https://www.bauer.uh.edu/gardner/docs/pdf/Exponential-Smoothing.pdf

Exponential smoothing: the state of the art, part ii

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

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

In this paper a method is suggested for predicting the distribution of scores in international soccer matches, treating each team’s goals scored as independent Poisson variables dependent o...

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2600997.pdf

A ratings based Poisson model for World Cup soccer simulation

Millions of dollars are wagered on the outcome of one day international (ODI) cricket matches, with a large percentage of bets occurring after the game has commenced. Using match information gathered from all 2200 ODI matches played prior to January 2005, a range of variables that could independently explain statistically significant proportions of variation associated with the predicted run totals and match outcomes were created. Such variables include home ground advantage, past performances, match experience, performance at the specific venue, performance against the specific opposition, experience at the specific venue and current form. Using a multiple linear regression model, prediction variables were numerically weighted according to statistical significance and used to predict the match outcome. With the use of the Duckworth-Lewis method to determine resources remaining, at the end of each completed over, the predicted run total of the batting team could be updated to provide a more accurate prediction of the match outcome. By applying this prediction approach to a holdout sample of matches, the efficiency of the “in the run ”wagering market could be assessed. Preliminary results suggest that the market is prone to overreact to events occurring throughout the course of the match, thus creating brief inefficiencies in the wagering market.



Key Points


In excess of 80% of monies wagered on the outcome of ODI matches are placed after the match has commenced.


Using all past data from ODI matches, multiple linear regression models are constructed to predict team totals and margin of victory.


By combining match information with prediction models, an ‘in the run’ prediction process is created for ODI matches.






Key words: Linear regression, live prediction, market efficiency, betting


Introduction
The first official one day international (ODI) match was played in 1971 between Australia and England at the Melbourne Cricket Ground. Whilst ODI cricket has developed over the past 35 years (2300 matches), the general principles have remained the same. Both sides bat once for a limited time (maximum 50 overs) with the aim in the first innings to score as many runs as possible, and in the second innings to score more than the target set in the first innings. The high scoring nature of ODI matches ensures that team totals and differences between scores can be well approximated by a normal distribution. As shown by (Bailey, 2005), this facilitates the use of multiple linear regression to predict a margin of victory (MOV) prior to the commencement of the match. Using a similar approach, a multiple linear regression is also used to predict the number of runs scored by the team batting first. With the use of (Duckworth and Lewis, 1999) approach of converting resources available into runs, as each over is bowled, the current total and the predicted total for the remaining overs are combined to produce an updated predicted total for the batting team. The difference between the pre-match predicted total and the updated predicted total provides a measure of how the batting team is performing through the course of their inning. This difference is then used to provide an updated prediction for the MOV.

https://researchbank.swinburne.edu.au/file/fe524bdf-2f50-4e52-977d-03c8005e9c3e/1/PDF (10 p).pdf

Predicting the match outcome in one day international cricket matches, while the game is in progress

With the growth in sports betting, it is possible to bet, both before and during a match, on a range of
outcomes in tennis. This paper shows how the standard statistics published by the ATP can be combined
to predict the serving statistics to be obtained when two given players meet. These statistics are then used
in a spreadsheet model to predict further match outcomes, such as the length of the match and chance
of either player winning. These calculations can be updated as the match progresses. The method is
demonstrated by focusing on a very long men’s singles match between Roddick and El Aynaoui played
at the 2003 Australian Open.

Combining player statistics to predict outcomes of tennis matches

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

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