This post is going to be a weekly re-occurring post where we’ll provide the projected scores for each NFL game. Right now, we have three different models for our game-specific predictions. This might cause some disagreement between the picks for the spreads, game-winners, and game scores. The solution to this is creating a more complex model that predicts all of them at the same time. Creating one robust solution is our long term plan.
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NFL Game Score Predictions for Week 13 2020
<table> <thead> <tr> <th style="text-align:left;"> HomeTeam </th> <th style="text-align:left;"> AwayTeam </th> <th style="text-align:right;"> HomeTeamScore </th> <th style="text-align:right;"> AwayTeamScore </th> </tr> </thead> <tbody> <tr> <td style="text-align:left;"> Atlanta Falcons </td> <td style="text-align:left;"> New Orleans Saints </td> <td style="text-align:right;"> 20 </td> <td style="text-align:right;"> 23 </td> </tr> <tr> <td style="text-align:left;"> Houston Texans </td> <td style="text-align:left;"> Indianapolis Colts </td> <td style="text-align:right;"> 25 </td> <td style="text-align:right;"> 24 </td> </tr> <tr> <td style="text-align:left;"> Miami Dolphins </td> <td style="text-align:left;"> Cincinnati Bengals </td> <td style="text-align:right;"> 25 </td> <td style="text-align:right;"> 20 </td> </tr> <tr> <td style="text-align:left;"> Minnesota Vikings </td> <td style="text-align:left;"> Jacksonville Jaguars </td> <td style="text-align:right;"> 28 </td> <td style="text-align:right;"> 19 </td> </tr> <tr> <td style="text-align:left;"> New York Jets </td> <td style="text-align:left;"> Las Vegas Raiders </td> <td style="text-align:right;"> 16 </td> <td style="text-align:right;"> 25 </td> </tr> <tr> <td style="text-align:left;"> Tennessee Titans </td> <td style="text-align:left;"> Cleveland Browns </td> <td style="text-align:right;"> 22 </td> <td style="text-align:right;"> 21 </td> </tr> <tr> <td style="text-align:left;"> Arizona Cardinals </td> <td style="text-align:left;"> Los Angeles Rams </td> <td style="text-align:right;"> 24 </td> <td style="text-align:right;"> 22 </td> </tr> <tr> <td style="text-align:left;"> Seattle Seahawks </td> <td style="text-align:left;"> New York Giants </td> <td style="text-align:right;"> 22 </td> <td style="text-align:right;"> 22 </td> </tr> <tr> <td style="text-align:left;"> Green Bay Packers </td> <td style="text-align:left;"> Philadelphia Eagles </td> <td style="text-align:right;"> 29 </td> <td style="text-align:right;"> 19 </td> </tr> <tr> <td style="text-align:left;"> Los Angeles Chargers </td> <td style="text-align:left;"> New England Patriots </td> <td style="text-align:right;"> 23 </td> <td style="text-align:right;"> 20 </td> </tr> <tr> <td style="text-align:left;"> Kansas City Chiefs </td> <td style="text-align:left;"> Denver Broncos </td> <td style="text-align:right;"> 31 </td> <td style="text-align:right;"> 19 </td> </tr> <tr> <td style="text-align:left;"> Chicago Bears </td> <td style="text-align:left;"> Detroit Lions </td> <td style="text-align:right;"> 24 </td> <td style="text-align:right;"> 21 </td> </tr> <tr> <td style="text-align:left;"> San Francisco 49ers </td> <td style="text-align:left;"> Buffalo Bills </td> <td style="text-align:right;"> 20 </td> <td style="text-align:right;"> 23 </td> </tr> <tr> <td style="text-align:left;"> Pittsburgh Steelers </td> <td style="text-align:left;"> Washington Football Team </td> <td style="text-align:right;"> 27 </td> <td style="text-align:right;"> 20 </td> </tr> <tr> <td style="text-align:left;"> Baltimore Ravens </td> <td style="text-align:left;"> Dallas Cowboys </td> <td style="text-align:right;"> 24 </td> <td style="text-align:right;"> 22 </td> </tr> </tbody> </table>Score Ranges
Below is a graph containing the 1 standard deviation of possible scores for a game. 68% of each team’s scores should fall between the leftmost and rightmost values.
This is useful for a few reasons:
- A broad range between the max and min, the less confident our model is about the final game score.
- You can see the overlap between outcomes and infer (I will work on using statistics to give you the probabilities) the number of results that would lead to a team winning and losing.
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