changed readme

README.md

@@ -48,12 +48,8 @@ To change the players and their ages, you must reference the `player_tokens.csv`
 
 For example, if you wanted to subtract Kristaps Porzingis from Boston's team and swap who was home / away, you would take the token representing Porzingis `4416` out of the `home_team_tokens` list, and replace him with, say, Payton Pritchard `4999`. You would then have to look up Pritchard's age (26), find the corresponding age token in `age_tokens.csv`, which is `11`, and replace Porzingis' age token (which is the second to last token).
 
-To swap home and away, you could replace the variables containing all of the player and age tokens, or just set the `swap_home_away` variable to `True`. The results are as follows:
-
-![NBA Finals prediction without Porzingis](porzingis-swapped-for-pritchard.png)
-
-As you can see, Dallas' win probability improved from 23% to 35%, and their chance of being blown out by 20+ points decreased from 14% to 10%. Clearly, the model thinks Porzingis is important to the Celtics' chances, but still considers Boston to be the superior team without him.
+To swap home and away, you could replace the variables containing all of the player and age tokens, or just set the `swap_home_away` variable to `True`.
 
 ## Training Process
 
-I downloaded data from stats.nba.com using the [https://github.com/swar/nba_api](swar/nba_api) package to get information on minutes played, game outcomes, and a few other dimensional elements to make everything fit together. Then, I ran a custom PyTorch training loop to train the model(s) on their chosen loss objective (spread, money line, or spread probability).
+I downloaded data from stats.nba.com using the [https://github.com/swar/nba_api](swar/nba_api) package to get information on minutes played, game outcomes, and a few other dimensional elements to make everything fit together. Then, I ran a custom PyTorch training loop to train the model(s) on their chosen loss objective (spread, money line, or spread probability).

example.ipynb

@@ -87,8 +87,8 @@
     "away_age_tokens = [11, 12, 19, 14, 23, 11, 13, 13]\n",
     "\n",
     "# Uncomment to take Tatum off team, replace with Pritchard\n",
-    "away_player_tokens = [4999, 5039, 5027, 4981, 4972, 5004, 4416, 4983]\n",
-    "away_age_tokens = [11, 12, 19, 14, 23, 11, 13, 13]\n",
+    "# away_player_tokens = [4999, 5039, 5027, 4981, 4972, 5004, 4416, 4983]\n",
+    "# away_age_tokens = [11, 12, 19, 14, 23, 11, 13, 13]\n",
     "\n",
     "# The model usually gives the home team a bump in win probability.\n",
     "# Change this to \"True\" to swap home and away teams.\n",