@WILLY_THA_LURCH
Good work here. Is this an actual system with rules/parameters set, and anything inside the parameters you play? OR is this just you handicapping the totals and firing where you see edges?
Just curious because I'm always trying to learn and improve on CBB totals. I do great in Non-Con and then when Conference plays comes in I am not good at all, so im trying to pick your brain on adjustments/methods/ What statistics hold the most weight for you when analyzing CBB totals? (adj tempo? Adj Defense? Adj Offense? Avg Secs per Possession on Offense or Avg Secs per possession on Defense? Turnover rate? Or is there a certain set of analytics you find works best for Totals: Haslametrics vs Torvik vs KenPom etc...?)
I know its a lot of questions but just trying to pick your brain to create discourse to learn and adjust my own.
@tland22
thank you man -
it is indeed an actual system that looks at all games with a posted total on a given day. nothing too complicated really; been working a lot with kenpom data which is why i started so late in the season. common sense says the bigger sample size we have the more accurate our data should be. im looking for differentials between the posted totals and "expected totals" based on AdjD, AdjO and AdjT metrics while trying to filter out outliers. a lot of data is measured in terms of 100 possessions so im working from AdjT numbers to find an expected # of possessions per game. that multiplied with a teams AdjO and AdjD ratings give a rough yet solid enough number for expected pts/g both for and against. averaging team a pts for/against together with team b pts for/against gives an estimated total that is compared to the books' posted total line. numbers further away from 0 = higher confidence in a pick.
i have been betting these "higher confidence" picks (differential # >=5 OR <=-5) without any further handicapping to test out how the numbers do by themselves. seeing some decent results but im always prepared for regression. call me a pessimist but 60%+ is just not sustainable over the long term so i am enjoying the benefits now lol. i think it should keep up thru the end of the season and the tourney but i really dont see it being a quality tool at the start of a new season as i suspect it would need a more robust dataset to pull from.
sorry for the wall of text but hope i was able to answer your questions - peace and BOL. 
@Thinkb4ubet
agreed lol its gonna be an absolute stinker for sure. we're banking on SDSU to not let off the gas and probably get up 75-80 by themselves to see this go over with how bad AFA is this year.
good to know others are getting to the same conclusions after addtl research. does make me feel a little better about my own process. i had HP / Upstate as my second highest confidence play today. FWIW they have only had 1 H2H matchup go over in the past 10 (not that previous year games matter at all in college hoops especially with the transfer portal how it is these days but i digress) and i had it scored around 153-154.
Decent night last night - got absolutely moosed on upstate / HP with 8 points scored in 19 seconds to send it over by 1 point. 4-3 on the night !
HP / SC UPSTATE - U162 - 
GW / UMASS - U154.5 - 
BU / NAVY - O131.5 -
MILWAUKEE / WRIGHT ST - U155 -
VALPO / DRAKE - O129 -
SDSU / AIR FORCE - O126.5 -
N TEXAS / TEMPLE O130.5 -
Established in 1995,
That SDSU Airforce game may be the most boring blowout snooze fest of the day
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