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Model Selection is a key part of many ecological studies, with Akaike's Information Criterion (AIC) being by far the most commonly used technique for this purpose. Typically, a number of candidate models are defined a priori and ranked according to their expected out-of-sample performance. Model selection, however, only assesses the relative performance of the models and, as pointed out in a recent paper, a large proportion of ecology papers that use model selection do not assess the absolute fit of the 'best' model. In this paper, it is argued that assessing the absolute fit of the 'best' model alone does not go far enough. This is because a model that appears to perform well under model selection is also likely to appear to perform well under measures of absolute fit, even when there is no predictive value. This paper proposes a model selection permutation test that assesses the probability that the model selection statistic of the 'best' model could have occurred by chance alone, whilst taking account of dependencies between the models. It is argued that this test should always be performed as a part of formal model selection. The test is demonstrated on two real population modelling examples of ibex in northern Italy and wild reindeer in Norway. In both cases, the model selection permutation test gives a highly significant result, indicating that the performance of the 'best' model is unlikely to be through chance alone. R code is provided with which to perform the tests.

Betting odds are generally considered to represent accurate reflections of the underlying probabilities for the outcomes of sporting events. There are, however, known to be a number of inherent biases such as the favorite-longshot bias in which outsiders are generally priced with poorer value odds than favorites. Using data from European soccer matches, this paper demonstrates the existence of another bias in which the match odds overreact to favorable and unfavorable runs of results. A statistic is defined, called the Combined Odds Distribution (COD) statistic, which measures the performance of a team relative to expectations given their odds over previous matches. Teams that overperform expectations tend to have a high COD statistic and those that underperform tend to have a low COD statistic. Using data from twenty different leagues over twelve seasons, it is shown that teams with a low COD statistic tend to be assigned more generous odds by bookmakers. This can be exploited and a sustained and robust profit can be made. It is suggested that the bias in the odds can be explained in the context of the “hot hand fallacy”, in which gamblers overestimate variation in the ability of each team over time.

District heating is expected to play an important role in the decarbonisation of the energy sector in the coming years since low carbon sources such as waste heat and biomass are increasingly being used to generate heat. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty, such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance) so that decision-makers can make robust decisions. The uncertainty in input parameters of the energy system model together with general scenarios are introduced to generate distributions of net present costs and emissions for each design. To perform inference about the induced distributions of outputs, we apply the orderings in the mean and dispersion. The proposed approach is demonstrated in an application to the waste heat recovery problem in district heating in Brunswick, Germany. The results obtained show that heat pump, a low carbon design option, is more robust in comparison to combined heat and power (CHP) and a mix of CHP and heat pump under all three scenarios, highlighting that robustness is an attractive feature of low-temperature waste heat recovery.

This paper considers the use of observed and predicted match statistics as inputs to forecasts for the outcomes of football matches. It is shown that, were it possible to know the match statistics in advance, highly informative forecasts of the match outcome could be made. Whilst, in practice, match statistics are clearly never available prior to the match, this leads to a simple philosophy. If match statistics can be predicted pre-match, and if those predictions are accurate enough, it follows that informative match forecasts can be made. Two approaches to the prediction of match statistics are demonstrated: Generalised Attacking Performance (GAP) ratings and a set of ratings based on the Bivariate Poisson model which are named Bivariate Attacking (BA) ratings. It is shown that both approaches provide a suitable methodology for predicting match statistics in advance and that they are informative enough to provide information beyond that reflected in the odds. A long term and robust gambling profit is demonstrated when the forecasts are combined with two betting strategies.

A scoring rule is a function of a probabilistic forecast and a corresponding outcome used to evaluate forecast performance. There is some debate as to which scoring rules are most appropriate for evaluating forecasts of sporting events. This paper focuses on forecasts of the outcomes of football matches. The ranked probability score (RPS) is often recommended since it is 'sensitive to distance', that is it takes into account the ordering in the outcomes (a home win is 'closer' to a draw than it is to an away win). In this paper, this reasoning is disputed on the basis that it adds nothing in terms of the usual aims of using scoring rules. A local scoring rule is one that only takes the probability placed on the outcome into consideration. Two simulation experiments are carried out to compare the performance of the RPS, which is non-local and sensitive to distance, the Brier score, which is non-local and insensitive to distance, and the Ignorance score, which is local and insensitive to distance. The Ignorance score outperforms both the RPS and the Brier score, casting doubt on the value of non-locality and sensitivity to distance as properties of scoring rules in this context.

Urban waste heat recovery, in which low temperature heat from urban sources is recovered for use in a district heat network, has a great deal of potential in helping to achieve 2050 climate goals. For example, heat from data centres, metro systems, public sector buildings and waste water treatment plants could be used to supply 10% of Europe’s heat demand. Despite this, at present, urban waste heat recovery is not widespread and is an immature technology. Based on interviews with urban waste heat stakeholders, investors interested in green investments, and experience from demonstrator projects, a number of recommendations are made. It is suggested that policy raising awareness of waste heat recovery, encouraging investment and creating a legal framework should be implemented. It is also recommended that pilot projects should be promoted to help demonstrate technical and economic feasibility. A pilot credit facility is suggested aimed at bridging the gap between potential investors and heat recovery projects.

The over/under 2.5 goals betting market allows gamblers to bet on whether the total number of goals in a football match will exceed 2.5. In this paper, a set of ratings, named ‘Generalised Attacking Performance’ (GAP) ratings, are defined which measure the attacking and defensive performance of each team in a league. GAP ratings are used to forecast matches in ten European football leagues and their profitability is tested in the over/under market using two value betting strategies. GAP ratings with match statistics such as shots and shots on target as inputs are shown to yield better predictive value than the number of goals. An average profit of around 0.8 percent per bet taken is demonstrated over twelve years when using only shots and corners (and not goals) as inputs. The betting strategy is shown to be robust by comparing it to a random betting strategy.

Wild reindeer have a range that extends across the circumpolar region. In the last few decades, however, populations of wild reindeer have been on the decline. The reasons for these declines are poorly understood, but are suggested to be linked to both local and global climatic factors, disease, and human interference. Hardangervidda plateau in Norway is home to the largest wild reindeer population in Europe, and is at the southern end of its European range. This population is therefore of particular importance, particularly in the light of climate change. We investigated how weather and hunting have affected the wild reindeer population in Hardangervidda over the last two decades. Our findings suggest that the wild reindeer population in Hardangervidda is most affected by winter temperature and hunting, where colder temperatures and lower harvest rates typically result in higher growth rates. We did not find significant evidence for linear density dependence. Our results show trends across Hardangervidda, and give an indication of how region-wide weather and hunting pressure can affect the wild reindeer population. As new data emerge, future investigations should look into the existence and nature of density dependence and the influence of other weather and human disturbance related factors.

Approximately 1.2 EJ of energy are potentially available for recovery each year from urban heat sources in the EU. This corresponds to more than 10 percent of the EU’s total energy demand for heat and hot water. There are, however, a number of challenges to be met before urban waste heat recovery can be performed on a wide scale. This paper focuses on the non-technical issues related to urban waste heat recovery and is written on the basis of opinions gathered from stakeholders in the field. Three non-technical issues are focused upon. First, a number of important barriers to wide scale urban waste recovery are identified, and where applicable, recommendations are made regarding how to overcome these barriers. Second, important issues and challenges regarding contract design are identified and discussed. Key elements of heat supply contracts between the district heating company and the owner of the waste heat are described. Finally, the impact on business models of properties specific to urban waste heat recovery are discussed. Data were collected from two separate sources, both related to the ReUseHeat Horizon 2020 project, which addresses the application of urban waste heat recovery in existing district heating networks. First, a number of interviews with stakeholders were carried out. Second, information was collected from demonstrator sites involved in the ReUseHeat project. It was concluded that, for urban waste heat recovery to be taken up on a wide scale, there is still a large amount of work to do to overcome these major issues. This paper is novel in that key non-technical issues of urban waste heat recovery are discussed from the perspective of a large sample of actual stakeholders and practitioners in the field.

Performance measures of point forecasts are expressed commonly as skill scores, in which the performance gain from using one forecasting system over another is expressed as a proportion of the gain achieved by forecasting that outcome perfectly. Increasingly, it is common to express scores of probabilistic forecasts in this form; however, this paper presents three criticisms of this approach. Firstly, initial condition uncertainty (which is outside the forecaster's control) limits the capacity to improve a probabilistic forecast, and thus a ‘perfect’ score is often unattainable. Secondly, the skill score forms of the ignorance and Brier scores are biased. Finally, it is argued that the skill score form of scoring rules destroys the useful interpretation in terms of the relative skill levels of two forecasting systems. Indeed, it is often misleading, and useful information is lost when the skill score form is used in place of the original score.

Predicting the weather is notoriously difficult because of its chaotic nature. In chaotic systems, small errors in the initial conditions can cause large errors in predicting the future. In this poster we describe the challenges of such systems and a method that has been developed to deal with the uncertainty it brings. Ensemble forecasting uses a variety of initial conditions that take into account any uncertainty in what is happening now to give us a variety of predictions of the future, each accounting for different circumstances. We compare a variety of different methods of transforming these ensembles into probability distributions which can be of great use to decision makers.