Another week, more profit -- and maybe future adaptations.

Week 14 was a pretty eventful week, my personal favorite moment being the Ravens and Browns having a fourth quarter touchdown frenzy. The model performed decently, but there was one obstacle that absolutely killed my win percentage: the relevancy of the NFC East! For such a losing division, they have been killing it these past two weeks and hopefully my model will reflect that more.

The adaptive spread is still becoming more accurate, seeing 62.5% accuracy (10 of 18). This may not sound like much, but let's do a quick probability trial right here. Spread is where they think 50% of betters will place on both sides, and is pretty indicative on how Vegas thinks the game will actually be played out. For math purposes, and for me not wanting to look at each percentage individually, let's assume the spread represents 50% of bettors on each side. Treating this as a binomial distribution (win or don't win), we can actually calculate the percentile my picks were for this week. P(x>=10) would represent the percentile, and this would be the sum from x=0 to 10 of (16 choose x) * 0.5^16, which is 0.895, or better than or equal to 89.5% of random picks. Check the math here if you want to see it. I'd say that's something to be proud of, but we have to keep working. 

​Something about the model that bothered me was that it didn't account for the strength of a team that was won against. The model only pulls PF/PA's for each team overall throughout the season and doesn't necessarily account for who they played. I want to reference the Steelers, possibly one of the worst 11-0 teams I have ever seen, and how they have lost two straight. My model LOVED the Steelers, but there has to be a way to get a better assessment of skill than straight ELO.

This is when I came up with an idea: why not combine the thought of a team's winning record and the ELOs of the teams they have played. I want to give you an example right here. Let's look at a team right here: 
Team 1 beats Team 2 (ELO 1200)
Team 1 beats Team 3 (ELO 1300)
Team 1 loses to Team 4 (ELO 1500). 
Team 1 elowin% = (1200+1300)/(1200+1300+1500) = 2500/4000 = 0.625. A straight up win percentage would have been 0.667 for this team, but they didn't play anyone of significance, really. My only hesitation for this is that it heavily penalizes for losing to a good team. I need to work a way around that before I can implement it into the model. 

I am still using the same version of the model as I did three weeks ago, as I have been bogged down with studying for the GRE and applying for jobs. Once I can secure something, I will invest much much more time into this. For now, let's keep winning. You can find an updated Tableau Dashboard where the old one was right here as usual. 

​Have a great week, guys!