Relative Skill Research Articles

Skill-biased technological change and intergenerational education mobility

This paper analyses the impact of skill-biased technological change (SBTC) on intergenerational education mobility. I set up an SBTC model with an overlapping-generations framework, where high and low-income households invest in their children’s skill development. Technology incentivizes these investments by raising the skill-premium and improving life-skills; it constrains investments by increasing inequality. I find that, for SBTC shocks within a critical range, intergenerational investments by both household-types are higher in the new steady-state, with the relative increase being larger for the low-income group. I use cross-U.S. commuting-zone data to examine if education mobility outcomes are better in locations characterized by (1) higher STEM-shares, and (2) larger shifts in the demand of relative skills. I empirically find that children from low-income households are not only more likely to attend college if they live in high-tech areas, but this likelihood increases by a larger margin compared to children from higher-income households.

Impacts of Sampling and Storm-Motion Estimates on RUC/RAP-Based Discriminations of Nontornadic and Tornadic Supercell Environments

Abstract This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in the relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0–1-km SRH becomes more skillful than 0–500-m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer convective available potential energy (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increase for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE is unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to the supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion. Significance Statement Researchers continue to explore reasons why some storms produce tornadoes and others do not. Some recent studies show conflicting results, including the best layers to analyze the environmental wind shear and the ability of wind speeds relative to the storm to distinguish nontornadic from tornadic storms. One study is rooted in observations taken for field projects that targeted tornadic storms and the others in model analyses. Restricting model analyses to a spatiotemporal region that emulates field projects does not explain the disagreement but reveals how the skill of some other variables changes. The tendency for field projects to target particularly strong storms and the use of actual storm motions versus estimates of storm motions both contribute to the conflicting results.

They are still children: a scoping review of conditions for positive engagement in elite youth sport

ObjectiveThe objective of this study is to characterise the key factors that influence positive engagement and desirable developmental outcomes in sport among elite youth athletes by summarising the methods, groups...

Massive open online course on breastfeeding during the COVID-19 pandemic in Mexico.

COVID-19 virus was reported to be transmitted through breastfeeding (BF), creating the need for rapidly available, standardized information and training for health personnel and the community about BF as an urgent action to reduce misinformation and unethical promotion of breast milk substitutes (BMS). In Mexico, a massive open online course (MOOC) was designed and implemented to protect, promote and support BF in emergency contexts. MOOC registration consisted of collection of quantitative data regarding participant characteristics, MOOC coverage, scores achieved and completion rates. A multiple linear regression analysis was performed to relate the absolute and relative skills earned in the MOOC to participant characteristics. In addition, factors associated with completion rate and dissemination of information from the MOOC were analyzed using multiple logistic regression and presented as odds ratios. During a period of 19 months, 52,426 participants across the country, including health personnel and general population, entered the Cursos en Linea del Instituto Mexicano del Seguro Social (CLIMSS) platform, of which 50.5% completed the MOOC. The level of participation was maintained from January 2021 to early 2022 when the perception of the risk of getting sick from COVID-19 and quarantining decreased. In adjusted analysis, completion rate was associated with being older or belonging to a health institution; furthermore, residing in the north of the country doubled the odds of completing the MOOC (odds ratio 2.24; 95% confidence interval 1.95-2.56). A MOOC can be a useful training strategy to disseminate information, especially in emergencies where physical distancing is important and reaching the largest possible population is required.

Long-run productivity changes in the Australian Northern prawn fishery

This study examines long-run changes in productivity within the Australian Northern prawn fishery, an input control managed fishery. Productivity change is estimated through a Malmquist Index over a 20-year period from 1999–2000 to 2019–2020, decomposing productivity into technical change and efficiency change components. Fluctuations in the Malmquist Index over the period of the study were driven primarily by the technical change component, reflecting changes in stock abundance, technical improvements, and the effects of management. The efficiency change index was found to be relatively stable throughout the study period. The findings suggests that changes in performance of the fleet are driven largely by exogenous factors impacting catches, with limited change in the relative skill of fishers.

Coastal Data Information Program: advances in measuring and modeling wave activity, climate, and extremes

ABSTRACT The Coastal Data Information Program (CDIP) provides wave data to the public in near real-time, maintains an operational wave model for the California coast, and is engaged in ocean wave research on a global scale. CDIP’s array of moored wave buoy stations are instrumented with Datawell Waveriders. CDIP’s recent work to improve monitoring capabilities for directional surface wave spectra, surface current, and temperature is described. This includes data quality assurance and control, real-time alerts, telemetry and dissemination information technology infrastructure and methodology, data visualization tools, and mooring and instrumentation innovation. CDIP’s goal is to maximize data reliability, availability, accuracy, and precision. Validation of commonly used wave models – operational, hindcast, and forecast – with CDIP data is automated to provide assessments of relative skills at a variety of coastal locations and wave conditions. Wave heights measured by the buoys typically exceed modeled heights during the most energetic events, such as hurricanes, nor’easters, and bomb cyclones. In a practical validation example, the global wave model used to drive CDIP’s California coastal wave forecasts was recently changed, based on comparisons against buoy data. Forecast performance has improved for swell events, which are a challenge to model accurately in the Southern California Bight.

Relative and Unified Skill of Environmental, Edaphic, and Management Factors to Explain Crop Yield Variance Using Machine Learning

Highlights Random forests equipped with recursive feature elimination were used to predict crop yield variation using diverse predictors. The predictors belonged to either environmental, soils, or management domains. The best performing models combined predictors from all three domains and explained 75%-80% spatiotemporal yield variance. Extreme heat was most important among soils, management, and seasonal environment predictors in maize and soybean. Irrigation intensity, tile drainage, % silt, and July rain were most important at the sub-seasonal aggregation scale. Abstract. Crop yields are dictated by a complex interplay between environment, edaphic (soils), and management that are subject to change across space and time. However, to what extent each of these influences and their interactions have been important in explaining yield variance is limitedly understood. The convoluted nature of this question motivates the application of modern machine learning approaches to decipher these influences and elucidate crop yield relations with their critical drivers. Here, we used random forest modeling with recursive feature elimination (RFE) to discern the diverse drivers of historical (1981-2019) county-level maize and soybean yields in the U.S. within the realms of environment (growing and extreme degree days, precipitation, vapor pressure deficit, evaporative demand, crop water use, soil moisture), soils (sand, silt, and clay contents, bulk density, soil organic carbon, available water capacity), and management (irrigation intensity, tile drainage, nitrogen input, depth to groundwater). We found that the most effective models selected predictors from all three realms and achieved remarkable explanatory capability, accounting for 75%-80% of the spatial and temporal variance combined. Environmental predictors exhibited a non-negotiable role in determining model performance, while their combination with either soil or management predictors approached the efficacy of the best-performing model. Specific variables within each individual predictor set and their combinations were analyzed for their relative importance to the model skill. The best performing model that used soils, management, and seasonal environmental predictors evaluated extreme heat as the most influential predictor for both crops. On further inclusion of sub-seasonal environmental variables with soil and management predictors, the relative importance shifted, with irrigation intensity assuming prominence as the most influential predictor, accompanied by tile drainage, silt content, and July precipitation for both crops. RFE revealed that only eight of the most relevant predictors were sufficient to explain >70% of the yield variance, even when the total predictors included were as high as 52. that Visual representations were developed to offer insights into the functional response of crop yield to changes in critical predictors, thereby facilitating predictions across diverse agricultural production systems and over time. This research underscores the value of including soil and management indicators alongside environmental predictors to improve understanding and predictability of the intricate dynamics governing crop yield variability. Keywords: Climate variability, Drainage, Irrigation, Maize, Nitrogen, Random forest, Soils, Soybean.

Motor Learning in Robot-Based Haptic Dyads: A Review.

Rehabilitation robots have the potential to alleviate the global burden of neurorehabilitation. Robot-based multiplayer gaming with virtual and haptic interaction may improve motivation, engagement, and implicit learning in robotic therapy. Over the past few years, there has been growing interest in robot mediated haptic dyads, or human-robot-robot-human interaction. The effect of such a paradigm on motor learning in general and specifically for individuals with motor and/or cognitive impairments is an open area of research. We reviewed the literature to investigate the effect of a robot-based haptic dyad on motor learning. Thirty-eight articles met the inclusion criteria for this review. We summarize study characteristics including device, haptic rendering, and experimental task. Our main findings indicate that dyadic training's impact on motor learning is inconsistent in that some studies show significant improvement of motor training while others show no influence. We also find that the relative skill level of the partner and interaction characteristics such as stiffness of connection and availability of visual information influence motor learning outcomes. We discuss implications for neurorehabilitation and conclude that additional research is needed to determine optimal interaction characteristics for motor learning and to extend this research to individuals with cognitive and motor impairments.

Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection

AbstractWith an increasing number of continental‐scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty and making improvements to the model(s). We hypothesize that any model, running a single set of physics, cannot be “properly” calibrated for the range of hydroclimatic diversity as seen in the contenintal United States. Here, we evaluate the NOAA National Water Model (NWM) version 2.0 historical streamflow record in over 4,200 natural and controlled basins using the Nash‐Sutcliffe Efficiency metric decomposed into relative performance, and conditional, and unconditional bias. Each of these is evaluated in the contexts of meteorologic, landscape, and anthropogenic characteristics to better understand where the model does poorly, what potentially causes the poor performance, and what similarities systemically poor performing areas share. The primary objective is to pinpoint traits in places with good/bad performance and low/high bias. NWM relative performance is higher when there is high precipitation, snow coverage (depth and fraction), and barren area. Low relative skill is associated with high potential evapotranspiration, aridity, moisture‐and‐energy phase correlation, and forest, shrubland, grassland, and imperviousness area. We see less bias in locations with high precipitation, moisture‐and‐energy phase correlation, barren, and grassland areas and more bias in areas with high aridity, snow coverage/fraction, and urbanization. The insights gained can help identify key hydrological factors underpinning NWM predictive skill; enforce the need for regionalized parameterization and modeling; and help inform heterogenous modeling systems, like the NOAA Next Generation Water Resource Modeling Framework, to enhance ongoing development and evaluation.

Public sector wage compression and wage inequality: gender and geographic heterogeneity

Abstract Studies of wage inequality concentrate on private wages. Public sector wages are typically assumed to contribute to the overall wage equality. We challenge this understanding in an analysis of the relative skill premium in the public versus private sectors. The analysis of heterogeneity across gender and geography is based on rich register data for Norway. The raw data confirm the relative wage compression in the public sector. However, this is a male phenomenon and only prevalent in large cities when unobserved worker and firm characteristics are taken into account. With identification based on shifters between private and public sectors and movers between city-size groups, wage setting for female workers in the public sector increases wage inequality in all regions, particularly in the periphery. The result is consistent with policies promoting the recruitment of high-educated female workers and the expansion of public services in the periphery counterbalancing the desired equality effect of public wages.

SimplETAS: A Benchmark Earthquake Forecasting Model Suitable for Operational Purposes and Seismic Hazard Analysis

Abstract The epidemic-type aftershock sequence (ETAS) model is the most effective mathematical description of the short-term space–time earthquake clustering. However, the use of such a model is sometimes hampered by the difficulty in estimating the high number of its unknown correlated parameters. Moreover, the most recent ETAS formulations introduce the space–time variability of some parameters that makes their estimation even more arduous. Here, we investigate the model in an opposite perspective, looking for the simplest ETAS parameterization that can satisfactorily describes the earthquake clustering in crustal tectonic regions; we named this model simplETAS. We show that simplETAS calibrated with the Italian seismicity of the last decades adequately describes the space–time occurrence of the out-of-sample largest earthquakes in the instrumental and historical catalog, confirming the validity of the assumptions made to build the model. Owing to its simplicity, simplETAS is easily applicable in most regions, and it has some important properties that are worth being remarked. First, simplETAS can be used as a benchmark model to assess the relative predictive skill of more complex earthquake forecasts. Second, it may be used for operational earthquake forecasting purposes in regions with limited earthquake catalogs. Third, it provides a straightforward, flexible, and effective approach to generate synthetic earthquake catalogs of variable length to be implemented in seismic hazard and risk analysis, overcoming all the declustering-related problems and the controversial Poisson assumption.

Bilingual Vocabulary Assessment: Examining Single-Language, Conceptual, and Total Scoring Approaches.

This study explored approaches for measuring vocabulary among bilingual children with varying levels of proficiency in Spanish and English. One hundred fifteen kindergarten and first-grade Spanish-English-speaking children completed measures of vocabulary and sentence repetition in Spanish and English. Scores were derived from their responses to the vocabulary measure: Spanish-only vocabulary, English-only vocabulary, conceptual vocabulary, and total vocabulary. Best language sentence repetition was also obtained. Using both visualization of data and statistical analysis, we tested for potential associations between children's relative language skills in Spanish and English and the scores they received on each of the vocabulary metrics. Participants' single-language vocabulary scores were linearly associated with their relative language scores. Higher relative Spanish language skills corresponded with higher Spanish-only vocabulary scores, and higher English language skills corresponded with higher English-only vocabulary scores. A quadratic association between children's relative language and their conceptual vocabulary scores was observed. Children with more balanced skills in Spanish and English received lower scores for conceptual vocabulary. No association between total vocabulary and relative language was observed. Results revealed evidence of differential test bias for single-language vocabulary scores and conceptual vocabulary scores. Spanish-only vocabulary underestimated knowledge of participants with higher English proficiency, whereas English-only vocabulary underestimated knowledge of participants with higher Spanish proficiency. Conceptual scoring yielded lower values for participants with relatively balanced proficiency in Spanish and English. There is need for further consideration of score and test functioning across the full continuum of bilinguals with dynamic proficiencies in each of their languages. https://doi.org/10.23641/asha.23796330.

Evaluating Medium-Range Forecast Performance of Regional-Scale Circulation Patterns

Abstract Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly and longer lead times. We hypothesize that by clustering atmospheric states into a number of predefined categories, the noise can be reduced and, consequently, medium-range forecasts can be improved. Self-organizing map (SOM)-based clustering was used on daily mean sea level pressure (MSLP) data from the North American Regional Reanalysis to categorize the synoptic-scale circulation for eastern North America from 1979 to 2016 into 28 discrete patterns. Then, using two goodness-of-fit metrics, the relative skill of four different forecasting methods over a 90-day lead time was studied: 1) a circulation pattern (CP) forecast, 2) raw forecast output from the Climate Forecast System (CFS) operated by the National Centers for Environmental Prediction (NCEP), 3) a simple climatology forecast, and 4) a simple persistence forecast. As expected, forecast skill of both the CP forecast and the raw CFS forecast generally decreased rapidly from the first day, coming to parity with the skill of climatology after 10–12 days when using correlation, and at 7–10 days when using the root-mean-square error (RMSE). Most importantly, this study found that the CP forecast was the most skillful forecast method over the 8–11-day lead time when using RMSE. On a spatial basis, the skill of the CP forecast and the raw CFS decreases latitudinally from north to south. This study thus demonstrates the potential utility of categorical or circulation pattern–based forecasting at 1–2-week lead times.

Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next-generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA's recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical depth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.

Reduced therapeutic skill in teletherapy versus in‐person therapy: The role of non‐verbal communication

AbstractObjectivesTherapists have experienced unique challenges and reported reduced competency when delivering teletherapy compared with in‐person therapy. This study aimed to examine the association between therapists' non‐verbal communication abilities and perceived skills in teletherapy.MethodsUsing a geographically diverse sample of 323 therapists, we assessed therapists' perceived skills in teletherapy and in‐person therapy and calculated the relative skills by subtracting one rating from the other. We performed multivariate regressions to explore whether and how therapists' non‐verbal communication abilities (i.e., emotion recognition ability, non‐verbal emotional expressiveness and affective communication) were associated with their perceived skills in teletherapy and in‐person therapy as well as the relative skills in teletherapy versus in‐person therapy.ResultsNon‐verbal emotional expressiveness and affective communication were positively associated with therapists' perceived skills in teletherapy. Therapists with stronger non‐verbal emotional expressiveness perceived their skills as relatively less reduced in teletherapy versus in‐person therapy compared to those with poorer non‐verbal emotional expressiveness.ConclusionMore research and training are needed to address the unique challenges in teletherapy, particularly around the ability to express emotions and facilitate non‐verbal communication.

Complex Syntax Methods of Assessment: Relative Clauses

Relative clauses are a type of complex syntax in which a clause modifies a noun (e.g., “the dog that runs in the house”). Some children struggle to produce relative clauses and their difficulties may impact their academic performance. Assessments that measure children’s relative clause skill are thus needed to assess and monitor progress of intervention in children’s relative clause skill to increase these children’s communication and in turn, academic performance. The aim of this study was to compare commonly used assessments in the research literature, two sentence imitation tasks (a straight imitation task and a delayed imitation task) and one elicitation task (a toy elicitation task), to identify the most efficient measure that captures child skill. An age effect was found for the straight imitation task which suggests that this task can capture growth over time. There were no statistically significant differences between the straight imitation and toy elicitation tasks. This lack of statistical significance indicates that the straight imitation task, which appeared to yield similar results to the elicitation task and is more efficient to administer, may provide an equally valid measure of relative clause production skills. The use of a straight imitation task is suggested to provide speech-language pathologists and researchers with a relative clause assessment tool that will aid in relative clause intervention that is efficient, captures growth, and truly captures relative clause skill.

Global Innovation Contests

The primary objective of this paper is to develop a two-country, dynamic, general equilibrium model with innovation contests to formally analyze the impact of globalization on the skill premium and fully-endogenous growth. Higher quality products are endogenously discovered through stochastic and sequential global innovation contests in which challengers devote resources to R&D, while technology leaders undertake rent-protection activities (RPAs) to prolong the expected duration of their temporary monopoly power by hindering the R&D effort of challengers. The model generates intra-sectoral trade, multinationals, and international outsourcing of investment services. Globalization, captured by a move from autarky to the integrated-world equilibrium, leads to convergence of wages and growth rates. Globalization and long-run growth are either substitutes or complements depending on a country’s relative skill abundance and the ranking of skill intensities between RPAs and R&D services. Trade openness between two countries that possess identical relative skill endowments but differ in size does not affect either country’s long-run growth.

A comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble

AbstractSeasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting.

Factors affecting cloud-based enterprise resource planning software adoption in Ethiopia

Enterprise Resource Planning (erp) are integrated software solutions that transform organizations’ internal processes, provide collaboration with partners, external applications and information systems. Extant literature reveals that organizations are showing interest to transit from an on-premise erp to the new cloud-based erp solutions due to their extra benefits. There are few recent initiatives in Ethiopia to adopt cloud-based erp but challenges faced in the course of the adoption are not explored. Using the technology-organization-environment, diffusion of innovation, and the model of innovation resistance frameworks as lenses, this research aims at identifying factors contributing to the adoption of cloud-based erp in the Ethiopian context. A quantitative approach is adopted and survey was conducted using a self-administered online questionnaire using Google’s online form to gather data from employees of Ethiopian Shipping and Logistics Services Enterprise. Out of 295 questionnaires distributed, 152 valid questionnaires were collected and considered for the data analysis. The proposed model was tested using a partial least square with the help of the Smart pls software. The proposed model explained 58.5 % of the variance in cloud-based ERP adoption factors. The empirical analysis indicated that Relative advantage, Trust, IT Skill, and External pressure had a significant influence on the adoption of cloud-based erp in Ethiopia whereas Organizational Culture, Observability, and Trialability had no significant impact on the adoption of cloud erp service. The study provides a comprehensive understanding of the factors which affect the adoption of cloud-based erp technology in Ethiopia.

Modelling Australian Rules Football as spatial systems with pairwise comparisons

Abstract Statistical analysis in competitive sport is an important tool for developing strategy and seeking competitive advantages. However, for complex team sports such as Australian Rules Football, major limitations occur when using possession event data for game analysis. First, focusing on counting possession events does not capture the impact of off-the-ball actions such as ground positioning of other players. Second, it is difficult to determine the extent that an event is due to either team’s relative proficiency or skill. Third, there is limited possession event data available from each match and modelling efforts often have low statistical power. Here we reinterpret event data into positional systems and utilise pairwise performance metrics to understand the relative team proficiency in each of these states. These metrics can then be used to construct transition probabilities between states for future games, and ultimately, absorbing probabilities of goal states. Our approach effectively predicts match outcomes using team ratings for forward, midfield and defensive systems and is sufficiently interpretable to support strategic decision-making by coaching departments in the Australian Football League (AFL).