PredictXI- Best Fantasy Team Forecasting

VIVA-Tech International Journal for Research and Innovation
ISSN(Online): 2581-7280
Volume 1, Issue 6 (2023)
Article No. 08
PP 01-07
1
www.viva-technology.org/New/IJRI
PredictXI- Best Fantasy Team Forecasting
Vivek Mistry1
, Saurabh Walanj2
, Shivam Singh3
, Prof. Akshata
Raut4
1
(Department Computer Engineering, Mumbai University, MUMBAI)
2
3
4
(Professor, Department Computer Engineering, Mumbai University, MUMBAI)
Abstract: Fantasy sports have gained immense popularity in recent years, and platforms like Dream11 and Vision11
have become the go-to destinations for sports enthusiasts looking to test their sports prediction skills. The success of a
fantasy team hinges on the selection of the right players, a task that requires a deep understanding of various factors
influencing a game. PredictXI is an innovative machine learning-based system designed to simplify and enhance the
process of creating winning fantasy teams for prediction apps like Dream11 and Vision11. PredictXI harnesses the
power of advanced machine learning algorithms to analyze a multitude of critical variables that impact a player's
performance and a team's success. The proposed system also considers multiple factors to make informed decisions on
player selection & team composition for a particular match. By processing these variables and employing sophisticated
predictive modeling techniques, PredictXI narrows down the best players to include in your fantasy team. PredictXI
stands as a cutting-edge solution that empowersusers with data-driven insights, increases the likelihood of assembling a
winning fantasy team, and enhances the overall fantasy sports experience.
Keywords – Fantasy sports, Sports prediction, Fantasy team, Player selection, Predictive Modeling, Winning
Strategy.
I. INTRODUCTION
Fantasy cricket has become an immensely popular pastime for cricket enthusiasts around the world. In fantasy cricket,
participants create their own dream teams comprised of real-life cricketers from various matches, and these teams
compete against each other based on the actual performances of the selected players in real games. The essence of
fantasy cricket lies in predicting player performance, strategizing team composition, and making crucial decisions to
earn points and win contests. Thereare various apps which provide a platform to create these fantasy teams popularly
known one's are Dream11, Vision11, My11Circle, etc. Dream11 is an Indian fantasy sports platform that allows users
to play fantasy cricket, hockey, football, kabaddi, basketball, baseball, volleyball & handball. To create a
successful Dream11 cricket team, you need to have a good understanding of the sport, knowledge of the players'
recent form, pitch conditions, team strategies, and other factors that can influence a player's performance. Keep in
mind that cricket is a dynamic sport, and player performance can vary from match to match. Therefore, creating a
successful Dream11 cricket team requires continuous research and adaptation to stay competitive in fantasy cricket
contests. The proposed system is a robust and efficient machine learning model that can process and interpret vast
amounts of data related to cricket matches, players' performance, and pitch conditions. The system also considers
multiple factors to make informed decisions on player selection and team composition for a particular match. The
primary objective is to maximize the users' chances of earning points in the prediction game by selecting players who
are likely to perform well based on historical data and current form. The system helps to increase the winning
probability of players rather than relying on luck.

Article No. 08
PP 01-07
2
II. REVIEW OF LITERATURE SURVEY
The following chapter is a literature survey of the previous research papers and research which gives
the detailed information about the previous system along with its advantages and disadvantages.
Sachin Kumar S, Prithvi H V, C. Nandini [1] proposes Data Science approach to predict the winning fantasy
cricket team- Dream11 Fantasy Sports which is a predictive model that predicts the performance of players in a
prospective game using a combination of Greedy and Knapsack Algorithms. It gives us visual insights on the team,
and player’s performances by accounting for the statistical, and subjective factors of a prospective game. It increases
the probability of winning big. The authors selected the best regression model according to dataset and problem
statement. The authors proved that to predict the performance of a player in a prospective game, we must use
regressor models & not classification models.
Karthik Kataara, Shashank Shetty, Gokul S Krishnan, Sanjay Bankapur [2] proposes Analysis and
Prediction of Fantasy Cricket Contest Winners Using Machine Learning Techniques using a feed- forward deep
neural network (DNN) classifier for predicting the winning contestant for the top three positions in a fantasy league
cricket contest. The DNN-based classifier is able to generalize features effectively, thereby making good
predictions on the contest-winning positions compared to other baseline classifiers. Performance of the DNN
classifier was compared against the state-of-the-art machine learning approaches like k-nearest neighbors (KNN),
logistic regression (LR), Naïve Bayes (NB), random forest (RF), support vector machines (SVM) & in predicting
the fantasy cricket contest winners.
G. Sudhamathy and G. Raja Meenakshi [3] proposes Prediction on IPL data using machine learning
techniques in R package and focuses on exploring IPL data and presenting its insights as graphical representation
and comparative analysis. Four machine learning algorithms Naïve Bayes, Decision Tree, K nearest neighbor &
random forest are applied and the results are compared to measure the accuracy, precision, recall and sensitivity. By
making use of this, Indian Premier League and the fan followers can take decisions on the team’s performance and
predict the trophy winners that will lead to success in future. Random Forest performed well than the other algorithms
as the measures are outstanding. It shows high accuracy and less error rate. It helps to understand the four different
machine learning algorithms, working principal and their algortihms.
Ryan Beal, Timothy J. Norman and Sarvapali D. Ramchurn [4] proposes Optimizing Daily Fantasy Sports
Teams with Artificial Intelligence proposed a range of time series prediction techniques to predict player
performance and benchmark them on a real-world data-set. Authors proposed a knapsack packing formulation for
the DFS problem & solve it optimally using MIP techniques. The model evaluates human prediction & optimization
approaches to determine how prediction and optimization, individually and in concert, influences others. Authors
evaluated all models on data from over four seasons from a well-known DFS game and show that the approach is
able to return a profit 81% of the time.
Nilesh M. Patil, Bevan H. Sequeira, Neil N. Gonsalves, Abhishek A. Singh [5] proposes Cricket team
prediction using machine learning techniques which aims to predict team success based on the player's past records.
The authors used the Random Forest Algorithm and Decision Tree classifiers to produce the problem's prediction
models. It was found that the Random Forest classifier is the most reliable for the problems proposed. Random Forest
proved to be the most effective classifier with optimum precision for the datasets. Model completely eradicates the
biased selections & gives the best decisions.
Eric Hermann and Adebia Ntoso [6] proposes Machine Learning Applications in Fantasy Basketball which
applies machine learning to fantasy sports in order to gain an edge over the average player. Basketball players’
fantasy scores were predicted using a linear regression algorithm and stochastic gradient descent as well as a naive
bayes classifier with discretized state space. An advantage of around 8% was gained over regular users of DraftKings.
With a large enough volume of teams, the algorithm can make money. The techniques were not implemented yet
due to difficulty to gauge their effectiveness without investing a large sum of money into the algorithm.
Paul Steenkiste [7] proposes Finding the Optimal Fantasy Football Team in which three models were used by
author namely Linear Regression, Random Forests, and Multivariate Adaptive Regression Splines. The author used
every player’s statistic in the previous weeks of the season, as well as the team he was playing in each week as
input. It was then predicted how each player would do against his specific opponent in each of the relevant fantasy
categories. All three models correctly choose four or five of the players who actually did end up ranked in the top 7.

Article No. 08
PP 01-07
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Nikhil Dhonge, Shraddha Dhole, Nikita Wavre, Mandar Pardakhe, Amit Nagarale [8] proposes IPL cricket
score and winning prediction using machine learning techniques by using two methods the first one is prediction of
score and the second one is team winning prediction. The score prediction included linear regression, lasso regression
and ridge regression whereas in winning prediction SVC classifier, decision tree classifier and random forest
classifier were used. The model used the supervised machine learning algorithm to predict the winning. Random
forest classifier provided good accuracy and the stable accuracy so that desired predicted output becomes accurate.
Random Forest’s accuracy was more than SVC and Decision Tree Classifier.
Ayush Tripathi, Rashidul Islam, Vatsal Khandor, Vijayabharathi Murugan [9] proposes Prediction of IPL
matches using Machine Learning while tackling ambiguity in results using algorithms like NaïveBayes, SVM,
kNearest Neighbor, Random Forest, Logistic Regression, Extra Trees Classifier,XGBoost were adopted to train the
models for predicting the winner. This paper is centered on the implementation of machine learning to foretell the
winner of an IPL match. The highest accuracy of 60.043% with Random Forest, with a standard deviation of 6.3%
and an ambiguity of 1.4%, was observed. Tree-based classifiers provided better results with the derived model. The
derived model has also solved the problem of multicollinearity and identified the issue of data symmetry.
Malhar Bangdiwala, Rutvik Choudhari, Adwait Hegde, Abhijeet Salunke [10] proposes Using ML models
to predict points in Fantasy Premier League using Linear Regression, Decision Tree, and Random Forest algorithms.
This paper attempts to build and compare three machine learning modelsto accurately predict the number of points
that each footballer would earn over the course of the season. Features such as fixture difficulty, form of the two
teams, creativity, and threat of the footballer have been considered. Helps the player of this game to make more
informed decisions while making their respective teams. Successfully predicted the points a player would accumulate
in the upcoming match of on the FPL platform. This decision was rather daunting given the amount of data and
statistics that is available.

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III. ANALYSIS
Analysis table summarizes the research papers on the Fantasy Team Prediction. Below is a detailed
description of various algorithms used in research papers.
Table 1: Analysis Table
Title Summary Advantages TechStack
Data Science
approach to predict the
winning fantasy cricket
team— Dream11
fantasysports. [1]
This paper proposes Data
Science approach to
predict the winning fantasy
cricket team- Dream11
Fantasy Sports which is a
predictive model that
predicts the performance
of players in a prospective
game using a combination
of Greedy and Knapsack
Algorithms.
Selected the best
regression model
according to dataset and
problem statement. Proved
that to predict the
performance of a player in
a prospective game, we
must use regressor models
and not classification
models.
Data Science.
Analysis and
prediction of fantasy
cricket contest winners
using machine
learning techniques.
[2]
The system proposes
using a feed-forward deep
neural network (DNN)
classifier for predicting the
winning contestant for the
top three positions in a
fantasy league cricket
contest. The DNN based
classifier is able to
generalize features
effectively, thereby
making good predictions
on compared to other
classifier.
Improved DNN showed
the best results for all three
positions showing its
consistency in predicting
the winners and
outperforms the state-of-
the-art machine learning
classifiers.
Deep Neural Network
(DNN).
Prediction on IPL data
using machine learning
techniques in R
package. [3]
This paper proposes
focuses on exploring IPL
data and presenting its
insights as graphical
representation and
comparative analysis. By
making use of this, Indian
Premier League and the
fan followers can take
decisions on the team’s
performance and predict
the trophy winners that
will lead to success in
future. The four
algorithms give the result
as KKR which the more
probability of winning
however the winning team
turned out to be CSK.
Random Forest performed
well than the other
algorithms as the
measures are outstanding.
It shows high accuracy
and less error rate.
R Software.

Article No. 08
PP 01-07
5
Optimizing Daily
Fantasy Sports Teams
with Artificial
Intelligence. [4]
This paper proposes
Optimizing Daily Fantasy
Sports Teams with
Artificial Intelligence
proposed a range of time
series prediction
techniques to predict
player performance and
benchmark them on a
real-world data-set.
Authors evaluated all
models on data from over
four seasons froma well-
known DFS game and
show that the approach is
able to return a profit 81%
of the time.
Provides solutions to both
the prediction and
optimization problems
posed by fantasy sports.
Improved on the previous
published models in this
space by 15.9% on
average for points
prediction and by 377.5ms
for the optimization run
time.
Artificial
Intelligence.
Cricket team prediction
techniques.[5]
This paper proposes
Cricket team prediction
techniques which aims to
predict team success based
on the player's past
records. The authors used
the Random Forest
Algorithm and
Decision Tree classifiers
to produce the problem's
prediction models.
Random Forest classifier
was the most reliable for
the problems proposed.
Random Forest proved to
be the most accurate
classifier with optimum
precision for the datasets.
Model completely
eradicates biased
selections and gives the
best decision.
Random Forest.
Machine learning
applications in Fantasy
Basketball. [6]
This paper proposes a
proposes Machine
Learning Applications in
Fantasy Basketball which
applies machine learning
to fantasy sports to gain
an edge over the average
player. Basketball
player’s fantasy scores
were predicted using
linear regression
algorithm as well as
Naïve Bayes classifier
with discretized state size.
An advantage ofaround 8
percent was gained over
regular users of
DraftKings. With a large
enough volume of teams,
the algorithm can make
money.
Linear Regression &
Naïve Bayes
Classifier.
Finding the optimal
fantasy football team.
[7]
This paper proposes
finding the optimal
fantasy football team in
which three models were
used by author namely
Linear Regression,
Random Forests, and
Multivariate Adaptive
Regression Splines. It was
then predicted how each
All three models correctly
chose four or five of the
players who did actually
end up ranked in the top
seven.
MATLAB.

Article No. 08
PP 01-07
6
player would do against
his specific opponent in
each of the relevant
fantasy categories.
IPL cricket score and
winning prediction using
machine learning
techniques. [8]
This paper proposes IPL
cricket score and winning
prediction using machine
learning techniques by
using two methods the
first one is prediction of
score and the second one
is team winning
prediction. The model
used the supervised
machine learning
algorithm to predict the
winning. The accuracy
and prediction results can
be further improved by
increasing the number of
attributes and current
datasets of players.
Random Forest Classifier
provided good accuracy
and the stable accuracy so
that desired predicted
output became accurate.
Random forest classifier
accuracy more than SVC
and Decision tree
classifier.
Flask Framework.
Prediction of IPL
matches using machine
learning while tackling
ambiguity in results. [9]
This system provides
proposes prediction of
IPL matches using
Machine Learning while
tackling ambiguity in
results using algorithms
like Naïve Bayes, SVM, k
Nearest Neighbor,
Random Forest, Logistic
Regression, Extra Trees
Classifier, XGBoost were
adopted to train the
models for predicting the
winner.
Tree-based classifiers
provided better results
with the derived model.
The derived model has
also solved the problem of
multi-collinearity and
identified the issue of data
symmetry.
Data Science.
Using ML models to
predict points in Fantasy
Premier League. [10]
This research paper
proposes proposes using
ML models to predict
points in Fantasy Premier
League using Linear
Regression, Decision
Tree, and Random Forest
algorithms. This paper
attempts to build and
compare three machine
learning models to
accurately predict the
number of points that each
footballer would earn over
the course of the season.
Helps the players of this
game to make more
informed decisions while
making their respective
teams.
Successfully predicted the
points a player would
accumulate in the
upcoming match on the
FPL platform.
Random Forest.

Article No. 08
PP 01-07
7
IV. CONCLUSION
The proposed system will help to develop an ML-based system called Predict XI that aims to provide users with
optimized fantasy teams for prediction applications such as Dream11, Vision11, and similar platforms. The PredictXI
system will analyze various variables, including pitch conditions, players’ recent form, and batting/bowling conditions,
to narrow down the best possible combination of players for users’ fantasy teams. The system will in-turn help user in
better understanding of Fantasy Cricket & increasing player’s winning probability with accurate and in-depth insights.
REFERENCES
[1] Sachin Kumar S, Prithvi H. V, and C. Nandini, "Data Science Approach to Predict the Winning Fantasy Cricket Team—
Dream 11 Fantasy Sports" 2022, IEEE.
[2] Karthik Kataara, Gokul S. Krishnan, Shashank Shetty, Sanjay Bankapur, "Analysis and Prediction of Fantasy Cricket
Contest Winners Using Machine Learning Techniques" 2020, IEEE.
[3] G. Sudhamathy and G. Raja Meenakshi, "PREDICTION ON IPL DATA USING MACHINE LEARNING
TECHNIQUES IN R PACKAGE" 2020, IEEEE.
[4] Ryan Beal, Timothy J. Norman, and Sarvapali D. Ramchurn, "Optimising Daily Fantasy Sports Teams with Artificial
Intelligence" 2020, IEEE.
[5] Nilesh M. Patil, Bevan H. Sequeira, Neil N. Gonsalves, and Abhishek A. Singh, "CRICKET TEAM PREDICTION
USING MACHINE LEARNING TECHNIQUES" 2020, IEEE.
[6] Eric Hermann and Adebia Ntoso, "Machine Learning Applications in Fantasy Basketball" 2015, IEEE.
[7] Paul Steenkiste, "Finding the Optimal Fantasy Football Team" 2015, IEEE.
[8] Nikhil Dhonge, Shraddha Dhole, Nikita Wavre, Mandar Pardakhe, and Amit Nagarale, "IPL CRICKET SCORE AND
WINNING PREDICTION USING MACHINE LEARNING TECHNIQUES" 2021, IEEE.
[9] Ayush Tripathi, Rashidul Islam, Vatsal Khandor, and Vijayabharathi Murugan, "Prediction of IPL matches using
Machine Learning while tackling ambiguity in results" 2020, IEEE.
[10] Malhar Bangdiwala, Rutvik Choudhari, Adwait Hegde, and Abhijeet Salunke, "Using ML Models to Predict Points in
Fantasy Premier League" 2022, IEEE.

PredictXI- Best Fantasy Team Forecasting

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