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Building an ELO prediction model suitable for gaming
Evangelos Matselis
RMIT University, Melbourne, Australia.
Introduction Results/Discussion
Season-to-season carryover
Methodology
.
Home Advantage
The ELO rating system was developed by the Hungarian-
American, Arpad Elo, and was originally used to rate chess
players. However, during the past years, the model was
used for other sports too, such as soccer, basketball,
baseball and hockey.
Dyte and Clarke (2005) were the first to use ratings to
predict match outcomes, but it was not until 2009 that
ELO proved to be the best model for predicting match
outcomes.
In this work, we will build an ELO model for the German
Soccer League to predict the outcome of the matches in
four consecutive seasons.
References
1. Bedford, A. and Da Costa, C., 2004, ‘A ratings based analysis of Oceania's
road to the world cup’, in Proceedings of the Seventh Australasian
Conference on Mathematics and Computers in Sport, R. H. Morton and S.
Ganesalingam (ed.), Massey University, Palmerston North, NZ (Seventh
Australasian Conference on Mathematics and Computers in Sport).
2. Dyte, D. and Clarke, S.R., 2001, ‘A ratings based poisson model for World
Cup Soccer simulation’, JORS, 5, 993-998
3. Wikipedia, (2015). Elo rating system. [online] Available at:
https://en.wikipedia.org/wiki/Elo_rating_system [Accessed 24 Jun. 2015].
4. Schiefler, L. (2015). Football Club Elo Ratings. [online] Clubelo.com.
Available at: http://clubelo.com/ [Accessed 24 Jun. 2015].
In every new season, there are teams that are promoted
from 2.Bundesliga and teams that are relegated from
Bundesliga. The starting rating for each promoted team is
calculated as follows:
New Season Rating=1500+(Previous season Final rating-
1500)/2
New Season Rating for promoted = 1500 + (Average Final
Rating of relegated teams – 1500)/2
In most models, predicting a draw as a match outcome
proves to be a riddle, often really hard to solve. There is no
difference in ELO models, since predicting a draw means
that the Expected winning probability for both teams
should be 0.50, which is practically impossible. However, as
long as the draw is a common result in soccer (more than
20% of matches worldwide end in a draw), a way had to be
found either to predict a draw, or never to predict a draw in
a profitable way, as the model is developed for gaming
purposes.
It was decided to take advantage of “Draw No Bet”. Draw
no Bet is a special bet offered by the majority of the
bookmakers worldwide. When betting on a draw no bet
game, the odds are lower than the actual ones for home
and away wins, but if the game ends in a draw, then the bet
is void and the gamer gets his money back. As shown in the
table, whenever a game ended in a draw, the model would
consider it as a void bet.
Figure: Predicting and evaluating match outcomes
Dealing with the draw
The improved ELO model was successfully applied to the
German Bundesliga during the seasons 2010/2011-
2013/2014. The total performance of the model was over
60% from the starting season, when all teams started at a
1500 rating and exceeded 70% in 2011/2012 and
2013/2014.
The model predicted correctly more than 80% of the match
outcomes in 42 rounds throughout the seasons (always
excluding the games that ended in a draw), while 12 of
them had a prediction percentage of 100%.
Finally, although it might be expected that the model would
fail to predict correctly the majority of the game outcomes
at the beginning of each season, due to the teams’ lack of
form and other factors, it managed to achieve more than
60% correct prediction in three of the four examined
seasons, while the worst results usually come in the middle
of each season.
What could be done in later studies, would be to modify
the W value, if a very high rated team plays a low rated
team. This could give extra rating points to the lower rated
teams when they manage to win a game against a very
strong opposition and of course would give less points to
the strongest teams when they win against a weaker team.
The study started predicting the match outcomes from season
2010/2011. At season start, the rating for all teams was set as
1500. However, Teams change every year, some of them
strengthen, some become weaker and form is lost due to
season break. Thus, carrying the previous year’s final ratings
would probably lead to false conclusions and
misinterpretations.
The solution is based on MARS ratings system for AFL. In
MARS, the team ratings are dragged back towards the starting
rating (in our example is 1500) by an amount that is equal to a
half of the difference between the rating at the end of the
season and 1500.
Figure: Calculations for home advantage