictive performance via the Rank Probability Score at the level of both domestic leagues and national teams. We find that the best models are the bivariate and independent Poisson models. We then illustrate the versatility and usefulness of our new rankings by means of three examples where the existing rankings fail to provide enough information or lead to peculiar results.

How strong are soccer teams? The ‘Host paradox’ and other counterintuitive properties of the FIFA’s ranking.

2019 ◽  
Author(s):  
Marek Kaminski
Keyword(s):  
Long Time ◽  
Soccer Teams ◽  
Major Loss ◽  
Host Effect ◽  
Large Investment

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Helvetica Neue'} <p>The FIFA's ranking of national soccer teams is plagued with paradoxes. One surprising paradox is a dramatic underrating of the hosts of main tournaments. The hosts, who are absent from the preliminaries, for a long time, play only friendlies that award few points. Three models estimate the magnitude of the resulting “Host Effect” at 14.1-16.7 positions. Such an estimate goes against the intuition that a large investment in hosting a tournament should result in improvement of the host team’s standing. Host’s low ranking decreases the interest in the tournament and may result in a major loss of advertisement revenue.</p>

A Method to Treat Some Dynamical Statistical Models

1998 ◽  
Vol 06 (04) ◽  
pp. 357-375
Author(s):  
Gabriela Ciuperca
Keyword(s):  
Maximum Likelihood ◽  
Experimental Design ◽  
Glucose Metabolism ◽  
Nonlinear System ◽  
Statistical Models ◽  
Optimal Experimental Design ◽  
State Variables ◽  
System Of Differential Equations ◽  
Likelihood Estimator ◽  
Jackknife Estimator

In this paper we present a method for the estimation of the parameters of models described by a nonlinear system of differential equations: we study the maximum likelihood estimator and the jackknife estimator for parameters of the system and for the covariance matrix of the state variables and we seek possible linear relations between parameters. We take into account the difficulty due to the small number of observations. The optimal experimental design for this kind of problem is determined. We give an application of this method for the glucose metabolism of goats.

scholarly journals A DDoS Attacks Detection Based on Conditional Heteroscedastic Time Series Models

2015 ◽  
Vol 20 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Tomasz Andrysiak ◽  
Łukasz Saganowski ◽  
Mirosław Maszewski ◽  
Piotr Grad
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Present Article ◽  
Network Traffic ◽  
Statistical Models ◽  
Likelihood Function ◽  
Estimation Error ◽  
Time Series Models ◽  
Ddos Attacks ◽  
Network Infrastructure

Abstract Dynamic development of various systems providing safety and protection to network infrastructure from novel, unknown attacks is currently an intensively explored and developed domain. In the present article there is presented an attempt to redress the problem by variability estimation with the use of conditional variation. The predictions of this variability were based on the estimated conditional heteroscedastic statistical models ARCH, GARCH and FIGARCH. The method used for estimating the parameters of the exploited models was determined by calculating maximum likelihood function. With the use of compromise between conciseness of representation and the size of estimation error there has been selected as a sparingly parameterized form of models. In order to detect an attack-/anomaly in the network traffic there were used differences between the actual network traffic and the estimated model of the traffic. The presented research confirmed efficacy of the described method and cogency of the choice of statistical models.

scholarly journals SWALO: scaffolding with assembly likelihood optimization

2016 ◽  
Author(s):  
Atif Rahman ◽  
Lior Pachter
Keyword(s):  
Maximum Likelihood ◽  
Genome Assembly ◽  
Statistical Models ◽  
Substantial Improvement ◽  
Generative Models ◽  
Large Datasets ◽  
Maximum Likelihood Estimates ◽  
Link Type ◽  
Genome Assemblies

AbstractScaffolding i.e. ordering and orienting contigs is an important step in genome assembly. We present a method for scaffolding based on likelihoods of genome assemblies. Generative models for sequencing are used to obtain maximum likelihood estimates of gaps between contigs and to estimate whether linking contigs into scaffolds would lead to an increase in the likelihood of the assembly. We then link contigs if they can be unambiguously joined or if the corresponding increase in likelihood is substantially greater than that of other possible joins of those contigs. The method is implemented in a tool called Swalo with approximations to make it efficient and applicable to large datasets. Analysis on real and simulated datasets reveals that it consistently makes more or similar number of correct joins as other scaffolders while linking very few contigs incorrectly, thus outperforming other scaffolders and demonstrating that substantial improvement in genome assembly may be achieved through the use of statistical models. Swalo is freely available for download at https://atifrahman.github.io/SWALO/.

scholarly journals How strong are soccer teams? The ‘Host paradox’ and other counterintuitive properties of the FIFA’s ranking.

2019 ◽  
Author(s):  
Marek Kaminski
Keyword(s):  
Long Time ◽  
Soccer Teams ◽  
Major Loss ◽  
Host Effect ◽  
Large Investment

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Helvetica Neue'} <p>FIFA's ranking of national soccer teams is plagued with paradoxes. One surprising paradox is a dramatic underrating of the hosts of main tournaments. The hosts, who are absent from the preliminaries, for a long time, play only friendlies that award few points. Three models estimate the magnitude of the resulting “Host Effect” at 14.1-16.7 positions. Such an estimate goes against the intuition that a large investment in hosting a tournament should result in improvement of the host team’s standing. Host’s low ranking decreases the interest in the tournament and may result in a major loss of advertisement revenue.</p>

A hybrid random forest to predict soccer matches in international tournaments

2019 ◽  
Vol 15 (4) ◽  
pp. 271-287 ◽  
Author(s):  
Andreas Groll ◽  
Cristophe Ley ◽  
Gunther Schauberger ◽  
Hans Van Eetvelde
Keyword(s):  
Random Forest ◽  
Predictive Performance ◽  
Building Blocks ◽  
Current Strength ◽  
Ranking Methods ◽  
World Cup ◽  
Validation Data ◽  
Data Set ◽  
Covariate Information ◽  
Betting Odds

Abstract In this work, we propose a new hybrid modeling approach for the scores of international soccer matches which combines random forests with Poisson ranking methods. While the random forest is based on the competing teams’ covariate information, the latter method estimates ability parameters on historical match data that adequately reflect the current strength of the teams. We compare the new hybrid random forest model to its separate building blocks as well as to conventional Poisson regression models with regard to their predictive performance on all matches from the four FIFA World Cups 2002–2014. It turns out that by combining the random forest with the team ability parameters from the ranking methods as an additional covariate the predictive power can be improved substantially. Finally, the hybrid random forest is used (in advance of the tournament) to predict the FIFA World Cup 2018. To complete our analysis on the previous World Cup data, the corresponding 64 matches serve as an independent validation data set and we are able to confirm the compelling predictive potential of the hybrid random forest which clearly outperforms all other methods including the betting odds.

scholarly journals Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 37 ◽  
Author(s):  
Golden ◽  
Henley ◽  
White ◽  
Kashner
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Data Model ◽  
Statistical Models ◽  
Incomplete Data ◽  
Likelihood Function ◽  
Model Misspecification ◽  
Complete Data ◽  
Regularity Conditions ◽  
Observable Data

Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is misspecified. In this article, we create a formal theoretical framework for developing statistical models and detecting model misspecification in the presence of incomplete data where maximum likelihood estimates are obtained by maximizing the observable-data likelihood function when the missing-data mechanism is assumed ignorable. First, we provide sufficient regularity conditions on the researcher’s complete-data model to characterize the asymptotic behavior of maximum likelihood estimates in the simultaneous presence of both missing data and model misspecification. These results are then used to derive robust hypothesis testing methods for possibly misspecified models in the presence of Missing at Random (MAR) or Missing Not at Random (MNAR) missing data. Second, we introduce a method for the detection of model misspecification in missing data problems using recently developed Generalized Information Matrix Tests (GIMT). Third, we identify regularity conditions for the Missing Information Principle (MIP) to hold in the presence of model misspecification so as to provide useful computational covariance matrix estimation formulas. Fourth, we provide regularity conditions that ensure the observable-data expected negative log-likelihood function is convex in the presence of partially observable data when the amount of missingness is sufficiently small and the complete-data likelihood is convex. Fifth, we show that when the researcher has correctly specified a complete-data model with a convex negative likelihood function and an ignorable missing-data mechanism, then its strict local minimizer is the true parameter value for the complete-data model when the amount of missingness is sufficiently small. Our results thus provide new robust estimation, inference, and specification analysis methods for developing statistical models with incomplete data.

Comparisons among Earthquake Codes

1989 ◽  
Vol 5 (4) ◽  
pp. 767-789 ◽  
Author(s):  
RenéW. Luft
Keyword(s):  
Review Paper ◽  
Construction Materials ◽  
Base Shear ◽  
Limit States ◽  
Hazard Exposure ◽  
Moderate Seismicity ◽  
Strength Based ◽  
Importance Factor ◽  
Seismic Hazard Exposure ◽  
High Seismicity

This review paper compares ANSI, NEHRP, SEAOC, and UBC. A few essential differences among these documents are as follows: (a) The NEHRP document gives force levels corresponding to a strength-based or limit states design, while the other three documents give force levels that correspond to working or service stress design; (b) the importance factor is used as a multiplier of base shear level in all documents except NEHRP, which treats building importance by a seismic hazard exposure group; (c) NEHRP and UBC-1988 contain detailing requirements for all common construction materials and all seismic zones, while UBC-1985 contains detailing requirements for zones of high seismicity but only limited requirements for zones of moderate seismicity; (d) P-delta analysis is specified by NEHRP for all buildings that must be analyzed, by SEAOC for buildings that exceed drift limits, by UBC-1988 for all buildings except those in Zones 3 and 4 meeting drift limits, and is not specified by ANSI.

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