The influence of crowd noise and experience upon refereeing decisions in football

Several leaf litter decay studies have indicated that decomposition occurs more rapidly when litter is placed beneath the plant species from which it had been derived than beneath a different plant species (i.e. home-field advantage, HFA), although support for this notion has not been universal. We provide the first quantification of HFA in relation to leaf litter decomposition using published litter mass loss data from forest ecosystems in North America, South America, and Europe. Our findings indicate that HFA is widespread in forest ecosystems; on average litter mass loss was 8% faster at home than away. We hypothesize that HFA results from specialization of the soil biotic community in decomposing litter derived from the plant above it. Climate and initial litter quality data can be used to explain about 70% of the variability in litter decomposition at a global scale, leaving about 30% unexplained. We suggest that HFA be recognized as a factor that explains some of this remaining variability.

Home-field advantage accelerates leaf litter decomposition in forests

A parametric model is developed and fitted to English league and cup football data from 1992 to 1995. The model is motivated by an aim to exploit potential inefficiencies in the association football betting market, and this is examined using bookmakers' odds from 1995 to 1996. The technique is based on a Poisson regression model but is complicated by the data structure and the dynamic nature of teams' performances. Maximum likelihood estimates are shown to be computationally obtainable, and the model is shown to have a positive return when used as the basis of a betting strategy.

Modelling Association Football Scores and Inefficiencies in the Football Betting Market

The aim of this study was to examine the effects of match location, quality of opposition, and match status on possession strategies in a professional Spanish football team. Twenty-seven matches fr...

The influence of match location, quality of opposition, and match status on possession strategies in professional association football

The selective travelling salesman problem

Least squares is used to fit a model to the individual match results in English football and to produce a home ground advantage effect for each team in addition to a team rating. We show that for a balanced competition this is equivalent to a simple calculator method using only data from the final ladder. The existence of a spurious home advantage is discussed. Home advantages for all teams in the English Football League from 1981-82 to 1990-91 are calculated, and some reasons for their differences investigated. A paired home advantage is defined and shown to be linearly related to the distance between club grounds.

https://researchbank.swinburne.edu.au/file/4b4ca583-30a6-4dc2-adc6-8848b6f095cd/1/PDF (Accepted manuscript).pdf

Home ground advantage of individual clubs in English soccer

A simple functional equation of dynamic programming is used to generate optimal orienteering routes in mountainous country. Comparisons are made between computed and actual results, further refinements in the handling of data considered, and the benefits to competitors and organizers of orienteering-type events discussed.

https://link.springer.com/content/pdf/10.1057%2Fjors.1984.161.pdf

Dynamic Programming in Orienteering: Route Choice and the Siting of Controls

In cricket, particularly near the end of an innings, batsmen of different abilities need to manage the rate at which they score runs. Either batsman can choose to bat aggressively or defensively, which alters their chances of scoring runs or being dismissed. Since they change ends when they score a run and at the end of an over, by scoring an odd or even number of runs the two batsmen also determine which of them will face the next ball. It may be worthwhile to refuse a run to keep the slower or lower scoring batsman from the strike. Some dynamic programming models are developed which could be used to maximise the total number of runs scored.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2600705.pdf

To run or not?: Some dynamic programming models in cricket

Dynamic programming is applied to the problem in tennis of whether to serve fast or slow on either or both serves at each stage in a game, and a simple policy is found.

https://link.springer.com/content/pdf/10.1057%2Fjors.1985.11.pdf

Dynamic Programming in Tennis—When to Use a Fast Serve

An experimentally derived formula has implications for (i) the measurement of a runner's workrate; (ii) choice of gradient in running uphill; and (iii) the use of Naismith's rule in deriving schedules for fell runners.

Running Uphill: an Experimental Result and its Applications

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

John M. Norman

Author Tools

Chat about Author