Do I Have Any Shot?

[omgkyleflavin]

Do I have any shot at creating a strong model without a strong stats background. I'm a freshman in college and I'll be taking stats next semester, but as of now I'd say I have a relatively weak math background. I ordered Sharp Sports Betting and Weighing the Odds today, but I'm wondering if reading them w/o a strong math background is a waste of time.

Been betting for years now but I'm sure my profits may mostly be due to luck, even though the first couple months of college basketball is easy for anyone. Anyways, hoping to get a model going for NCAAB and NBA games this year but I've seen some people hammered on here before so I'm sure someone isn't afraid to tell me it's a complete waste of time.

Thanks in advance

[sayhey69]

if you have to ask...

[Waterstpub87]

Build a math background. If you want to learn math, you can learn it on your own time. I learn more outside of college classes through the internet then I did in them. Depending on the math faculty at your school, they might not mind if you ask them questions even if you don't have them in class. Professors I had found sports modeling interesting, and were willing to help with the statistics parts even if they didn't understanding the betting.

[omgkyleflavin]

Thanks for the reply.

I own a couple of basic stats books I was planning on digging in to. Any recommendation on key things to focus on? I know professors always skip around to cover the key areas so I'm thinking it might be a waste of time to take in the whole thing. I'm starting to learn Python now but it sounds like I'm going to need some more math skills first anyways.

[easyliving]

I minored in math along with my engineering degree. Won't ever consider building any type of model as I think its a waste. Bet for fun and recreation not to make a large profit.

[Miz]

Not a waste of time (elec eng, physics minor). I still work a day job as an engineer however. Learn VB, Python. Learn how to apply math to real world problems. Learn how to make conclusions from results based on imperfect datasets. GL