Currently working on analyzing and predicting the result of football matches with greater accuracy using the Dixon Coles algorithm.

1. FOOTBALL PREDICTOR
USING
MACHINE LEARNING
AAKASH M
SRIRAM V
VISHNU M

2. INTRODUCTION
Machine learning (ML) is one of the intelligent methodologies that have shown promising results in
the domains of classification and prediction. One of the expanding areas necessitating good
predictive accuracy is sport prediction, due to the large monetary amounts involved in betting. In
addition, club managers and owners are striving for classification models so that they can
understand and formulate strategies needed to win matches. These models are based on numerous
factors involved in the games, such as the results of historical matches, player performance
indicators, and opposition information. This paper provides a critical analysis of the literature in ML,
focusing on the application of Artificial Neural Network (ANN) to sport results prediction. In doing so,
we identify the learning methodologies utilised, data sources, appropriate means of model
evaluation, and specific challenges of predicting sport results.

3. OBJECTIVE
1. To identify the most important attributes of player’s performance which determine their ratings given by
experts. In this way we are finding out the latent knowledge which the experts use to assign ratings to
players.
2. To find out which performance attributes of the players of the two competing teams affect the match
outcome and to what extend the match outcome is characterised by the performance attributes of players.
3. Find out good ways in which individual player ratings can be aggregated resulting in a set of team ratings
and investigate how closely these team ratings can determine the match outcome. This will indicate the
correlation between expert ratings and match outcomes. Hence, it will show the influence of match
outcomes on the expert ratings.
4. To investigate how well the expert ratings given to the players of a team in the past performances of the
team predict the next match outcome.

4. LITERATURE SURVEY
Ranking of Sports Teams
via the AHP
The hierarchical structure is practical
because most organizations and decision
processes are complex and often are
organized in hierarchical form.
Accuracy of using Eigen-vector is less
compared to neural networks and ML
techniques.
Predicting Football Match
Results with Logistic
Regression
It’s advantages are it’s very suitable to
explain the relationship between output
variable and input, and it can solve the
problem which ordinary least squares
regression cannot.
There is so many unexpected result in
2015/2016 records and when incorporated in
training data, the model built was twisted and
does not produce the expected result.
A Survey of Content-
Aware Video Analysis for
Sports
We review the developments in sports video
analysis, focusing on content-aware
techniques that involve understanding and
arranging the video content on the basis of
intrinsic and semantic concepts.
Many open problems remain because of the
diversity of game structures among sports
domains. Developing a unified framework
that enables processing data from diverse
sports is still challenging.

5. ADVANTAGES
● Find a good ratings aggregation method.
● Using this aggregation strategy we find the set of attributes which can best characterise the match
performance.
● Characterise the match outcome using the attributes generated from aggregated player ratings.
● Predict the outcome of next match using the attributes created from the aggregated ratings of player
performances over the past matches.
● Best result for characterising match outcome was an accuracy of 90% using the best 8 attributes
only.
● This shows a positive correlation between match outcome and the ratings given by the soccer
experts.

6. DISADVANTAGES
● The best accuracy we obtained was 53.39% which is better than a random guess.
● This low accuracy was partly due to the extreme difficulty of predicting a ‘Draw’ for a match.
● This low accuracy also means that the outcome of a match depends only the players’ performances in
the current match and not the performances of the past matches.

7. PROPOSED MODEL
To increase the efficiency and to improve the probability ranking by :
● Using Dixon-Coles model and time waiting.
● Include goal times data.
● Add ranked probability score.
● Add standard errors to parameter estimates.
● Include more wide data sets such as physicality of players, experience, etc.

8. REQUIREMENTS
● Data set of Barclays Premier League (BPL).
● R language Studio.

9. ARCHITECTURE DIAGRAM

10. IMPORTING DATASETS

15. DIXON COLES ALGORITHM

22. REFERENCES
● Ranking of Sports Teams via the AHP by ZILLA SINU ANY -STERNA Department of Industrial
Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel, July 2016.
● Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP
Synchronization by Mihai Cucuringu, Jan 2018.
● Generic temporal features of performance rankings in sports and games by José A Morales, Sergio
Sánchez , Jorge Flores , Carlos Pineda , Carlos Gershenson, Germinal Cocho, Jerónimo Zizumbo ,
Rosalío F Rodríguez and Gerardo Iñiguez, Springer Journal 2016.
● Predicting the winner of NFL-games using Machine and Deep Learning by Pablo Bosch, Feb 2018.
● A Network-Driven Methodology for Sports Ranking and Prediction by Vincent Xia, Kavirath Jain,
Akshay Krishna, and Christopher G. Brinton, Department of Operations Research and Financial
Engineering, Princeton University ,Department of Electrical Engineering, Princeton University, 2018.

23. REFERENCES
● Predicting Football Match Results with Logistic Regression by Darwin Prasetio,2017.
● IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 28, NO. 5, MAY 2018
A Survey of Content-Aware Video Analysis for Sports Huang-Chia Shih, Member, IEEE
● Developing Analytical Tools to Impact U.Va. Football Performance by Jack Corscadden, Ross Eastman,
Reece Echelberger, Connor Hagan, Clark Kipp, Erik Magnusson, Graham Muller, Stephen Adams,
James Valeiras, and William T. Scherer, 2018.
● Predicting sports results using latent features: a case study Stefan Dobravec , May 2018.
● https://www.transfermarkt.co.uk/

24. BELIEVE THAT.