How well do Elo-based ratings predict professional tennis matches?

AbstractThis paper examines the performance of five different measures for forecasting men’s and women’s professional tennis matches. We use data derived from every match played at the 2018 and 2019 Wimbledon tennis championships, the 2019 French Open, the 2019 US Open, and the 2020 Australian Open. We look at the betting odds, the official tennis rankings, the standard Elo ratings, surface-specific Elo ratings, and weighted composites of these ratings, including and excluding the betting odds. The performance indicators used are prediction accuracy, calibration, model discrimination, Brier score, and expected return. We find that the betting odds perform relatively well across these tournaments, while standard Elo (especially for women’s tennis) and surface-adjusted Elo (especially for men’s tennis) also perform well on a range of indicators. For all but the hard-court surfaces, a forecasting model which incorporates the betting odds tends also to perform well on some indicators. We find that the official ranking system proved to be a relatively poor measure of likely performance compared to betting odds and Elo-related methods. Our results add weight to the case for a wider use of Elo-based approaches within sports forecasting, as well as arguably within the player rankings methodologies.

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Study of a discrete grey forecasting model based on the quality cost characteristic curve

Grey Systems Theory and Application ◽

10.1108/gs-06-2017-0016 ◽

2017 ◽

Vol 7 (3) ◽

pp. 376-384 ◽

Cited By ~ 5

Author(s):

Wenjie Dong ◽

Sifeng Liu ◽

Zhigeng Fang ◽

Xiaoyu Yang ◽

Qian Hu ◽

...

Keyword(s):

Prediction Accuracy ◽

Characteristic Curve ◽

Grey System Theory ◽

Forecasting Model ◽

Quality Level ◽

Cost Models ◽

Quality Cost ◽

Content Type ◽

Grey Forecasting ◽

Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

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Measuring The Tensile Strain of Wood By Visible And Near-Infrared Spatially Resolved Spectroscopy

10.21203/rs.3.rs-570550/v1 ◽

2021 ◽

Author(s):

Ma Te ◽

Tetsuya Inagaki ◽

Masato Yoshida ◽

Mayumi Ichino ◽

Satoru Tsuchikawa

Keyword(s):

Light Scattering ◽

Prediction Accuracy ◽

Tensile Strain ◽

Near Infrared ◽

Mean Squared Error ◽

Calibration Model ◽

Spatially Resolved ◽

Squared Error ◽

Stiffness Evaluation ◽

Spatially Resolved Spectroscopy

Abstract Wood has various mechanical properties, so stiffness evaluation is critical for quality management. Using conventional strain gauges constantly is high cost, also challenging to measure precious wood materials due to the use of strong adhesive. This study demonstrates the correlation between light scattering changes inside the wood cell walls and tensile strain. A multifiber-based visible-near-infrared (Vis–NIR) spatially resolved spectroscopy (SRS) system was designed to rapidly and conventiently acquire such light scattering changes. For the preliminary experiment, samples with different thicknesses were measured to evaluate the influence of thickness. The differences in Vis–NIR SRS spectral data diminish with an increase in sample thickness, which suggests that the SRS method can successfully measure the whole strain (i.e., surface and inside) of wood samples. Then, for the primary experiment, 18 wood samples with the same thickness (2 mm) were tested to construct a strain calibration model. The prediction accuracy was characterized by a determination coefficient (R2) of 0.86 with a root mean squared error (RMSE) of 297.89 με for five-fold cross-validation; for test validation, The prediction accuracy was characterized by an R2 of 0.82 and an RMSE of 345.44 με.

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Foundation Settlement Prediction Based on a Novel NGM Model

Mathematical Problems in Engineering ◽

10.1155/2014/242809 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 9

Author(s):

Peng-Yu Chen ◽

Hong-Ming Yu

Keyword(s):

Prediction Accuracy ◽

Forecasting Model ◽

Foundation Settlement ◽

Engineering Construction ◽

Settlement Prediction ◽

Exponential Law ◽

Grey Forecasting ◽

Index Sequence ◽

Settlement Time ◽

Time Sequences

Prediction of foundation or subgrade settlement is very important during engineering construction. According to the fact that there are lots of settlement-time sequences with a nonhomogeneous index trend, a novel grey forecasting model called NGM(1,1,k,c)model is proposed in this paper. With an optimized whitenization differential equation, the proposed NGM(1,1,k,c)model has the property of white exponential law coincidence and can predict a pure nonhomogeneous index sequence precisely. We used two case studies to verify the predictive effect of NGM(1,1,k,c)model for settlement prediction. The results show that this model can achieve excellent prediction accuracy; thus, the model is quite suitable for simulation and prediction of approximate nonhomogeneous index sequence and has excellent application value in settlement prediction.

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A COMPARISON OF SCORING METRICS FOR PREDICTING THE NEXT NAVIGATION STEP WITH MARKOV MODEL-BASED SYSTEMS

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622010003956 ◽

2010 ◽

Vol 09 (04) ◽

pp. 547-573 ◽

Cited By ~ 4

Author(s):

JOSÉ BORGES ◽

MARK LEVENE

Keyword(s):

Markov Model ◽

Prediction Accuracy ◽

Prediction Models ◽

Markov Models ◽

Real Data ◽

Absolute Error ◽

Brier Score ◽

Data Sets ◽

Extensive Evaluation ◽

The Impact

The problem of predicting the next request during a user's navigation session has been extensively studied. In this context, higher-order Markov models have been widely used to model navigation sessions and to predict the next navigation step, while prediction accuracy has been mainly evaluated with the hit and miss score. We claim that this score, although useful, is not sufficient for evaluating next link prediction models with the aim of finding a sufficient order of the model, the size of a recommendation set, and assessing the impact of unexpected events on the prediction accuracy. Herein, we make use of a variable length Markov model to compare the usefulness of three alternatives to the hit and miss score: the Mean Absolute Error, the Ignorance Score, and the Brier score. We present an extensive evaluation of the methods on real data sets and a comprehensive comparison of the scoring methods.

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Hybrid Forecasting Model of Reactive Load Based on FOA and Linear Exponential Smoothing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.823.500 ◽

2013 ◽

Vol 823 ◽

pp. 500-504

Author(s):

Yuan Sheng Huang ◽

Te Li ◽

Wei Pi

Keyword(s):

Prediction Accuracy ◽

Exponential Smoothing ◽

Forecasting Model ◽

Hybrid Forecasting ◽

Parameter Values

During the prediction of Linear exponential smoothing model,the value of the coefficient has a little blindness,which is always valued by experience.This paper uses FOA model to optimize it in Linear exponential smoothing model,constructing the hybrid forecasting model this paper uses to predict the reactive load of a substation.Then,this paper uses the hybrid forecasting model to forecast reactive load.The results show that:compared with the traditional Linear exponential smoothing model,the hybrid forecasting model is effective not only on selecting parameter values ,but also improving the prediction accuracy of the reactive load to a big extent.

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Predictive model of effective sustainable operation for sustainable development of enterprises

E3S Web of Conferences ◽

10.1051/e3sconf/202021402044 ◽

2020 ◽

Vol 214 ◽

pp. 02044

Author(s):

Haitong Wang ◽

Ziyi Cheng ◽

Hongyan Li

Keyword(s):

Capital Market ◽

Information Disclosure ◽

Prediction Accuracy ◽

Forecasting Model ◽

Business Operation ◽

Forecasting Models ◽

Sustainable Operation ◽

Business Forecasting ◽

Chinese Scholars ◽

Cart Model

Business forecasting has a very important impact on the future development of listed companies. Especially in the current era of information, corporate financial information disclosure is more comprehensive, so a reasonable business forecasting model is particularly important in the market. For the study of business operation forecasting models, Chinese scholars have achieved relevant results. This article is mainly based on the existing models for innovation and development. By establishing two models, SR + CART and ANN + CART, and testing their prediction accuracy, it provides a more diverse and reasonable tool for business forecasting, which is beneficial to the efficient development of capital market. The results show that the ANN + CART model has higher prediction accuracy, and the overall prediction accuracy is 92%.

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Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

10.21203/rs.3.rs-45941/v1 ◽

2020 ◽

Author(s):

Elise Ai Hwee Kho ◽

Jill N. Fernandes ◽

Andrew C. Kotze ◽

Glen P. Fox ◽

Maggy T. Sikulu-Lord ◽

...

Keyword(s):

Haemonchus Contortus ◽

Prediction Accuracy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Diagnostic Methods ◽

Calibration Model ◽

Good Prediction ◽

Blood Concentrations ◽

Calibration Models ◽

Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces.Methods: Vis-NIR spectra were obtained from worm-free sheep faeces from different environments in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations collected. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected Queensland (QLD) faeces. Naturally occurring blood in QLD samples was quantified using Hemastix® and FAMACHA© scores.Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions in sheep faeces. The calibration models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood.Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic for the accurate prediction of H. contortus infections in these regions.

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Influence of environmental factors on the detection of blood in sheep faeces using visible-near infrared spectroscopy as a measure of Haemonchus contortus infection

10.21203/rs.3.rs-45941/v2 ◽

2020 ◽

Author(s):

Elise Ai Hwee Kho ◽

Jill N. Fernandes ◽

Andrew C. Kotze ◽

Glen P. Fox ◽

Maggy T. Sikulu-Lord ◽

...

Keyword(s):

Haemonchus Contortus ◽

Prediction Accuracy ◽

Near Infrared ◽

Nir Spectroscopy ◽

Diagnostic Methods ◽

Calibration Model ◽

Good Prediction ◽

Blood Concentrations ◽

Calibration Models ◽

Good Prediction Accuracy

Abstract Background: Existing diagnostic methods for the parasitic gastrointestinal nematode, Haemonchus contortus, are time consuming and require specialised expertise, limiting their utility in the field. A practical, on-farm diagnostic tool could facilitate timely treatment decisions, preventing production and welfare loss in the flock. We previously demonstrated the ability of visible-near infrared (vis-NIR) spectroscopy to detect and quantify blood in sheep faeces with high accuracy. Here we investigate whether variation in sheep type and environment affect the prediction accuracy of vis-NIR spectroscopy in quantifying blood in faeces. Methods: Vis-NIR spectra were obtained from worm-free sheep faeces collected from different environments and sheep types in South Australia (SA) and New South Wales (NSW), Australia and spiked with various sheep blood concentrations. Spectra were analysed using principal component analysis (PCA), and calibration models were built around the haemoglobin (Hb) wavelength region (387 – 609 nm) using partial least squares (PLS) regression. Models were used to predict Hb concentrations in spiked faeces from SA and naturally infected sheep faeces from Queensland (QLD). QLD samples were quantified using Hemastix® and FAMACHA © scores. Results: PCA showed that location, class of sheep and pooled/individual samples were factors affecting the Hb predictions. The models successfully differentiated ‘healthy’ SA samples from those requiring anthelmintic treatment with moderate to good prediction accuracy (sensitivity: 57 – 94%, specificity: 44 – 79%). The models were not predictive for blood in naturally infected QLD samples, which may be due in part to variability of faecal background and blood chemistry between samples, or the difference in validation methods used for blood quantification. PCA of QLD samples, however, identified a difference between samples containing high and low quantities of blood. Conclusion: This study demonstrates the potential of vis-NIR spectroscopy for estimating blood concentration in faeces from various types of sheep and environmental backgrounds. However, the calibration models developed here did not capture enough environmental variation to accurately predict Hb in faeces collected from environments different to those used in the calibration model. Consequently, it will be necessary to establish models that incorporate samples that are more representative of areas where H. contortus is endemic.

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A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3447988 ◽

2021 ◽

Vol 12 (6) ◽

pp. 1-24

Author(s):

Shaojie Qiao ◽

Nan Han ◽

Jianbin Huang ◽

Kun Yue ◽

Rui Mao ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Forecasting Model ◽

Learning Models ◽

Bike Sharing ◽

Demand Forecasting Model ◽

Machine Learning Models

Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, and other dynamic factors, which challenges the scheduling of shared bikes. In this article, a new shared-bike demand forecasting model based on dynamic convolutional neural networks, called SDF , is proposed to predict the demand of shared bikes. SDF chooses the most relevant weather features from real weather data by using the Pearson correlation coefficient and transforms them into a two-dimensional dynamic feature matrix, taking into account the states of stations from historical data. The feature information in the matrix is extracted, learned, and trained with a newly proposed dynamic convolutional neural network to predict the demand of shared bikes in a dynamical and intelligent fashion. The phase of parameter update is optimized from three aspects: the loss function, optimization algorithm, and learning rate. Then, an accurate shared-bike demand forecasting model is designed based on the basic idea of minimizing the loss value. By comparing with classical machine learning models, the weight sharing strategy employed by SDF reduces the complexity of the network. It allows a high prediction accuracy to be achieved within a relatively short period of time. Extensive experiments are conducted on real-world bike-sharing datasets to evaluate SDF. The results show that SDF significantly outperforms classical machine learning models in prediction accuracy and efficiency.

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Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Energies ◽

10.3390/en12061140 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1140 ◽

Cited By ~ 15

Author(s):

Xin Gao ◽

Xiaobing Li ◽

Bing Zhao ◽

Weijia Ji ◽

Xiao Jing ◽

...

Keyword(s):

Feature Selection ◽

Prediction Accuracy ◽

Load Forecasting ◽

Support Vector ◽

Forecasting Model ◽

Data Sets ◽

Short Term ◽

Model Based ◽

Electricity Load ◽

Electricity Load Forecasting

Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

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