Afterthoughts Week 11
Every week is a learning experience. In order to become a better handicapper I have to avoid weeks with no wins. Week 11 turned out to be one of these. If I would have stayed within game plan, I could have done slightly better. Let me explain. We are hanging by a thread on 60% and need to make solid picks in the coming weeks in order to beat my goal and finish the season above 60.
In the table from the previous post, Oakland was as good a pick as Atlanta, so why was it not included in one of the picks. One word: Emotions.
Lesson #1: No matter how bad a team is, there is always a possibility that the team will cover the spread. If model indicates a pick for this underdog, go for it.
I do not regret the Pittsburgh pick because we got cheated out of it. Although Pittsburgh covered the spread with a last second touchdown, after various reviews, it was determined that the last TD did not count and the final score was 11-10. Referees admitted it was an error, no forward pass was don, but still that did not change the score.
The Atlanta pick I still think was a good one. They did not play as well as they could and lost. Fine I'll take that one.
The confidence measure is not providing reliable results. I can't remember a week where the highest confidence game covered. Also, when the confidence is far below 50%, doing the opposite is also not being as consistent as it should. I should consider doing each confidence dependent on the teams in the game. Currently, the measure takes into account the spread and the prediction only.
The other possibility is considering the wisdom of the crowds. Crowds are not always good predictors of games, but their knowledge tied with my statistical analysis might work well. Wagerline provides more than 3,000 people's picks of for each game. For this week for example, picks above 59% went 5-2 (something you do not see every week). These picks coincided with my point spread predictions for all games except Miami which my model correctly predicted would not cover. Their top picks were: ARI, PIT, TB, MIA, SF, NO, and TEN.
For today's game, Monday Night Football, between Cleveland and Buffalo, 52% is picking Buffalo, a no pick in my new hypothesis. The model predicts Cleveland will win the game by a few points. So the best thing to do for this game is to stay away from it.
I will not change my philosophy, what I will do is tabulate historical consensus picks and see if my hypothesis is true. Hypothesis: My statistical analysis complemented by consensus picks does fairly better than just my picks or just consensus picks.
In the table from the previous post, Oakland was as good a pick as Atlanta, so why was it not included in one of the picks. One word: Emotions.
Lesson #1: No matter how bad a team is, there is always a possibility that the team will cover the spread. If model indicates a pick for this underdog, go for it.
I do not regret the Pittsburgh pick because we got cheated out of it. Although Pittsburgh covered the spread with a last second touchdown, after various reviews, it was determined that the last TD did not count and the final score was 11-10. Referees admitted it was an error, no forward pass was don, but still that did not change the score.
The Atlanta pick I still think was a good one. They did not play as well as they could and lost. Fine I'll take that one.
The confidence measure is not providing reliable results. I can't remember a week where the highest confidence game covered. Also, when the confidence is far below 50%, doing the opposite is also not being as consistent as it should. I should consider doing each confidence dependent on the teams in the game. Currently, the measure takes into account the spread and the prediction only.
The other possibility is considering the wisdom of the crowds. Crowds are not always good predictors of games, but their knowledge tied with my statistical analysis might work well. Wagerline provides more than 3,000 people's picks of for each game. For this week for example, picks above 59% went 5-2 (something you do not see every week). These picks coincided with my point spread predictions for all games except Miami which my model correctly predicted would not cover. Their top picks were: ARI, PIT, TB, MIA, SF, NO, and TEN.
For today's game, Monday Night Football, between Cleveland and Buffalo, 52% is picking Buffalo, a no pick in my new hypothesis. The model predicts Cleveland will win the game by a few points. So the best thing to do for this game is to stay away from it.
I will not change my philosophy, what I will do is tabulate historical consensus picks and see if my hypothesis is true. Hypothesis: My statistical analysis complemented by consensus picks does fairly better than just my picks or just consensus picks.
Comments
example: arizona prediction by pickles is +10, but the spread is +3.5. pickles confidence in saying that's true is only 43%. so, is it true that either: the prediction is ambiguous, or the pick vs. the spread is ambiguous. the perfect algorithm is a tricky problem.
good luck pickles!