Senior Design Expo 2019

Football Simulator 2019
Thomas Mecattaf, Mohnish Chakravarti,
Cameron Terry
Advisor: Prof. Hardeep Johar
The goal of this project is to create a
recommendation system for coaches to use
on their players on a daily basis, such as to
provide detailed insights on what areas of his
game each player should improve in training.
These insights are extremely specific, as they
take into account the opponent the team is
facing, the teammates a specific player will
play alongside with, and the match-day
conditions the player will be exposed to.
Fundamentally, we view a player’s training
regime as an optimization problem for the
coach: there are many areas of the game to
improve in a player, but a limited time such
that crucial choices must be made to
prioritize regions of improvement. Hence, if
we can create a model that points out the best
points to focus on, we are indeed optimizing
the training regime of a player.
To do so, we scrape multiple datasets: first,
the FIFA video game ratings for each player,
which include detailed stats (between 0-100)
that describe their physical and mental
attributes, and in-game instincts. The second
dataset is a list of in-game performance of
each team (from whoscored.com). For the
sake of this proof-of-concept, we focus only
on the English Premier League.
Then, after this preliminary study, we run a
LASSO regression relating the make-up of a
team (11 vs 11 players) to the resulting game
performance. In our case, very detailed
statistics are available for 4 seasons in total,
so we have more than 1600 games at our
disposal. We are training an algorithm in
which we feed it each player’s statistics, and
it outputs a predicted in-game performance.
Finally, for each player, we would vary a
feature by +1 compared to the original
ratings, leaving all other features equal for all
other players. We then run that for all the
features of that player, and see the ones which
have the biggest “positive delta” impact on
the game. This would allow us to pinpoint
what feature the coach has to focus on while
training his players for this specific game.
References:
[1] Junyuan Gao, Predicting Premier League
Final Points andRank Using Linear
Modeling Techniques
[2] Suraj Keshri, Min-hwan On, Garud
Iyengar, Optimal Trajectory based Player's
ability in NBA
[3] Rory P. Bunker, Fadi Thabtah, A
machine learning framework for sport result
prediction
We conduct an analysis on the entire season
performance, to see what factors have the
most impact on performance, what are the
attributes that have the biggest influence on a
game for individual players, and what the
ratings that distinguish player types are.
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