Actions Speak Louder Than Goals: Valuing Player Actions in Soccer

Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.Comment: Significant update of the paper. The same core idea, but with a clearer methodology, applied on a different data set, and more extensive experiments. 9 pages + 2 pages appendix. To be published at SIGKDD 201

Reporting performance of prognostic models in cancer: a review

<p>Abstract</p> <p>Background</p> <p>Appropriate choice and use of prognostic models in clinical practice require the use of good methods for both model development, and for developing prognostic indices and risk groups from the models. In order to assess reliability and generalizability for use, models need to have been validated and measures of model performance reported. We reviewed published articles to assess the methods and reporting used to develop and evaluate performance of prognostic indices and risk groups from prognostic models.</p> <p>Methods</p> <p>We developed a systematic search string and identified articles from PubMed. Forty-seven articles were included that satisfied the following inclusion criteria: published in 2005; aiming to predict patient outcome; presenting new prognostic models in cancer with outcome time to an event and including a combination of at least two separate variables; and analysing data using multivariable analysis suitable for time to event data.</p> <p>Results</p> <p>In 47 studies, Cox models were used in 94% (44), but the coefficients or hazard ratios for the variables in the final model were reported in only 72% (34). The reproducibility of the derived model was assessed in only 11% (5) of the articles. A prognostic index was developed from the model in 81% (38) of the articles, but researchers derived the prognostic index from the final prognostic model in only 34% (13) of the studies; different coefficients or variables from those in the final model were used in 50% (19) of models and the methods used were unclear in 16% (6) of the articles. Methods used to derive prognostic groups were also poor, with researchers not reporting the methods used in 39% (14 of 36) of the studies and data derived methods likely to bias estimates of differences between risk groups being used in 28% (10) of the studies. Validation of their models was reported in only 34% (16) of the studies. In 15 studies validation used data from the same population and in five studies from a different population. Including reports of validation with external data from publications up to four years following model development, external validation was attempted for only 21% (10) of models. Insufficient information was provided on the performance of models in terms of discrimination and calibration.</p> <p>Conclusions</p> <p>Many published prognostic models have been developed using poor methods and many with poor reporting, both of which compromise the reliability and clinical relevance of models, prognostic indices and risk groups derived from them.</p

Reporting methods in studies developing prognostic models in cancer: a review

Development of prognostic models enables identification of variables that are influential in predicting patient outcome and the use of these multiple risk factors in a systematic, reproducible way according to evidence based methods. The reliability of models depends on informed use of statistical methods, in combination with prior knowledge of disease. We reviewed published articles to assess reporting and methods used to develop new prognostic models in cancer.We developed a systematic search string and identified articles from PubMed. Forty-seven articles were included that satisfied the following inclusion criteria: published in 2005; aiming to predict patient outcome; presenting new prognostic models in cancer with outcome time to an event and including a combination of at least two separate variables; and analysing data using multivariable analysis suitable for time to event data.In 47 studies, prospective cohort or randomised controlled trial data were used for model development in only 33% (15) of studies. In 30% (14) of the studies insufficient data were available, having fewer than 10 events per variable (EPV) used in model development. EPV could not be calculated in a further 40% (19) of the studies. The coding of candidate variables was only reported in 68% (32) of the studies. Although use of continuous variables was reported in all studies, only one article reported using recommended methods of retaining all these variables as continuous without categorisation. Statistical methods for selection of variables in the multivariate modelling were often flawed. A method that is not recommended, namely, using statistical significance in univariate analysis as a pre-screening test to select variables for inclusion in the multivariate model, was applied in 48% (21) of the studies.We found that published prognostic models are often characterised by both use of inappropriate methods for development of multivariable models and poor reporting. In addition, models are limited by the lack of studies based on prospective data of sufficient sample size to avoid overfitting. The use of poor methods compromises the reliability of prognostic models developed to provide objective probability estimates to complement clinical intuition of the physician and guidelines

The baroclinic secondary instability of the two-dimensional shear layer

The focus of this study is on the numerical investigation of two-dimensional, isovolume, high Reynolds and Froude numbers, variable-density mixing layers. Lagrangian simulations, of both the temporal and the spatial models, are performed. They reveal the breaking-up of the strained vorticity and density-gradient braids, connecting two neighboring primary structures. The secondary instability arises where the vorticity has been intensified by the baroclinic torque. A simplified model of the braid of the variable-density mixing layer, consisting of a strained vorticity and density-gradient filament, is analyzed. It is concluded that the physical mechanism responsible for the secondary instability is the forcing of the vorticity field by the baroclinic torque, itself sensitive to perturbations. This mechanism suggests a rapid route to turbulence for the variable-density mixing layer

Increased water intake to reduce headache: Learning from a critical appraisal

Clinical Bottom Line Water intake is a cost effective, non-invasive and low-risk intervention to reduce or prevent headache pain. Rationale: Chronic mild dehydration may trigger headache. Increased water intake could help. A small trial shows modest benefit; however, a larger methodologically sound randomized controlled trial is needed to confirm efficacy. Critically Appraised Paper Spigt, M., Weerkamp, N., Troost, J., van Schayck, C. P., & Knottnerus, J. A. (2012). ‘A randomized trial on the effects of regular water intake in patients with recurrent headaches.’ Family practice, 29(4), 370–5. Doi: 10.1093/fampra/cmr112 Clinical scenario Patients from primary care registered as ‘headache’, ‘tension headache’ and/or ‘migraine’ for more than one year who suffer at least two episodes of moderately intense headache or more than four mildly intense episodes of headache per month with a daily fluid intake of less than 2.5 litres per day. PICO (M) Patient/Problem = Headache > 1 year with 2 moderately intense or 4 mildly intense episodes per month Intervention = 1.5 litres water per day + stress control and sleep hygiene Comparison/Control = stress control and sleep hygiene Outcome = Reduce or eliminate headache Methodology = Therapy RCT Table 1: Final Search Terms TRIP Data Base: hits = 517 used filter Extended Primary research 4 found 1 paper applicable; 'Water intake '[MeSH Terms] AND 'Headache '[All Fields]'; Best match to PICO, (2012) RCT Selection Criterion and Overall Results 102 headache patients in16 primary care clinics were randomized into control (n = 50) and intervention groups (n = 52) Inclusion criteria = two > episodes of moderately intense headache or five > mildly intense headaches per month and total fluid intake > 2.5 litres per day, Follow-up @ 3 months. 79% intervention and 66% of controls completed RCT. Drinking more water resulted in a statistically significant improvement of 4.5 (confidence interval: 1.3–7.8) points on Migraine-Specific Quality of Life (MSQOL). 47% in the intervention (water) group self-reported improvement (6 > on a 10-point scale) against 25% in controls. Drinking water did not reduce headache days. Comments The transparency from the author of this critically appraised paper enables others to use this study as a teaching tool and to learn from the shortcomings in the trial. The study was underpowered and contains methodological shortcomings. Participants were partially un-blinded during the trial increasing the risk for bias. Only the subjective measures are statistically significant and attrition was significant. The intervention is low risk and of negligible cost. A methodologically sound RCT is recommended to evaluate if the intervention has beneficial effects

Flue gas injection control of silica in cooling towers.

Injection of CO{sub 2}-laden flue gas can decrease the potential for silica and calcite scale formation in cooling tower blowdown by lowering solution pH to decrease equilibrium calcite solubility and kinetic rates of silica polymerization. Flue gas injection might best inhibit scale formation in power plant cooling towers that use impaired makeup waters - for example, groundwaters that contain relatively high levels of calcium, alkalinity, and silica. Groundwaters brought to the surface for cooling will degas CO{sub 2} and increase their pH by 1-2 units, possibly precipitating calcite in the process. Recarbonation with flue gas can lower the pHs of these fluids back to roughly their initial pH. Flue gas carbonation probably cannot lower pHs to much below pH 6 because the pHs of impaired waters, once outgassed at the surface, are likely to be relatively alkaline. Silica polymerization to form scale occurs most rapidly at pH {approx} 8.3 at 25 C; polymerization is slower at higher and lower pH. pH 7 fluids containing {approx}220 ppm SiO{sub 2} require &gt; 180 hours equilibration to begin forming scale whereas at pH 8.3 scale formation is complete within 36 hours. Flue gas injection that lowers pHs to {approx} 7 should allow substantially higher concentration factors. Periodic cycling to lower recoveries - hence lower silica concentrations - might be required though. Higher concentration factors enabled by flue gas injection should decrease concentrate volumes and disposal costs by roughly half

Long-Term (10-Year) Gastrointestinal and Genitourinary Toxicity after Treatment with External Beam Radiotherapy, Radical Prostatectomy, or Brachytherapy for Prostate Cancer

Objective.To examine gastrointestinal (GI) and genitourinary (GU) toxicity profiles of patients treated in 1999 with external beam radiotherapy (RT), prostate interstitial brachytherapy (PI) or radical prostatectomy (RP). Methods. TThe records of 525 patients treated in 1999 were reviewed to evaluate toxicity. Late GI and GU morbidities were graded according to the RTOG late morbidity criteria. Other factors examined were patient age, BMI, smoking history, and medical co-morbidities. Due to the low event rate for late GU and GI toxicities, a competing risk regression (CRR) analysis was done with death as the competing event. Results. Median follow-up time was 8.5 years. On CRR univariate analysis, only the presence of DM was significantly associated with GU toxicity grade >2 (P = 0.43, HR 2.35, 95% Cl = 1.03–5.39). On univariate analysis, RT and DM were significantly associated with late GI toxicity. On multivariable analysis, both variables remained significant (RT: P = 0.038, HR = 4.71, CI = 1.09–20.3; DM: P = 0.008, HR = 3.81, 95% Cl = 1.42–10.2). Conclusions. Late effects occur with all treatment modalities. The presence of DM at the time of treatment was significantly associated with worse late GI and GU toxicity. RT was significantly associated with worse late GI toxicity compared to PI and RP

Sex differences in health at ages 11, 13 and 15

This paper tests the hypothesis of an emerging or increasing female excess in general ill-health and physical symptoms, as well as psychological distress, during early to mid-adolescence. Self-reported data on general health (longstanding illness and health in the last 12 months), recent symptoms (classified as ‘physical’ and ‘malaise’) and depressive mood were obtained from a large, Scottish, school-based cohort at ages 11, 13 and 15. Generally high levels of health problems at age 11 tended to increase with age, these increases being greater for females than males, not only in respect of depression and ‘malaise’ symptoms, but also limiting illness, ‘poor’ self-rated health, headaches, stomach problems and dizziness. The consequence, by age 15, is the emergence of a female excess in general ill-health and depressive mood, and a substantial strengthening of the small excess in both ‘physical’ and ‘malaise’ symptoms already apparent at 11 years. These findings are discussed in relation to explanations for the adult female excess in poorer health, and the emergence of a female excess of depression during adolescence

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies