I am using Random Forests, XGBoost and SVMs to classify whether the home team wins or the away team wins their bowl game (in college football). I trained the models on all the games during the season. 

I've come across something that is a bit weird and can't explain. I calculated a prediction confidence by subtracting the class probabilities. The XGBoost confidence values are consistency higher than both Random Forests and SVM's. I've attached the image below. 

I did some hyper-parameter tuning for all of my models and used the best parameters based on testing accuracy. 

 - Random Forest:
   -  700 trees
   -  15 variables randomly sampled (mtries)
   -  minimum split criteria of 5 rows. 
 - XGBoost:
   -  0.5, Learn rate 
   -  gbtree as my booster
   -  max depth of 6
 - SVM: 
   - RBF kernel
   - C (slack) of 1
   - 0.01, Sigma 


[![First half][1]][1]
[![Second half][2]][2]
  [1]: https://i.sstatic.net/WTCo5.png
  [2]: https://i.sstatic.net/aDyqz.png