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A Method for Inferring Batting Conditions in ODI Cricket from Historical Data

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Subject- Finance
Topic- A Method for Inferring Batting Conditions in ODI Cricket from
Historical Data
University name- UNIVERSITY OF CANTERBURY

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Introduction
The outcomes that take place on a sports field are obviously heavily influenced by the ability
and performance on the day of the athletes taking part; however, these are not the sole
determinants. In many sports outcomes are also influenced by random influences, ranging
from human error by match officials to the proverbial “rub of the green”. For empirical
researchers interested in analysing sports data, most of these external influences can simply be
modelled as exogenous sampling error. There is one influence, however, that is potentially
less benign—the impact of weather and venue conditions at the time of the sporting event. In
many sports, particularly those played outside, the ease with which player skill and effort can
translate into positive outcomes can depend heavily on these conditions. If the variation in
conditions during the course of a match is small relative to the variation in conditions between
different matches, then conditions within a match cannot reasonably be modelled as
independent draws from some random distribution.
One sport where this issue can be particularly problematic is one-day-international (ODI)
cricket. In ODI cricket one team bats and has a single “innings” in which it seeks to score as
many runs as possible. The innings ends when the other team has bowled 300 deliveries to the
batsmen, or when ten batsmen have been dismissed, whichever comes first. The teams then
change roles and the other team has an innings of 300 deliveries or 10 dismissals with which
to try and achieve a higher score.
ODI cricket has been the subject of a lot of empirical research in the academic literature of
statistics, operations research and economics, in part because of enthusiasm for the game of
researchers in those areas, but also because of its highly quantitative nature, with the state of
the game being quantifiable after each of the up-to 600 deliveries that constitute a match.
Statistical analysis of ODI cricket typically consists of estimates of distributions of likely
outcomes as a function of the state of a game at a particular point. For instance, the
Duckworth-Lewis system currently used in all ODI matches to make adjustments to target
scores when bad weather forces an interruption in a match with a consequent reduction in the
time available for play, originated as an academic paper (Duckworth and Lewis, 1998) that

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Subject- Finance Topic- A Method for Inferring Batting Conditions in ODI Cricket from Historical Data University name- UNIVERSITY OF CANTERBURY? Introduction The outcomes that take place on a sports field are obviously heavily influenced by the ability and performance on the day of the athletes taking part; however, these are not the sole determinants. In many sports outcomes are also influenced by random influences, ranging from human error by match officials to the proverbial "rub of the green". For empirical researchers interested in analysing sports data, most of these external influences can simply be modelled as exogenous sampling error. There is one influence, however, that is potentially less benign-the impact of weather and venue conditions at the time of the sporting event. In many sports, particularly those played outside, the ease with which player skill and effort can translate into positive outcomes can depend heavily on these conditions. If the variation in conditions during the course of a match is small relative to the variation in conditions between different matches, then conditions within a match cannot reasonably be modelled as independent draws from some random distribution. One sport where this issue can be particularly problematic is one-day-international (ODI) cricket. In ODI cricket one team bats and has a single "innings" in which it seeks to score as many runs as possible. The innings ends when t ...
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