[Ganchrow]

All comments, questions, and criticisms welcome:



Note that you can use the push probability estimates embedded in the calculator or can enter your own.

Currently supports:
NBA, NFL, NCAA BB, and NCAA FB Sides and Totals
MLB Totals

[zootiehead]

Looks nice. Why doesn't it work with MLB spreads?

[Ganchrow]

Originally Posted by zootiehead

Looks nice. Why doesn't it work with MLB spreads?

Because that works a little a differently than spread/totals in other sports. Specifically, the marginal value of half-points in MLB spreads is considerably more sensitive to the level of total than in other sports and also depends on whether the favorite team is playing at home or away.

MLB spreads will certainly be included in an upcoming version, but in the meantime I'm really just trying to get users' opinions and feedback on the current state of the interface and features.

[Santo]

It looks very usable at the moment. Speaking personally I prefer to keep these things in excel spreadsheets than web tools because of the speed differential.

I haven't checked all your numbers with my spreadsheets, but before I do I'll ask whether you believe them all to be accurate, or are just "placeholders" so to speak.

[flyingillini]

Great and I like it. I will use it!

[Ganchrow]

Originally Posted by Santo

I haven't checked all your numbers with my spreadsheets, but before I do I'll ask whether you believe them all to be accurate, or are just "placeholders" so to speak.

We'll say I believe them to be fairly accurate.

The MLB totals you can take to the bank.

The NFL spreads are very accurate except for the fact that they don't adjust for the relative magnitude of the totals.

The NBA and NCAA BB totals are currently the figures in which I have least confidence.

[vanzack]

Ganchrow - most of the time you talk way above my head (that is a compliment in case you are confused) but in simple terms what data are you using?

For example, how many years of NFL data go in to determining that a 6.5 point fav has a 5.7% chance of pushing a 7 point spread?

Thanks in advance.

[Ganchrow]

Originally Posted by vanzack

Ganchrow - most of the time you talk way above my head (that is a compliment in case you are confused) but in simple terms what data are you using?

For example, how many years of NFL data go in to determining that a 6.5 point fav has a 5.7% chance of pushing a 7 point spread?

Thanks in advance.

MLB goes back to 1999.
NBA goes back to 1990.
NFL goes back to 1990.
NCAA BB does back to 1997.
NCAA FB goes back (in theory) to 1990.

[vanzack]

Originally Posted by Ganchrow

MLB goes back to 1999.
NBA goes back to 1990.
NFL goes back to 1990.
NCAA BB does back to 1997.
NCAA FB goes back (in theory) to 1990.

So my first question / comment to you is can we assume that the NFL game has remained constant since 1990 and in 2008 is it prudent to use statistics from the 90's? (I dont know the answer to this, throwing it out as discussion...)

Are the years weighted in importance, and if so / not what is the best way to weight the statistics?

[Ganchrow]

Originally Posted by vanzack

So my first question / comment to you is can we assume that the NFL game has remained constant since 1990 and in 2008 is it prudent to use statistics from the 90's? (I dont know the answer to this, throwing it out as discussion...)

That's the assumption. Examining the data, it does indeed appear to hold.

Originally Posted by vanzack

Are the years weighted in importance, and if so / not what is the best way to weight the statistics?

No.

Here's how I footnoted the relevant chart on the NFL forum:

• The push frequency for a given point spread was determined by the percentage of games with a closing spread within 2 points of the spread in question ending with a favorite margin of victory equal to that spread. For example, the push frequency of a spread of 3 was determined from all games with a closing spread between 1 and 5.
• This is based on Stanford Wong's methodology as described in Sharp Sports Betting.

[vanzack]

Originally Posted by Ganchrow

That's the assumption. Examining the data, it does indeed appear to hold.

No.

Here's how I footnoted the relevant chart on the NFL forum:

• <span style="font-size: 11px;">The push frequency for a given point spread was determined by the percentage of games with a closing spread within 2 points of the spread in question ending with a favorite margin of victory equal to that spread. For example, the push frequency of a spread of 3 was determined from all games with a closing spread between 1 and 5.</span>
• <span style="font-size: 11px;">This is based on Stanford Wong's methodology as described in Sharp Sports Betting.</span>

Thanks, and thanks for the efforts on the calculator.

I would be curious to hear others thoughts on the validity of using non-weighted stats since 1990.

I dont have any evidence one way or the other but think it would be an interesting discussion.

[Ganchrow]

Originally Posted by vanzack

Thanks, and thanks for the efforts on the calculator.

I would be curious to hear others thoughts on the validity of using non-weighted stats since 1990.

I dont have any evidence one way or the other but think it would be an interesting discussion.

Testing a data set for stationarity is not difficult.

If you're interested on the underlying mathematics, see the Wolfram entry on the stationary of time series.

	1990s				2000s	

Spread	n	p		Spread	n	p
0	312	0.00%		0	195	0.00%
1	901	2.55%		1	780	2.44%
2	1,105	1.99%		2	917	1.96%
3	1,210	9.67%		3	1,006	9.94%
4	1,233	3.41%		4	1,038	2.50%
5	1,278	1.64%		5	1,159	1.73%
6	891	3.93%		6	726	2.75%
7	824	5.83%		7	628	5.57%
8	772	1.94%		8	627	2.39%
9	618	0.81%		9	490	1.02%
10	417	4.80%		10	276	5.07%
11	343	2.33%		11	243	2.06%
12	279	0.72%		12	179	0.00%
13	202	0.99%		13	105	1.90%
14	164	4.88%		14	81	4.94%
15	135	2.22%		15	70	0.00%
16	79	3.80%		16	36	2.78%
17	43	4.65%		17	23	4.35%

[ProlinePlayer]

Originally Posted by Ganchrow

MLB goes back to 1999.
NBA goes back to 1990.
NFL goes back to 1990.
NCAA BB does back to 1997.
NCAA FB goes back (in theory) to 1990.

I don't really know if it makes much of a difference, but for this kind of thing I prefer to use a cutoff of 1994 for the NFL.

The year the 2-point conversion was introduced. I would suspect that the probabilities around the key numbers may be somewhat different before/after that change.

[Ganchrow]

Originally Posted by ProlinePlayer

I don't really know if it makes much of a difference, but for this kind of thing I prefer to use a cutoff of 1994 for the NFL.

The year the 2-point conversion was introduced. I would suspect that the probabilities around the key numbers may be somewhat different before/after that change.

Certainly a good point.

In reality, however, it doesn't make much of a difference. Stanford Wong's apparent assessment is that the increase in sample size makes up for the slight regime change that may have coincided with the reimplementation of the NFL 2-point conversion in the 1994-95 season. Wong's data set, however, stretches all the way back to the mid 80s.

For what it's worth, with my particular (and to be blunt, somewhat arbitrary) choice of data set years, the figures barely hiccough if you exclude the four pre-1994 seasons. In reality, the sampling is pretty noisy to begin with, and it's probably even more disingenuous of me to take those percentages out of to 3 decimal places. (Of course the tiny sample sizes are probably a big part of why I personally don't much bet the NFL to begin with.)

[Bill Dozer]

This is a very cool tool that takes line shopping to the next lext level. I think this will be the most popular tool hands down.