I just get the free stuff from KP. It doesn't explicitly tell you the lines, but you can back into them very easily. I just do the calc quickly in my head.
If you want a site that has more data for free it is Bart Torvik. But not sure how well they approximate opening lines.
I just take the AdjEM of both teams, and take the difference, adjust pace, and add 3.5 for home.
Syracuse ADJ EM per 100 = 10.3
Miami ADJ EM per 100 = 8.4
Delta in EM is 1.9, Pace of both teams is about 70 ... so 1.9 * 70% = 1.3.
Then add the home court advantage of 3.5 ... so 1.3 - 3.5 = (2.2) for Miami.
Torvik is very close to Kenpom because he uses mostly Kenpom methodology. He says it in his FAQs
How is T-Rank different from Kenpom?
The short answer is that T-Rank is very similar to Kenpom, which is no surprise given that T-Rank is basically an offshoot of Kenpom. But there are three main sources of difference:
GameScript and Garbage Time
The incorporation of the GameScript stat, and its omission of garbage time gives T-Rank a slightly unique aspect. Whether it's a good aspect is another question.
Pythags versus Efficiency Margins
Prior to the 2017 season, Kenpom
switched away from the pythagorean expectancy / log5 method, to a still very similar system that uses adjusted "efficiency margins" (EMs) instead. The main difference is that instead of being multiplicative, the new Kenpom system is additive. So the basic formula is:
Game Adj. OE = (PPP - Average PPP) - (Opponent's Adj. DE - Average PPP) + Average PPP
For our neutral court example above that would be:
(110 - 100) - (90 - 100) + 100 =
120
So, similar, but a little different. When Kenpom decided to go to adjusted EMs, I decided to stick with the Barthag, for old time's sake.
Secret Sauce
Here are the additional adjustments I make:
- There's a recency bias—all games in the last 40 days count 100%, then degrade 1% per day until they're 80 days old, after which all games count 60%.
- An adjustment that discounts blowouts in mismatches—if the margin of victory (MOV) is more than 10 points and the difference in Barthags is above a threshold, the game starts getting discounted. If the MOV is 20 points or higher, the discount is (Higher Barthag - Lower Barthag - .5) * 2. So if a team with a Barthag of .8000 is playing a team with a Barthag of .2000, and it wins by 20 points, the game value will be 1 - (.8 - .2 -.5) * 2, or 80%
- As with Kenpom, there is also a preseason component that is phased out once a team has played 13 adjusted games (since not all games count for 100% of a game, it typically sticks around for 15 or 16 games).
Ultimately, because of these differences, the final numbers are similar but different. Notably, T-Rank has a wider "spread" between top and bottom teams, probably because Kenpom has a much more significant cap on margin of victory