This document discusses predicting the outcome of cricket matches and assisting coaches. It will use algorithms like Naive Bayes and ID3 to predict matches based on factors such as home advantage, toss result, and team combination. These predictions will determine betting odds. The system will also assist coaches by selecting the best team using player records and using algorithms like Gale-Shapley to determine the optimal batting order. The document reviews several research papers on related topics and summarizes previous work on analyzing cricket matches.

1. CRICKET PREDICTOR
BY:
NAMAN JAIN(9911103490)

2. INTRODUCTION
In this work we address the winning factors in the sport of ONE DAY CRICKET.
Winning in ODI depends on factors such as scoring as well as physical strength
of the teams. But there is scope of further research on these factors and
analyzing them. Interesting factors include home game-advantage, day
night effect, winning the toss and batting first.
Our main aim is to predict the outcome of the cricket match and assisting the
coach in selecting the best possible team.

3. PROBLEM STAEMENT
Part 1
To predict the result of particular match based on various parameter such as
Home advantage, First Bat, Ground Conditions, Toss, Team Combination etc.
On the basis of these results, decide the betting rates.
Part 2
To assist the coach in various areas such as Team Selection, Batting Order,
Strategy etc.

4. PROPOSED SOLUTION
For Part 1
We are predicting the outcome of a match by simulating research papers and improving upon
them by adding more parameters and also comparing the accuracy in prediction of various
techniques. Betting rates will also be decided based on these techniques.
Some of the algorithms and techniques:
Naive Bayes
ID3
For Part 2:
We are assisting the coach of the cricket teams in selecting the best possible team using individual
player records to determine the best team for a particular match. Also use set matching algorithms
such as Gale-Shapley to decide the batting order.

5. ALGORITHMS
We will do so using 2 algorithms :
1. Naïve Bayes Algorithm : It can be seen as a way of understanding how the
probability that a theory is true is affected by a new piece of evidence.
2. ID3:- The ID3 algorithm begins with the original set S as the root node. On
each iteration of the algorithm, it iterates through every unused attribute
of the set S and calculates the entropy H(S) (or information gain IG(A)) of
that attribute. It then selects the attribute which has the smallest entropy
(or largest information gain) value. The set S is then split by the selected
attribute (e.g. age < 50, 50 <= age < 100, age >= 100) to produce subsets
of the data.

6. COACH ASSISTOR
STABLE MARRIAGE PROBLEM
Stable marriage problem (SMP) is the problem of finding a stable
matching between two sets of elements given a set of preferences for each
element. A matching is a mapping from the elements of one set to the
elements of the other set. A matching is stable whenever it is not the case
that both:
 some given element A of the first matched set prefers some given
element B of the second matched set over the element to which A is
already matched, and
 B also prefers A over the element to which B is already matched

7. RESEARCH PAPER 1
Auto-play: A Data Mining Approach to ODI Cricket Simulation and Prediction
In this paper, we build a prediction system that takes in historical match data
as well as the instantaneous state of a match, and predicts future match
events culminating in a victory or loss. We model the game using a subset of
match parameters, using a combination of linear regression and nearest-
neighbour clustering algorithms

8. RESEARCH PAPER 2
Fuzzy Logic based Cricket Player Performance Evaluator
We propose a fuzzy logic based technique to evaluate the performance of
cricket players. Various input parameters are being considered which are
scaled using linguistic variables and a very simple yet effective software tool is
developed to compute the effect of input parameters on the ranking of the
players.

9. RESEARCH PAPER 3
Gale-Shapley Stable Marriage,Problem Revisited: Strategic Issues and
Applications
We study strategic issues in the Gale-Shapley stable marriage model. In the
first part of the paper, we derive the optimal cheating strategy and show that
it is not always possible for a woman to recover her women-optimal stable
partner from the men-optimal stable matching mechanism when she can
only cheat by permuting her preferences. In fact, we show, using simulation,
that the chances that a woman can benefit from cheating are slim

10. LITERATURE SUMMARY
From our literature survey, it was found that very limited machine learning work has
been done on game of cricket. Though cricket shares some attributes with other
sports such as baseball, it still remains unique in certain respects and deserves to
be analyzed independently. Most of analyzing studies on cricket so far have been
conducted using statistical methods. Furthermore, many of them have addressed
the five day long test matches but not the One Day Internationals. We present
some relevant studies below.
The statistical research on Cricket has been started very early stage of the cricket.
In 1945 Wood used the geometric distribution to model the total score in cricket .
This was not a study on the ODI form of the game but has been recognized
among the pioneering research in the game of cricket. Bailey and Clarke
conducted a study to predict the outcome in one day international cricket while
the game is in progress .

11. SUMMARY(cont)
This study was performed using statistical models. The interesting fact about
this article is that the authors have statistically proved how the match
resources (number of overs and batsmen left) affect the final result. However,
they deal with analysis during the current game. They do not predict in
advance the chances of winning a new game based on previous matches.
Chedzoy studied the issue of umpiring errors in cricket matches . An umpire is
the term used for a referee in cricket. This article focuses on umpiring
decisions and how they affect the outcome of the match. This study was also
based on a statistical approach. Moreover, it focused only on one aspect of
the game, namely, the effect of umpires. Sparks and Abrahamson developed
a mathematical model to predict award winners in a game.

12. SUMMARY(cont)
This study was conducted using machine learning techniques and focused on
baseball matches. They have employed this model in the national league
and correctly predicted winners prior to the award announcement. Smith
and Lipscomb have done similar study for Predicting CY Young award
winners for Baseball Pitchers. Interestingly, they have found that Naïve Bayes
performed well in their research as well. Bandulasiri has written an interesting
article on predicting the winner in an ODI cricket match. This article
addressed similar datasets as we used in our research. In this paper, the
author has used statistical methods to find wining factors for an ODI match.
We have explored the machine learning path, considering popular classifiers,
and developed a software tool based on our results. This AI-based tool would
be very helpful in predictive analysis in cricket.

13. SUMMARY(cont)
It is also possible to apply the machine learning techniques we used in our
research to predict the outcome in other outdoor sports such
as baseball. The specific approach used may depend on the nature of the
given datasets and applications

14. GALE SHAPLEY
In this approach, 2 11*11 matrix are considers
1: Preference list of Players
2.Preference list of Coach

15. GALE SHAPLEY(cont)

16. THANK YOU