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Joe's selections dominate early soft lines and turn March into Magic with his Proprietary RATS Theory, combined with key statistical indicators.  It is why Joe is the All Time Net Winner in Basketball Profits.

There are 250 CBKB teams which have regularly posted lines with an average of 12 (viable) players on each team. That means there are 3000 players in our “universe”. Transitioning one’s ratings from year-to-year was normally challenging enough in the past when integrating graduation losses with incoming players.  Today’s environment makes it even more challenging.  With new transfer rules, there are 750 to 1000 TRANSFERS EACH YEAR.  THAT IS A 25% to 33% ROSTER TURNOVER, ON AVERAGE, IN TRANSFERS ALONE.  This makes it virtually impossible to accurately assess a team in the early season.  

The NBA is a long and  grueling grind. It normally exceeds 100 games for teams in the NBA  Finals. As a result, what happens in the first couple months of the  season is often not indicative of the current form of the team.  Injuries, trades and chemistry can change tremendously through the  course of the season. THAT IS WHY THE BEST WAY TO HANDICAP THE NBA IS  WITH A RECENCY MODEL. I use two Recency Models. One is based on the  highly successful RAT Model I built for CBKB handicapping. The other is a  computer based model which isolates current form. Together, these will  isolate our daily plays which will perform as well as any you can find.

Once the NBA Playoffs begin, it’s a whole new ballgame. The Recency  Models are again indicative, but with teams playing the same opponent  for as long as a 7 game set, it is important to understand the value of  the Zig/Zag Model. Simply stated, it means playing ON the team who lost  the previous game of the series. Though this simple idea normally  provides nominal profit, there are key parameters and tighteners which  push this level of success to over 60%.

With the advent of the 2002 movie “Money Ball”, sabermetric  handicapping has become a major part of a handicapper’s analysis when  betting the bases.  Since that time, I used just about every metric  available in search of a methodology which would provide consistent  winners.  In the final analysis, I emerged with my “OPS Theory of MLB  Handicapping”.

This OPS Theory, simplified, is the sum of a team’s OPS batting, plus  the inverse to 1.000, of a team’s pitching.  After ranking these  numbers for each team from 1-30, I then compare it to the W/L rank of  each MLB team.  When there is a difference of 5 or more positions, we  have a play. That is to say, a PLAY ON a team if their OPS rank is 5  points superior to their W/L rank, and PLAY AGAINST a team if their OPS  rank is 5 points inferior to their W/L rank.  As a subjective opinion, I  compare the bullpen OPS rank of each team.

In OPS, a walk counts one for on-base and zero for slugging, while  any hit counts one for on base and the number of bases for slugging.  So  counting both OBA and SLG, a single counts 2 and a homer 5.  These are  in about the same proportion as in linear weights.  That is why OPS  works almost as well.

I perform the operation of OPS rankings every Monday and Thursday  morning prior to the start of each new series.  That is why you often  see the same teams on the selection sheet in consecutive days.  When  plays are mutually exclusive, I immediately exclude them from  consideration.  In addition, I break down the plays into 4 distinct  classifications.

ON home teams

ON away teams

AGAINST home teams

AGAINST away teams

What about pitching?  Of course it is important!  But starting  pitching is accurately reflected in the line, my OPS Model includes  pitching, and the Bullpen OPS is now worth 33% of the pitching handicap.

As a further consideration, I EXCLUDE money line plays where the line  is more than 1.50.  Games where the line is more than 1.50 are included  as run line selections only when my run line chart, which I update  daily, shows that a PLAY ON or PLAY AGAINST team qualifies in an over  80% situation.