Optimal cricket team selection using machine
learning
Abstract: Building a team in any sport requires a careful
assessment process, and the team's overall performance in a
particular match depends on which individuals are chosen to
be on the team. A range of criteria and algorithms are
employed to selectively choose players. The strengths and
weaknesses of the opposition, pitch conditions, player analytics,
and climatic data are all carefully considered while selecting a
cricket squad. A key component of player performance
prediction is machine learning algorithms, which use past data
on team dynamics and individual player statistics to predict the
success of the team as a whole. The process of forming a team
is streamlined by this predictive analysis of individual player
performance, which promotes the best possible team
chemistry. Even though there have been recent academic
attempts to build prediction models for cricket player
performance, previous studies frequently ignore important
variables like weather dynamics and field characteristics,
which have a significant influence on player production. By
completing a thorough evaluation of the literature, our study
closes this gap and develops a solid framework for predicting
player performance in cricket. By using our technique, team
managers are able to make well-informed judgments that lead
to the selection of a lineup that is most suited to improve the
team's performance as a whole.
Keywords— Cricket, Performance Prediction, Machine
Learning, Decision Tree, Random Forest
I.
INTRODUCTION
The ultimate goal of a cricket match is to win. Right now,
cricket draws more viewers than soccer [1]. Sports need a
professional approach as they grow more and more
competitive [2]. Numerous teams started using scientific
approaches to determine their game plan and plan of attack.
A machine learning technology aids in the analysis of
opponent team members' performances by the team
management. A machine learning method may be used to
identify the strengths and weaknesses of both teammates
and the adversary [5].
A lot of researchers have been working on developing
algorithms that can forecast players' performances in recent
times [1] [2]. Predicting player performance in cricket
assists team management in making the final team selection
prior to the toss.
Victory in cricket is determined by several variables,
including previous results, playing on home or away ground,
match experience, venue-specific performance, performance
against a particular opposition, and the team's and player's
form.
As technology advances more quickly and the public's
desire for cricket grows, so does the need for machine
learning calculations to predict cricket match results. Cricket
is one of the most well-known and popular sports in the
world today, particularly in the Indian subcontinent. Strong
incentives to model and train the game from several
viewpoints have been provided by a large betting market, a
multiplicity of natural elements impacting the game, and
extensive media coverage [5].
Every element of life is made simpler by the use of artificial
intelligence, machine learning, deep learning, and data
science. The coaches and players will be able to assess the
areas for growth through the use of machine learning and
result prediction prior to the real game [5]. Machine learning
is a rapidly expanding field that is closely related to
computational insights and often overlaps with it. It is
centered on using technology to make predictions. It is
closely related to numerical improvement, which is
hypothesized to provide the field with methods and
application areas. Sometimes, data mining—a subset known
as supervised learning—is confused with machine learning.
Data mining focuses more on exploratory information
processing.
Assessing players' performances is a difficult
undertaking. The team managers and team selectors may
find it useful to have an intelligent system that forecasts
player performance based on historical data [1] [2].
Numerous studies have been conducted in an attempt to
forecast player performance [1] [2] [5]. Nevertheless, none
of them take advantage of weather-related elements, which
might have an impact on players' performance. As a result,
we are working on a literature review to provide the best
approach for predicting each player's performance
throughout a cricket match.
II. BASIC INTRODUCTION OF CRICKET GAME
Three forms are available for playing cricket: 1. ODIs (50
overs matches), 2. T20 (20 overs matches), and 3. Tests
(five day games). Confined overs cricket refers to both
Twenty20 and One-Day Internationals, whereas Test
matches are confined to play for 5 days.
Every format has two innings, with one bowling and one
batting opportunity for each team. One inning in an ODI
consists of a maximum of 50 bowling overs. In T20 cricket,
a bowling innings consists of a maximum of 20 overs. When
the bowling team bowls 50 overs or more in an ODI or T20
match, or before the batting team loses 10 wickets, the
inning is over.
Teams switch roles after the first inning; for instance, the
batting team bowls, and the bowling team bowls. In ODI
and T20 cricket, the bowling team chases down a target set
by the opposing side within 50 overs or more after the
batting team bowls. Similarly, the bowling team attempts to
stop the other side from chasing the target in less than 50
overs, 20 overs, or 10 wickets via bowling. Each side can
bat and bowl in the two innings in ODIs and T20s. In ODIs,
an inning may consist of up to 50 overs, but in T20s, an
inning may consist of 20 overs or fewer. Test matches last
up to five days each, and single game, each side may play
up to two innings. Both teams in limited cricket have eleven
players apiece, and each will bat and bowl once in an
inning.
Each team switches roles after the first inning. The bowling
team attempts to reach the opposite side's target in 50 overs
or less than 10 goals conceded. In addition to bowling, the
attacking team attempts to prevent the other side from
pursuing the target or scoring ten goals in 50 overs or fewer.
Once the batting team has lost 10 wickets or 50 overs to the
opposing team, the batting innings comes to an end. In the
same manner, the opposing team will play after the fielding
team attacks. To win the game, the batting team must pursue
the goal established by the first betting team. To win, the
opposition must limit the batting team's ability to chase the
objective. For both bowlers and batsmen, limited-overs
cricket presents greater challenges. Batsmen need to get
runs as fast as they can, while bowlers need to limit them by
conceding the fewest runs and picking up the fewest
wickets. The most widely used format in modern
international cricket is the One-Day International (ODI)
format.
A. CRICKET FOR STATISTICS
The two primary types of statistics are bowling and
batting. The match's ultimate result is mostly determined by
these two statistics.
STATS ON BATTING [4]
The following are all of the batting statistics:
1. Innings: The total number of innings a player has
played.
2. Not Outs: The quantity of times the batter is still in
the game.
3. Runs: The total amount of runs a batsman has
scored.
4. Highest Score: The greatest score that the batsman
has ever achieved in the past.
5. Batting Average: The batsman’s batting average.
6. Centuries: How many times a batsman reaches 100
runs in a game.
7. Half-Centuries: The frequency with which a
batsman reaches 50 runs.
8. Balls Faced: The total quantity of balls that the
batter has faced.
9. Strike Rate: Strike Rate = (100 * Runs) / Ball Faced
10.
10. Run Rate: The typical amount of runs a team scores
per over.
STATS ON BOWLING [4]
Statistics related to bowlers are:
1. Over: A bowler's six balls bowled constitutes an
over.
2. Maiden Overs: The overs in which the bowler
gave up no runs in an over.
3. Wickets: The quantity of wickets a bowler has
claimed.
4. No-Balls: How many no-balls a bowler has
bowled.
5. Wide: The quantity of wide balls that the bowler
bowls.
6. Bowling Average: The mean quantity of runs lost
per wicket.
7. Strike Rate: The typical quantity of balls bowled.
III Correlated Works
Only a few documents on player performance prediction in
cricket were found using search [5]. Based on player data
and attributes, Passi and Pandey [5] reconstructed hitting
and bowling records. Other elements that could not be taken
into consideration yet have an impact on player performance
include the kind of wickets and the weather. To forecast
player performance, Siripurapu [5] created an adaptive
Neuro-fuzzy model, although it only takes into account team
members based on certain bowling and batting metrics.
Therefore, there's a chance that using every participant at
once will raise the rating.
In a one-day cricket match, Barr et al. [7] suggested criteria
for comparing and choosing batsmen. When choosing a
hitter, authors simply take into account the strike rate and
batting average. Nonetheless, weather and wicket types have
a big impact on how well players perform .
Bhattacharjee et al. [8] conducted a study in 2012
to forecast the performance of bowlers. The pace at which a
bowler bowls is one crucial factor that writers overlook
when analyzing a player's performance. This research aims
to forecast bowler performance exclusively.
Bowlers and batters are divided into three groups by Iyer
and Sharda [9]: performance, failure, and middling. The
authors' method for predicting a player's performance is
based on neural networks. This method was used to choose
the 2007 World Cup team based on each player's
performance rating.
Rising stars in cricket were forecasted by Hasseb Ahmed
et al. [10] in 2017. Based on performance development and
weighted average, they compiled a list of the top 10
emerging cricket players using a variety of machine learning
and mathematical techniques. Lastly, they made a
comparison between the International Cricket Council ranks
and rising star scores.
.
Predict player performance in an ODI cricket match using
the SVM algorithm. For their study, they only use six
players, though. SVM requires greater processing time when
there is non-linear data separation. Additionally, for best
performance, the right kernel function must be chosen.
A. MODEL OF GENERIC PREDICTION
The machine learning technique for predicting players'
performance was proposed by several academics. Prediction
is done using machine learning techniques such as random
forest, SVM, naïve Bayesian, and decision tree. With the
exception of random forest, the majority of algorithms
operate below optimally. Figure 1 displays the generalized
prediction model derived from previous studies together
with the output of the corresponding machine-learning
method.
FIG 1: PREDICTION MODEL FOR OPTIMAL CRICKET
B. PROBLEM IDENTIFICATION
Predicting a player's performance is becoming increasingly
desirable in a competitive setting. In this field, machine
learning
has
a
significant
potential
impact.
The majority of previous studies did not include the weather
in their analysis. Weather conditions may have an impact on
players' and teams' performances [5]. Also, a limited
collection of datasets is used in the majority of current
studies [3]. They focus just on a small aspect of the cricket
game and restrict their study efforts.
C. PROPOSED SYSTEM FLOW
The majority of previous research solely includes statistics
pertaining to cricket matches and leaves out weather-related
information [1, 3, 7, 10]. The weather has a significant
impact on players' performances. The wind that blows in the
bowler's direction enhances their performance and hinders
that of the batsman. The following factors might affect the
outcome of a day-night match: humidity, wind direction,
rain, and cold. We suggested a method that predicts a
player's performance by utilizing meteorological data and
statistics from cricket matches.
.
We discovered that a machine learning strategy using
meteorological attributes has the potential to enhance
prediction performance after reviewing a survey of the
literature on players' performance prediction. We suggested
a technique that combines meteorological and cricket
datasets. After that, feature extraction will be used to extract
the most valuable traits. In order to ensure that every class
has an equal impact on the final forecast, we additionally
balance the uneven cricket dataset. The suggested system
flow is provided in Figure 2.
FIG 3: Hyper Plane of SVM
The SVM hyperplane is depicted in figure 2. The type of
issue statement and the application we are designing will
determine how the hyperplane's value is set up.
FIG 2: FLOW MODEL OPTIMAL TEAM
B. Random Forest Classifier.
IV. Classification Steps
. The challenge of classification is to determine, given a
training set of data that includes observations whose
category is known, which of a set of categories a new
observation belongs to.
Random Forest is a classifier that contains a number of
decision trees on various of the given dataset and takes the
average to improve the predictive accuracy of that dataset.
The greater number of trees in the forest leads to higher
accuracy an prevents the problem of overfitting.
A.
C. Decision Tree
Support Vector Machines (SVM) Classifier
The foundation of SVM (Support Vector Machine) is
supervised learning. SVM needs training sets and labels to
go along with them. If test data is added after training, the
model classifies it into one of the two categories. It
functions effectively in linear classification. By translating
the inputs into a high dimensional feature space and
employing a kernel method, it can even do well on a
nonlinear classification. It builds the classification's hyper
plane. The hyper plane is selected to maximize the
separation between the closest data points on either side.
Formally, SVM is a machine learning technique that uses a
hyperplane to categorize datasets according to a given
criteria. The data is divided into 2-D space using a
hyperplane based on input variables.
Decision tree algorithms segment data by feature
values, optimizing criteria like Gini impurity or entropy.
They provide transparent insights into fraud indicators but
may overfit complex datasets. Techniques like pruning
mitigate this. Despite limitations, decision trees are
foundational in fraud detection, aiding in feature
significance understanding and serving as building blocks
for advanced models.
Linear Classification:
In its simplest form, SVM performs linear classification by
finding the hyperplane that best separates th data into two
classes. This hyperplane can be expressed as:
W.x+b=0
(1)
FIG 4: DECISION TREE OF RANDOM FOREST
V. RESULT AND DISCUSSION
Where w is the weight vector perpendicular to the
hyperplane, x is the input feature vector, and b is the
bias term.
This model emphasizes skill alignment, clear goals,
effective communication, feedback, adaptive leadership, and
celebrating achievements to optimize team performance and
collaboration within project environments.
Accuracy table :-
Algorithm
Accuracy
Decision
Random
Tree
Forest
86.50
92.25
Weighted
SVM
Random
Forest
68.78
93.73
TABLE 1: COMPARSION OF ACCURACY.
Table 1 illustrates that our weighted random forest
technique yields the highest accuracy of 93.73 when
compared to other conventional classification algorithms.
FIG 6: ACCURACY GRAPH
VI. CONCLUSION
In order to create an intelligent system that can anticipate
players' performances for one-day international cricket
matches, it is necessary to take into account factors other
than bowling and batting. The weather aspect is a crucial
consideration that greatly affects how well players perform.
We suggested a machine learning approach that makes use
of weather-related datasets and cricket statistics. We suggest
data balancing as a preprocessing step before using a
machine learning method, as combined data is inherently
unbalanced. We evaluated our suggested model on a
combined balanced dataset of meteorological and cricket
match data, and it employs a novel weighted random forest
classifier with hyperparameter tweaking. When compared to
other algorithms, we discovered that the accuracy of our
suggested model was good.
FIG 5: COMPARISON ANALYSIS FOR ALGORITHMS.
REFERENCES
The performance of many machine learning models for
choosing cricket teams is displayed in the pie chart:
1. M. G. Jhanwar and V. Pudi, "A team composition based
approach
for predicting the outcome of One-day International cricket matches,"
CEUR Workshop Proc., vol. 1842, no. September, 2016.
- 86.50% in Decision Tree
- 92.25% in Random Forest
2. "Improved Prediction Accuracy in the Game of Cricket Using Machine
Learning," by K. Passi and N. Pandey, International Journal of Data
Mining, Knowledge Management, and Process, vol. 8, no. 2, pp. 19–36,
2018.
- Support Vector Machine (SVM): 68.78 percent
- 93.73% in Weighted Random Forest
The accuracy % attained by a particular model is
represented by each slice of the pie. As shown, Random
Forest and Weighted Random Forest were the two models
that performed the best in terms of accuracy. Among the
models examined, Decision Tree fared very well, although
SVM had the lowest accuracy, shown in figure 4.
3. Int. J. Trend Res. Dev., vol. 5, no. 1, pp. 2394–9333; R. Lokhande and
A. Professor, "Live Cricket Score and Winning Prediction."
4. M. G. Jhanwar and V. Pudi, "A team composition based approach for
predicting the outcome of One-day International cricket matches," CEUR
Workshop Proc., vol. 1842, no. September, 2016.
5.
"Intelligent system for team selection and decision making in the
game of cricket," N. Siripurapu, A. Mittal, R. P. Mukku, and R. Tiwari, Smart
Innov.
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77,
pp.
467–474,
2018.