Section I – Handicapping

Why College Basketball

This particular section and its corresponding spreadsheet is most effective
when handicapping college basketball. This is simply due to the fact that a mistake or turnover in football can result in a 7- or 14-point swing in the score of the game. In a game of few scores, that one blown play could have a major adverse effect on the game’s betting line. However, in basketball, scores are higher and a turnover results in just a 2 or 4 point difference out of many in total.
Also during a basketball season, teams play many more games than in a football season. When analyzing statistics, there is an important factor called “confidence limits” in which a higher number of results gives a corresponding higher level of certainty of an outcome. This is particularly evident in basketball towards the end of the season. In football, by the time there is a gain of confidence in the stats, the season is almost over.

Seasonal Changes
College basketball lasts about four months, from mid-November to the national championship held in early April. During this time, the performance level of a team can change significantly. In other words, and as expected, a team’s performance level is seasonal. These seasonal changes are noted simply by looking at the spreadsheet trends.
In conclusion, it becomes imperative to test different theories and systems seasonally.

Start of the Season
At the start of the basketball season, there is not enough spreadsheet data to
develop a reliable confidence level in rating a teams performance. However, there
is still a decent way to predict early season ratings. I simply use the following chart where I adjust the teams rating by subtracting the total points per game of those players not returning:

Return chart
Grad Losses	0	-10	-20	-30	-40	-50	-60
Rating Change	0	-3	-4	-5	-6	-7	-8

For example, if a team lost 4 players to graduation who averaged 14, 12, 8, and 6
points respectively totaling 40 points, I would reduce the teams rating from last year by 6 points.
Note the ratings for teams with large grad losses don’t change nearly as much as
you would expect. There is a reason for this, good recruiting makes up for major
losses and also the returning players simply step up and play more minutes to make up for much of the slack.
Please consider the above chart as just a guideline. It’s best to wait until sometime around mid-December and certainly by the 1st of January when conference play starts. There is then a sufficient number of games played that you can and should start using the spreadsheet data.

Power Ratings
Power ratings define a team’s level of performance and accordingly, are the
fundamental elements, the basis for effectively predicting the outcome of athletic
events My Rating System.
I call this my “one half” rating system for the following reason: If a particular team
improves over the line, I credit its Vegas rating by only one half of the amount
improved. If a team performs worse than the line, I decrease its Vegas rating by only one half of that amount.

Mathematical Examples
In my rating system, low numbers are for stronger teams and higher numbers are for weaker teams. In this example, Team A is playing Team B on a neutral court.
Team A has a Vegas rating of 10.
Team B has a Vegas rating of 18.
Therefore, Team A is favored by 8 points over Team B (18-10=8, which is the line)
Now assume Team A beats Team B by 16 points, or 8 points better than the line. I would then credit Team A with half of the 8 points variance between the line and the actual score difference, of 4 points. Its new performance rating is then 6. (Previous rating of 10 – 4 = 6.)
Team B’s rating is decreased by half of the 8 points variance between the line and the actual score difference, so Team B’s new rating is 22. (Previous rating of 18 + 4 = 22.)
This is now mathematically correct (22 – 6 = 16), which is the exact score differential of the game played.
Other handicapping systems improve or reduce a team’s ratings by the full amount. This is mathematically invalid and, in many cases grossly exaggerates a team’s performance level.

Favorites and Underdogs
For many years, I have examined favorite/dog data to see if there is any particular
category that is well worth noting. As many as five different levels of favorites, both home and away (ten categories) have been examined.
Based on the collective data for all of the teams combined together as a whole, the results clearly show that there is no standout against-the-spread favorite category of all noticeable merit! Vegas is definitely not biased when posting favorites or dogs of any amount, home or away!
Against-the-spread favorite data for individual teams can and does vary from one team to another and accordingly, this individual team data is worth noting when handicapping.

Summary
The best time to capitalize on handicapping is when there is significant variation/
difference between the line posted by the odds maker based on his ratings, and the line or outcome predicted using this rating system. These specialized, crafted ratings have constantly provided me with outstanding returns, earning the right to now be released nationally to the interested public. They are considered intellectual property and have now received copyright protection.

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