A Bradley-Terry Artificial Neural Network Model for Individual ...

18 Joshua Menke, Tony Martinez
Table 1 Bradly-Terry ANN Enemy Territory Prediction Errors
Heuristic None Inflation Certainty Both
Prediction Error 0.1034 0.0635 0.0600 0.0542
5 Results and Analysis
The results are shown in table 1. Each column gives the prediction error
resulting from a different combination of heuristics. The first column uses
no heuristics, the second uses only the inflation prevention heuristic (see
section 3.6), the third column uses only the rating certainty heuristic (see
section 3.5), and the final column combines both heuristics. Each heuristic
improves the results and combining both gives the lowest prediction error
at 5.42%. This value means that a given estimate is, on average, 5.42% too
high or 5.42% too low. Therefore, when the model predicts a team has a
75% chance of winning, the actual % chance of winning may be between 70-
80%. One question is on which side does the prediction error tend for a given
probability. To examine this, the predictions for the case that combines both
heuristics are plotted against their prediction errors in figure 2 taking a 5%
interval histogram of the results. The figure shows which direction the error
goes for a given prediction. Notice that with only two low-error exceptions on
the extremes, the prediction error tends to be positive when the prediction
probability is low, and negative when it is high. This means the prediction
is slightly extreme on average. For example, if the model predicts a team
is 75% likely to win, they will be on average only 70% likely to win. A