This page displays predictions for games in the 2018 CFB season. These predictions come from a model built on historical college football play by play and game data in order to simulate upcoming games. All data is from collegefootballdata.com.
To see regular season win projections and conference championship / playoff / national championship probabilities for all teams as of this week, go to:
This table displays the results of simulating week 15 of the 2018 CFB
season params$nsims times. These simulations are run one
week in advance, using information available through week 14. Quality is
the (harmonic) mean of the two team’s overall rating, with 100
indicating a game between highly rated opponents. Pr(HomeWin) is the
percentage of times that the home team won the game across every
simulation.
How has the model performed so far in2018?
The model predicts the probability that the home team will win and their expected margin of victory (or defeat). I assess the model’s performance by assessing the accuracy of its predictions (win/loss), the logloss of its probabilities, and the mean absolute error of the margin.
Outcome | Margin | ||||
Season | Week | N | Accuracy | LogLoss | MAE |
2018 | 1 | 88 | 0.82 | 0.41 | 16.4 |
2018 | 2 | 78 | 0.78 | 0.38 | 15.2 |
2018 | 3 | 66 | 0.79 | 0.43 | 14.3 |
2018 | 4 | 62 | 0.76 | 0.46 | 13.0 |
2018 | 5 | 58 | 0.76 | 0.47 | 11.8 |
2018 | 6 | 56 | 0.73 | 0.54 | 13.5 |
2018 | 7 | 55 | 0.69 | 0.57 | 14.5 |
2018 | 8 | 55 | 0.82 | 0.44 | 12.8 |
2018 | 9 | 56 | 0.54 | 0.67 | 13.8 |
2018 | 10 | 62 | 0.68 | 0.54 | 14.2 |
2018 | 11 | 62 | 0.84 | 0.46 | 11.8 |
2018 | 12 | 66 | 0.82 | 0.39 | 13.6 |
2018 | 13 | 64 | 0.72 | 0.55 | 14.4 |
2018 | 14 | 9 | 0.78 | 0.47 | 14.4 |
Season Average: | 0.75 | 0.48 | 13.9 | ||
This table displays the results of simulating the rest of the 2018 season times. These simulations are run weeks in advance, where the remainder of the season is simulated using only the information as of week 14.
For example, we simulate the entire season before any games are played by simulating week two based on the (simulated) results of week one, then simulating week three based on the (simulated) results from week two. As a result, predictions for future weeks tend to be less accurate than predictions made one week in advance and are likely to change as we get closer to the actual game.